Discreteness and Interactivity in Spoken Word...

Psychological Review Copyright 2000 by the American Psychological Association, Inc. 2000, VoL 107, No. 3, 460-499 0033-295X/00/$5.00 DOI: 10.1037//0033-295X.107.3.460

Discreteness and Interactivity in Spoken Word Production

Brenda Rapp and Matthew Goldrick Johns Hopkins University

Five theories of spoken word production that differ along the discreteness-interactivity dimension are evaluated. Specifically examined is the role that cascading activation, feedback, seriality, and interaction domains play in accounting for a set of fundamental observations derived from patterns of speech errors produced by normal and brain-damaged individuals. After reviewing the evidence from normal speech errors, case studies of 3 brain-damaged individuals with acquired naming deficits are presented. The patterns these individuals exhibit provide important constraints on theories of spoken naming. With the help of computer simulations of the 5 theories, the authors evaluate the extent to which the error patterns predicted by each theory conform with the empirical facts. The results support a theory of spoken word production that, although interactive, ptaces important restrictions on the extent and locus of interactivity.

In much of current theorizing on cognitive representation and processing, the default assumption is that cognitive systems are highly interactive. Nonetheless, relatively little attention has been directed at examining what is meant by "interactivity" or what empirical evidence is required to decide that a system is interactive. In this article, we consider these issues by examining interactivity in the domain of spoken word production. Specifically, we consider the role of interactivity in the processes involved in going from a word's meaning to its phonological form. Rather than attempting to decide for or against interactivity, we ask the following questions: Which aspects of spoken naming are best accounted for by discrete processes and which require that we assume interactivity? Where there is interactivity, what is the nature and extent of the interaction?

In this article, we identify five theories of spoken word production that represent different points along a continuum from high discreteness to high interactivity. These five theories differ systematically with respect to the features of cascading activation, feedback, domains of interaction, and seriality. We identify a set of

Brenda Rapp and Matthew Goldrick, Cognitive Science Department, Johns Hopkins University.

This research was supported by National Institute of Mental Health Grant R29MH55758 and by the Johns Hopkins University Provost's Award for Undergraduate Research.

We would like to acknowledge the dedication and insights that Seth Dennis and Brian Glucroft contributed to the scoring and analysis of the patient data. Argye Hillis and Alfonso Caramazza kindly provided us with R.G.B.'s data for reanalysis. We are grateful to Gary Dell and David Plaut for their willingness both to share their simulation programs and to assist us in getting them up and running. Michael McCloskey and Paul Smulen- sky provided invaluable intellectual and moral support throughout the project. Finally, we very much appreciate the patience and the help- ful comments of Luigi Burzio, Argye Hillis, Gary Dell, and Michael McCloskey on earlier versions of this article.

Correspondence concerning this article should be addressed to Brenda Rapp, Cognitive Science Department, Johns Hopkins University, Krieger Hall, 3400 North Charles Street, Baltimore, Maryland 21218-2585. Elec- tronic mail may be sent to [email protected].

findings from normal and impaired spoken production that need to be accounted for by any theory of spoken word production. Then, with the help of computer simulation, we evaluate the extent to which the error patterns predicted by each of the five theories conform with the empirical facts. We find support for a theory of spoken word production that, although interactive, places important restrictions on the extent and locus of interactivity. Before turning to the specific issues of spoken word production, we consider several general matters regarding discreteness and interactivity.

Interactivity: Concepts and Mechan i sms

Concepts

The term interactivity has been applied to a range of situations that differ from one another in significant ways (see Boland & Cutler, 1996). We use interactivity to refer to multiple processes (e.g., Processes A and B) that influence one another as they carry out their computations. This use of interactivity is intended to capture the notion of "mutual influence" that the term typically evokes.

A distinction can be made between forward-backward and lateral (side-to-side) interactivity (Figure 1). Forward-backward interactivity occurs when information flows not only forward, from "earlier" to "later" processes, but also backward, from later to earlier ones. In contrast, lateral interactivity refers to the situation in which the flow of information is between processes that are not ordered with respect to one another.

There are, of course, extremely difficult questions regarding what counts as a process that we do not address here. We use the term process in an informational sense to refer to transformations or mappings of one type of information to another (the mapping of semantic features onto words, the transformation of abstract word representations into phonemes, etc.). Furthermore, we would like to emphasize that although it may be possible to localize a process to a particular part of a network, a direct relationship between processes and layers or nodes in a network is not necessary. Thus, a process may involve multiple layers, and furthermore, these layers may be involved in more than one process.

460

INTERACTIVITY IN SPOKEN WORD PRODUCTION 461

@ Forward-Backward Lateral Integration

Interactivity Interactivity

Figure 1. Different types of interactivity.

Forward-backward interactivity has been invoked to explain a number of phenomena, such as the word superiority effect--the observation that people identify letters more accurately in brief visual presentations of words than in brief visual presentations of single letters or random letter strings (Reicher, 1969; Wheeler, 1970). McClelland and Rumelhart (1981; Rumelhart & McClel- land, 1982) proposed that a letter identification process feeds forward to a word recognition process and that the word recognition process not only feeds forward to subsequent processes (e.g., semantic ones) but also feeds back to the letter identification process. The letter identification process then again feeds forward to the word recognition process, creating an interactive cycle (see Elman & McClelland, 1988, for an example of forward-backward interactivity in spoken language perception; see also Samuel, 1997).

Lateral interactivity can be illustrated by a hypothesis that edge-based and motion-based processes (which can independently compute form) may influence one another in the course of object recognition. According to such a hypothesis, edge- and motion- based processes function successfully and autonomously with stimuli that lack either motion or edge cues, respectively. Further- more, these processes are not ordered with respect to one another. Only with stimuli containing both edge and motion information would the two processes interact.

As well as referring to reciprocal effects, the term interactivity is sometimes applied to a somewhat different situation in which the outputs of multiple processes come together and jointly contribute to some other subsequent process or processes (Figure 1). Note that in such cases the computations of the contributing processes themselves are not affected as a result of their joint action. To distinguish this from the reciprocal action situation, we refer to it as integration.

An example illustrating integration is the hypothesis that the outputs of relatively early and independent visual and auditory processes jointly contribute--are integrated--to determine the out-

put of some subsequent process (e.g., object identification). This proposal stands in contrast not only to a hypothesis that assumes that the two prOcesses interact but also with a nonintegrative (e.g., horse race) hypothesis that assumes that although the two independent processes are engaged, only one (e.g., the quickest) contributes to the object identification decision.

Although it is useful to distinguish between interactivity and integration, it is also important to note that the consequences of integration and interactivity are similar in the sense that both serve to reduce the isolation of processing systems by increasing the extent to which different processes jointly contribute to an outcome. In this article, we are concerned specifically with forward- backward interactivity and with a particular form of integration.

Despite the fact that interactive and integrative mechanisms are often assumed in cognitive theorizing, solid evidence for interactivity has often been difficult to come by. For example, the word superiority effect is often cited as the prototypical result requiring forward-backward interactivity; nonetheless, this account has not gone unchallenged. The argument for interactivity rests on establishing that the effect stems from word-driven enhancement at the letter level. This is crucial because alternative hypotheses based on strictly feedforward processing assume that the superiority effect originates at the word level (see Johnston, 1978; Massaro & Cohen, 1991; Paap, Newsome, McDonald, & Schvaneveldt, 1982). Research on this topic illustrates that establishing strong evidence for interactivity is generally not straightforward and, crucially, that it requires a clear demonstration that the effects cannot be accounted for within a noninteractive architecture.

Mechanisms of lnteractivity

A variety of mechanisms or architectural features may play a role in implementing interactlvity and integration. These include cascading activation, feedback, connectivity distance, extent of interactivity domains, and seriality. Cascading activation refers to

462 RAPP AND GOLDRICK

the flow of activation to a later level before a decision has been reached at the earlier level, feedback refers to the flow of information from later to earlier levels, connectivity distance refers to the directness of connections between levels, domains of interaction refer to the number and types of processes that are assumed to interact, and seriality refers to the degree to which there are processing steps or decision points between input and output. Later, we elaborate on the contribution these mechanisms make to the level of discreteness-interactivity in the system.

Theor ies o f Spoken W o r d Product ion

Word retrieval in spoken production is generally referred to as a two-stage process in which the first stage is meaning-based and the second is phonology-based (Butterworth, 1989; Garrett, 1980; Levelt, 1989). Accordingly, it is assumed that a nonverbal meaning is first mapped onto a word and that the word is then mapped onto its corresponding phonology.

In this context, the terms stage and process are often used interchangeably. However, we draw a distinction between them. As stated earlier, we use process in the informational sense; we use stage in a temporal sense to refer to activities occurring before or after some event or point in time. Furthermore, as is the case for process, we assume that there need not be a direct relationship between stages and layers or levels of a network.

Current theories of spoken word production differ primarily regarding (a) the number, type, and organization of representations; (b) whether or not activation spreads or cascades to subsequent levels before processing is finished at an earlier level; and (c) whether processing across levels is strictly feedforward (But- terworth, 1989; Levelt et al., 1991a; Roelofs, 1992, 1997; Schrie- fers, Meyer, & Levelt, 1990) or involves feedback (Dell, 1986; Harley, 1984). Although we focus on points (b) and (c), it is useful to first briefly review the debate on representational type and number.

Representational Issues

Most accounts of word production posit three major representational types: semantic, syntactic, and phonological. 1 These are typically conceptualized as pools of units or nodes that both accumulate activation and pass it on to other units. For example, Dell and O'Seaghdha (1991, 1992; see also Harley, 1993) posited semantic feature nodes, lexical nodes, and phonological segment nodes (with the lexical nodes representing lexically specific syntactic information such as noun, masculine, etc.; see Figure 2A); Levelt (1992) referred to lexical concepts, lemmas, and word forms (in which lemmas are syntactic objects; see Figure 2B); similarly, Roelofs (1992) proposed conceptual, syntactic, and word form strata; and Caramazza (1997) described a lexical semantic network, a syntactic network, and phonological lexemes (see Fig- ure 2C). In addition, Dell (1986) and Roelofs (1992, 1997) posited an additional lexical level between the 1emma level and the phoneme level (Figure 2D) that is sometimes referred to as the morpheme level. 2

At the conceptual-semantic level, most theories assume a decomposed semantic representation and depict processing as the

activation of a set of semantic features (but see Roelofs, 1992, 1997, for a nondecompositional semantics position).

Beyond the semantic level, all of the theories assume at least one level of lexical representation (i.e., where units correspond to words) that mediates between the conceptual-semantic level and the level of individual phonemes. However, the nature and organization of this lexical level differs across theories.

According to the lemrha-based theories, there is, for each word, an amodal, lexically specific node (lemma) that represents the syntactic characteristics of that word. This node mediates between the conceptual and phoneme levels and serves as the basis for retrieving both orthographic and phonological information. It is important to note that the lexically specific syntactic information represented by the lemma must be accessed to gain access to a word's phonological characteristics)

In contrast, Caramazza's (1997) independent network theory assumes that the nodes mediating between semantic features and phonemes are modality-specific phonological lexeme nodes (with a corresponding set of orthographic lexeme nodes mediating between semantics and graphemes). These phonological lexemes have pointers to features in a syntactic network as well as to phonemes in a segmental network. A central claim of the independent network theory is that access to phoneme information does not require prior access to syntactic information.

In sum, the lemma and independent network theories differ in two key respects: (a) whether the lexical representations mediating between semantics and phonemes are m o d a l or modality-specific and (b) whether access to lexical syntax is a prerequisite for access to phonological form.

The debate between lemma theories and the independent network account is ongoing, and it is beyond the scope of this article to review the relevant evidence (see Caramazza, 1997; Caramazza & Miozzo, 1997; Miozzo & Caramazza, 1997). Given that our primary concern is with the discreteness-interactivity question, we emphasize the commonalities among the theories by assuming a generic architecture (Figure 3). According to this account, there is a semantically decomposed level of representation followed by an L level that corresponds to a representation that may be either modality-specific (e.g., phonological lexemes) or amodal (e.g., lemmas) and that may or may not be fundamentally syntactic in nature.

1 The representation of orthographic form is largely ignored in most accounts, although it turns out that issues of orthographic representation . play an important role in establishing which aspects of lexical knowledge are modal (common to both written and spoken forms) and which are modality specific (Caramazza, 1997; Caramazza & Hillis, 1990; Rapp & Caramazza, 1998).

2 Furthermore, some theories posit additional sublexical levels of phonological representation corresponding to syllables or syllable constituents, whereas others do not.

3 Roelofs's (1992) theory is in the lemma class; however, because syntactic feature information is accessed from the amodal lemma node, it is not clear whether accessing syntactic feature information is essential for phonological retrieval. That is to say, the activation of the "empty" lemma node (without proper access to syntactic feature information) could serve as the basis for phonological encoding.


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Figure 2. Schematics of the following theories: (A) Dell and O'Seaghdha (1991, 1992): distributed semantic concepts map onto amodal lexical nodes, which, in turn, map onto phonemes; (B) Levelt (1989): lexical concepts map onto lemmas specifying syntactic information, and these, in turn, map onto word forms; (C) Caramazza (1997): distributed semantic concepts directly access modality-specific lexemes as well as syntactic features; and (D) Roelofs (1992, 1997): nondecomposed concepts map onto modal lemmas, and these map onto modality- specific (phonological) morphemes, which, in turn, map onto phonemes.

Cascading Act ivat ion and Feedback

The literature on spoken word production has been dominated by a debate between proponents of strictly discrete accounts (But- terworth, 1989; Garrett, 1980; Kempen & Huijbers, 1983; Levelt, 1989, 1992; Levelt et al., 1991a; Roelofs, 1992, 1997; Schriefers et al., 1990) and advocates of interactive theories (Dell, 1985, 1986, 1988; Dell & O'Seaghdha, 1991, 1992; Harley, 1984, 1993; MacKay, 1987; Stemberger, 1985). Although the work of Dell and colleagues and Levelt and colleagues refers to a number of detailed aspects of spoken production that we do not consider here, their proposals with regard to the discreteness-interactivity issue are representative of discrete and interactive positions, and thus we take them as a starting point.

The central characteristics of a strict, discrete two-stage position (e.g., Levelt et al., 1991a) are as follows: (a) Stage 1 begins when semantic information regarding the target (e.g., CAT) produces activation of the target and its semantically related competitors (e.g., DOG, HOG, RAT) at the semantic and L levels; (b) Stage 1

ends when a single L-level unit is selected, where selection means that the activation of the competing L-level units (corresponding to semantic competitors) is reduced to 0; (c) during Stage 2, phonological retrieval takes place for only the selected L-level unit (see Figure 4A).

In contrast, the central, characteristics of a highly interactive position (e.g., Dell & O'Seaghdha, 1991, 1992) are as follows: (a) Stage 1 begins when semantic information regarding the target produces activation of the target and its semantically related competitors, and furthermore, Stage 1 continues as all of the activated L-level units pass on activation to the phoneme level; (b) activation throughout Stages 1 and 2 involves not only a forward flow of activation but also a backward flow of activation between the phonological and L levels as well as between the L and semantic levels; (c) Stage 1 "ends" with the selection of the most active L-level unit, where selection means that the activation level of the selected unit is raised above that of its competitors (but the competitors are not turned off); (d) during Stage 2, processing at


Semantic Activation

L-Level Selection

Phoneme Selection

Figure 3. The generic two-stage account of spoken naming. Distributed semantic concepts map onto lexical nodes, which, in turn, map onto phonemes.

all levels continues until the end of the stage, at which time the most active phoneme units are selected (again by raising their activation levels above those of their competitors; Figure 4D).

These two theoretical positions are similar, as indicated earlier, in terms of the representational types adopted and their commit- ment to a two-stage framework. In addition, they share the assumption that both the target and its semantic competitors are active during Stage 1 processing. However, as pointed out by Levelt et al. (1991a; see also Stemberger, 1985), the interactive theory differs from the discrete theory in terms of two key features: the cascading activation assumption and the feedback assumption.

Cascading Activation

The most important 'consequence of cascading activation is that multiple units at the L level (e.g., those corresponding to the target CAT and its semantic neighbors DOG and RAT) send activation to the phoneme level. This allows the phonemes corresponding to the target's semantically related neighbors (e .g . , /d /and/ r / f rom DOG and RAT, respectively) to become "players" at the phoneme level (see Figure 5B). 4

In fact, one could say that cascading activation implements a form of integration (albeit in a somewhat different manner than illustrated in Figure 1). It is a form of integration in that it allows activation generated by two processes (L-level selection and phonological retrieval) to be combined in determining the final output of the system. This contrasts with a highly discrete account in which the nonselected candidates of the semantic process (DOG-RAT) do not influence events at the phonological level (Figure 5A).

Feedback

Feedback allows phonological neighbors of the target to be competitive throughout the system. Specifically, through the backward flow of activation, the phoneme level can influence the L level, and (with full feedback) the phoneme level can influence the semantic level. One particularly significant consequence of this is that phonological units receive activation not only from the target word but also from the target's phonological neighbors (e.g., HAT,

MAT, CAN, COT). For example, if CAT activates the phonemes /k/,/ae/, and/t/, then feedback from those phonemes back to the L level will both reactivate CAT and activate word nodes corresponding to any words sharing any of those phonemes (e.g, HAT; Figure 5C). In turn, these active L-level nodes will contribute, through their forward connections, to the activation of their corresponding phonemes at the phoneme level (e.g, /h/ from HAT) and, through their backward connections, also activate their corresponding semantic features.

Cascading Activation Plus Feedback

As just indicated, feedback alone adds the phonological neighbors of the target to the "cast of characters." When combined with cascading activation, feedback also serves to further strengthen the semantic competitors of the target at the L level and at the semantic level. As a result, in a system with cascading activation and full feedback, semantic and phonological neighbors of the target will be active at all levels of the system.

