When Ottoman is Easier than Chair: An Inverse Frequency Effect in Jargon Aphasia

WHEN OTTOMAN IS EASIER THAN CHAIR: AN INVERSEFREQUENCY EFFECT IN JARGON APHASIA

Jane Marshall, Tim Pring, Shula Chiat and Jo Robson

(Department of Language and Communication Science, City University, London)

ABSTRACT

This paper presents evidence of an inverse frequency effect in jargon aphasia. Thesubject (JP) showed a pre-disposition for low frequency word production on a range oftasks, including picture naming, sentence completion and naming in categories. Her realword errors were also striking, in that these tended to be lower in frequency than the target.Reading data suggested that the inverse frequency effect was present only when productionwas semantically mediated. It was therefore hypothesised that the effect was at least partlydue to the semantic characteristics of low frequency items. Some support for this wasobtained from a comprehension task showing that JP’s understanding of low frequencyterms, which she often produced as errors, was superior to her understanding of highfrequency terms. Possible explanations for these findings are considered.

Key words: inverse frequency effect, jargon aphasia

INTRODUCTION

There is compelling evidence that at least some aspects of lexical processingare influenced by word frequency. For example, pictures with low frequencynames take longer to name than pictures with high frequency names (Oldfieldand Wingfield, 1965) and such effects are observed even over repeatedadministrations of a naming task (Jescheniak and Levelt, 1994). A similaradvantage for high frequency words has been observed in lexical decision (e.g.Balota and Chumbley 1984), and reading aloud (e.g. Balota and Chumbley,1985; Seidenberg, 1985, 1989).

There is also evidence that speech difficulties are influenced by wordfrequency, in that low frequency words are more likely to induce phonologicalerrors (Dell, 1989, 1990) or tip of the tongue states (Harley and Brown, 1998).Some have also argued that word substitutions tend to be higher in frequencythan the intended target, both in the case of semantic (Levelt, 1989) andphonological errors (del Viso, Igoa and Garcia-Albea, 1991), although this viewhas been disputed (Garrett, 1992). Stemberger (1985) also argues that adjacentshift errors (in which two words exchange places in connected speech) aresusceptible to frequency, in that the most frequent word typically ends up first.Finally, Levelt (1983) found a negative correlation between prelexical pausesand word frequency, suggesting that extended word searches were most likely tooccur prior to the production of low frequency targets.

Various computational accounts of these frequency effects have beenproposed (e.g. Stemberger, 1985; Dell, 1989). For example, it is argued that low

Cortex, (2001) 37, 33-53

frequency words have comparatively low resting levels of activation. As a result,when these words are targeted, greater input activation is needed than is the casefor high frequency words. Furthermore, in these competitive systems, lowfrequency items will be vulnerable to substitution by their higher frequencyneighbours.

Given that lexical models incorporate an advantage for high frequencywords, it might be anticipated that frequency effects would be ubiquitous incases of aphasia. Yet, although there is both group and single case evidence ofsuch effects (e.g. Newcombe, Oldfield and Wingfield, 1965; Ellis, Miller andSin, 1983; Kay and Ellis, 1987; Panzeri, Semenza and Butterworth, 1987), theyare by no means universal. For example, Nickels and Howard (1995) found thatonly two of their 27 subjects were significantly influenced by frequency innaming and there are numerous single case investigations where frequency is nota major factor (e.g. Hillis, Rapp, Romani et al., 1990).

There could be two explanations for the absenceof frequency effects inaphasia. One is that not all levels of the lexical system are influenced byfrequency. Jescheniak and Levelt (1994) propose that frequency effects ariseonly at the word form or lexeme level. Impairments arising at other levels mighttherefore be impervious to frequency. Consistent with this view, Nickels (1995)found that aphasic phonological errors were sensitive to frequency, whereassemantic errors were not.

The other explanation makes reference to alternative variables which areknown to influence naming and which may correlate with frequency. Theseinclude imageability (e.g. Franklin, Howard and Patterson, 1995), familiarity(Funnel and Sheridan, 1992), age of acquisition (Hirsh and Ellis, 1994), andlength (e.g. Ellis, Miller and Sin, 1983; Nickels, 1995). Indeed, it has beensuggested that findings previously attributed to frequency might, in fact, arisefrom these alternative variables. For example, Morrison, Ellis and Quinlan(1992) reanalysed Oldfield and Wingfield’s data (1965) to show that age ofacquisition, rather than frequency, might be the key variable. Furthermore, someof these variables, such as imageability and length, can produce inverse effectsfollowing brain damage (Breedin, Saffran and Coslett, 1994; Marshall, Pring,Chiat et al., 1996; Best, 1995). Therefore, a dominant influence of one of theseother variables could either mask or eliminate an effect of frequency.

Although we might be able to explain the absence of frequency effects inaphasia, an inverse effect would be more challenging, since here we would haveto argue that the deficit has overturned the normal processing advantage for highfrequency items. A number of commentators have observed that people withjargon aphasia often use rather ‘high sounding’ speech, containing unusual, lowfrequency terminology (e.g. Brown, 1981; Weinstein and Lyerly, 1976). Forexample, one individual studied by Weinstein, Lyerly, Cole et al. (1966)described a plastic straw as a ‘little apparatus called a sucking utensil’. Theseobservations suggest that further investigation might uncover an inversefrequency effect in jargon aphasia.

This study offers such an investigation. The individual studied named lowfrequency words better than high frequency words across a variety of tasks,including picture naming, naming in categories and sentence completion.

34 Jane Marshall and Others

Furthermore the subject’s real word errors reversed the trend identified by Levelt(1989), in that low frequency items tended to replace higher frequency targets.These findings are very difficult to explain within current theories of wordproduction. We speculate that they may arise from the interaction of weaknessesat different levels of processing.

