Conflict monitoring and cognitive control

Nick Yeung

Department of Experimental Psychology

University of Oxford

(h1) Abstract

Neural mechanisms of cognitive control are hypothesized to support flexible, goal-

directed behavior by representing task-relevant information in order to guide thought and action.

The conflict monitoring theory proposes that anterior cingulate cortex (ACC) contributes to

cognitive control by detecting conflicts in information processing and signaling when increased

top-down control is required. This theory provides a computationally specified framework for

understanding how cognitive control is recruited, and explains a large literature of human

neuroimaging studies reporting ACC activity in conditions of increased cognitive demand.

Predictions from the theory have been tested and consistently confirmed in behavioral and

neuroimaging experiments with human subjects. However, challenging findings from patients

with ACC lesions, and from studies of ACC function in non-human primates, suggest that

conflict monitoring may be just one facet of the broader role of ACC in performance monitoring

and the optimization of behavior.

(h1) Keywords

cognitive control; conflict monitoring; anterior cingulate cortex; neuroimaging;

computational models; neuropsychology; reinforcement learning; performance monitoring.

(h1) Introduction

A central challenge in cognitive neuroscience research is to account for intelligent,

purposive human behavior without relying on an unspecified intelligent agent—a controlling

homunculus—for explanatory power (Monsell & Driver, 2000). Framed differently, the

challenge is to explain how the richness and flexibility of human cognition emerges from

interactions among component processes that are themselves fixed and algorithmically

specifiable. As a first step toward meeting this challenge, it is often proposed that dedicated

mechanisms of cognitive control support flexible cognition by representing task-relevant

information and using this information to guide thought and action in accordance with current

goals and intentions (Miller & Cohen, 2001). Regions in lateral prefrontal cortex (PFC) are

thought to play a particularly important role in exerting this control (Sakai, 2008).

However, the notion that lateral PFC exerts control raises the question of how this region

itself knows which information is task-relevant: there is a clear danger here of theoretical

regress. Recent years have therefore seen considerable effort devoted to understanding how the

inputs to this region might support its functioning. Various functional connections of lateral PFC

are relevant in this regard: Interactions with the medial temporal lobe might support the recall of

goals set during earlier prospective planning (Cohen & O'Reilly, 1996; Schacter, Addis, &

Buckner, 2007), input from orbitofrontal cortex could provide information about the likely

payoffs of those plans (Koechlin & Hyafil, 2007; Wallis, 2007), and anterior prefrontal regions

might facilitate the coordination of multiple task goals (Badre & D'Esposito, 2009; Koechlin,

Ody, & Kouneiher, 2003; Shallice & Burgess, 1991). Complementing these ideas, a major focus

of recent research has been the hypothesis that recruitment of control by lateral PFC might

depend on input from medial PFC regions that monitor ongoing performance for indications of

success or failure in the chosen task.

The conflict monitoring theory is the most influential articulation of the hypothesis that

medial and lateral PFC regions form a regulatory feedback loop (Figure 1). [Figure 1 here].

According to this theory, control is recruited following the detection in medial PFC of

competition—or conflict—in information processing (Botvinick, Braver, Carter, Barch, &

Cohen, 2001; Botvinick, Cohen, & Carter, 2004; Carter et al., 1998). This chapter surveys recent

empirical and theoretical developments in research on this hypothesis. The first section reviews

the conceptual and empirical foundations of the theory. The second section reviews empirical

debates stimulated by the theory, for example regarding the neural basis of conflict monitoring

and the varying forms of conflict that might be monitored in the human brain. The final section

places the theory in a broader theoretical context, discussing challenging findings from studies of

medial PFC function in other species that suggest a wider conception of the role of this region in

the control of behavior.

(h1) Theoretical foundations

(h2) Conflict and competitive interactions

The idea that processing conflicts impose a fundamental constraint on human cognition

has been articulated in several influential theories of visual attention (Desimone & Duncan,

1995; Treisman & Gelade, 1980), action selection (Allport, 1987; Norman & Shallice, 1986),

working memory (Baddeley, 1996), and executive function (Logan, 2003; Miller & Cohen,

2001). In each domain, top-down cognitive control is thought to be needed to impose coherence

on thought and action. In particular, the central role of processing conflict, and the need for top-

down modulation, is inferred to follow from three basic and interrelated features of human

cognition: that behavior is flexible, that it is goal-directed, and that it is an emergent property of

coordinated activity across distributed neural circuits.

The flexibility of human cognition is apparent even in the artificial simplicity of the

laboratory. Presented with words on a screen, for example, subjects can read them aloud,

memorize them, classify each as a living or manmade thing, search for a certain word, etc., and

can switch fluently between these tasks according to whim or instruction (Logan, 1985; Monsell,

1996). Given that behavior is only loosely constrained by the environment, some form of control

must be exerted to select among the available tasks and actions (Duncan, 1986; Miller & Cohen,

2001; Monsell, 1996). However, purposeful behavior need not always rely on effortful control:

We often follow established routines that we execute with little thought or effort. The essence of

flexible, goal-directed behavior lies in our ability to act effectively when we lack these routines

and, perhaps even more critically, when achieving our behavioral goals requires us to override

our habits (Norman & Shallice, 1986; Shiffrin & Schneider, 1977). Control is required in the

Stroop task, for example, to suppress an established habit of reading words to allow naming the

ink color of those words (Cohen, Dunbar, & McClelland, 1990; Stroop, 1935). Cognitive control

is therefore needed because our behavioral goals are often in conflict with established response

tendencies.