Further Interactivity: Connectivity Distance, Seriality, and Domains of Interaction

Cascading activation and feedback do not exhaust the possibilities for implementing interactivity in spoken word production. In fact, as Dell and O'Seaghdha (1991) argued, the architecture they proposed is "globally modular but locally interactive" (p. 604). As mentioned earlier, at least three other factors may contribute to a system's discreteness-interactivity: connectivity distance, seriality, and the extent of interaction domains.

Connectivity Distance

In Dell and O'Seaghdha's (1991), Dell (1986), and Dell, Schwartz, Martin, Saffran, and Gagnon (1997), there is interaction only between adjacent representational levels; nonadjacent levels interact only indirectly. However, one can imagine a system in

4 This activation of phonemes of semantic neighbors results from two aspects of the way in which cascading activation is typically assumed to be implemented: (a) The activation of phonology begins before the semantically driven L-level selection process has been completed (preactivation of phonology), and (b) all candidates of this semantically driven process (the target and its semantically related competitors) are allowed to influence second-stage phonological retrieval beyond the point of L level selection. In point of fact, either (a) or (b) would be sufficient for introducing semantically related words as players at the phoneme level. That is, if activation of phonology is allowed to occur as soon as Stage 1 processing begins but only the top candidate is allowed to continue beyond the end of Stage 1, then the "pre"activation of phonology would allow the semantically driven process to exert an influence on the outcome of Stage 2 processing. Alternatively, even if there were no preactivation of phonology but the multiple candidates activated at the L level by the semantically driven first-stage process were allowed to pass on their activation to Stage 2, then semantically generated candidates would also compete in the phonological arena. Although these two aspects of cascading activation can be distinguished, they have largely the same consequences and are typically assumed to go together. Therefore, we assume both when referring to cascading activation.


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which all levels interact directly with one another. For example, Van Orden, de Haar, and Bosman (1997) sketched an account of reading involving full connectivity among and between all levels. We do not further consider connectivity distance as a mechanism for increasing interactivity; instead, we examine seriality and domains of interaction.

Seriality

In Dell and O'Seaghdha (1992), Dell (1986), and Dell et al. (1997), at the end of each stage, a selection occurs whereby the activation of the most highly active node is increased. Although processing is interactive until each selection point, selection intro- duces a strong ordering of the processing stages (seriality), thereby reducing interactivity. If interactivity serves to strengthen the influence of competitors that share a target's characteristics across

multiple levels of the system, then any process that serves to exaggerate the difference between a winner and its competitors will reduce the influence of the competitors on all subsequent processing. In this way, strong selection points reduce the extent and the time course over which full and free interaction among levels is allowed.

Domains of lnteraction

The theories considered thus far assume (at least implicitly) that the most, if not the only, relevant domains for understanding spoken naming are semantic and phonological (as well as syntactic). However, the input domain (e.g., visually based processes in the case of picture naming) could also make a significant contribution to spoken output (Humphreys, Riddoch, & Quinlan, 1988).

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vation has been used to argue that the spoken production system is biased to produce words over nonwords.

Presumably, in a highly discrete system, errors in selecting or encoding phonemes should result in words or nonwords simply on the basis of chance. That is, according to a discrete account, the phoneme level "knows" nothing about words (it is "blind,' to lexicality), and thus there is no reason why disruptions at the phoneme level should preferentially result in word responses.

It has not been straightforward to establish the statistical probability with which a phoneme-level disruption in DFA should result in another word of the language. Nonetheless, a number of studies involving both corpora of spontaneous and experimentally elicited speech errors have made use of a variety of analysis procedures and claimed to find lexical error rates higher than would be expected by chance under DFA (Baars, Motley, & MacKay, 1975; Dell, 1986; Dell & Reich, 1981; Stemberger, 1985). In addition, researchers have found evidence of lexical bias in the errors of certain aphasic individuals. For example, Best (1996) and Gagnon, Schwartz, Martin, Dell, and Saffran (1997) found that formal errors (phonologically related word responses) occurred at rates higher than would be expected under DFA (see also Blanken, 1998; Laine & Martin, 1996).

Although the existing lexical bias data are not unassailable, a true lexical bias effect would be problematic for DFA. Proponents of the discrete feedforward position typically account for an apparent lexical bias effect by assuming a postencoding (and presumably prearticulation) "editor" that monitors the output before it is articulated and vetoes nonword outcomes (Baars et al., 1975; Butterworth, 1981; Kempen & Huijbers, 1983; Levelt, 1989). The detection of an error by the editor stops the speech production process, and word retrieval begins anew. This editorial mechanism is, of course, error-prone, and word responses are more likely to slip past unnoticed than are nonword responses. This would result in a lexical bias effect. 5

However, the postencoding editor (at least in its current form) is a relatively unsatisfactory means of accounting for lexical bias. As pointed out by Dell and Reich (1980) and Martin, Weisberg, and Saffran (1989), not only does it require seemingly needless redu- plication of information, but because the specific nature of the mechanism has remained unclear, the proposal is overly powerful.

Rather than assume this type of editorial process, interactive theories account for lexical bias by pointing to the natural consequences of cascading activation and feedback. Lexical bias can be understood as follows: As activation passes from the L-level representation of a target (CAT) to its phonemes (/kL /ae/, It/), feedback connections send activation from these phonemes back to all L-level units that share phonemes with the target, including L-level units corresponding to formal neighbors of the target (e.g., HAT, BAT, MAT, RAT). The L-level nodes of these formal neighbors, in turn, activate all their constituent phonemes, including those that are not shared with the target (/h/for HAT). These then reactivate their corresponding L-level nodes, creating what Dell (1986) referred to as "positive feedback loops. ''6 Nonword outcomes (e.g., GAT) do not enter into such relationships. When a disruption occurs at the phoneme level--for example, difficulty in retrieving the first phoneme of CAT--the phonemes corresponding to the phonological neighbors of CAT that have been activated through feedback connections will have the opportunity to more

successfully compete for selection than the phonemes corresponding to nonwords (/h/should be a stronger competitor than/g/for the onset position). This is possible only because there is an active L-level process "pushing" the final selection outcome in the direction of the phonemes corresponding to alternative words of the language, such as HAT, BAT, MAT, and RAT (as well as words that are phonologically related to semantic neighbors of the target and only indirectly related to the target itself; e.g., CAT ~ DOG LOG). In contrast, the phonemes corresponding to nonwords do not have this L-level support. This contrasts with the situation in DFA, in which disruption in encoding the initial phoneme of CAT should result in a word or a nonword simply on the basis of the number of words and nonwords that can be formed by changing the first phoneme.

This discussion illustrates that feedback is necessary for lexical bias effects. Cascading activation, in contrast, is not strictly necessary for lexical bias (see Best, 1996, on this point). For this reason, lexical bias effects are problematic for theories of spoken word production that do not allow phonologically related neighbors of a target to compete for output. Of the five theories under consideration, lexical bias effects are clearly problematic for DFA and CFA. The other three--RIA, HIA, and FILSA--adopt feedback mechanisms that should, in principle, support lexical bias effects. Nonetheless, there may be specific limitations, which, with the help of computer simulations, we explore later in this article.

The Mixed Error Effect

Speal~ers sometimes substitute a target word for one that is related to it in meaning (zebra for giraffe, oyster for lobster). A question that has received considerable attention is the extent to which targets and errors in these semantic substitutions are also phonologically similar. A number of analyses of spontaneous and experimentally elicited speech errors claim that semantic errors show a higher degree of phonological similarity than would be predicted by DFA (Butterworth, 1981; Dell & Reich, 1981; Har- ley, 1984; Martin et al., 1989; Stemberger, 1983; but see del Viso, Igoa, & Garcia-Albert, 1991; Levelt, 1983).

As is the case for lexical bias, a central issue has been establishing the "chance" rates of mixed errors that would be expected under DFA. In this regard, mixed error analyses can generally be divided into two types: (a) analyses based on predictions of rates of mixed, semantic, and other errors and (b) analyses of the errors themselves based on predicted degree of phonological overlap between targets and errors. The former are more typically carried out with experimentally induced errors and the latter with corpora of spontaneous errors where a determination of rate is often impossible because the number of items attempted and the number of correct responses are typically not known.

5 Levelt (1989) argued that independent evidence for the postencoding, prearticulatory editor comes from findings indicating that the magnitude of lexical bias effects is strongly influenced by factors one might expect an editor to be sensitive to. These are factors such as task demands and the conversational setting. For example, Baal's et al. (1975) found no lexical bias effect in an elicitation paradigm when all of the stimuli are nonwords.

6 Positive feedback loops are similar, if not comparable, to attractors referred to in PDP modeling (e.g., Plaut & Shallice, 1993a).


The baseline rate (chance rate) of mixed errors or the degree of phonological overlap between targets and errors that would be expected under DFA is typically assumed to be (a) the rate of mixed errors or phonological overlap that could be expected if a true semantic error (arising from L-level misselection) just happened to be phonologically similar to the target plus (b) the rate of mixed errors or phonological overlap that would be expected if a true phonological error just happened to be semantically related to the target plus (c) some correction for the possibility that errors may occur at both levels on some trials (Martin et al., 1989; Plant & Shallice, 1993a; Shallice & McGill, 1978). Analyses based on these assumptions (Dell & Reich, 1981; Martin, Gagnon, Schwartz, Dell, & Saffran, 1996; Martin et al., 1989) report mixed error rates significantly greater than predicted. In addition, significant mixed error effects have also been reported for aphasic individuals (e.g., Blanken, 1998; but see Best, 1996).

The discrete feedforward position has typically responded to claims of significant mixed error effects either by pointing to the weaknesses in the analyses or by invoking the postencoding editor. It can be argued that just as the editor is more likely to overlook word versus nonword errors, it is also less likely to detect mixed versus either semantic or phonological errors because mixed errors share both semantic and phonological characteristics with the target.

The interactive position accounts for these "mixed" errors by assuming that the feedback connections from phonology to L level and from L level to semantics allow for interaction between semantic and phonological processes. Given this connectivity, if something were to go wrong in the course of naming, mixed neighbors would be favored over competitors that were either only semantically or only phonologically related to the target. For example, if there were difficulties in encoding the first position phoneme for CAT, the phonemes corresponding to its phonological neighbors HAT, MAT, and RAT would be active as well as those corresponding to its semantic neighbors DOG and RAT (these semantic competitors would not have been turned off at L-level selection, and their activation would be further maintained through the feedback structure). Other things being equal, the phonemes corresponding to mixed neighbors such as RAT (which received support from both the semantic and phonological processes) should have the competitive advantage over the phonemes of either a semantic or a formal neighbor such as DOG or MAT (which has been supported by either semantic or phonological processes, but not both).

Although interactive accounts that include both cascading activation and full feedback connectivity can certainly account for mixed error effects, the respective contribution of each of these features has not been systematically explored. We show through our simulation work that cascading activation alone is sufficient to account for a mixed error effect arising at the phonological level and, furthermore, that cascading activation plus feedback to the L level is sufficient to account for mixed error effects that arise in the course of L-level selection. Thus, mixed error effects that are problematic for a strictly discrete feedforward account such as DFA can be accommodated under all of the other architectures (CFA, RIA, HIA, and FILSA). In our simulation work, we explore the specific capacities and limitations of the five architectures in this regard.

Other Evidence

In addition to analyses concerned with errors, there are a number of experimental results relating to other types of predictions derived from discrete and interactive theories. For completeness, we review these very briefly.

For example, a number of researchers have evaluated the adequacy of discrete and interactive accounts with regard to the time course of semantic and phonological effects (see Levelt, Roelofs, & Meyers, 1999, for a review). Some of these studies provide support for cascading activation and/or feedback (see Cutting & Ferreira, 1999; Damian & Martin, 1999; Peterson & Savoy, 1998; Starreveld & La Heij, 1995, 1996; but see Roelofs, Meyer, & Levelt, 1996; Schriefers et al., 1990).

In addition, Nickels (1995) examined predictions of interactive versus discrete accounts regarding the effects of semantic (concreteness-abstractness) and formal (length and frequency) variables on the errors of aphasic individuals and found that the evidence favored a discrete position (but see Harley, 1995, for a response).

Furthermore, in a very comprehensive study, Dell et al. (1997) examined the picture-naming performance of a set of individuals with acquired neurological damage in order to determine whether an interactive two-stage theory (with cascading activation and feedback) could account for the range of individual error patterns (see also Foygel & Dell, in press; Martin & Saffran, 1992; Martin et al., 1989; Schwartz, Saffran, Bloch, & Dell, 1994). Through a series of simulation studies, Dell et al. showed (among other things) that the fit between observed and simulated patterns was substantially better than the fit obtained for randomly generated patterns of errors (pseudopatients). This successful fit between patient data and simulation data lent general support for the interactive two-stage account of naming. We return to this study in the General Discussion section.

Plan for the Remainder of This Article

In sum, the evidence from mixed errors and lexical bias provides support for the assumptions of cascading activation and feedback. As indicated earlier, this evidence has not gone unchallenged. However, even if we were to assume its validity, the existing evidence does not distinguish between the various theoretical positions that assume cascading activation and feedback (e.g., RIA, HIA, and FILSA).

This article has two empirical sections. In the first,, we present three case studies of neurologically injured individuals. These case studies establish two further sets of empirical results that provide important constraints on theories of spoken word production. In the second empirical section, with the assistance of computer simulation, we carry out a detailed evaluation of the five theoretical positions we have identified. They are evaluated with respect to their ability to account for four sets of findings: the mixed error and lexical bias effects in unimpaired individuals as well as the two sets of findings from our study of the neurologically injured individuals. We show that these latter results not only provide further evidence in support of cascading activation and feedback but also allow us to go some distance in adjudicating among the various interactive accounts of spoken word production.


Table 1 Results of Lexical Testing for K.E.

Task Correct Semantic Similar word Nonword Other word "Don't know"

Oral picture naming 56% 38% 0% 0% 0% 6% (n = 335)

Written picture naming 55% 32% 0% 7% a 1% b 5% (n = 335)

Oral reading 58% 35% 1% 0% 0% b 7% (n = 335)

Writing to dictation 59% 25% 1% 10% a 2% 4% (n = 335)

oral tactile naming 53% 45% 0% 0% 0% 2% (n = 47)

Written tactile naming 60% 34% 0% 2% 0% 4% (n = 47)

Auditory comprehension 58% 40% 0% 2% ¢ 0% (n = 191)

Reading comprehension 65% 28% 1% 7% ~ 0% (n = 191)

a Includes misspelled, possible semantic errors (e.g., crab ---* [clam] ~ clim), b Includes possible visual ---> semantic errors (waist ~ [wrist] --* watch) and semantic --~ visual errors (check ~ [rouge] ~ rug). ¢ Rejection of correct word-picture matches.

Case S tudies

Case 1: K . E . m S e m a n t i c Deficit, Semant ic Errors Only

K.E. was a 52-year-old, right-handed man with a master of business administration degree who worked as a high-level manager in a large corporation when he had a thromboembolic stroke 6 months prior to the initiation of the testing reported in this article (see Hillis, Rapp, Romani, & Caramazza, 1990, for a detailed report). A C T scan 5 days postonset revealed a large area of decreased density in the left fronto-parietal area, consistent with an infarct in the distribution of the left middle cerebral artery. Testing indicated intact visual perception, reasoning, and arithmetic skills. K.E.'s spontaneous speech was sparse, consisting primarily of nouns, semantic paraphasias, and overlearned phrases. His repetition, however, was good. K.E. had significant difficulties in spoken and written comprehension and production of single words, and a number of tasks were administered to examine more systematically these lexical difficulties.

K.E. was tested with two sets of stimulus items. Set 1 consisted of 144 stimulus items from 10 semantic categories. Set 2 consisted of 47 items from 5 semantic categories. Both sets of stimuli were presented in four production tasks (oral picture naming, written picture naming, oral reading, and writing to dictation) and in two comprehension tasks (auditory word-picture matching and written word-picture matching). In addition, items from Set 2 were also presented for spoken and written tactile naming. For details of test administration, see Hillis et al. (1990).

Responses were scored as semantic errors if they clearly shared a semantic relationship with the target; nonword responses if they did not correspond to words of the language; similar words if they were not semantically related to the target but shared 50% of the target 's phonemes; don't know responses if K.E. did not produce a response; or other, which included words that were neither phonologically similar nor obviously semantically related to the target. Results for all tasks are reported in Table I.

One important characteristic of K.E. 's performance is that he produced no phonologically related responses in oral naming, but

instead he produced only semantic errors or "don ' t know" respons-

es. 7 K.E. 's (6%) "don ' t know" responses may represent instances when the semantic system was not able to settle on any likely candidate. For our purposes, what is important is that there is no

evidence that these responses originated in a faulty phonological process. A deficit at a phonological level should result in at least

some phonological errors; the fact that none were observed allows

us to assume that the "don ' t know" responses did not have a phonological origin.

The second striking observation is that K.E. made semantic

errors at similar rates on all tasks-- in production and comprehension; across auditory, written, and tactile input modalities; and across written and spoken output modalities. This pattern points to

a common error source--presumably a lexical semantic-level deficit affecting comprehension and production in all modalities, s The

only alternative account would be that of multiple impairments to

numerous input-output mechanisms. This possibility was examined and rejected in Hillis et al. (1990) on the basis of item consistency analyses.