CASE REPORT

JP is a retired deputy warden of a nursing home. She is married with adult children.She is right handed and a monolingual English speaker. In December 1994, at the age of75, JP fell, fracturing her arm and skull. She was unconscious for two days. When sheregained consciousness she displayed concussion and some disinhibited behaviours,although her production and comprehension of speech seemed normal. Eight days after herfall, while still in hospital, JP apparently sustained a left CVA. She acquired a righthemiplegia (which virtually resolved) and a severe dysphasia. A CT scan revealed ischaemicchanges in the left Sylvan Fissure. This study began 12 months after the onset of JP’sdysphasia.

LANGUAGE PRESENTATION

Observations of Input

On a non verbal test of picture semantics (Pyramids and Palm Trees, Howardand Patterson, 1992) JP made 5 errors, which is just outside normal limits. Herperformance on verbal assessments was more impaired. In matching spoken andwritten words to pictures she scored 35/40 and 34/40 respectively, with all errorsinvolving selection of the semantic distracters (tests drawn from thePsycholinguistic Assessments of Language Processing in Aphasia, PALPA: Kay,Lesser and Coltheart, 1992). She was also poor at auditory synonym judgements(22/30 concrete items; 21/30 abstract items; PALPA: Kay et al., 1992).Misunderstandings equally arose in daily conversation, e.g. she respondedinappropriately to specific requests and yes/no questions. JP was partially awareof her comprehension difficulties. For example, when attempting to matchwritten words to pictures, she remarked ‘I don’t take what they tell me’.

Observations of Output

Reports indicate that JP’s speech immediately following her CVA was almostcompletely incomprehensible, with neologisms and frequent verbal paraphasias.She made heavy use of a corpus of stereotyped utterances which included:‘fibula’, ‘fistula’, ‘fibrositis’, ‘epiglottis’ and ‘potting shed’. At the time of thisstudy she was still producing extended English jargon and neologisms.Perseveration also featured, although some of her previous repetitive utteranceswere no longer present.

Striking in JP’s output was the presence of low frequency terminology. Someof this was only tenuously linked to the target. For example, when asked whereshe had been on holiday she replied ‘blank harbour of the differentiate’ (target:

Inverse frequency effect in aphasia 35

The Isle of Wight). On other occasions there was a more obvious connection.For example, when describing travelling in France she produced: ‘three monthsrainbow going to Alaska’; and when wondering where the therapist had parkedher (very modest) car, she asked: ‘Where’s your Daimler?’. In one informaltherapy task she was asked what she might buy in a number of shops. Herresponses included ‘salt beef’ at the butcher, ‘sally lunds’ at the baker and‘locusts’ at the pet shop!

JP produced virtually no spontaneous writing and was unwilling to carry outwriting tasks. On rare occasions when she did attempt written naming lowfrequency terms were again observed. For example, she wrote ‘pigeon’ for apicture of a bird.

JP’s repetition of both words and non words was virtually faultless (78/80,PALPA, Kay et al., 1992). She also achieved a high level of success when reading words aloud (e.g. 32/40 with high imageability items), but notwith non words (0/24, Kay et al., 1992). This pattern is typical of phonologicaldyslexia.

INVESTIGATION OF THE REVERSEFREQUENCY EFFECT IN OUTPUT

The above observations suggest that low frequency words might be morereadily available to JP than high frequency words. In this section of the paperwe present evidence, in the form of spoken naming data and error analysis,which supports this view.

Picture Naming

Preliminary testing with JP assembled a large corpus of picture naming data(259 items, drawn mainly from the Snodgrass and Vanderwart set, 1980). Thiscorpus was initially analysed in order to derive a general profile of her namingperformance (see Table I). The table also outlines the error classification systemused throughout this study. Frequency values were taken from Francis andKucera (1982).

This preliminary data suggested that JP named low frequency words moresuccessfully than high frequency words (51/109 vs 30/113, chi square = 8.89, p < 0.01). She was also significantly better at naming the medium frequencywords than the high frequency words (18/37 vs 30/113, chi square = 6.247, p < 0.05).

A second, more highly controlled corpus of pictures was administered fornaming (Nickels, 1992). The 130 items comprised 3 sets: 50 1 syllable words,50 two syllable words and 30 three syllable words. Within each set half theitems were low frequency (< 15 occurrences per million, Kucera and Francis,1967) and half were medium/high frequency (> 20 occurrences per million). The3 sets were matched across the groups for imageability. With these materials JPstill showed an advantage for the low frequency items (low frequency vsmid/high frequency 23/65 vs 9/65, chi square = 7.004, p < 0.01). No effect forlength was seen. (See Table II).


The final picture naming task explored whether naming was broadly affectedby semantic category. JP was asked to name 20 animates and 20 inanimates,which were matched for frequency and familiarity. Her scores were identicalwith the two sets (8/20).

Naming in Response to Sentence Completion Cues

JP’s picture naming indicated superior performance with low frequencyitems. Of interest was whether this result would be sustained across variants ofthe naming task. The observation that JP responded well to closure cuesencouraged us to explore sentence completion as one variant.

Fifty sentences were devised for completion. Half the items were non-constrained, in that a wide range of closures would be acceptable, e.g.:

She went to the department store and bought a ...The remaining items were constrained, or targeted more specific responses.

These sub-divided into 13 items targeting low frequency responses and 12targeting high frequency responses, e.g.:

The mother of the sick child phoned the ... (target: doctor, high).The cat sharpened his ... (target: claws, low).


TABLE I

JP’s Naming Responses with 259 Pictures

No of responses % of total

Correct 99 38%Semantic errors 70 27%Verbal paraphasias 35 13.5%Neologisms 22 8.5%Phonological errors 5 2%Other 28 11%

High Frequency words (41 +) 30/113 (26%)Medium Frequency words (20 – 40) 18/37 (49%)Low Frequency words (< 20) 51/109 (47%)

Error classification

Semantic errors real word responses bearing a semantic relationship to the target, e.g. ‘elbow’ for‘arm’; related compounds were included, e.g. ‘drinking straw’ for ‘glass’.