7 Although immediately after the stroke K.E. exhibited some oral-verbal apraxia in attempts to produce words or nonverbal oral movements, this quickly resolved such that, by the time of the testing reported here, sound-based errors were virtually absent from his spoken production. In testing with a different set of items, K.E. made two or three errors in oral reading that consisted of responses that were ambiguous in terms of whether they were either visually or phonologically similar to the target. Given the absence of such errors in his naming, however, we assumed that in reading these errors arose as a result of the contribution of sublexical processing to the oral reading output (Hillis & Caramazza, 1995).

8 In addition to the central semantic deficit, K.E. showed evidence of an additional impairment specific to written production. This impairment manifested itself in numerous misspellings that involved letter deletions,

INTERACTIV1TY IN SPOKEN WORD PRODUCTION

Table 2 Results of Lexical Testing for P. W.


Oral picture naming 72% 19% 0% 0% 3% 6% Written picture naming 46% 12% 3% 17% 6% 16% Spoken word-picture

matching 95% 5% 0% 0% Written word-picture

matching 94% 6% 0% 0% Repetition 100% 0% 0% 0% 0% 0%

471

In sum, the results of K.E.'s testing clearly point to a lexical semantic locus of impairment. Importantly, the fact that K.E. produced semantic errors and no sound-based errors indicates the lexical production system, must be structured in such a way that damage at the semantic level can result in a pattern of pure semantic errors.

Case 2: P. W.--Postsemantic Deficit, Semantic Errors Only

P.W. was a 51-year-old, right-handed man who had completed one semester of college and was employed as a manager of a meat department when he had a left-hemisphere stroke that resulted in written and spoken language difficulties. CT scanning revealed an ischemic infarction of the left parietal and posterior frontal areas. The results we report were obtained between 24 and 48 months after the cerebral vascular accident (CVA; for further information, see Rapp, Benzing, & Caramazza, 1997; Rapp & Caramazza, 1998).

P.W. was asked to carry out the following tasks with 258 items from Snodgrass and Vanderwart's (1980) picture set: spoken picture naming, written picture naming, repetition, and written and spoken word-picture matching. The tasks were administered as for K.E., and the results are reported in Table 2.

P.W.'s performance was similar to that of K.E. in one important respect and crucially different in another. In oral picture naming, P.W., like K.E., produced a considerable proportion of semantic errors but no phonological errors (similar words or nonwords); sound-based errors were also not observed in repetition. However, P.W. was markedly different from K.E. in that his word comprehension was largely normal, as indicated by his accuracy of 95% on the spoken word-picture matching task (for normal elderly control participants [n = 14], accuracy ranged from 96% to 100%).

P.W.'s performance on the comprehension task was essentially normal, indicating that a semantic deficit was unlikely to

be the source of his semantic errors in spoken and written naming. Several additional pieces of evidence support this conclusion. P.W.'s semantic errors were often accompanied by a verbal indication that he knew he had not produced the correct word (e.g., picture of a zebra ---> "horse, not r e a l l y . . , a horse in the jungle"). This suggests that he was not confused about the word's meaning. Also, on many occasions when he made a semantic error in spoken naming, he was able to spell the correct word (e.g., picture of a tiger ---> "lion"; T-I-G-E-R). In fact, in a series of picture-naming tasks in which P.W. was required to say, write, say (or write, say, write) the name of each picture on each triM, he was able to write the name of the picture on about 20% of trials when he was unable to provide the correct name in spoken form (Rapp et al., 1997).

ff semantic errors do not originate from a semantic deficit, then how do they come about? Caramazza and Hillis (1990) argued that semantic errors may have either a semantic or a postsemantic basis. They assumed that semantic representations activate phonological lexemes to a degree proportional to their shared semantics, such that when one is intending to say "tiger," the phonological lexemes of lion, cat, leopard, and so forth will also be activated. Given this, if (as a result of postsemantic damage) the tiger lexeme is relatively unavailable, then the most highly activated word (e.g., "lion") may be produced (see also Nickels, 1992). With regard to P.W., Rapp et al. (1997) argued that he had postsemantic damage affecting the retrieval of both phonological and orthographic lexemes. 9

Case 3: CS.S.--Postsemantic Deficit, Multiple Error Types

C.S.S. was a 62-year-old, right-handed man with 3 years of college who worked as a jet engine testing engineer before having a left-hemisphere stroke 26 months before the initiation of our work with him. CT scanning revealed a left parietal infarct and a

substitutions, additions, and transpositions, for example, pelican ---> PELI- CAR, and a number of errors that, presumably, are misspellings of semantic errors (e.g., shark -->ALLIGATE). In other work (Caramazza, Micefi, Villa, & Romani, 1987), these types of errors have been attributed to damage to the graphemic buffer--a postlexical locus of impairment within the spelling system.

9 We further argued that the deficit in spoken naming was lexical and prephonological because we found highly significant effects of lexical frequency on accuracy but we found no significant effects of length or of phonological complexity (number of clusters; Rapp et al., 1997). These are factors that, it has been argued (Butterworth, 1992), affect postlexical phonological processing.


Table 3 C.S.S. ' s Performance on a Set of Lexical Processing Tasks


Oral picture naming 82% 6% 3% 7% 2% 0% Written picture naming 82% 2% 0% 8% 8% 0% Spoken word-picture

matching 100% 0% 0% 0% Written word-picture

matching 100% 0% 0% 0% Repetition 96% 1% 1% 2% 0% 0%

small lacunar infarct in the fight basal ganglia. In addition, and prior to the infarct, C.S.S. had had, for a number of years, an asymptomatic and stable left frontal lobe menlngioma.

C.S.S.'s spontaneous speech was marked by frequent hesitations and word-finding difficulties as well as semantic and phonological errors. We examined his single-word processing in a number of tasks, including Spoken picture naming, repetition, and spoken and written word-picture matching. Stimuli for these tasks were Snodgrass and Vanderwart's (1980) 258 items that were used with P.W. Task administration and error classification procedures were the same as those described above. The list was administered twice for the tasks of oral naming and repetition and once for spoken and written word comprehension. The results are presented in Table 3.

Like P.W., C.S.S. performed well in spoken and written word comprehension. However, unlike P.W. and K.E. and although his overall accuracy was higher, C.S.S. produced multiple error types in spoken naming: semantic errors, similar word responses, and a significant number of nonword responses. Furthermore, unlike P.W. and K.E., C.S.S. made similar types of errors (albeit at far lower rates) in repetition as well. The intact comprehension indicates a postsemantic deficit; additionally, the presence of phonologically similar word and nonword errors is potentially indicative of a deficit to a level of phonological processing. Further indica- tions of phonological involvement are that repetition was also error-prone and there was a significant word length effect. On this basis, we assume that C.S.S. is likely to have some difficulty affecting his ability to retrieve and/or encode the phonemes corresponding to the words he is trying to produce. We cannot, however, be sure that this is the only, or even the primary, source of his errors. We consider single and multiple damage loci for C.S.S. in evaluating the five candidate theories.

Interim Summary

We have described the performance of 3 individuals with deficits affecting their spoken naming abilities. Both K.E. and P.W. exhibited a pattern of only semantic errors in naming, whereas C.S.S. produced multiple error types. Importantly, the data indicate that a pattern of only semantic errors can arise either from a semantic locus of impairment (K.E.) or from a postsemantic locus (P.W.). A pattern of mixed error types (C.S.S.) may involve additional damage affecting phonological retrieval or encoding. In this regard, C.S.S. resembles others in the literature (Dell et al., 1997; Ruml, Caramazza, Sbelton, & Chialant, in press). These results establish the first set of findings from impaired performance that we are concerned with: The spoken naming system can

apparently be damaged such that a pattern of only semantic errors can arise from either a semantic or a postsemantic locus of damage.

Phonological Influence on Semantic Errors:

Mixed Error Effects ?

A pattern of only semantic errors in naming in the absence of phonologically similar word or nonword errors would seem to suggest little or no influence of phonology at the level of the system at which the errors were generated. The absence of such errors does not, however, rule out the possibility that the semantic errors themselves show a mixed error effect. Given this, we carried out a series of analyses to determine the extent to which the semantic errors produced by K.E., P.W., and C.S.S. were phonologically similar to the target words. Under DFA, semantic errors should be no more phonologically similar to the target than one would expect by the random pairing of words. This is because, as indicated earlier, according to this theory there is no mechanism by which words that are both semantically and phonologically similar to a target should have an advantage over words that are only semantically (or only phonologically) similar.

To obtain a larger set of errors for the mixed error analyses, we collected responses from the administration of various additional sets of stimuli. To create similar conditions across the three individuals, we restricted the analyses to items from eight categories with which all three were tested (vehicles, vegetables, clothing, birds, furniture, fruit, animals, and body parts). 1° This procedure provided us with 67 target-error pairs for C.S.S., 109 for P.W., and 63 for K.E.

Analysis 1: Overall Phonological Overlap Index

The Phonological Overlap Index (POI) between targets and errors was determined for each individual as follows: (a) Each phoneme of a target word was compared with each phoneme of the

1o Most semantic errors consisted of items with a coordinate relationship to the target. There were, however, some responses that were semantic associates of the target (lemon ~ sour) or that were the target's superor- dinate category (e.g., caterpillar ----> bug). There were few such responses (two errors for K.E., three errors for C.S.S., and eight errors for P.W.). Nonetheless, it is possible that these do not represent "true" semantic errors but rather an attempt to explain the meaning of the target. If so, a true phonological effect in naming might be obscured. To prevent this, we removed all of these errors.


Table 4 Phonological Overlap Index (POI) Values for the Three Participants as Well as the Characteristics of a Baseline Distribution of POI Values Obtained From 5,000 Random Re-Pairings of Stimulus-Response Sets

Participant POI Estimated p Adjusted POI Estimated p Readjusted POI Estimated p

K.E. .134 >.32 .124 >.722 P.W. .220 <.0002 .201 <.0002 C.S.S. .301 <.0002 .291 <.0002 .259 <.0002

Mean POI SD Minimum Maximum Baseline distribution .129 .01 .097 .169

response, and each match was assigned a value of 2 (indicating that two of the phonemes in the target and response pool were shared); (b) the proportion of shared phonemes for each target-response pair was calculated by dividing the number of matches by the total number of phonemes in target plus response; and (c) the propor- tions of shared phonemes for each target-response pair were then averaged to generate an overall POI value for each individual.H Using this technique, we obtained the following overall POI values: .134 for K.E., .220 for P.W., and .301 for C.S.S.

Interpreting these POI values requires determining the baseline POI values that would be expected given the general characteristics of the English language and the specific types of stimuli with which the these individuals were tested. To determine this, we randomly paired all targets and errors produced by the 3 individuals. We then computed a mean baseline POI in the same manner as described above. We carded out 5,000 such random pairings of the items in the target-error list, generating 5,000 baseline POI values. The mean POI value of this set was .129, with a standard deviation of .01 and minimum and maximum POI values of .097 and .169, respectively (Table 4):

The frequency with which a value equal to or greater than each individual's observed POI was generated in the 5,000 baseline runs provides a direct estimation of p values (see Krauth, 1988, for a discussion of distribution-free statistics). As shown in Table 4, the results indicate that the POI values observed for P.W. and C.S.S. were never observed in 5,000 random pairings (p < .0002). However, a POI value equal to or greater than K.E.'s was generated 1,605 times (p > .32). Thus, whereas P.W.'s and C.S.S.'s semantic errors were clearly more phonologically similar to the target words than would be expected given a random pairing of words, K.E.'s responses were not.

Analysis 2: Adjusted Phonological Overlap Index

We assumed baseline rates to be comparable with the overlap observed by randomly pairing items. However, given that the errors are from the same semantic category as the targets, there is a possibility that words within the same category are more phonologically similar to one another than to words from other categories. If so, then some proportion of the overlap observed in the semantic errors would come "for free" merely by virtue of their being semantic errors. This is important because if we take a significant mixed error effect to be an indicator of external pho-

nological influence on semantically driven processing, then it is crucial to eliminate any possible artifactual phonological effects.

We carried out an analysis to determine the average POI value for items within each of the eight semantic categories, as well as the average POI value for items from different categories. To do so, we first specified the members of the eight categories by taking, for each category, all items listed as a member of that category by at least 20 of the 442 participants in Battig and Montague's (1969) norms. This provided us with reasonably large categories made up of fairly typical members.

A mean within-category POI for each category was calculated by randomly pairing items within a category and then computing the mean POI. Random within-category pairings were carded out 1,000 times for each category, and then the mean POI for the 1,000 values was calculated for each category. The mean across-category POI for each category was determined by randomly pairing the items of one category with items from the other seven categories and then computing a mean POI. Again, this was carded out 1,000 times for each category, and the mean POI values for each set of 1,000 values were calculated (Table 5). Averaging across the eight categories, the average within-category POI was .175, and the average across-category POI was .148. That is, on average, words within a category shared 17.5% of their phonology, whereas words across categories shared 14.8%. Importantly, an average of 2.7% of the phonemes of two items from the same category were shared simply by virtue of their common category membership. 12 This difference was significant in one-tailed test-

t ~ A number of other features could have been considered in calculating phonological overlap, including number of phonemes in the same syllabic position, number of syllables, stress pattern, and so forth. Any choices are obviously theory dependent, in that they rely on assumptions about what "counts" as similar to the activation process; for example, are only words with the same phonemes in the same syllabic position confusable? Current understanding of these issues is so limited that our choice was driven primarily by ease of computation. What is critical is that the same criteria used in establishing the observed POI values were applied in establishing chance POI rates.

~2 Because some categories include items with possibly shared morphology (e.g., bicycle, tricycle, motorcycle), we re.computed the within- and across-category POI, excluding these morphologically related items. There were relatively few such items, and the re.computed values were virtually identical to the original values.


Table 5 Average Phonological Overlap Index (POI) Values for Items From the Same Semantic Category and POI Values for Items Across Semantic Categories

Within Across- Category category POI category POI Difference

Transportation .248 .170 .078 Vegetables .214 .169 .044 Clothing .193 .143 .050 Birds .150 .138 .012 Furniture .143 .145 - .002 Fruit .211 .153 .058 Animals .124 .136 -.012 Body Parts .117 .127 -.010

M .175 .148 .027

possibility that some of C.S.S.'s semantic errors arose from damage to more than one part of the system. Under DFA, we must consider the possibility that some of these errors may have come from a phonologically based error-generation mechanism that produces phonological errors that just happen to be semantically related to the target. Any such errors need to be removed from C.S.S.'s corpus of semantic errors and a mean POI value needs to be recalculated.

C.S.S. is an interesting case in that the analysis of his data presents some of the same difficulties that one faces in analyzing normal slips of the tongue--in both cases it is possible (indeed, likely) that errors arise at multiple levels of the processing system.

Analysis 3: Readjusted Phonological Overlap Index for C.S.S.

ing, t(7) = 2.19, p < .05, and marginally so in two-tailed testing. The finding of an overall POI advantage for within- versus across- category items has implications not only for the evaluation of phonological effects in the semantic errors of the 3 individuals we studied but also for the evaluation of mixed error effects in slips of the tongue of normal speakers.

The finding of a positive and significant POI effect of category membership indicates that we may have overestimated the effect of external phonological influence in the genesis of the semantic errors. To take into account the within-category POI advantage, we calculated an adjustment value for each of the 3 individuals. To compute these adjustment values, we first counted the number of items from each individual's data set in each of the eight categories. We multiplied these numbers by the within- versus across- category POI difference for each category, and then an average adjustment value was calculated for the entire list. For K.E., this adjustment value was .009; for P.W., it was .019; and for C.S.S., it was .010.13 We adjusted the POI values reported above for each individual by subtracting the adjustment values from them. The resulting adjusted POI values were .124 for K.E., .201 for P.W., and .298 for C.S.S. When we compared the adjusted values with the values in the baseline distribution, we found (Table 4) that the adjustment had no significant consequences for the pattern reported prior to adjustment. A POI value at least as large as that observed for K.E. occurred in the random pairings 3,611 out of 5,000 times (p > .72). In contrast, even with the adjustment, P.W.'s and C.S.S.'s POI values were never generated by a random pairing of items.

The results of the mixed error analyses can be summarized as follows: The individual with semantic-level damage (K.E.) showed no mixed error effect, whereas the individual with independently established postsemantic damage (P.W.) showed a strong and robust mixed error effect. The situation with C.S.S. is more complex, however. The mixed error analysis carded out thus far assumes a single semantically driven, error-generating mechanism. As described above, on the basis of performance across a range of tasks, this assumption is motivated for K.E. and P.W. For K.E., this mechanism is presumably at the conceptual-semantic level, whereas for P.W., it may be a process involved in L-level activation, selection, or both. However, we cannot rule out the

To determine the proportion of C.S.S.'s semantic errors that may have been generated by a phonologically based error mechanism, we collected all of C.S.S.'s nonsemantic word responses to items within the eight semantic categories, that is, all real word responses that bore no apparent semantic relationship with the target (e.g.,/g o t / - -~ /n o t/); there were 35 such errors. We then calculated the phonological overlap for each of the target-error pairs and compared the distribution of overlap values for these nonsemantic word errors with the overlap distribution for the semantic errors. The two distributions were quite different. The mean POI value for the distribution of nonsemantic word errors was .60 (SD = .18, minimum = .25, maximum = .89), and the most frequent overlap values fell in the .60-.80 range. In contrast, the mean POI value for the semantic errors was .301 (SD = .21, minimum = 0, maximum = .71), and the most common overlap values were in the .21-.40 range. To eliminate all errors in the semantic distribution that might have been produced by the phonological process, we needed to reduce the semantic distribution by the largest possible proportion of such errors. At the same time, the adjustment needed to conform to the characteristics of the distribution of the phonologically based errors. To accomplish this, we assumed that all of the semantic errors within the .61-.80 range (the peak overlap range for the phonological process) were actually generated by the phonological process. We eliminated all of these and then further reduced the remaining semantic error distribution in a manner proportional to the distribution of nonsemantic word errors (see Figure 7). Removing these values (13% of the total number of semantic errors) presumably left us with the set of semantic errors that were not likely to have been generated by the phonologically based error mechanism. 14

We then recalculated the POI over the remaining items and found that the readjusted POI value of .259 (actually .269 - .01 for the within-category error adjustment) was, as before, never observed in the random baseline distribution. In sum, when we eliminated all semantic errors that could have arisen accidentally

13 For all 3 individuals, the adjustment value was lower than the average across all categories because the data sets included many items from the categories of animals and body parts, where there are slightly negative within- versus across-category POI differences.

14 Gislin Dagnelie deserves credit for proposing this adjustment method.

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Figure 7. Distribution of C.S.S.'s semantic errors in terms of percentage of phonological overlap between target and error. The line with open squares indicates the proportion of the distribution removed in order to correct for the possible contribution of a phonological error process.

from a phonological process, we still observed a strong and significant effect of phonology on C.S.S.'s semantic errors.

Summary of Findings From Impaired Naming

We first determined the locus of damage for each of the 3 participants on the basis of the pattern of accuracy and naming errors across a range of tasks. We then examined the possibility of phonological influence on the semantic errors. We determined the locus of damage independently of, and prior to, examining the extent of phonological influence. There are two sets of findings from these case studies that may provide important constraints on theories of naming: (a) The naming system can be damaged such that a pattern of only semantic errors can result from either a semantic locus of damage or some postsemantic locus, and (b) whereas a semantic locus of damage need not give rise to a mixed error effect (K.E.), a postsemantic locus may do so (P.W. and C.S.S.).

Of particular interest is the pattern exhibited by P.W.--a pattern of only semantic errors with a significant mixed error effect. Given the potential importance of this pattern and in order to consider its replicability, we carded out an analysis of the semantic errors of another individual who had a functional deficit apparently quite similar to P.W.'s. R.G.B. (Caramazza & Hillis, 1990) was described as producing a pattern of only semantic errors in spoken naming as a result of postsemantic damage. Hillis and Caramazza kindly provided us with the set of R.G.B.'s spoken naming errors, and we computed the POI value for R.G.B.'s semantic errors. The

obtained value of .319 indicates a highly significant mixed error effect. The similarity in the results for P.W., R.G.B., and C.S.S. increases our confidence in the finding that semantic errors arising from a post,semantic locus of damage may exhibit a robust mixed error effect.

The Evaluation of Five Theories

In this section, we use computer simulation to assist us in evaluating the extent to which five theories, representing five points along a continuum of discreteness-interactivity, can account for the following four sets of findings: (a) a mixed error effect in unimpaired individuals, Co) a lexical bias effect in unimpaired individuals, (c) a pattern of only semantic errors arising from either a semantic (K.E.) or a postsemantic (P.W.) locus of damage, and (d) the differential effects of phonology according to impairment locus--the absence of phonological effects on semantic errors resulting from semantic damage and the presence of phonological effects on semantic errors resulting from postsemantic damage. Additionally, we consider the adequacy of each of the theories with regard to the pattern of multiple error types exhibited by C.S.S. because this represents a commonly reported pattern (see Dell et al., 1997; Ruml et al., in press).

As indicated in the introduction, the five theories we simulate are (a) DFA: no cascading activation, no feedback; (b) CFA: cascading activation, no feedback; (c) RIA: cascading activation, partial feedback from phoneme levels to the L level; (d) IliA: cascading activation, feedback between all adjacent levels; and (e) FILSA: cascading activation, feedback between all adjacent levels, low seriality, and extended interaction domain.

This work has two objectives: (a) to determine which one of these particular theoretical positions best accounts for the data and (b) to draw more general conclusions regarding discreteness and interactivity in spoken word production.

Simulation Information

The simulations of DFA, CFA, RIA, and HIA all use the same lexicon and the same localist connectionist processing architecture. These are described in the following section. The simulation of FILSA is based on a PDP architecture used by Plant and Shallice (1993a) and is described in the section on the evaluation of FILSA.

Simulation Neighborhood

Following Dell et al. (1997), we simulated spoken naming with the naming of a single consonant-vowel-consonant (CVC) target word presented in a neighborhood with the error opportunities of an "average" English neighborhood. The error opportunities for English were reported by Dell et al. and are presented in Table 6. This table indicates that for any given target word, a random selection of phonemes of the language will yield a nonword outcome 80% of the time and a word outcome 20% of the time. When the outcome is a word, it will be an unrelated word 10% of the time, and so forth. The simulation neighborhood was also designed to be consistent with our findings (reported above) of


Table 6 Error Opportunities in English (Based on Dell et al., 1997) and for the Neighborhood Adopted in the Simulations of the Discrete Feedforward Account, the Cascading Feedforward Account, the Restricted Interaction Account, and the High Interaction Account

Estimated error Simulation error Error category opportunity opportunity

Nonword .80 .75 Word .20 .25

Semantic .01 .018 Mixed .004 .009 Formal .09 .099 Unrelated .10 .126

greater phonological overlap within versus across semantic categories. 15

To match the error opportunity and phonological overlap constraints, we designated words from three independent pools of position-specific phonemes--corresponding to the onset, vowel, and coda of CVC words. We used seven onsets, four vowels, and four codas. The lexical neighborhood we created consisted of a target word and 28 neighbors (Table 7). This neighborhood well matched the estimated error opportunities in the language (Table 6) and had a greater within (.22) versus across (.173) semantic

category POI value.

Simulation Architecture

L-level units, whereas HIA assumes additional feedback connections between the L level and semantic feature layers.

Processing Characteristics and Parameters

Processing time in the network takes place in a series of steps; at each step, the activation of units is updated synchronously. Each unit 's activation is determined by its own activation at the previous time step as well as by the activation (at the previous time step) of the units that connect to it. We used the following activation function, adapted from Dell et al. (1997):

Aj(t) = Aj(t - 1)(1 - Qj) + .~,P,fi,(t - 1) + GN(Aj(t - 1), Nj).

Aj(t) is the activation (A) of any unit j at time (t). Q is the decay parameter (set in our simulations to .517). P is the parameter determining the weight on the connections between units. All weights connecting one layer with another had the same positive value of .04 except when we carried out manipulations of feedback strength. Noise is generated by selecting a random number from a Gaussian function (GN). This function has a normal distribution with a mean of 0 and a standard deviation proportional to the activity of the unit at the previous time step (Aj[t - 1]) and the noise parameter (Nj). In normal processing, we assume that N is at or near 0, and we assume that brain damage corresponds to a pathological increase in N.

In addition to the parameters P, Q, and N, additional parameters include step number and jolt size. Processing in the simulation begins with an initial activation or "jolt" to each of the 10 semantic units comprising the semantic representation of the target word.

We used a single four-layer network to simulate the naming process from conceptual processing to phoneme selection (Figure 8). The network builds on the three-layer network of Dell et al. (1997), to which we added a concept layer. We did so to examine K.E.'s pattern of semantic/conceptual-level damage. In effect, we simulated a three-stage theory of naming by adding a stage that is assumed, although not usually discussed, in current theorizing (see Levelt et al., 1999). The three stages are Stage 1 (conceptual processing), Stage 2 (L-level selection), and Stage 3 (phonological retrieval).

The first layer consists of 29 lexical concept nodes, one for each word in the neighborhood. The second layer consists of 281 semantic feature nodes. Each concept is connected to 10 semantic feature nodes that make up each word 's semantic representation. Words within the same semantic category (the target and its three semantic neighbors) share 3 semantic feature units, and words outside the semantic category share none. The semantic feature nodes are, in turn, connected to any of the 29 L-level nodes that they serve to define. Finally, each L-level node is connected to one phoneme in each of the three (onset, vowel, coda) phoneme pools.

In addition to these feedforward connections, all simulations have feedback connections from semantic feature units to concept units. The feedback from semantic features to concept units ensures that the simulations of the different theories are identical with respect to everything except for the architectural features that are being manipulated. 16 Additionally, for the simulation of RIA, there are feedback connections between phoneme-level and

is Dell et al. (1997) used two networks with different neighborhood structures to simulate the structure of a single prototypical neighborhood. One neighborhood consisted of a target, one semantic and two formal neighbors, and two unrelated words, and the other neighborhood consisted of a target, one semantic neighbor, one mixed neighbor, one formal neighbor, and two unrelated words. The first was sampled on 90% of naming trials and the second on 10%. Dell et al.'s neighborhoods were potentially problematic in two ways. First, the manner in which sampling from the lexical neighborhood was implemented put a ceiling on the number of mixed errors that could be observed. Second, the greater across-category versus within-category POI values for Dell et al.'s lexicon overpredicted formal error rates. In the neighborhoods adopted by Dell et al., the overall (across the two neighborhoods) within-category POI was .033, and the across-category POI was .32. That is, the structure of the neighborhoods actually favored the activation of formal neighbors over mixed neighbors.

In a simulation investigation, Ruml et al. (in press) examined the role of simulation neighborhood characteristics under various types of simulated damage. Somewhat surprisingly, and despite the obvious aforementioned shortcomings, Ruml et al. concluded in favor of Dell et al.'s (1997) neighborhood.

16 In the simulations of DFA, CFA, and RIA, the fact that there is feedback between semantic features and concept units has no consequences for evaluating the role of interactivity-discreteness in L-level selection and phonological encoding because the absence of feedback from L-level units to semantic feature units ensures that phonological effects cannot contribute to semantic-conceptual processing and selection.

x7 With the exception that, largely for "historical" reasons, Q was set to .7 at the concept level.


Table 7 The Number and Characteristics of the Words in the Prototypical Neighborhood Adopted in the Simulations of the Discrete Feedforward Account, the Cascading Feedforward Account, the Restricted Interaction Account, and the High Interaction Account

Category No. of words Phonemes shared with the target

Target 1 Mixed 1 2/3 phonemes Semantic 2 0/3 phonemes Formal 11 n = 2, 2/3 phonemes

n = 9, 1/3 phonemes Unrelated 14 0/3 phonemes

After this, there are three subsequent selection points: at the semantic level, at the L level, and at the phoneme level. These selection points can be thought of as marking the end of each processing stage (conceptual processing, L-level selection, and phoneme retrieval) and the beginning of the next. In our simulations, the following are adopted: 10 steps for Stage 1 (conceptual processing), 8 steps for Stage 2 (L-level selection), and 8 steps for Stage 3 (phoneme retrieval).

At each selection point, the most active unit in a particular layer is selected by the introduction of external activation. In the case of a tie, the selection process randomly chooses among all equally active units. A trial begins with an initial jolt of 1 unit to each of the 10 semantic units corresponding to the target, the semantic selection jolt increases the activation of the semantic feature units corresponding to the most active concept unit to 10, and the L-level selection jolt sets the most highly active L-level unit to 4. Phoneme selection does not involve a jolt; rather, it simply consists of noting the identity of the most highly active phoneme unit in each of the three phoneme pools.

Simulating Damage

We simulated damage by introducing what Dell et al. (1997) referred to as activation noise, which is GN(Aj(t - 1), Nj) in the activation function above, at either single or multiple levels. Un- less stated otherwise, each simulation was run for 10,000 trials at each of five values of N.

Given that the amount of noise a unit receives is directly proportional to its own activation level, the most frequent error types will involve units whose activation levels are normally very close to the target, the next most frequent error types will involve those units whose activation levels are the next closest to the target, and so forth. We also carded out extensive manipulations of other parameters and found that all of these (with the possible exception of P) have similar consequences (see Appendix A for a more detailed discussion).

In evaluating each theoretical position, we discuss response rates subsequent to "damage" at each of three levels: the concept level, the L level, and the phoneme level. It is essential to keep in mind that, regardless of the locus of damage, the responses always correspond to the most active phonemes at the end of each simulated naming trial.

Evaluation of the Discrete Feedforward Account

The critical theoretical assumptions of a DFA are (a) processing proceeds in a strictly forward direction, (b) processing is confined to the current processing stage only, and (c) only the item selected at the end of a given stage is processed at the following one. The predictions of DFA with regard to the four sets of facts with which we are concerned are fairly transparent; nonetheless, we simulate this position as a check on the basic simulation architecture.

Mixed Error Effect

DFA does not predict a mixed error effect because it does not allow for the integration of semantically and phonologically driven activation required to provide an advantage to a mixed neighbor over both semantic and formally related neighbors.

Concept- or L-level noise. If noise at the concept level or the L level results in the lowering of the target's activation levels, then an error will originate from among the target' s competitors at those levels: semantic and mixed neighbors. Given that these two neighbor types are identical at the concept and L levels, the long-term probabilities of producing a mixed neighbor or one of the two semantic neighbors should be equivalent. Because there are twice as many semantic neighbors as mixed neighbors, we should expect a 2:1 ratio of semantic to mixed errors from damage at either the concept level or the L level.

Figures 9A and 9B illustrate these consequences. With both concept- and L-level noise, semantic and mixed error rates increase as accuracy decreases, with the rate of mixed errors remaining a constant 50% of semantic errors. Not surprisingly, Z scores comparing the rates of mixed to semantic errors 18 are not significantly different from predicted (ranging from -0 .64 to 1.30 with concept-level noise and from -1 .61 to 1.19 with L-level noise).

Phoneme-level noise. Noise at the phoneme level simulates a situation in which damage is restricted to the phonological retrieval stage. With noise at this level, the activation of a target phoneme may drop, and another phoneme might be selected instead. A true mixed error effect at the phoneme level is supported only when the phonemes of a mixed neighbor have an advantage over the phonemes of an equivalent formal neighbor (and semantic neighbors). Although in the simulation neighborhood there are a

t o t a l of 11 formal neighbors of the target (2 sharing 2 target phonemes and 9 sharing 1 target phoneme), only 1 of them, which we refer to as formal*, is precisely matched to the mixed neighbor in terms of phonological overlap with the target, with the other formal neighbors of the target, and with words unrelated to the target. Under DFA, mixed and formal* errors result from the "accidental" misselections of phonemes and should be equivalent; therefore, we expect the ratio of mixed:formal* errors to be 1:1. Even though absolute nonword rates are greater than formal error rates (as seen in Figure 9C) Z scores comparing rates of matched mixed and formal* errors are, as predicted, nonsignificant (range: -0 .65 to 0.47).

is Because the output of the system under noisy conditions at the concept level is unstable with high levels of noise, we ran 60,000 naming trials at the highest concept-level noise values.


S e l e c t s e m a n t i c f ea tu res .

S e l e c t an L - L e v e l unit .

S e l e c t p h o n e m e s .

Figure 8. Schematic of the simulation architecture used in implementing a discrete feedforward account-high interaction account.

Phoneme plus L-level noise. It is possible, however, that the normal spoken word production scenario is better approximated by noisy conditions at both phonological and L levels. Accordingly, we submitted the DFA simulation to 20,000 trials at each noise value at both phonological and L levels. Determining the predictions of DFA with regard to mixed error rates subsequent to multilevel noise is fairly complex, and the details of the algorithm used are described in Appendix B. When we compared the observed DFA simulation results with those predicted by a discrete feedforward theory, we found nonsignificant Z scores (ranging from -0 .63 to 1.55).

Lexical Bias

As discussed earlier, under DFA, there is no mechanism that biases the naming system to produce words over nonwords. As a consequence, we expect rates of word-nonword outcomes to de- pend entirely on their phonological overlap with the item selected at L level. We can document the absence or presence of lexical bias by comparing the rates of formal neighbors with those of nonwords that are matched to the formal neighbors in terms of phonological overlap with target. Although in a system with true lexical bias we expect that words will have the advantage over matched nonwords, under DFA, we expect them to be comparable.

Figure 9C depicts the overall rate of nonwords that are produced as phoneme-level noise is increased. For the lexical bias analysis, however, we need to specifically compare rates of matched formal* and nonword* responses. When we do so, we find, as expected, nonsignificant Z scores (ranging from - 1.40 to 1.37).

Only Semantic Errors: K.E. and P.W.

DFA should have no difficulty in accounting for a pattern of only semantic errors originating from either a concept or a postsemantic (L-level) locus. These patterns are readily accounted for because at these levels only the target and its semantic neighbors are active. Figures 9A and 9B indicate that damage at either level results in only semantic or mixed errors--matching both K.E.'s and P.W.'s patterns of only semantic errors. In addition (although not depicted in the figures), damage at multiple levels can easily reproduce the pattern of multiple error types exhibited by C.S.S.

Differential Effects of Phonology on Semantic Errors

DFA does not predict a mixed error effect even under severe damage-noise conditions. This can be seen clearly in Figures 9A and 9B, in which the mixed error rate remains the predicted 50% of the semantic error rate (and Z scores are nonsignificant). Thus, although this allows DFA to account for K.E.'s pattern, it cannot reproduce P.W.'s pattern.

Summary of the Evaluation of the Discrete Feedforward Account

A simulation incorporating the key assumptions of a discrete feedforward theory of spoken naming did not exhibit either mixed error or lexical bias effects under conditions ranging from low levels of noise to more severe damage. DFA was, however, able to recreate P.W.'s and K.E.'s patterns of only semantic errors as well


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- 0 - Ooralot - 0 - Mixed

Onnfleled Som~ Formal

-O- Ner~,~ora

Figure 9. Output at the phoneme level resulting from introduction of activation noise at each level in the discrete feedforward account simulation. A: Concept-level noise. The dashed line indicates K.E.'s accuracy level. B: L-level noise. The dashed line indicates P.W.'s accuracy level. C: Phoneme-level noise.

as C.S.S.'s pattern of multiple error types (see Table 8 for a summary).