Verbal paraphasias real word responses which were semantically and phonologically unrelated to thetarget, e.g. ‘helicopter’ for ‘jacket’; unrelated compounds were included, e.g.‘Chile con carne’ for ‘radio’.

Neologisms non word responses which contained fewer than 50% of target phonemes, e.g./ti:fil/ for bowl

Phonological errors word and non word responses which contained over 50% of the targetphonemes, e.g. ‘shock’ for ‘sock’.

TABLE II

Naming Responses to 130 Pictures Controlled for Length, Imageability and Frequency (% correct)

1 syll 2 syll 3 syll Total

Mid/high frequency 3/25 (12%) 3/25 (12%) 3/15 (20%) 9/65 (14%)Low frequency 9/25 (36%) 11/25 (44%) 3/15 (20%) 23/65 (35%)Total 12/50 (24%) 14/50 (28%) 6/30 (20%) 32/130 (25%)

The sentences were read aloud to JP and she was asked to think of a closingword. The task was also administered to 14 non-aphasic control subjects.

The control subjects generated semantically acceptable closures for allprovided sentences. As expected, their responses to the non constrainedsentences were more varied than to the constrained sentences (mean number ofdifferent responses produced with constrained sentences: 2.72; mean number ofdifferent responses produced with unconstrained sentences: 6.76, t (13) = 6.648,p < 0.001). This is illustrated by the following examples:

Stimulus: She went to the department store and bought a ....(unconstrained).

Controls’ responses:dress (2), frying pan, blouse, curtain, cardigan, jacket,ironing board, outfit, rug, hair dryer, mop, handbag, frock.

Stimulus: The mother of the sick child phoned the ... (constrained).Controls’ responses:doctor (14).

JP scored 43/50 on the sentence completion task (unconstrained items 22/25;constrained items with high frequency targets 9/12; constrained items with lowfrequency targets 12/13). Her errors comprised 3 items on which she failed togenerate a response and 4 anomalous (although related) closures, examplebelow:

Stimulus: The child was allergic to ... (unconstrained).Response: eczema

With some items JP offered more than one closure, in which case her firstresponse was included in the frequency analysis.

Although acceptable, many of JP’s correct responses were unexpected, andlower in frequency than the typical responses of the controls. Examples:

On the farm they saw a ... salamander.(non-constrained item; although 11 controls responded with ‘cow’).

The man carried his new wife into the ... parlour.(high frequency constrained item; 13 controls responded with ‘house’).

Table III presents the mean frequency of responses in the sentencecompletion task. The analysis only includes items on which JP had achieved acorrect result. Both JP and the controls produced the predicted frequencydifferentials between the constrained sets of items, in that responses to itemswith high frequency targets were higher in frequency than those to lowfrequency targets. However, with all groups of items, JP’s responses are lower in


TABLE III

Mean Frequency of Responses on the Sentence Completion Task

Type of stimuli No of items JP Controls

Unconstrained 22 28.12 70.68High Frequency Targets 9 117.08 318.55Low Frequency Targets 12 17.8 29.14

frequency than the controls’, with a significant overall effect (related t test, t(42) = 3.002, p = 0.005).

A final analysis compared the length of JP’s responses with the mean lengthof the controls’ (in number of phonemes). Although JP’s responses weremarginally longer than the controls’, the difference was not significant (meanlength of JP’s responses: 5 phonemes; mean length of controls’ responses: 4.48phonemes, related t test, t (42) = 1.73, not significant).

Naming in Categories

A final naming task required JP to generate items from 10 categories (nopictures were provided). The categories were: animals, flowers, vegetables,furniture, kitchen items, countries, transport, musical instruments, tools andclothes. Ten people without aphasia acted as control participants on this task(see Table IV).

Controls were asked to stop after 15 items were named in each category.With this constraint, the mean number of items named by the controls was 119.6and the mean number per category ranged from 7.7 (tools) to 14.3 (countries).JP found the category naming task difficult, naming just 29 items overall, witha maximum of 5 items per category. Her performance was not obviouslyaffected by the nature of the category. For example, the mean number of itemsproduced in the animate and inanimate categories was identical (3).

Although JP named far fewer items than the controls, many of her responseswere very unusual (such as ‘ottoman’ in the furniture category and ‘Liberia’ inthe countries). Indeed ten of her responses were named by none of the controls.A one factor repeated measures ANOVA with categories as the random effectwas used to compare the mean frequency of JP’s responses in each categorywith the mean of the controls’ responses and with the mean of the controls’ firstand last 5 responses. A significant main effect [F (3, 27) = 4.51, p < 0.01] wasfound. A Newman-Keuls unplanned comparison found that the controls’ first 5responses were significantly more frequent than their last 5 and JP’s responses(both p < 0.05). The overall frequency of JP’s responses was very similar to thatof the last 5 responses produced by the controls (see Table IV).

A second analysis explored the typicality of responses (Battig and Montague1969). Typicality norms are derived by asking subjects to name items withincategories, the most typical being the most commonly named. These norms,therefore, offer a direct measure of naming frequency within the category task.Norms were available for 9 of the ten categories tested with JP. Table IV provides the mean typicality ratings for JP’s responses within these 9categories, together with the mean ratings of the controls’ overall responses andof their first and last 5 responses within each category. A one factor repeatedmeasures ANOVA was used to compare the mean typicality of JP’s responseswith the mean of the controls’ overall responses, and with the mean of thecontrols’ first and last 5 responses. This revealed a significant main effect [F (3,24) = 17.94, p < 0.0001]. A Newman Keuls test showed that the controls’ first 5responses were significantly more typical than their overall responses (p < 0.05)and their last 5 responses (p < 0.001); JP’s responses were less typical than the


controls’ first 5 responses (p < 0.001) and their overall responses (p < 0.001).The table shows that JP’s responses had similar typicality values to the controls’last five responses.