Evaluation of the Cascading Feedforward Account

CFA is similar to DFA in that it also assumes a strictly forward flow of information. However, it differs from DFA in that it incorporates the assumptions that (a) activation is not restricted to the current processing stage but instead continues forward to subsequent processing stages and (b) although "winning" items are selected at each stage, processing of all items proceeds to subsequent stages throughout the entire naming trial.

Mixed Error Effect

Phoneme-level noise: Direct phoneme-level disruption. For a true mixed error effect to arise, it is not enough for mixed neigh-

bors (e.g., RAT for the target CAT) to be better competitors than semantic neighbors (e.g., DOG); they must also be better competitors than comparable formal neighbors (e.g., HAT). With cascading activation, phonemes of mixed neighbors receive activation from both (a) the mixed neighbor nodes themselves at the L level (as a consequence of shared semantics with the target) and Co) the target at the L level (as a consequence of shared phonology with the target). This contrasts with the situation of the phonemes of semantic or formal neighbors. The phonemes of purely semantic neighbors benefit only from the semantically driven activation of the semantic neighbor nodes at the L level. The phonemes of formal neighbors receive their primary input from the target at the L level as a consequence of shared phonology (and they may also receive input from semantic neighbors of the target with which they share phonemes). On this basis, we expect that, with cascading activation, the phonemes corresponding to a mixed neighbor should be more active than the phonemes of a matched semantic or a formal neighbor.

Figure 10C shows that, although nonwords constitute the principal error type subsequent to phoneme-level damage, the phonemes of the mixed neighbor are indeed selected more often than those of either the semantic or formal neighbors. We can statistically evaluate the specific hypothesis that more mixed errors are observed under CFA than would be expected under the assumptions of DFA by comparing the observed simulation response rates for the mixed neighbor and the matched formal neighbor (formal*) with those predicted by a discrete feedforward theory (again see Appendix B). Analyses of the errors at each noise value yield highly significant Z score values that increase with increasing noise levels and range from 233.79 to 366.67.

Concept- and L-level noise: Indirect phoneme-level disruption. We can also consider the consequences of cascading activation on noise at either the concept level or the L level. Given that cascading activation exerts its consequences only at the phoneme level, mixed neighbors should not be favored in selection at either the concept level or the L level. If, however, the phonological retrieval process can be indirectly disrupted by noise from higher levels, then we might expect the same mixed error effect observed subsequent to direct disruption to the phoneme level. With cascading activation, we should see indirect phoneme retrieval disruption. This should occur because the L level continues to provide input to the phoneme level beyond the point of L-level selection. This allows L-level instability to create instability throughout phonological retrieval. Whether phonological instability is direct or indirect, it should favor the phonemes of mixed neighbors.

In the simulation indirect disruption of phoneme retrieval subsequent to concept-level damage is difficult to achieve. This is indicated by the absence of nonword errors in Figure 10A. How- ever, indirect phonological disruption subsequent to L-level damage is observed (Figure 10B). We see that although semantic and mixed errors predominate, as noise increases, nonword errors appear and increase. The appearance of nonword errors is an indication that L-level instability has disrupted phonological retrieval (recall that the output we consider is always based on the most active phonemes). The mixed error analysis indicates a small, yet reliable, mixed error effect at high (P = .7 and .9) noise levels (with Z scores of 4.44 and 4.60 respectively).


Table 8 Summary of the Ability of the Various Theoretical Positions to Account for the Four Sets of Facts Under Consideration

Only semantic errors: Only semantic errors: Absent mixed error Mixed error effect: Normal mixed Normal lexical Semantic locus Postsemantic locus effect: Semantic locus Postsemantic locus

Theory error effect bias effect (K.E.) (P.W.) (K.E.) (P.W., R.G.B.)

DFA x x ~/ CFA ,/ x ,/

Only for errors arising at the phoneme level

m A 7 ,/ ,/ ,/ ,/ Need to limit

phoneme to L-level feedback

HIA ,/ ./ ,/ ,./ ~/ Need to limit L-level to Need to limit Need to limit L-level to

concept-level feedback phoneme to concept-level feedback L-level feedback

FILSA Not tested but Not tested but Not tested but likely likely to be likely to be to be

,/ ,/ x x x

,/ ,/ x ,/ ,/ x

,/

Not tested but likely to be

,/

Note. DFA = discrete feedforward account; CFA = cascading feedforward account; RIA = restricted interaction account; HIA = high interaction account; FILSA = further interaction, low seriality account.

In sum, cascading activation allows for mixed error rates beyond those expected under the assumptions of DFA subsequent to even relatively minor disruptions at the phoneme level (e.g., at 99% accuracy level) and also subsequent to major disruptions at the L level (below 50% accuracy). It is worth noting, however, that under CFA a significant mixed error effect cannot arise at L-level selection itself because mixed neighbors are not favored over other semantic neighbors at the time of L-level selection. 19

Lexical Bias

As is the case for DFA, under the assumptions of CFA, there are no reasons to expect a true lexical bias effect. The analyses required to confirm this, however, are more complex than under DFA. Under DFA, the activation levels of all nontarget phonemes (e.g., word initial /r/ or /g/ given the target CAT) should be comparable. However, this is not necessarily the case with cascading activation. The complication is that in a system with cascading activation, the activation of nontarget phonemes depends on the composition of the target word 's semantic neighborhood. Nontarget phonemes shared by a greater number of semantic neighbors of the target (e.g., word i n i t i a l / r / i n the context of a semantic neighborhood composed of CAT, DOG, and RAT) will be more active than nontarget phonemes supported by fewer (or no) semantic neighbors (e.g., word in i t i a l /g / in such a neighborhood). Given this, it is necessary to consider the phonological inventory of a target word 's semantic neighborhood in order to find "matched" words and nonwords whose response rates can be compared. Indeed, when such items were identified, comparable response rates were observed in simulation testing (see Appendix C).


CFA (like DFA) should easily account for a pattern of only semantic errors originating from either a concept-level or an

L-level deficit. Given that at these levels the only active units are those corresponding to the target and its semantic neighbors, damage should result in the misselection of a semantic neighbor (see Figures 10A and 10B). Although, as noted earlier, noise at higher levels may indirectly affect the phoneme level and give rise to multiple error types at the end of the phonological retrieval stage, the pattern of only semantic errors exhibited by P.W. and K.E. is readily observed over an extensive range of parameter settings.

Differential Effect of Phonology on Semantic Errors

K.E.'s pattern of only semantic errors without any evidence of phonological influence is also readily observed under the assump-

19 Some researchers have supposed (e.g., Dell et al., 1997; Martin et al., 1996) that mixed errors may arise primarily during L-level selection. This supposition is based, in large part, on the observation that word errors (semantic, mixed, and formal) typically respect the grammatical category of the target and on the assumption that L-level activation is driven by both grammatical and semantic considerations. However, under these same assumptions, the preservation of grammatical category in mixed and semantic errors might be expected even under CFA as well. Specifically, if we assume that grammatical information is an additional source of input to the L level, then at the phoneme level, the phonemes corresponding to mixed or semantic neighbors that share the target's grammatical category will be more highly activated than the phonemes corresponding to mixed or semantic neighbors that do not. Under this scenario, disruption of phonological encoding in CFA should yield a significant mixed error effect favoring mixed neighbors that share the target's grammatical category. Thus, it seems plausible that preservation of grammatical category in lexical errors might be expected under CFA (although not necessarily at the high rates that have been observed). Nonetheless, it is certainly true that to the extent to which there are good reasons to think that mixed errors arise in L-level selection, CFA's credibility is diminished.


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Noise Value

0.10 0.30 0.50 0.70 0.90

Noise Value

- 0 - Correct Mixed Unrelated Semantic Formal

- 0 - " Nonword

Figure 10. Output at the phoneme level resulting from introduction of activation noise at each level in the cascading feedforward account simulation. A: Concept-level noise. The dashed line indicates K.E.'s accuracy level. B: L-level noise. The dashed line indicates P.W.'s accuracy level. C: Phoneme-level noise.

dons of CFA. As Figure 10A indicates, damage to the concept level (at K.E.'s 55% accuracy level) yields a pattern of only semantic errors. Z scores evaluating a mixed error effect at all levels of noise at the concept level were nonsignificant (range = -0 .96 to 1.12).

CFA can also account for C.S.S.'s pattern (a significant mixed error effect in the context of the production of multiple error types). CFA does not predict C.S.S.'s high rate of semantic and mixed errors (6%) with phoneme-level damage alone; an additional L-level damage locus is required to yield these rates. When we simulate multiple damage loci, C.S.S.'s pattern is readily matched: simulation accuracy = 85% (C.S.S. = 83%), simulation semantic + mixed = 8% (C.S.S. = 6%), simulation formal errors = 1% (C.S.S. = 3%), and simulation nonwords = 6% (C.S.S. = 6%).

Crucially, however, CFA cannot account for the pattern of only semantic errors with a significant mixed error effect exhibited by

P.W. and R.G.B. (Caramazza & Hillis, 1990). The problem for CFA, simply put, is that a significant mixed error effect necessarily goes hand-in-hand with nonword error production. This is true for both direct and indirect phonological disruption. Phoneme-level noise gives rise to multiple error types (nonwords in particular) as soon as accuracy falls below 95% (Figure 10C). Furthermore, noise values at the L level that are sufficient to cause a significant mixed error effect downstream also produce nonword errors. We illustrate and discuss these issues in more detail in the next section.

Summary of the Evaluation of the Cascading Feedforward Account

The incorporation of the assumption of cascading activation allows for the occurrence of true mixed errors. Additionally, CFA (like DFA) can account for the pattern of only semantic errors exhibited by K.E. and P.W. CFA does not, however, predict a significant lexical bias. Most important, the pattern of only semantic errors accompanied by a significant mixed error effect exhibited by P.W. (and R.G.B.) represents a seemingly insurmountable obstacle for CFA (Table 8). 20

Further Reductions in Discreteness Under the Cascading Feedforward Account: Jolt Size, Selection Strength, and Seriality

W e have moved along the discreteness-interactivity continuum from DFA to CFA by incorporating the assumption of cascading activation. However, selection strength (jolt size) also contributes to the degree of discreteness-interactivity of a system. Recall that at the end of each processing stage the activation of the winning node is elevated above that of its competitors. 21 As a result, the influence exerted by competitors on all subsequent processing is diminished. It is in this way that selection points reduce the extent and time course over which full and free interaction is allowed.

To gain a fuller understanding of CFA, we examined the effects of manipulating selection strength under conditions of L-level noise (comparable to P.W.'s hypothesized damage locus). The relationships between selection strength and nonword error rates and mixed error effects are depicted in Figure 1 I. The results reveal that indirect phonological disruption subsequent to L-level noise (as indexed by nonword errors) depends on the strength of the selection process ("jolt" size) at the L level: the lower the jolt, the more likely the phonological disruption. Figure 11 also illustrates the point made above that under CFA a significant mixed error effect is necessarily accompanied by nonword errors. Thus, P.W.'s pattern of no nonword errors is observed only with jolts greater

20 Given the importance of the absence of phonologically based errors in P.W.'s performance, we reviewed all of the spoken picture-naming data for P.W. (reported in Rapp et al., 1997). This consisted of a total of 2,189 trials of spoken picture naming. Across all of these trials, P.W. produced no formal errors and only two nonword responses: "rhinoceros" was named as /r ai n a s e s e s/and "accordion" was named as/a k o r d i n/.

21 Although in the particular algorithm we have adopted this "enchance- ment" of the winner is implemented through an external jolt, there are other means of accomplishing the same end. For example, a competitive output mechanism should yield comparable results.

A 10%

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80% 70% 60% 50%

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Figure 11. Indirect phoneme-level disruption. Effect of L-level jolt size on phoneme-level output under conditions of L-level noise. The vertical dashed line indicates P.W.'s accuracy level. A: Percentage of nonword responses at 50%-80% accuracy in cascading feedforward account simulations with varying jolt sizes. B: Z scores testing for a mixed error effect in the same simulations. The horizontal dashed line indicates the Z score value for two-tailed probability less than .05.

than five. The problem for CFA is that (at P.W.'s accuracy level) these large jolt sizes do not produce a significant mixed error effect.

If the selection process is "strong" (enhancing the difference between winner and losers), then the primary consequence of cascading activation--the introduction of multiple semantic competitors on the phonological stage---is diminished or overridden. In other words, as selection strength increases, interactivity decreases. One can easily imagine, in fact, that if selection strength at the L level were strong enough, a system with cascading activation could be effectively transformed into DFA. The simple lesson is that to increase interactivity it is not sufficient to simply incorporate cascading activation (or any other mechanism); other aspects of processing (in this case, selection strength) contribute to the "functionality" of the interactive mechanism.

Evaluation of the Restricted Interaction Account

Under RIA, discreteness is further reduced with "lower level" feedback connections from the phoneme level back to the L level.

The primary consequences of allowing phonological activation to influence L-level processing are to (a) create an activation advantage for mixed over semantic neighbors at the L level and (b) provide L-level support for the phonemes of formal neighbors.

Mixed Error Effect

In addition to the mixed error effect at the phoneme level observed under CFA, the feedback in RIA allows for a mixed error effect in the course of L-level selection. This is because the feedback allows the advantage of the phonemes for mixed neighbors at the phoneme level (seen in CFA) to be "carded back" to the L level.

It is not surprising, therefore, that when noise is introduced at either the phonological level or the L level, mixed error rates are greater than 50% of the semantic error rates (see Figures 12B and

B 100%

8O%

i

'15

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80% j° "6

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0.10 0.30 0.50 0~70 0.90

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I

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I

',P ",r PW ",r -,r" T 0.10 0.30 0.50 0.70 0.90

Noise Value


- - 0 - Cornmt Mixed

- ~ - Unrelated Semantic Formal

- ~ - - Nonworcl

Figure 12. Output at the phoneme level resulting from introduction of activation noise at each level in the restricted interaction account simulation. A: Concept-level noise. The dashed line indicates K.E.'s accuracy level. B: L-level noise. The dashed line indicates P.W.'s accuracy level. C: Phoneme-level noise.


12C). If we analyze the phoneme-level output, Z scores are significant at every value of noise at the phoneme level or the L level (range: 8.50 to 27.16).

Additionally (and in contrast to CFA), if we consider the output of L-level selection itself (not depicted in Figure 12), we also fred significant mixed error effects at all noise values (Z scores ranging from 4.20 to 9.64).

Lexical Bias

The feedback connections in RIA should provide a mechanism by which phonemes of formal neighbors may be preferred over phonemes of matched nonword outcomes. As activation passes from the target to its phonemes, the feedback connections send activation from these phonemes back to the L-level units that share phonemes with the target, including formal neighbors. The L-level nodes of these formal neighbors, in turn, activate all their constituent phonemes, including those that are not shared with the target. These will then reactivate their corresponding L-level nodes.

We carded out simulations with a smaller network that allowed us to compare appropriately matched word and nonword responses. 22 The results revealed significant word bias effects (Z score = 2.82), providing confn-mation of this account of lexical bias under RIA.


Concept-level damage should have comparable consequences under RIA as it does under CFA and DFA because, at this level, disruption can easily result in the misselection of one of the target's semantically related neighbors. The misselected item can then pass activation downward in a relatively undisturbed manner such that what was misselected at the concept level is encoded correctly at the phoneme level. Figure 12A depicts this outcome of concept-level damage.

The patterns arising from L-level damage, however, may be expected to be somewhat more complex. The feedback from the phonological level to the L level allows formal neighbors to become competitors at the L level itself, both in the course of L-level selection and afterward. This creates an opportunity for formal neighbors to be selected at the L level. This leads us to expect that, at least under certain circumstances, L-level damage may lead to both formal and nonword errors.

Given this possibility, it is first important to establish if there are conditions under which P.W.'s pattern will be observed under the general assumptions of RIA. Figure 12B indicates that the pattern of only semantic errors (<1% nonsemantic errors) is observed with L-level damage at accuracy levels well below P.W.'s. Only as accuracy falls further do nonword and formal errors begin to emerge. Thus, under RIA, P.W.'s pattern of only semantic errors resulting from postsemantic damage can be readily accounted for with L-level damage.

Differential Effect of Phonology on Semantic Errors

Under RIA, concept-level damage easily results in a pattern of only semantic errors and no mixed error effect, with Z scores

ranging from -0 .89 to 0.54 (Figure 12A). These results extend down to accuracy levels well below K.E.'s.

RIA can also account for C.S.S.'s multiple error pattern plus significant mixed error effect either with multiple damage loci (as under CFA) or by assuming phoneme-level damage alone. For example, phoneme-level damage that results in 85% accuracy for the simulation (C.S.S.: 82%) also results in 5% semantic plus mixed errors (C.S.S.: 6%), 2% formal errors (C.S.S.: 3%), and 8% nonwords (C.S.S.: 7%).

With respect to P.W.'s pattern, as indicated above, disruption to the L level can produce a pattern of only semantic errors at accuracy levels even below P.W.'s. Importantly, at accuracy levels at which only semantic errors are observed, there are also highly significant mixed error effects. Thus, in Figure 12B, it is evident that with L-level damage (at P.W.'s accuracy level) not only is there a pattern of only semantic errors but also the rate of mixed errors is clearly greater than 50% of the semantic errors. This result is the most important difference between CFA and RIA: Whereas CFA cannot account for P.W.'s full pattern, RIA can.

Summary of the Evaluation of the Restricted Interaction Account

This section has revealed that the combined assumptions of cascading activation and restricted interactivity can readily account for all four sets of facts under consideration. Critically, and in contrast to CFA, the introduction of limited feedback provides a mechanism that (a) predicts a true lexical bias effect and (b) accounts for P.W.'s pattern of only semantic errors plus mixed error effect arising from postsemantic damage (Table 8).

There are, however, limits to the degree of interactivity that can be tolerated within RIA. Under RIA, the effects of further increasing interactivity were explored by manipulating selection and feedback strength. We found that as selection strength at the L level was decreased and feedback from the phonological to the L level was increased, P.W.'s pattern of only semantic errors became increasingly difficulty to match. This is illustrated in Figure 13, A

22 We were unable to check our thinking on this matter with the full network because it was (accidentally) constituted in a way that did not allow for identical L-level input to any word-nonword pair. Instead, we implemented the "BabyFeedbackNet." This simulation consisted of two layers, corresponding to the L level and the phoneme level in our full simulation. The neighborhood consisted of the target (CAT), two semantic neighbors (RAT and BAT), and two formal neighbors (BAD and RAP). Each trial consisted of a four-unit activation jolt to the L-level target, CAT. Semantic neighbors of the target received one tenth the activation of the target (to simulate the effects of prior semantic processing). Activation was then allowed to proceed for eight time steps, at which point the most active phonemes in each position were selected. Activation noise at the phoneme layer was set to .9, and 100,000 iterations were run. Feedforward connections were set to P = .04 and feedback connections were to set to P = .01. Under these conditions a significant word bias (BAD matched nonword RAD) was observed (Z = 2.82, p < .05). The strength of such a word bias effect is clearly dependent on the strength of the feedback connections, and when we varied feedback strength from P = .0001 to P = 1.0, we found that Z scores increased from 0.34 to 7.85.


A

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Figure 13. The relationship between L-level jolt size and feedback strength (strong and weak) under conditions of L-level noise (restricted interaction account [RIA]). The dashed line indicates P.W.'s accuracy level. A: Percentage of nonword responses at 50%-80% accuracy in RIA simulations with varying jolt sizes and weak feedback (phoneme- to L-level feedback = .01). B: Percentage of nonword responses at 50%- 80% accuracy in RIA simulations with varying jolt sizes and stronger feedback (phoneme- to L-level feedback = .1).

and B. This figure compares the effects of lower (.01) and higher (. 1) feedback values on nonword error rates. First, it is important to note that all jolt size-feedback combinations produced a mixed error effect at P.W.'s 70% accuracy level. In addition, as Figure 13 indicates, there was a range of selection strength-feedback values that also produced nonword rates of 0% to 0.5% at P.W.'s accuracy level. However, these numbers also reveal that as selection strength decreased (from jolt size = 10 to 3) and feedback increased (from P = .01 to .1), unacceptably high nonword error rates were observed at P.W.'s accuracy. This is due to the fact that decreases in selection strength serve to increase indirect phonological disruption and increases in feedback serve to amplify support for formal neighbors at the L level. Both of these, in turn, translate into support for nontarget phonemes and an increase in nonword responses.

Not unexpectedly, feedback strength also plays an important role in the rate of formal errors that occur at L-level selection itself. Whereas under CFA, formal errors occur only in the course of phonological retrieval (either as a result of direct or indirect disruption), under RIA, phonologically related lexieal neighbors are competitive at the L level and may be selected. As indicated in Figure 14 (with L-level noise), as feedback strength increases from P = .01 to P = .1, formal error rates dramatically increase at L-level selection. This is another way in which increases in feedback strength make it increasingly difficult to match P.W.'s pattern.

In sum, the restricted feedback assumption in RIA allows for a pattern of only semantic errors in both concept processing and L-level selection at the accuracy levels exhibited by P.W. and K.E., while at the same time matching the differential effects of phonology on their performance. It is also true, however, that generating a significant mixed error effect at the L level while maintaining a pattern of only semantic errors (P.W.'s pattern) requires a careful balancing of selection strength and feedback.

Evaluation of the High Interaction Account

Given RIA's ability to account for the four sets of facts under consideration, we could assume that an even higher degree of interactivity in the naming system is not required to account for this set of facts. Nonetheless, it is important to establish whether more interactive accounts can also account for these facts and, if not, to understand why.

HIA adds "upper level" feedback between the L level and the concept level. This has a number of general consequences; here, we highlight two of them. First, this feedback provides an opportunity for conditions downstream--at both the phonological and L levels--to contribute to upstream concept-level processing. Spe- cifically, we might expect that at the concept level itself mixed

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Figure 14. Formal errors occurring at L-level selection in restricted interaction account simulations with different phoneme- to L-level feedback values.


neighbors of the target should develop an advantage over semantic A neighbors and, additionally, that formal neighbors should develop 10o~ - an advantage over unrelated words. Second, with this feedback, the

8 0 % - concept level receives downstream input throughout each naming trial. As a consequence, the concept level maintains a higher level ~ 60% - of activation throughout the naming trial than under any of the other theories we have examined. The primary consequences are ~ ,ms- both to keep activation levels higher throughout the entire network

20*/. and to maintain a stronger and more prolonged semantically driven pressure on the downstream processes. Because HIA supports 0% many of the same predictions as RIA, we review these only very briefly, focusing instead on the differences between the two accounts. B

100% -

Mixed Error Effect 80% -

It is not surprising that noise at the phoneme or L level results ~ s0~. - in statistically significant mixed error effects at all levels of accuracy, with Z scores ranging from 10.37 to 85.90 (Figure 15, i 4 0 % -

B and C). 2 0 % -

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For the same reasons discussed above for RIA, a true lexical C

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80% Only Semantic Errors

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match the pattern of only semantic errors exhibited by both P.W. i 40%

and K.E. This can be seen in Figure 15A and B. We discuss limitations on this below, a0%

Differential Effect o f Phonology on Semantic Errors

Both P.W.'s and K.E.'s patterns can be matched under HIA. However, as we discuss in some detail below, matching either of the patterns occurs under a very limited range of parameters. This is especially true for K.E.

Summary of the Evaluation of the High Interaction Account

HIA can account for the full set of facts under consideration. It is important to note, however, that it can do so under conditions that highly restrict interactivity. We now consider those conditions.

The Limits on Interactivity in the High Interaction Account: Selection Strength and Feedback

P.W.'s pattern of exclusively semantic errors is matched only if interactivity is limited. In our evaluation of RIA, we observed that to match a pattern of only semantic errors arising from postsemantic (L-level) damage, it was crucial to balance phoneme- to L-level feedback and L-level selection strength. Otherwise, formal errors arose in L-level selection, and nonword errors were produced on output. Because of HIA's greater potential for interaction, restrict- ing interactivity becomes an even more significant concern than it is under RIA.

0%

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- ' O - Correct

Mlxod

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- - 0 - Nonword

Figure 15. Output at the phoneme level resulting from introduction of activation noise at each level in the high interaction account simulation. A: Concept-level noise. The dashed line indicates K.E.'s accuracy level. B: L-level noise. The dashed line indicates P.W.'s accuracy level. C: Phoneme-level noise.

For example, Figure 16, A and B, indicate that I-I/A requires a jolt size of 10 or larger to prevent nonword errors, whereas this was achieved under RIA (cf. Figure 13, A and B) with much smaller jolt sizes. Thus, the limitations of RIA that are related to "lower level" feedback strength are magnified in HIA.

Also regarding P.W.'s pattern, we found that upper level feedback allows damage at the L level to indirectly disrupt concept-level processing. Figure 17 reveals that as upper level feedback strength increases from .0001 to 1.0, L-level damage produces increasingly greater rates of errors (semantic and mixed) at the point of concept-level selection (Figure 17). Such errors are kept to minimal levels only with feedback values below .01. These findings suggest that individuals (e.g., P.W.) with presumed postsemantic damage should show conceptual confusion and, presumably, make errors in comprehension tasks (something that P.W. does not do).

Most problematic for I l iA is the fact that K.E.'s pattern is


matched under only a very restricted set Of conditions. As indicated in Figure 18, as upper level feedback strength increases, a mixed error effect is observed on output. The absence of a mixed error effect is seen only at parameter settings that dramatically limit feedback levels (e.g., upper level feedback p = .004; lower level feedback p = .04). In other words, only when the upper level feedback signal is reduced to 4/1,000 of the downward signal are significant mixed error effects eliminated.

Although the full connectivity assumed under HIA can account for P.W.' s and K.E. 's pattern of only semantic errors, it is important to note that this is achieved by severely limiting the actual interactivity in the system. This again illustrates the point that the mere presence of some mechanism-- in this case, connect ivi ty-- does not ensure its functionality. Thus, although HIA can account for the same set of facts as RIA, it does so only to the extent to which it approximates RIA's assumption of null upper level inter-

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Figure 17. Accuracy at the concept level following indirect concept-level disruption due to L-level noise in the high interaction account.

activity (Table 8). In other words, HIA's upper level feedback is at best irrelevant and at worst a liability (see Harley & MacAndrew, 1995, for a similar point).

Evaluation of the Further Interaction, Low Seriality Account

The central tenet of FILSA is that the mapping between input and output domains is a highly interactive, continuous process with low discreteness and seriality. We have already demonstrated that as interactivity increases and seriality and discreteness decrease, it becomes increasingly more difficult to match the patterns of data under consideration. FILSA corresponds to a theoretical position that is more interactive than any examined thus far, and on this basis we can anticipate that it will encounter important difficulties. FILSA's interactivity assumptions are stronger than those of HIA in two ways: (a) interaction from visual input to phonological output and (b) extremely limited seriality.

Neighborhood and Architecture in the Further Interaction, Low Seriality Account

We used much of the architecture developed by Plaut and Shallice (1993a). 23 We used their set of 40 words in five different semantic

Figure 16. The relationship between L-level jolt size and feedback strength (strong and weak) under conditions of L-level noise (high interaction account [HIA]). The dashed line indicates P.W.'s accuracy level. A: Percentage of nonword responses at 50%-80% accuracy in HIA simulations with varying jolt sizes and weak feedback (phoneme- to L-level feedback = .01 ). B: Percentage of nonword responses at 50%- 80% accuracy in IliA simulations with varying jolt sizes and stronger feedback (phoneme- to L-level feedback = . 1).

23 Extremely low seriality was something that we found difficult to accomplish in our implementation of HIA, presumably because of certain characteristics of the simulation architecture. We found that extreme reductions of selection strength (jolt size) in HIA resulted in poor performance even under minimal noise conditions. In contrast, our implementation of FILSA corresponds to a low seriality system that is more resilient under damage conditions.


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categories as well as their semantic and phonological representations. For the input representations, we adopted the visual representational scheme described in Plaut and Shallice (1993b). 24

Each of the visible layers (visual, semantic, phonological) is completely internally connected. In addition to these visible layers, two hidden layers of 40 units each were used. The first hidden layer is completely connected to both visual input and semantics, and the second is completely connected to both semantic representations and phonological output. All weights are bidirectional: Activation can pass both forward and backward along any connection, and the weight has the same value in both directions (see Figure 6).

Processing in the Further Interaction, Low Seriality Account

We used the deterministic Boltzmann machine/mean field theory framework adopted in Plaut and Shallice (1993a). The state of a particular unit in the network ranges from - 1 to 1 and is determined by the following function:

Aj(t) = Aj(t - 1)(1 - Q) + Q tanh[ l /T~ wi:A,(t - 1)].

The activity (A) of any unit j at time (t) is determined by its activation at the previous time step (t - 1) and the activation of each unit i that connects to unit j (Ai[t - 1]), modulated by the weight on the connection between i andj (wij). Q determines how much each factor is allowed to contribute to the new activation. In our simulation, Q was globally equal to .4, meaning that 40% of the unit's activation came from other units and 60% from its own activation at the previous time step. "tanh" is the hyperbolic tangent function (a sigmoid function), and T is the temperature parameter. When T is high, less input is supplied to the tanh function, reducing the range and sharpness of the sigmoid.

Processing in the network begins with fixing ("clamping") the visual input unit states to the input values corresponding to some word in the simulation lexicon. All other units have an initial activation of 0. The initial temperature is set to 50, from which it exponentially decays toward 1. At each time step, the temperature is lowered, and each noninput unit in the network synchronously updates according to the activation function outlined above. This process is allowed to continue until no unit state changes more than 0.01.

Learning in the Further Interaction, Low Seriality Account

The training process used was contrastive Hebbian learning and is described in Appendix D (see also Plant & Shallice, 1993a).

Seriality and Emergent Seriality

As indicted, FILSA was designed to explore a theoretical position that is more interactive than HIA with regard to seriality and extent of interaction domains. The expansion of the interaction domains was implemented with additional representational levels and connectivity. However, establishing whether or not the simulation actually implemented extremely low seriality was less straightforward.

Smolensky (1986) examined a simulation that was based on the same class of learning and processing principles we have adopted and that lacked externaUy imposed seriality. He found, however, that seriality emerged in the course of learning. Specifically, in the simple four-unit system he examined, the units did not reach their final settling state simultaneously. Instead, two units settled at much the same time, followed by the third and then the fourth. He concluded that "out of the statistical din of parallel microdecisions emerges a sequence of macrodecisions"(Smolenksy, 1986, p. 246). Given this possibility, it was important to determine if FILSA, despite the absence of explicit selection points, could have (through learning) developed a serial processing structure consisting of a largely semantically driven stage followed by a phonologically driven stage.

To look at this possibility, we tracked the processing (in the undamaged system) of each unit by examining changes in distance from final activation state. We did this by computing (at each processing step) a mean sum squared error based on the difference between the units' current activation and their activations when the network had settled to its final stable state. We then computed the mean of this distance for all units during a full set of naming trials.

Figure 19A presents hypothetical results that would be expected in a highly serial system where a "decision" regarding the semantics of the target is made before much progress has been made regarding its phonological encoding. Figure 19B depicts the actual pattern obtained in the simulation. It shows that the observed pattern is clearly quite different from the idealized serial pattern

24 Forty-four visual features were used as an input representation. The first 25 of these features encode information about the three principal components that comprise each object, as well as information about their relative size and placement. The remaining 19 features describe each object's general characteristics (e.g., texture, color, and absolute size of the entire object).


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and thus that there is little evidence of emergent seriality. If anything, phonological units drop toward their final state slightly sooner than do the semantic units. Only at the very end of the naming process do the semantic units settle slightly before the phonological units. These results provide good evidence that the simulation of FILSA we developed does, in fact, correspond to a theoretical position that assumes very low seriality.

Only Semantic Errors From Semantic or Postsemantic Damage ?

We can fairly safely assume that with its features of cascading activation and feedback FILSA will exhibi t mixed error and lexical bias effects. For this reason, and because establishing chance rates of mixed errors under FILSA is complex, we focus


our evaluation of FILSA on whether or not it allows for the pattern of only semantic errors exhibited by P.W. and K.E.

To examine this question, we simulated semantic and postsemantic damage by "lesioning" a proportion of the weights by setting them to 0. Semantic-level damage was simulated by lesioning the weights among semantic units; postsemantic damage was simulated by lesioning the weights between the semantic and hidden units. A random set of weights was selected and zeroed, and each damaged network was then presented with the entire set of 40 words. For each damage level, 20 repetitions of damage were performed to obtain an average damage effect.

The network's output for a given trial corresponded to the most highly active unit in the onset, vowel, and coda pools. Addition- ally, for a response to be well formed, one unit in each pool had to be active above a certain threshold level, and all other units had to be relatively inactive. In this regard, we followed the procedure used by Plaut and Shallice (1993a) and adopted their criterion level (see Plaut & Shallice, 1993a, for formal specifications).

The following categorization of target-error types was adopted: semantic, phonological, visual, visual-semantic, visual-phonological, or visual-semantic-phonological. Because target-error pairs vary continuously in their similarity along visual-semantic- phonological dimensions, we developed a method for discrete error categorization, which is described in Appendix E. The following are examples of errors under this categorization scheme: purely semantic = BONE-PORE, phonological = BOG-DOG, visual = NUT-ROCK, semantic-visual = DOG-CAT, semantic- phonological = LEG-LIP, visual-phonological = GUT-NUT, and visual-phonological-semantic = RAT-CAT.

Resul~

All outputs were well-formed patterns that could be classified as words in the simulation lexicon. K.E.'s pattern would be best matched if semantic damage produced only semantic errors. One complication is the possibility that errors categorized as semantic- visual, semantic-phonological, or semantic-visual-phonological might be "true" semantic errors. To be as generous as possible with the hypothesis that only semantic errors were observed following semantic-level damage, we considered all of these error types as semantic errors. Nonsemantic errors were members of the visual, phonological, and visual-phonological categories.

Figure 20A depicts the percentage of semantic and nonsemantic errors that resulted from damage to semantic units. It is evident that at every accuracy level, semantic damage resulted in a considerable percentage of errors that were clearly nonsemantic. For example, at 50% correct (near K.E. 's 55% accuracy level), 30% of the responses were considered to be semantic errors (of these, 3% were purely semantic, 2% were semantic- phonological, 15% were semantic-visual, and 10% were visual-semantic-phonological) , but 11% of responses were nonsemantic errors. It is interesting to note that, of the nonsemantic errors, visual errors (7%) were the most common, followed by visual-phonological (4%) and then phonological (<1%). Nine percent of the responses at this accuracy level were not clear exemplars of any error category and could be considered to be unrelated responses.

A perfect match to P.W.'s pattern would similarly require a pattern of only semantic errors. However, as Figure 20B depicts, nonsemantic errors were observed at all accuracy levels subsequent to postsemantic damage. In particular, at P.W. 's 70% accuracy level, 20% of the errors were semantic errors (of these, 4% were purely semantic, 1% were semantic- phonological, 9% were semantic-visual, and 6% were visual- phonological-semantic), whereas 5% were nonsemantic. These consisted of similar rates of visual-phonological errors and visual errors (2.1% and 1.8% respectively), followed by phonological errors (< 1%). Five percent of the responses could not be classified as clear exemplars of any error type.