Conclusions from the Category Naming Task

Controls’ category naming tended to become less frequent and less typicalwith serial position, i.e. they started with common items (such as ‘table’) andprogressed to less common ones (such as ‘chaiselong’). As JP produced fewitems per category, it was not possible to compare the frequency of her first andlast responses formally. However, when she did produce a sequence ofresponses, these did not obviously decline in frequency or typicality.

Category naming was a difficult task for JP and she never achieved morethan 5 items per category in any one attempt. It therefore might be anticipatedthat her responses would be similar in nature to the first 5 responses producedby controls. However, this was not the case. When JP’s responses werecompared to the first 5 responses produced by the controls, we found that theywere both less frequent and less typical. Furthermore, JP’s responses were lesstypical than the overall responses produced by the controls. In other words, JP’slimited naming attempts in this task were characteristic of the (uncommon)responses produced in late serial positions by the controls.

Conclusions from the Naming Tasks

JP’s naming shows a significant advantage for low frequency items across avariety of tasks. Comparative data from control subjects in the sentencecompletion and category naming task confirmed that her response mode was notinduced by an unforseen bias in the assessments. Furthermore the effect waspresent even when length and imageability were controlled for.

It is possible that the apparent inverse frequency effect was due to anothervariable, such as familiarity or typicality. However, given that these variablescorrelate very highly with frequency, the problem to be explained remainsessentially the same. Why does JP name uncommon items better than commonones, particularly when this reverses the trend seen in both normal productionand in aphasia?

Although JP’s naming was dominated by low frequency words, highfrequency items were occasionally achieved (e.g. see table III). This indicated that


TABLE IV

Mean Frequency and Typicality of Responses Produced by JP and Controls in the Category Naming Task

Mean frequency Mean typicality

JP’s responses (overall) 15.76 88.12

Controls’ responses – overall 24.61 127.87– first 5 items named per category 37.70 181.78– last 5 items named per category 14.77 77.73

high frequency items were retained in her lexicon, even though they hadapparently lost their normal naming advantage.

Analysis of Semantic Errors

JP’s most common type of naming errors were real word errors bearing asemantic relationship to the target (semantic errors). For example, when asked toname 259 pictures, she produced 70 semantic errors, compared to 99 correctresponses, and semantic errors represented approximately 44% of all errorsproduced (see Table I). JP’s awareness of these errors was poor. For example,when she was asked to judge whether her responses were correct or not on anaming task she spotted only 25% of her semantic errors.

The naming assessments suggested that low frequency words were moreaccessible to JP than high frequency words. If this were the case, we might seesupportive evidence in her substitutions, in that low frequency substitutionsmight replace higher frequency targets.

In order to explore this hypothesis, all semantic errors produced in picturenaming were analysed. There were a total of 115 such errors, which wereelicited from 389 targets. Table V provides a breakdown of these errors. Itshows that, in line with the above expectation, 89 of the errors (77%) werelower in frequency than the intended target, compared to just 26 (23%) whichwere higher in frequency. Examples (frequency values in parenthesis):

Target Error

arm (217) elbow (17)chicken (49) pheasant (19)steps (228) escalator (0)grave (20) crucifix (3)flute (1) ukulele (0)

As these examples show, JP’s errors bore a variety of relationships to thetarget. Striking, however, was the number of subordinate errors, e.g.:

dog (147) collie (2)flower (78) daffodil (1)doctor (349) surgeon (12)car (393) Mercedes (1)knife (86) pen knife (0)ladder (19) step ladders (1)onion 19) shallot (0)

There were 31 errors of this type, compared to just 3 superordinate errors(‘bird’ for owl, ‘insect’ for spider and ‘gun’ for cannon). There were also7 errors in which a subordinate error seemed superimposed upon a shared

feature error, eg when a pie was named ‘treacle tart’ and spider was named‘viper’.


It might be argued that JP’s subordinate errors are really highly specificcorrect responses. However, with 23 of her 31 subordinate errors this wasclearly not the case. In other words, the pictured flower was not a daffodil, thedog was not a collie and the car was not a Mercedes.

JP’s semantic errors also showed interesting phonological properties. Firstly,there was a very high number of compounds (37 errors). Although many ofthese were also subordinate errors, 21 were not. Examples:

stable horse boxski snow bootglass drinking straw

Secondly, there was a strong tendency for errors to be longer than the targetand this effect was evident even if the compound errors were removed from theanalysis (see Table V). As a result, the predominant error type is both lower infrequency and longer than the target word.

Analysis of Verbal Paraphasias

JP’s second most common error category was verbal paraphasias, or realword responses which were both phonologically and semantically unrelated tothe target. JP produced 63 verbal paraphasias from 389 picture stimuli. Here aresome examples of her verbal paraphasias:

Target Errors

jacket helicoptertable horse shoeroad vertebrae


TABLE V

Length and Frequency Analysis of JP’s Semantic Errors

Shorter * Longer than Equal Totalthan target target to target

All errors (N = 115)Lower frequency 13 68 8 89than target (11%) (59%) (7%) (77%)Higher frequency 6 18 2 26than target (5%) (16%) (2%) (23%)Total 19 86 10

(16%) (75%) (9%)

Errors excluding compounds (N = 78)Lower frequency 17 34 8 59than target (22%) (44%) (10%) (76%)Higher frequency 6 11 2 19than target (8%) (14%) (2%) (24%)Total 23 45 10

(29%) (58%) (13%)

* Length calculated in terms of number of phonemes.

Table VI presents an analysis of all the verbal paraphasias produced by JP inpicture naming. These showed the same trend as her semantic errors, in that theytended to be both lower in frequency than the intended target and longer. A verysimilar proportion were also compounds (16/63).