Summary of the Evaluation of the Further Interaction, Low Seriality Account

FILSA is clearly unable to account for patterns of only semantic errors arising from either semantic or postsemantic loci of damage. Even adopting a classification scheme that favors semantic over nonsemantic errors, we found significant numbers of nonsemantic errors (visual, visual-phonological, or phonological; see Table 8).

The difficulties of F1LSA are unsurprising given that we have already shown that incrementing interactivity even within the architectural constraints of HIA or RIA leads to increasing difficulties in matching the observed patterns. One might, nonetheless, wonder if the FILSA simulation failures are attributable to some aspect of the way we implemented the central assumptions of the FILSA theory, and whether perhaps another implementation of a position with the same assumptions would have worked. Although for the reasons stated this seems unlikely, it cannot be ruled out.

Genera l Discuss ion

With the help of computer simulations, we have evaluated five theories of spoken naming with respect to their ability to account for four sets of facts: (a) a mixed error effect in the speech errors of normal unimpaired individuals; (b) a lexical bias effect in the speech errors of unimpaired individuals; (c) the pattern of only semantic errors resulting from either a semantic locus or a postsemantic locus of damage; and (d) the differential phonological effects on the semantic errors, specifically the absence of phonological effects on semantic errors arising from a semantic locus of damage and the presence of phonological effects on semantic errors arising from postsemantic loci of damage. The five theories vary along the discreteness- interactivity dimension, and we have specifically considered the roles played by cascading activation, feedback, seriality, and extent of interaction domains in accounting for the set of critical observations.

The principal results of this investigation can be summarized rather simply. To satisfy the constraints provided by these four sets of facts, we must assume that, at a minimum, the naming system incorporates the following: (a) a mechanism--such as cascading activation--that allows the phonemes corresponding to the semantic neighbors of a target word to be active during phonological


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Figure 20. Results on output after removing a random set of connections in the further interaction, low seriality account (FILSA) simulation. "Semantic" includes phonological-semantic, pure semantic, visual-semantic, and visual-phonological-semantic errors. "Nonsemantic" includes visual-phonological, pure visual, and pure phonological errors. Results are reported as a percentage of 800 trials (20 replications of each damage level, with 40 words presented in each replication). A: Results at different accuracy levels after removing intersemantic connections. B: Results at different accuracy levels after removing connections between the semantic and the hidden layer preceding the phoneme layer.

retrieval and (b) a mechanism--such as functional feedback connections from a phonological layer to a preceding lexical-level layer--that provides a means by which lexical outcomes will be preferred over nonlexical ones and phonological information can influence semantically driven, lexical-level processing. A mechanism such as cascading activation allows us to understand the mixed error effects that occur in normal and impaired performance. A mechanism such as phonological/L-level feedback accounts for lexical bias effects and mixed error effects occurring prior to phonological encoding in either impaired or normal performance.

As previously stated, we had two objectives with this research: (a) to determine which of the specific theoretical positions examined best accounts for the data and (b) to draw more general conclusions regarding discreteness and interactivity in spoken word production. With regard to the first objective, we found that RIA most readily accounts for the relevant facts. Additionally, HIA is satisfactory to the extent that upper level L-level/semantic feedback levels approximate RIA 's null feedback condition. That is, there are conditions under H I A - - namely, strong seriality combined with extremely low levels of L-level/semantic feedback-- that do not create an obstacle for

INTER.ACTIVITY IN SPOKEN WORD PRODUCTION 491

matching K.E.'s error pattern. However, unless there are independent grounds motivating this upper level feedback, it would seem unnecessary at best.

In additionto providing support for a restricted interactivity account of spoken word production, this research more generally provides insight into the architectural and processing features that are minimally required to account for the relevant facts. In this regard, the important generalization is that although interaction is necessary, it is also true that interactivity is problematic as it increases beyond some optimal point--whether this increase is achieved by means of lowered seriality, increased feedback strength, or expansion of domains of interaction. Any architecture that allows for interaction across domains--be it FII_~A, HIA, RIA, or even CFA--will encounter difficulties in matching the patterns exhibited by P.W. and K.E. if the interaction is too great.

The Respective Contributions of Patterns of Impaired and Unimpaired Performance

Although P.W.'s and K.E.'s errors are similar to the errors of unimpaired individuals, what proves to be especially useful about K.E.'s and P.W.'s errors is the fact that only semantic errors are produced and that we have independent grounds (from comprehension and spelling tasks) for determining the loci (semantic and postsemantic, respectively) of these errors. Their contributions can be summarized in the following way: P.W.'s pattern helps us to begin to set a lower bound on interactivity, whereas K.E.'s pattern contributes to setting an upper bound. Simply put, there must be sufficient interactivity for a true mixed error effect to arise in L-level selection (P.W.'s pattern), yet interactivity must be suffi- ciently restrained so that phonological effects are not observed subsequent to conceptual-level disruption (K.E.'s pattern).

Table 8 presents the key observations we have been concerned with and summarizes the ability of the five theoretical positions to account for them. Table 8 makes it quite clear that (a) P.W.'s pattern creates insurmountable obstacles for DFA and CFA, (b) K.E.' s pattern puts limitations on the amount of upper level interactivity permitted under HIA, and (c) both P.W. and K.E. create fundamental problems for FILSA.

The data from normal speech errors make their primary contribution in discriminating between DFA and CFA because DFA does not provide a mechanism that would predict mixed errors or a lexical bias effect. If an L-level locus could be convincingly established for a mixed error effect in normal speech errors, then these would play the same role as P.W.'s pattern of mixed errors originating at a postsemantic-prephonological locus. That is, a clear L-level locus for the normal mixed error effect would also be a fundamental obstacle for CFA.

L-Level Nodes, Lemmas, and Lexemes

In the introduction, we adopted the convention of referring to the lexical level of representation mediating between concept and phoneme levels as the L level. We did so to capture the commonalities between the lemma-based theories of Dell and O'Seaghdha (1992), Levelt (1989), Garrett (1980), and others and the lexeme- based independent network model proposed by Caramazza (1997). Recall that these theories differ with regard to whether they as-

sume that units at this intermediate level correspond to amodal (lemmas) or modality-specific (lexemes) lexical representations. One might well wonder if these differences have any bearing on the results that we have reported.

The answer is that they generally do not. First, to account for the four sets of facts under consideration, no theory can do without a mechanism such as cascading activation that allows the phonemes corresponding to the semantic neighbors of a target word to be active at the phoneme level. Furthermore, a mechanism such as feedback connections from the phonological layer to some preceding lexical level is necessary to account for lexical bias effects and mixed errors arising at some lexical level. In this regard, one could say the theories simply vary in terms of what is represented at the lexical level preceding the phoneme level--in some accounts this is an amodal lemma level, whereas in others it is a modahty- specific lexeme level. The data we have examined certainly do not distinguish between these possibilities.

There is, however, one sense in which the lexeme-lemma difference is indeed relevant. It concerns the broader generalizations one may wish to draw from this work. The evidence presented here indicates that the levels that most strongly interact are the phoneme and L levels. Given this, if we assume that the L level is a modality-specific phonological lexeme level, then the generalization may well be that levels within a common representational domain (in this case, phonological) interact more strongly than levels from different representational domains (phonology vs. semantics). In contrast, if the L level corresponds to an modal lexical level, then this kind of domain-based account of strong versus weak interactivity cannot be put forward. Thus, a lexeme- based interpretation of the L level supports a broader and more elegant generalization.

We have also reported that there are theories that assume that both lemma and lexeme levels mediate between concept and phoneme levels (Dell, 1986; Roelofs, 1992; see Figure 2D). It is worth considering the implications of this assumption. First of all, such a theory would allow for the domain-based generalization just proposed above. Second, if one were to require P.W.'s semantic errors (or the mixed errors produced by normal individuals) to arise specifically in the course of lemma (rather than lexeme) selection, then functional feedback connections would be required not only from the phoneme level to the lexeme level but also from the lexeme level to the lemma level. Although it is not clear that there are good reasons to assume that mixed errors arise in lemma versus lexeme selection, we point this out as an imphcation of the lemma plus lexeme architecture.

Finally, there are implications of a lemma plus lexeme-based architecture for the problematic nature of K.E.'s pattern under HIA. As connectivity distance increases, the functional consequences of feedback connections decrease. In an architecture with feedback and feedforward connections only between adjacent levels, an additional level increases the distance between phonological and concept levels. In the context of K.E.'s data, increasing connectivity distance has the consequence that--at uniform feedback values throughout the system--the influence of phonology on conceptually generated errors will decrease. On this basis, a system with full feedforward and feedback connectivity as well as with both lemmas and lexemes will have less difficulty with K.E.'s data. Crucially, however, to the extent to which connectivity


distance decreases the problematic nature of K.E.'s error pattern, it also renders the system less interactive functionally. To retain true interactivity across increasing connectivity distance, one must do things such as increase feedback strength, reduce seriality, and so forth. This underscores the point made earlier that the mere presence of connectivity does not ensure its functionality. Thus, it is not enough to note that feedback connections are in place; it is critical to determine if the connections actually serve to bring together information from distant parts of the system. We have made this point strongly with respect to feedback connections, but it applies, of course, equally to all other means of implementing interactivity.

Dell et al. (1997)

Dell et al. (1997) cautiously drew the conclusion that "parameter alterations affect all layers of the network equally and, hence, differences in error rates across patients are explicable without differential involvement of semantic, lexical or phonological units" (p. 832). They reached this conclusion, which they referred to as "the globality assumption," because parameter alterations across the entire simulated naming system generated a fairly good match to the patterns exhibited by the patients they studied (but see Foygel & Dell, in press). Dell et al. did indicate, however, that it may be difficult for the globality assumption to account for patients (e.g., K.E. and P.W.) who make only semantic errors at relatively low levels of overall accuracy. 25 In fact, for these reasons (among others), Foygel and Dell backed away from the globality claim.

Although we did not carry out an exhaustive exploration of all possible parameter permutations, we did examine a wide range of damage situations that involved manipulating multiple parameters (P, Q, intrinsic noise, activation noise, jolt size, and step number). In the course of doing so, we did not find any global damage setting that produced a pattern of only semantic errors at the rates observed for K.E. and P.W. The results we report that indicate a good fit of the data under HIA were achieved only through local damage. In addition, we were also able to show that C.S.S.'s pattern of multiple error types plus a significant mixed error effect (similar to that of Dell et al.'s [1997] high-weight individuals) could (under both RIA and HIA) be accounted for with only local damage. On this basis, we propose (see also Ruml & Caramazza, in press) that patterns such as those exhibited by K.E., P.W., and R.G.B. constitute evidence that compels rejection of the assumption that brain damage necessarily affects global aspects of processing (see also Caplan, Vanier, & Baker, 1986, and Hillis, Rapp, & Goldrick, 1998, for other potentially problematic patterns). 26

Implementing lnteractivity

Our work illustrates that interactivity can be achieved in a variety of ways: with cascading activation, increasing feedback, decreasing seriality, and extending the interaction domains. We have focused primarily on establishing the locus and extent of interactivity rather than on determining the specific contributions of each of these mechanisms. Although the different means of achieving interactivity may make empirically distinguishable predictions, this has not been our principal concern here.

The expansion of interaction domains in FILSA does, however, make verifiable predictions regarding error types. That is, if a functional expansion of interaction domains (visual features through phonemes) has been achieved, we should see error types that are not expected in a system in which these domains do not interact. The fact that we observed visual-phonological errors in the FILSA simulation is evidence that domain expansion was achieved and that extensive domain interaction is indeed problematic.

It is important, however, to make clear that the failures of the FILSA simulation to account for the relevant findings are not a consequence of the fact that the FILSA theory was implemented in a distributed connectionist architecture rather than in the localist connectionist architectures used to implement the other positions. We assume that FILSA's failures stem from the fact that the interaction domains were too extensive given the overall level of interactivity in the system. Importantly, we assume that the question of distributed versus localist connectionist principles is generally orthogonal to the question of interactivity-discreteness with which we have been concerned. That is, the particular distributed connectionist architecture we used simply provided a means for implementing a more interactive position than HIA. It is presumed that there would have been a way of accomplishing the same objective within a localist architecture. Likewise, we assume that PDP principles could also be used to implement more discrete theoretical positions than FILSA. Our conclusions concern the locus and extent of interactivity and discreteness in the spoken naming system and not the classes of algorithms used for simulating the various theoretical positions.

The Role of Simulation in Evaluating Theoretical Claims

We have used simulation as a tool to help us in thinking through the predictions and implications of various theoretical claims. We

2~ In response to these potentially problematic patterns, Dell et al. (1997) suggested that these patients might have made other kinds of errors had different testing materiais and scoring methods been used. These method- ological concerns are, in our view, completely groundless. First, both K.E. and P.W. were tested with hundreds of line-drawing stimuli from a wide variety of semantic categories (taken largely, but not exclusively, from Snodgrass and Vanderwart's [1980] set). These did not differ in any apparent way from the stimuli used to test Dell et al.'s participants. Furthermore, C.S.S., who did produce multiple error types, was tested with most of the same materials. Regarding scoring criteria, we too scored the first complete response produced and counted single phoneme errors as errors. In addition, Dell et al. suggested that patients exhibiting a pattern of only semantic errors produced a high degree of no responses and semantic descriptions. This was not, however, the case for either P.W. or K.E., who produced approximately 6% omissions. This rate was well within the range exhibited by Dell et al.'s participants. Thus, the pattern of only semantic errors that we report cannot be simply dismissed as artifactual.

26 A further claim of Dell et al. (1997) is of a distinction between deficits of representational integrity (implemented with increased decay parameter values) and deficits of transmission strength (implemented with decreased weights on connections between units; see also Schwartz et al., 1994, and Martin, Dell, Saffran, & Schwartz, 1994). The predictions derived from this distinction were matched to an impressive degree in Dell et al. (but see Foygel & Dell, in press). For this reason, the proposed distinction may prove to be a fruitful one.


have proceeded by first identifying the specific theoretical claims and the empirical phenomena with which we were concerned. Only then did we attempt to instantiate the various theoretical claims in simulations. The claims that we have been concerned with are fairly broad--they refer to the degree and locus of interactivity and discreteness in spoken word production. Further- more, the phenomena we have been concerned with are fairly qualitative and "broad" as well--presence of lexical bias, mixed error effect, mixed error effect in the absence of phonological errors, and so forth. Clearly, one could have detailed theoretical claims that make specific quantitative predictions; the theoretical claims could include such things as the exact number of processing steps, the relationship between the strength of feedforward and feedback connections, the type and number of semantic features, and aspects of the activation function. In addition, one could be interested in far more detailed empirical phenomena--the specific magnitude of a lexical bias effect, the specific rates of different erro r types, and so forth.

It is important for empirical phenomena and theoretical claims to be commensurate in "grain." It clearly makes no sense to evaluate theoretical claims that make precise quantitative predictions with qualitative behavioral findings. It is equally senseless, however, to evaluate broad theoretical claims with detailed empirical results. For this reason, we have not been overly concerned wi'th data "fitting." We have assumed that, given the theoretical issues with which we are concerned, the minor details of the data are largely irrelevant. For example, C.S.S.'s rate of formal errors was 3%, and his nonword error rate was 7%. Although CFA's best matches were 1% and 6%, respectively, whereas RIA's best matches were 2% and 8%, respectively, we did not consider such things in adjudicating among theories. We ignored them because the theoretical claims we were evaluating simply did not make predictions that were specific enough to make these distinctions.

We do not consider the simulations to be theories of spoken word production. They are merely attempts to capture the central aspects of various theoretical positions in ways that are, hopefully, relatively insensitive to the multitude of details that need to be specified to get a simulation up and running but that, given the theoretical issues under consideration, are irrelevant. To this end, we have examined the robustness of the behavior of the simulations under a range of parameter settings, noise types, and so forth and have reported on the extent to which these factors influence the results we have observed. On the basis of these explorations and because we have developed some understanding of the mechanisms of discreteness and interactivity, we consider our conclusions to be fairly general and not restricted to the specific algorithms and sets of implementational decisions that were made. Nonetheless, without formal proof, this must remain an open question. 27

Neurobiological Plausibility

Much has been made of the implications for cognitive theorizing that, presumably, derive from the observation of extensive neural interconnectivity (and particularly extensive feedback connectivity). In simulations of interactive cognitive systems, this extensive connectivity is typically implemented by means of full and comparable feedforward and feedback connectivity between levels.

Our results suggest, however, that interaction may not be comparable between all adjacent levels. Specifically, the results indicate the presence of greater interactivity between the phoneme level and the L level than between the concept level and the L level. If, as indicated earlier, we take the L level to correspond to a modality-specific lexeme level, then we can take our work as evidence for greater within- versus across-domain interaction. It is interesting to note that this notion is consistent with generally accepted facts about cortical connectivity, namely, that short- distance connections far outnumber long-distance connections. 28 This lends itself to the supposition of a greater degree of functional interaction between adjacent versus nonadjacent networks. If we adopt the further (also widely held) assumption that adjacent neurons are likely to be involved in computations within a common domain, then we would expect within-domain neural connectivity to support greater interactivity than across-domain connectivity.

Although this certainly does not count as evidence in favor of RIA, we mention it to indicate that conclusions regarding restricted interactivity are at least broadly consistent with our current understanding of cortical connectivity.

Why Discreteness ? Why Interactivity ?

The traditional argument for modularity (discreteness) as a general design principle is that a modular system is easier to "debug" or to improve than a highly interactive one. The rationale underlying this is that in a modular system problems in one part of the system will not necessarily have widespread consequences. Likewise, improvements to some aspect of functioning can be carded out locally and will not necessarily require widespread modifications (Marr, 1982). A further argument (in the context of spoken word production) was made by Lever et al. (1991b), who argued that

lexical selection and phonological encoding fulfill wildly different functions. The former involves fast search in a huge lexicon for an appropriate item; the latter creates an articulatory program for that item. Any feedback from the latter to the former level only makes the system more error prone than necessary. Modularity, one could say, is nature's protection against error. (p. 618)

Interestingly, arguments favoring interactivity make very similar points. For example, in terms of design principles, distributed interactive systems are claimed to be highly robust under damage (Hinton & Sejnowski, 1986). The reasoning here is that unless damage to some part of a system is very substantial, input from (or interaction with) other parts of the system will often be sufficient to "clean up" the noise. Additionally, Dell et al. (1997), in striking contrast with Levelt et al. (1991b), argued that

the function of lexical access is to get from conceptual representations to phonological forms. It would be worthwhile if the decision about

27 Rural et al. (in press) adopted a "data-fitting" approach to evaluating the adequacy of various simulations including Dell et al.'s (1997) and RIA. We consider this to reflect overconcem with matching aspects of the data that are not predicted by the theoretical claims under consideration.

2a By some estimates, as many as 70% of synapses may be of local or intrinsic rather than extrinsic origin (White, 1989).


which word to choose at lemma access were to be informed about how retrievable that word's phonological form is. (p. 830)

More generally, arguments favoring interaction stem from the possibilities that interactivity provides for multiple constraint sat- isfaction. In fact, McCleUand, Rumelhart, and Hinton (1986) argued that people are smart because the brain uses a computational architecture that is ideally suited for simultaneously considering many and diverse pieces of information. This capacity favors behaviors that are optimal in the sense that they are shaped by the multiple constraints an organism is trying to satisfy.

Although this is not the place for an in-depth discussion of this topic, we would like to briefly make three points. First, it is fairly easy to see that either modularity or interactivity taken to an extreme would be likely to have unfavorable consequences. Sec- ond, the extent to which either is advantageous is likely to vary across domains and even across tasks within a domain. And third, there is a wide range of methods and means by which the necessary degree of interactivity-discreteness may be achieved.

With regard to extremes, in accord with Dell et al. (1997), it is clear that a consideration of the phonological forms that are available should play some role in expressive language. We would not want to be able to only utter the word corresponding to the precise meaning we have in mind or remain mute. For example, if one is unable to retrieve an intended phonological form (sofa), a synonym may often (although certainly not always) be satisfactory (couch). However, if one is thinking canine, one does not want the fact that cat is the most highly accessible phonological form to lead one to think that the meaning one wants to express is, in fact, cat.

Furthermore, it is likely that success in certain tasks requires maximal consideration of multiple, comparably important constraints, whereas other tasks require that certain constraints be given high or even exclusive priority. 29 In spoken naming, for example, the meaning to be expressed is the driving consideration; accessibility of the phonological forms is a lesser one. In contrast, when one is planning a reaching movement to a salt shaker on a crowded stove top with hot burners, the locations of the obstacles and the hot burners may provide constraints comparable to those of the location of the salt shaker itself.

In conclusion, we have discussed a variety of mechanisms that can be used to implement discreteness and interactivity. The work reported in this article represents an attempt to determine the balance between discreteness and interactivity that exists in the human word production system and to begin to understand the mechanisms by which it may be achieved.

29 These ideas are related to the more technical notions of harmony maximization, constraint ranking, and strict domination expressed in harmony theory (Smolensky, 1986) and optimality theory (Prince & Smolen- sky, in press).

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A p p e n d i x A

O t h e r M e a n s fo r S i m u l a t i n g B r a i n D a m a g e

Dell et al. (1997) simulated damage by increasing values of P or Q as well as using intrinsic and activation noise. In spite of apparent differences between our respective methods of implementing damage, the consequences are highly similar. Note that Dell et al.'s activation function is identical to the one we adopted except that in addition to the activation noise term, it incorporates an "intrinsic error" term.

The first thing to note is that changing the Q (decay) value for a particular layer (or globally) will merely decrease every unit 's activation by the same proportion. Only absolute activation values will be affected, and thus the relationships among units will be unaffected. In fact, the only way that an increase in decay rate can result in any errors at all is if absolute activation values are reduced to a level where noise processes can generate errors. Thus, an increase in decay per se does not result in errors; it only indirectly allows noise to have an impact on the outcome.

Activation noise will preserve the activation relationships among units that are apparent in the noiseless situation--strong competitors of the target (highly active units) are more likely errors than weak competitors. Intrinsic noise also has much the same effect as activation noise, A1 although because it affects all units comparably, the consequences are less predictable as absolute activation levels fall to very low levels. As absolute activation values begin to fall (as time passes or if Q is increased), the difference in activation levels between the target and other units becomes small enough for intrinsic noise to have an effect and push a competitor's activation level

over that of the target. At this point, errors will again correspond fairly directly to the activation functions observed under noiseless conditions. However, when absolute activation values become very small relative to intrinsic noise levels, intrinsic noise will begin to approximate a random error selection process. In sum, Q manipulations merely create the conditions whereby noise processes can generate errors, whereas intrinsic or activation noise largely respects the relationships present in the nondam- aged state.

The consequences of manipulating P in a feedforward architecture are much like those for Q. However, the consequences of P manipulation in interactive systems are less transparent. Dell et al. (1997) argued that reductions in P have the effect of reducing the "concurrence" and interactivity between levels. They claimed that this manifests itself in a variety of ways, including an attenuation of mixed error effects and an increase in nonword and unrelated word errors. We have not been able to characterize the effects of P damage in any systematic manner (but see Foygel & Dell, in press).

A! For example, intrinsic noise is thought to be the primary cause of unrelated lexical errors. However, high activation noise at the L level produces high levels (e.g., 48%) of unrelated errors.


Appendix B

Computation of Predicted Mixed Error Rates

Our analysis of mixed errors is based on the following formula and is designed to determine if the number of mixed errors we observe in the simulations under noisy conditions at phoneme and L levels is what would be predicted by the assumptions of a discrete feedforward theory. Accord- ing to a discrete feedforward theory,

Sob . . . . . d = k ( S ) + (1 - k ) ( S ) + c ( F " )

or (given the simulation lexicon)

Sob ..... d = 2/3(S) + 1/3(S) + 1/2(Fm).

Sob~ve,t consists of all errors that are semantically related to the target (pure semantic plus mixed semantic and phonological errors). These errors can be generated by either a semantic process or a phonological process. S corresponds to the number of Sobser~ ~ that are generated from the semantic process. Some percentage of S will, because of the structure of the lexicon, not have a phonological relationship with the target. We denote this percentage by k, equal to 2/3 in our neighborhood (two of the three semantic neighbors do not share phonology with the target). The first term, k(S) + (1 - k)(S), expresses this: Some percentage k of the errors generated by the semantic process will share no phonology with the target, whereas 1 - k of the errors will share phonology.

Semantic errors can also be generated by a phonological process. F m corresponds to the number of errors arising from the phonological processes that are as similiar to the target as the mixed error is. Some percentage of these errors will also, because of the lexicon structure, have a semantic relationship with the target. We call this percentage c, which is equal to I/2 in our neighborhood (formal* and the mixed error are equally similiar to the target; thus, 1/2 of these errors will bear a semantic relationship). Note that mixed and formal* neighbors are only 2 of the 12 words in the neighborhood that are formally related to the target.

From this formula, using the pure semantic and formal* errors produced by any of the simulations, we derive the estimated rate of mixed errors expected according to the assumptions of DFA. We then compare (by a Z test) the rate of expected mixed errors and the rate of simulated mixed errors. If they do not differ, then we conclude that the simulation is compatible with DFA. If they differ, then we assume a mixed error effect that is incompatible with a discrete account.

We derive this estimation as follows. To a first approximation (see below), pure semantic errors arise only through the semantic process. As shown above, this will be equal to 2/3 of S. Because 1/3 o rS will be mixed errors, we estimate the number of mixed errors arising through the semantic process to be 1/2 of the observed pure semantic errors. Furthermore, because formal* errors arise only through the phonological process, their observed error rate will be equal to 1/2 of Fm. This is equal to the number of mixed errors arising through the phonological process. The estimated rate of mixed errors will therefore be the sum of mixed errors from both processes: 1/2 the number of pure semantic errors plus the number of formal* errors.

This estimation does not state several minor complications, which we, nonetheless, did take into account. First, we do not wish to include formal* errors that are not strictly the result of the phonological process, for example, a mixed error at L-level selection followed by a phonological encoding error that yields formal*. Consequently, our calculations use the number of formal* errors that specifically come about as a result of correct selection at the L level followed by an error in phonological encoding.

In addition, there are also slight adjustments for the mixed and pure semantic error rates. The actual mixed error rate M is

M = (1 - k )S + c F ~ + (M1Sn~vho~) - (Sno~phJM) -- (Non-S~o~pho, errodM),

whereas the "nonphonologicar' semantic error rate, S,o,pnon, is actually

Sno,pho, = k S + (SnonphonlM) - (MlSnonphon) -- (Non-MIS.o~pbon).

For the mixed errors, these include such things as mixed errors that result from the misselection of a semantic neighbor that is then followed by the misselection of phonemes at the phoneme level (MlSnonpbon) or the situation in which the selection of a mixed neighbor at the L level is followed by a phonological error that results either in a semantic e r r o r (SnonphonlM) or in some other response (Non-Snonpho n IM). Similar adjustments must be made for the pure semantic errors.

If we assume that (SnonphonlM) and (MlSnonpbon) ale essentially equal, then these contributions will cancel out in each formula. If we also assume that (Non-Snonpho n errorIM) and (Non-M errorlSnonphon) are equal, they will affect each error rate in the same manner and thus will not change the relationship between them. Therefore, we can ignore these corrections.

Appendix C

Lexical Bias Effects and the

As discussed in the text, it may be necessary to consider the phonolog-

ical inventory of a target word's semantic neighborhood in order to establish that the rate of formal errors observed conforms to that predicted

under the theoretical assumptions of CFA.

To demonstrate the role of the semantic neighborhood in determining word and nonword outcomes, we ran the CFA simulation with some minor

modifications. We identified two pairs (A and B) of word and nonword outcomes. All four outcomes had the same vowel and coda, but they differed in their onset phonemes. Each pair was matched in connectivity in the onset position. Word A and" Nonword A had onsets that were not constituents of any semantic neighbor (pairs lacking semantic support). Word B and Nonword B each shared an onset with a single semantic neighbor of the target (pairs with semantic support). The simulation was

Cascading Activation Account

run as before, except that just prior to phoneme selection the onset and vowel phonemes of the target were "deselected" by getting their activity levels to 0. This mimics a trial on which an error is made in both the onset and vowel.

The results of 10,000 runs conformed to what was predicted. First, when

matched for similarity to semantic neighbors of the target, comparable error word and nonword rates were observed for the nonsemantic pair (9 words vs. 10 nonwords) and the semantic pair (1,179 words vs. 1,195 nonwords). Second, outcomes (word or nonword) supported by a semantic neighbor (the semantic pair) were far more likely than outcomes not supported by semantic neighbors (nonsemantic pairs).

This suggests that in evaluating empirically obtained measures of word and nonword outcomes, it may be important to assess the potentially

4 9 8 RAPP AND GOLDRICK

distorting effect of the phonological makeup of the semantic category. To determine the extent to which such considerations may actually be necessary, we established word and nonword error rates predicted under CFA for onset errors in eight semantic categories.

For each category, we listed all the phonemes that were in word onset positions. These were assumed to be the most likely competitors for the onset position of a target word because they would be receiving "cascading" activation from higher levels. (Onset-less items as well as morphologically complex items such as compounds or plausibly prefixed items, for example, blackberry-blueberry and bicycle-tricycle, were removed.) From this group, the three onsets that appeared first, second, and third most frequently in each semantic category were identified. These would be the competitor phonemes receiving the most support from higher level units. For each semantic category, we then paired the word body of each item with each of the most frequent competitor onset phonemes. The results of pairing word bodies with the most frequent onset competitor constitute the most likely error for each word in each category and provide an estimate of predicted rates of word-nonword outcomes in a system with cascading

activation. The results (Table C1) of word body pairings with the most frequent onset competitor indicate a wide range of word outcome rates across categories---from a high of 87% in the category of body parts to a low of 0% in the category of fruits. The results obtained from pairings with the lower frequency onset phonemes indicate that there is also considerable within-category variability with word outcomes ranging from 0% to 57% in the fruit category and from 33% to 88% in transportation.

Given these results, it is possible that if empirically determined word-nonword rates are higher than would expected under DFA, they may, depending on the semantic category membership of the stimuli (and especially in cases in which stimuli are from a single or limited number of categories), be consistent with a cascading activation account. In that case, further interactivity assumptions (e.g., feedback) would be unwarranted. Because of these possibilities, definitive conclusions regarding whether reported word-nonword rates in normal speech errors are actually problematic for CFA await further analyses that take into account the phonological composition of the semantic neighborhoods of target items.

Table C1 Predicted Percentage of Word Outcomes for Onset Errors Across Different Semantic Categories Under the Assumptions of the Cascading Feedforward Account

Most frequent 2rid most frequent 3rd most frequent Category onset competitor onset competitor onset competitor

Animals 51 45 58 Furniture 27 37 37 Body parts 87 74 88 Fruit 0 57 29 Clothes 69 39 35 Birds 39 24 9 Transportation 33 88 33 Vegetables 18 28 8

A p p e n d i x D

L e a r n i n g in t h e F u r t h e r I n t e r a c t i o n , L o w S e d a l i t y A c c o u n t

The procedure involved two phases: a positive phase and a negative phase. In the positive phase, all three visible layers are clamped, and the hidden unit activations are calculated. The positive phase corresponds (in some sense) to the target of learning. In the negative phase, only one visible layer is clamped, and hidden and visible layers are allowed to update their states. In contrast to the positive phase, the negative phase measures how well the network has already learned the task. Learning then involves changing the weight between two units in proportion to the difference in the products between the two units for the positive and negative phases.

In the FILSA simulation, the negative phase had three subparts, each corresponding to the clamping of one of the three visible layers. This was done to make the system highly interactive, forcing it to be responsive to the demands of all three visible layers. Essentially, the network was learning three tasks: how to generate phonology from visual input through

semantics, how to generate visual representations from phonological input through semantics, and how to generate phonology and visual representations from semantic input.

To balance the positive and negative phases, the pairwise product of the units' positive phase activation was multiplied by 3 before subtracting the three negative phase products. This difference was added into a list of pending weight changes. The entire process was repeated for each word, with the positive and negative phase product differences for each weight accumulating until every word had been processed. After the set was done, the weights were changed (using a weight step of 0.01 and no momentum). In our network, the 40 words were presented 7,900 times before the state of each visible unit was within 0.2 of its correct value during each of the three negative phases. No attempt was made to minimize the training time.


Appendix E

Error Categorization Under the Further Interaction, Low Seriality Account

To determine the most appropriate category for each of the target-error pairs actually produced by the simulation, we carded out the following three-step categorization process. For Step 1, we characterized all possible errors in the simulation lexicon by taking every possible word pair in the simulation lexicon and computing what we refer to as visual, semantic, and phonological similarity ratings. This was done by comparing the activation vectors for each pair across the visual, semantic, and phonological units separately and computing the normalized dot-product. For each word pair, this provided us with 3 values between -1 and 1. We then normalized the dot-products separately for the visual, semantic, and phonological sets so that the minimum value of each was 0 and the maximum value for the range was 1. (We did this because, although the dot-product has a range of -1 to 1, the vectors in each [visual, semantic, or phonological] set more ranges that differed. For example, the dot-products among visual vectors ranged from -.23 to .95, whereas the phonological dot-products ranged from .64 to .88.) This provided us with normalized visual, semantic, and phonological similarity ratings for each target-error pair.

Step 2: the three similarity ratings for each pair can be jointly considered as a vector describing the pair's position in a 3-D visual-semantic- phonological similarity space. To establish membership within one of the seven error categories, we described the "prototypical" member of each error category as a vector in this space. We then used distance from the prototype to determine category membership for any given target-error pair. For example, a prototypical semantic error would have a semantic similarity rating of 1 with visual and phonological similarity ratings of 0.

Likewise, a prototypical mixed semantic-phonological error wouid have semantic and phonological similarities of 1 and a visual similarity of 0. For every pair of words in the lexicon, we determined the Euclidean distance between their actual visual, semantic, and phonological similarity ratings and the idealized prototypical vectors for each of the seven possible error categories. After calculating the distance to each category prototype, we ranked all the possible pairs of words (in each category) in terms of the distance-from-prototype values. This provided a rank for each pair, within each category, based on distance from the prototype. In this way, for each word pair, we had seven values corresponding to the pair's ranking in each of the seven error categories.

For Step 3, each word pair was assigned to the single error category in which it had the highest rank. This allowed us to discretely categorize each target-error pair, and it also provided a measure of how strong a member of the category it was. Finally, we considered a target-error pair to be a strong member of a category if its distance from prototype was within the top 10% of all the word pairs. Thus, many possible target-error pairs (45%) were not strong exemplars of any category (e.g., ROCK-CAT). These were considered to be unrelated items.

Received June 25, 1998

Revis ion received August 3, 1999

Accepted September 22, 1999 •

New Editors Appointed: Emotion

The Publications and Communications Board of the American Psychological Associa- tion announces the appointment of Richard J. Davidson, PhD (Department of Psychol- ogy, University of Wisconsin--Madison), and Klaus R. Scherer, PhD (Department of Psychology, University of Geneva), as co-editors for the new APA journal Emotion for the term 2001-2006.

Effective immediately, please submit manuscripts (two copies plus disk) to

Richard J. Davidson, PhD Emotion Journal Office Department of Psychology and Waisman Center University of Wisconsin--Madison 1500 Highland Avenue Madison, W153705-2280

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