Conclusions from the Error Analysis

The inverse frequency effect seen in JP’s naming was also evident in her realword errors, in that these tended to be lower in frequency than the target word.Errors also tended to be longer and/or compounds. Finally, her semantic errorswere often subordinates of the target. This feature contrasts strikingly with‘normals’, who predominantly respond with basic level terms in picture naming(e.g. Rosch, Mervic, Gray et al., 1976; Jolicoer, Gluck and Kosslyn, 1984).

The following sections attempt to identify which processing level may giverise to the inverse frequency effect. In the first, we use reading aloud data toexamine the proposal that the effect originates in phonological processing. In thesecond comprehension tasks are presented to explore the possibility that theeffect arises from semantic processing.

THE PHONOLOGICAL HYPOTHESESAND INVESTIGATIONS OF READING

For JP, the phonological representation of low frequency words may be moresalient, possibly because these items tend to be longer and have fewerphonological neighbours. Under this account the inverse frequency effect seen inJP’s errors was a byproduct of length. Thus JP prefers ‘collie’ to dog simplybecause it is longer. The phonological hypothesis is supported by the inverselength effect seen in JP’s errors (although no such effect was present in hernaming as a whole).

If the inverse frequency effect arises at the phonological level it should beevident in all tasks which require the processing of lexical phonology. One suchtask might be repetition. However, JP repeats words and non-words equally well,suggesting that she uses sub-lexical mechanisms. In contrast, she was completelyunable to read non words, and her errors on these items bore no relationship tothe target. This suggested that, when reading, JP was compelled to access lexicalphonologies. If the inverse frequency effect is a general feature of such access,it should be evident in reading.


TABLE VI

Length and Frequency Analysis of JP’s Verbal Paraphasis

Shorter * Longer than Same lenght Totalthan target target as target

Lower frequency than target 6 25 9 40Higher frequency than target 8 6 4 18Same frequency as target 1 3 1 5Total 15 34 14 63

* Length calculated in terms of number of phonemes.

Reading Aloud

JP’s reading was evaluated first by asking her to read 100 words which shehad also named from pictures. As Table VII shows, her reading wassignificantly better than her naming (McNemar chi square = 34.9, p < 0.001)and unlike naming, no inverse frequency effect was seen. The set included 20regular and 20 irregular items which were matched for length and frequency.Predictably, there was no effect of regularity.

JP’s error profile was very different in naming and reading. When naming,JP’s commonest error was semantic (44% of errors). In reading there was onlyone such error (10%). Here, her largest class of error was visually orphonologically related to the target (5 errors). For example, BALL was read as‘fall’ and HAND as /kand/.

The role of frequency in reading was further explored by administeringPALPA test no 31 (reading aloud by imageability and frequency, Kay et al.,1992). JP read aloud 32/40 of the high imageability words (80%). Herperformance with the low imageability set was poorer (24/40, 60%). Of greatestinterest was the effect of frequency. With high imageability words JP wasunaffected by frequency (hi imag hi freq = 16/20, hi imag low freq = 16/20).However, with low imageability words she was significantly worse with the lowfrequency set (16/20 vs 8/20, chi square = 5.1, p < 0.05). As before, themajority of JP’s reading errors were phonologically or visually related to thetarget (12 errors). There were 6 neologisms, 3 verbal paraphasias and 2derivational errors. No semantic errors occurred.

JP’s reading failed to show the inverse frequency effect, indeed the onlyeffect which was found was in the normal direction. Her far superior readingover naming suggested that she was exploiting a direct lexical reading route,linking the visual input lexicon with the phonological output lexicon. Thisconclusion was supported by two strands of evidence. First, the sublexical routewas clearly unavailable, given that JP completely failed to read non words.Second, JP’s reading was very different from her naming, in that she madevirtually no semantic errors. This suggested that reading aloud, unlike naming,was not semantically mediated.

One difficulty for this account was the apparent imageability effect. Previousinvestigations have suggested that imageability effects may not arise solely fromthe semantic system, but may also occur within the input or output lexicons (e.g.Franklin, Howard and Patterson, 1994). To explore this possibility a visuallexical decision task was administered. The stimuli comprised 136 high and lowimageability words (plus non word partners) which were subdivided into high


TABLE VII

JP’s Reading and Naming of 100 Items

Naming Reading

High freq words 14/43 (32%) 38/43 (88%)Low freq words 30/57 (53%) 52/57 (91%)

and low frequency sets. Items were matched for length and for the error positionin the non word.

In the normal version of the lexical decision task JP showed a powerful ‘yes’bias, or a strong tendency to accept non words. For example, in PALPA lexicaldecision test 25 (Kay et al., 1992), she scored 59/60 with words and 41/60 withnon words. Therefore an adapted version of the task was administered in whichJP was presented with both the correct word and its non word partner and hadto point to the correct word. Critically, JP was not required to understand thewords in the task, only recognise them. In processing terms, the task can beaccomplished purely by accessing entries in the visual input lexicon.

JP performed almost perfectly with the high imageability words (34/34 highimag/high freq; 33/34 high imag/low freq). Her performance was significantlyworse with the low imageability set (67/68 vs 57/68, chi square = 9.14, p < 0.05). However, this effect was almost entirely due to the low frequencyitems (low imag/high freq = 32/34; low imag/low freq = 25/34). Indeed, evenwithin the low imageability set there was a significant effect of frequency (chisquare = 5.3, p < 0.05).

The lexical decision task mirrored the results obtained in reading aloud. Itseems that JP had a specific problem recognising low imageability/lowfrequency words. This would account for her difficulties in reading these wordsaloud, since an inability to access entries in the visual input lexicon wouldprevent her from exploiting her direct lexical reading route.

Conclusions from the Reading Assessments

The reading assessments failed to replicate the inverse frequency effects seenin naming. JP’s reading of high imageability words was good and significantlybetter than her naming. Her reading of low imageability words was poorer, butshowed a normal frequency effect. The lexical decision task suggested that theseeffects were attributable to the input, rather than output components of the task.

It seems that an inverse frequency effect does not emerge whenever JPaccesses phonological entries for output. Rather, this pattern is only present innaming. This in turn suggests that the effect requires some semantic involvement.

THE SEMANTIC HYPOTHESISAND COMPREHENSIONDATA

This section uses input testing to explore the hypothesis that the inversefrequency effect arises at the semantic level of processing. The hypothesis wouldbe supported if it could be shown that JP’s comprehension is inversely affectedby frequency. In particular, we might expect that her comprehension of the lowfrequency, subordinate terms which often feature in her output should be betterthan her comprehension of more general, high frequency terms.

The section presents two comprehension assessments. In the first JPperformed best with high frequency terms, which seemed to challenge thesemantic hypothesis. However, this assessment required finer discriminations tobe made with the low frequency terms. The second task eliminated thisinequality and revealed an advantage for low frequency terms.


(i) Spoken Word to Picture Matching with General and Subordinate Terms

In this task JP was shown two semantically related pictures and had to pointto one named item. The stimuli for the task consisted of 25 general terms (plusfoils) and 25 ‘subordinates’ (plus foils), e.g.:

General stimuli Foils Subordinate stimuli Foils

bird insect robin woodpeckershoe glove slipper sandaldog cat collie Dalmatian

In all cases the general/subordinate terms were derived from JP’s own errors.Thus she had called a bird a ‘robin’ and a shoe a ‘slipper’. Each pairing wasadministered twice; i.e. on one occasion, JP was shown a picture of a bird andan insect and had to point to the bird, on the next occasion she had to point tothe insect. All subordinate stimuli were lower in frequency than their generalcounterparts.

If JP is more able to process highly specific semantic representations sheshould perform best with the subordinate items. In fact, she showed the oppositetrend. With the general terms she scored 49/50. With subordinates she wassignificantly worse (40/50, chi square = 8.27, p < 0.05). Her errors weredistributed across the two administrations of the stimuli (5 errors occurred in thefirst administration of the pictures and 6 in the second).

(ii) Yes/No Questions

In the picture task JP had displayed superior comprehension of general oversubordinate terms. However, this may have been partly induced by the task,since JP was required to make finer judgements with the subordinate terms.Indeed, experiments with unimpaired subjects have shown that categorising basiclevel terms like ‘bird’ and ‘insect’ is easier than categorising subordinate terms,like ‘robin’ and ‘woodpecker’ (Rosch et al., 1976).

The final comprehension task aimed to remove this bias. Once again thestimuli for the task consisted of general and subordinate terms, although now intriads, e.g.:

General Subordinate set 1 Subordinate set 2

tree willow oakflower rose daisy

Set 1 of the subordinates were JP’s own errors, as were most of set 2. Therewere 15 triads. As before, subordinate stimuli were lower in frequency thangeneral stimuli (general stimuli had a mean frequency of 166.5; set onesubordinates had a mean frequency of 6.9; set two subordinates had a meanfrequency of 6.5).

Four basic questions were developed for each triad. The questions were


identical for each member of the triad and only probed general semanticfeatures. Thus, in order to answer the basic questions about the first triad JPonly had to access the general features of all trees, and not the specific featuresof oaks and willows:

Does a tree/willow/oak have foliage?Does a tree/willow/oak have feathers?Can you mow a tree/willow/oak?Can you fell a tree/willow/oak?

Overall, there were 180 basic questions: 60 for the general terms and 60 foreach set of the subordinate terms. Questions with the general terms wereadministered twice, on separate occasions. Thus JP answered a total of 240questions. In this level of the task, the demands were identical across the sets.Therefore any difference between them must be attributable to JP’scomprehension of the terms.

A second corpus of questions was also developed. These were specificquestions and were only asked of one set of the subordinate terms (60 items). Inorder to answer these, JP had to access the specific features of the item, e.g.:

Do you see willows in the desert?Does a willow have drooping branches?Does a willow have cones?Are willows often seen by water?

These questions enabled us to compare JP’s ability to access specificsemantic features of the subordinate terms with her ability to access their generalfeatures.

The yes/no questions were also administered to 20 control subjects. Eachcontrol subject answered 150 questions: 60 basic questions on the general terms,60 basic questions on the subordinate terms and 30 specific questions.

As Table VIII shows, the controls achieved at least a 96% success level withall question types. There were no significant differences between the groups ofquestions. In contrast, JP’s performance across the groups varied. She wassignificantly better at responding to basic questions with subordinate terms thanto basic questions with general terms (97/120 vs 110/120, McNemar chi square= 5.33, p < 0.05). However, JP’s comprehension of the subordinate terms wasnot intact. With one set of specific terms, JP answered both basic and specific


TABLE VIII

JP’s and Controls’ Responses to the Yes/No Comprehension Questions

General (high freq) terms Specific (low freq) terms

JPBasic questions 81% (N = 120) 92% (N = 120)Specific questions – 75% (N = 60)

Controls (mean)Basic questions 97.8% (N = 60) 97.4% (N = 60)Specific questions – 96.8% (N = 30)

questions. Her performance was significantly better with the basic questions(basic 55/60 vs 45/60, chi square = 6, p < 0.05).

JP displayed an interesting pattern of performance with the yes/no questions.When answering basic questions, which only probed the general semanticfeatures of a triad, JP performed best with the subordinate terms. In other words,it seemed that she could derive more ‘tree’ properties from subordinate termslike ‘willow’ and ‘oak’, than from the general term ‘tree’. Despite this, JP wasunsure about the specific features of these items. Thus she did not know whetherwillows had cones or whether roses are grown from a bulb (despite the fact thatshe had been a keen gardener pre-morbidly). This latter finding would beconsistent with her performance in the picture tasks, which showed a tendencyto confuse closely related items.

Conclusions from the Comprehension Assessments

The comprehension assessments aimed to explore whether JP could derivemore semantic information from low frequency, subordinate terms than fromhigher frequency, general terms. If she could, this might help to explain whysuch terms often featured in her output.

The tasks demonstrated that JP’s comprehension of subordinate terms wasimpaired. She confused members of the same class in word to picture matchingand failed to answer specific yes/no questions about these items. It seemed thatJP could not access full semantic representations for the subordinate terms. Inmany ways this was consistent with her output data. Although JP often producedthese terms, they were almost always produced as errors. Furthermore whenasked specifically to name these subordinate items, i.e. label a picture of acollie, her performance was poor and entailed further semantic errors. It seemedthat the semantic representations for these subordinate terms were not preserved.

Although subordinate terms were impaired, they were superior to generalterms in one task. JP was more able to answer basic questions about subordinateterms than about general terms. Possible reasons for this finding are consideredin the concluding discussion.

DISCUSSION

This paper described an individual with jargon aphasia who produced lowfrequency words better than high frequency words. This effect was seen acrossdifferent variants of the naming task, such as picture naming, sentencecompletion and naming in categories, and contrasted sharply with the dataobtained from non-aphasic controls on the same tasks. The trend observed inJP’s naming was also evident in her real word errors in that these werepredominantly lower in frequency than the target word. Her errors showed twofurther patterns. They tended to be longer than the target, and, in the case ofsemantic errors, were often subordinates of the target (see Tables V and VI).

Before discussing the reason for these findings the role of other variables inJP’s naming must be considered. JP’s naming was unaffected by gross semanticcategory, in that the naming of matched animates and inanimates was equal.


There was also no effect of length in her naming (although there was in hererrors) and the inverse frequency effect was present even when imageability andlength were controlled for (see Table II). Therefore, it seems that somepotentially confounding variables can be dismissed.

Frequency is only one measure of a word’s occurrence. The others includefamiliarity, which is derived from subjects rating how often they encounter anobject or word, and typicality. One way of assessing typicality is to ask subjectsto name items within a category, the most typical being the most commonlynamed. These different measures of occurrence correlate very highly, i.e. wordswhich are atypical also tend to be infrequent. Therefore, it is possible that whatwe have described as an inverse frequency effect, is in fact an inverse effect oftypicality or familiarity. Indeed, JP’s responses in category naming were notonly less frequent than controls’ but also less typical. Similarly, the fact thatmany of her semantic errors were subordinates to the target means that theymust also have been less familiar. It is difficult to tease apart the effects of thesedifferent variables and even if we could achieve this the question to be answeredremains essentially the same. Why does JP produce uncommon words (whichare low in frequency, typicality and familiarity) more easily than common ones?In this final section we consider a number of possible responses to this question,although we acknowledge that none satisfactorily accounts for the data.

One hypothesis, proposed by Weinstein and colleagues (e.g. Weinstein, Cole,Mitchell et al., 1964; Weinstein et al., 1966; Weinstein and Lyerly, 1976;Weinstein, 1981) argues that low frequency words are produced in jargonaphasia as part of a psychological response to the disorder:

‘A major aspect of jargon is that it is an attempt to imitate normal – evenelegant – speech in order to avoid depression and preserve a sense of identityand social relatedness’ (Weinstein, 1981).

In developing this argument, Weinstein claims that low frequency productionis part of a complex of symptoms which include denial of deficit, ludicbehaviour and highly self referential use of language. It is also a typical featureof ‘non-aphasic misnaming’, or a selective anomia for objects connected withillness or personal status.

This hypothesis has difficulty accounting for JP’s performance. She displayedfew of Weinstein’s behavioural symptoms. Despite her poor self monitoring, shedid not deny her aphasia, rather she was frustrated by it and willinglyparticipated in speech and language therapy. Her language was not excessivelyself referential and there were no signs of ludic behaviour (if anything, her moodwas rather low). Above all, her naming errors were not topic specific. Forexample, her semantic errors occurred in response to words drawn from a widerange of categories including: tools, domestic/kitchen items, clothing, personalbelongings, animals, body parts, buildings and parts of buildings, musicalinstruments, vehicles, natural phenomena, food and drink, and furniture.

Even if Weinstein’s psychological account is accepted, we still need toexplain why low frequency vocabulary is preferentially available to JP, in thecontext of an otherwise severe naming impairment. This calls for a processingaccount of her naming performance.

The first processing explanation to be considered is that JP’s lexicon has lost


the high frequency words. This hypothesis can be dismissed immediately, sinceJP did name some high frequency words on all tasks. For example, in thesentence completion task she scored 9/12 with sentences targeting highfrequency words and the mean frequency of her responses to this set was117.08. It seemed that high frequency words were available to JP. They hadsimply lost their normal naming advantage.

Another possibility is that JP is naming at an abnormal level of specificity.There is strong evidence that normal naming typically targets basic level terms.So, when presented with a picture of a robin, most unimpaired subjects will say‘bird’ (Rosch et al., 1976; Jolicoeur et al., 1984). Aspects of JP’s performancesuggest that she is no longer targeting such basic level terminology, particularlyher tendency to produce subordinate semantic errors. However, this does notfully explain her production. First of all her subordinate production is usuallyincorrect, e.g. when naming a flower she produced ‘daffodil’ despite the fact thatthe pictured flower was more like a daisy. Furthermore, when asked to providesubordinate names (such as the breed of a pictured dog) JP performed poorlyand made additional semantic errors. Finally, many of JP’s errors (73%) did notinvolve subordination.

The third hypothesis is phonological. Although length effects were not seenin JP’s naming, they were present in her errors, which were typically longer thantargets, and were often compounds (see Tables V and VI). Such words arehighly distinctive, or have few phonological neighbours, and it is possibly thischaracteristic which for JP was facilitatory. Put another way, words with severalneighbours may be difficult to access because those neighbours have the effectof inhibiting the target.

The main difficulty for the phonological hypothesis came from JP’s readingdata. In its pure form, the phonological account would suggest that all taskswhich involve accessing lexical phonologies should show the inverse frequencyeffect. Reading was a good candidate for testing this, since sub-lexicalmechanisms (as tested by reading non words) were not available. However,contrary to the phonological hypothesis, the inverse frequency effect was notpresent in reading. Indeed, the only frequency effect seen was in the normaldirection, at least with low imageability items.

JP seemed to read aloud via the direct lexical reading route, given thatreading was better than naming and induced virtually no semantic errors. It wastherefore hypothesised that the inverse frequency effect was present only whenoutput was semantically mediated, as in naming. This gave rise to the fourth,semantic hypothesis. JP showed some evidence of a semantic impairment. Shemade a high proportion of semantic errors in naming, and had poor awareness ofthose errors. She also made semantic errors in comprehension tasks, regardlessof modality. Of course, a semantic impairment per se is insufficient to explainthe inverse frequency effect. We would also have to argue that, for JP, lowfrequency items have semantic properties which are in some way facilitatory.

If the origin of the inverse frequency effect was semantic, it should beevident in input. Two tasks were administered comparing JP’s comprehension ofhigh frequency general terms with her comprehension of the low frequencyspecific terms which she often produced as errors. These revealed that JP’s


comprehension of the low frequency terms was far from intact. She confused themeanings of related terms in a word to picture matching task and made errors inanswering specific questions about them. However, in one task the lowfrequency terms did reveal an advantage. JP was more able to answer basicquestions about these terms than about their general counterparts. In otherwords, she seemed to derive more basic semantic features from specific termslike ‘willow’ than from general terms like ‘tree’.

The input testing supplied some evidence to support the view that semanticrepresentations of low frequency terms were more available than semanticrepresentations of high frequency terms. Why might this be? It could be that lowfrequency terms have more elaborate semantic representations than their generalcounterparts. Thus ‘willow’ contains all the features of ‘tree’, plus additionalfeatures which distinguish it from ‘oak’ and ‘elm’. With any item JP may accessa reduced inventory of semantic features. For unelaborated items, such as ‘tree’,this reduced inventory is very impoverished. As a result errors are made evenwith basic yes/no questions and naming of such items is very poor. For morespecific items, such as ‘willow’, the original semantic entry is richer, or containsmore features. As a result, even a reduced inventory yields sufficient informationfor the general properties of the item to retrieved. Thus basic questions can beanswered with these items. However, the loss of some features means thatspecific properties, such as whether or not a willow has cones, are lost. Thiswould account for JP’s tendency to confuse subordinate terms and would explainher errors with the specific questions about such terms. In line with this account,there is some evidence in the literature that low frequency, specific verbs maybe more available to some aphasic people than their very high frequency, butgeneral, counterparts, possibly because these low frequency items have richersemantic representations (Breedin, Saffran and Schwartz, 1998).

Although this proposal accounts for some of JP’s data, there are majorproblems. It is challenged by the evidence that both aphasic and non aphasicpeople typically retrieve high frequency words best. If low frequency words weremore richly represented this would surely not be the case. Secondly, it is difficultto reconcile even with JP’s own production. Many of the words produced by JP,such as ‘salamander’ and ‘ottoman’, must have had very sparse semanticrepresentations, even pre-morbidly. Indeed, in discussion, JP indicated that shehad very little knowledge about the nature of these items (as did her husband).

An alternative account again appeals to the notion of neighbourhood. It ispossible that low frequency items are facilitatory for JP because they are highlydistinctive, or share few semantic features with other items. One way ofexplaining this would be to suggest that JP’s deficit has resulted in an excess ofspreading activation. When targeting a word like ‘dog’, competitors like ‘cat’receive a disproportionate degree of activation (via shared semantic nodes). As aresult, no single item achieves superiority and a response cannot be generated. Incontrast, ‘semantic outliers’, such as ‘salamander’ or ‘ottoman’, which share fewsemantic features with other items, send out very little spreading activation. Anyactivation which is available can therefore converge on the target, thusgenerating a naming advantage.

This discussion has argued that JP may have a predisposition for words with


low levels of neighbourhood. She seems aided by words with few semanticneighbours and, in the case of her naming errors, with few phonologicalneighbours. These observations may be explained by suggesting that herimpairment has resulted in excessive, rather than reduced spreading activation atall levels of the language system. Tasks which ‘bypass’ one level of the system,such as reading, remain relatively spared. Tasks which engage all levels, such asnaming, are impaired and show the inverse frequency effect. Others havesimilarly argued for over activation of lexical entries in Wernicke’s aphasia (e.g.Milberg, Blumstein and Dworetzky, 1988).

One problem for this account is that it predicts that JP would be mostsuccessful in naming items which are both long and low in frequency, or itemswhich are both semantically and phonologically distinct. Yet this was notobserved (see Table II). It is also challenged by evidence that neighbourhoodnormally aids, rather than inhibits retrieval (Harley and Brown, 1998).

It has been noted that people with jargon aphasia may show a preference forunusual, flowery vocabulary. This paper presents data to support thisobservation. The fact that such data can occur is intriguing. Explaining itsoccurrence is another matter.

Acknowledgements. This study was supported by MRC Grant No: G9231810N. Wethank Wendy Best for her helpful comments and Ruth Gilcrist for referring JP to us. Wealso thank Lindsey Nickels for making her testing materials available. We thank JP forparticipating.

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Jane Marshall, Department of Language and Communication Science, City University, London EC1V OHB, U.K. E-mail:[email protected]

(Received 2 May 2000; reviewed 20 June 2000; revised 18 August 2000; accepted 28 September 2000)


When Ottoman is Easier than Chair: An Inverse Frequency Effect in Jargon Aphasia

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