The importance of conflict in constraining our ability to behave coherently arises

critically from the distributed nature of neural processing: Even the simplest cognitive task

requires the coordination of activity across multiple brain regions. The distributed architecture of

cognition is exemplified by the structure of the cortical visual system in which a proliferation of

functionally specialized regions feeds into memory and action systems in a massively parallel

manner. Within this parallel processing system, coordination is vital to ensure the coherent

activation—or binding—of object features represented in distant regions (Treisman & Gelade,

1980), and to ensure that features of selected objects are transmitted to appropriate action

systems (Allport, 1980). This coordination is thought to depend on mechanisms of top-down

cognitive control implemented in lateral PFC (Desimone & Duncan, 1995).

These considerations converge to suggest that regulating conflict is a core function of

cognitive control. It follows that identifying these conflicts as they arise could provide a crucial

signal of when increased control is required. This idea is at the heart of the conflict monitoring

theory (Botvinick et al., 2001; Carter et al., 1998), which proposes specifically that anterior

cingulate cortex (ACC), a region in medial PFC, monitors for the occurrence of conflict between

incompatible actions. A canonical example of response conflict comes from the Stroop task, in

which the presence of competing responses associated with incongruent color and word

attributes (e.g., BLUE in red ink) results in impaired performance relative to the case in which

color and word are congruent (e.g., RED in red ink). Response conflict similarly occurs in the

flanker task when the central target is flanked by distracting peripheral stimuli associated with a

different response (e.g., > > < > >) relative to the case when target and flankers are congruent

(e.g., > > > > >). The conflict theory proposes that ACC detects these conflicts as they emerge

during the action selection process, and uses this information to signal the need for increased

control. Increased control by lateral PFC regions, which serve to enhance task-relevant stimulus

and response representations, in turn leads to reduced conflict during subsequent task

performance, thus closing the conflict-control loop (Figure 1).

(h2) Anterior cingulate cortex

ACC lies bilaterally around the rostrum of the corpus callosum (Figure 2) [Figure 2 here].

The conflict monitoring theory focuses in particular on dorsal-caudal ACC, Brodmann’s areas

24c′ and 32′ (Devinsky, Morrell, & Vogt, 1995; Vogt, Nimchinsky, Vogt, & Hof, 1995). As well

as being densely interconnected with neighboring premotor regions (Dum & Strick, 1993), this

part of ACC shares strong anatomical connections with lateral PFC and parietal cortex

(Beckmann, Johansen-Berg, & Rushworth, 2009; van Hoesen, Morecraft, & Vogt, 1993), and

co-activates with these regions in human neuroimaging studies during task performance (Duncan

& Owen, 2000; Koski & Paus, 2000; Nee, Wager, & Jonides, 2007; Paus, Koski, Caramanos, &

Westbury, 1998) and in a slowly evolving manner at rest (Margulies et al., 2007; Raichle, 2010;

but see Dosenbach et al., 2007). The region thus forms part of a well-characterized control (or

“executive”) network with precisely the connectivity required by a conflict monitoring system:

Interconnections with motor structures would provide access to information about co-activation

of competing actions—at the level of both abstract motor plans and specific planned

movements—while connections with lateral PFC would enable the region to signal the need for

increased top-down control when such conflicts are detected.

Evidence that ACC plays this role in response conflict monitoring has come primarily

from human neuroimaging studies. Using a variety of methods including PET, fMRI, and EEG,

increased ACC activity has been found in conditions characterized by conflict between different

actions: when subjects override habitual responses, when they select one of a number of

potentially correct responses, and when they make errors (Figure 2; Botvinick et al., 2001;

Botvinick et al., 2004). Perhaps the most replicated neuroimaging finding regarding ACC is its

increased activity when the required response conflicts with a prepotent or habitual alternative.

Thus, ACC activity is observed in the Stroop task when the written word is incongruent with the

required color naming response (Bench et al., 1993; Liotti, Woldorff, Perez, & Mayberg, 2000;

Pardo, Pardo, Janer, & Raichle, 1990), in the flanker task when peripheral distractors cue a

different response than the attended target (Botvinick, Nystrom, Fissell, Carter, & Cohen, 1999;

Kopp, Rist, & Mattler, 1996), and in corresponding conditions of many other tasks (e.g., Braver,

Barch, Gray, Molfese, & Snyder, 2001; Nieuwenhuis, Yeung, van den Wildenberg, &

Ridderinkhof, 2003; Paus, Petrides, Evans, & Meyer, 1993; Peterson et al., 2002; Rubia et al.,

2001; Taylor, Kornblum, Minoshima, Oliver, & Koeppe, 1994).

ACC activation is similarly observed in underdetermined response tasks that induce

conflict between multiple potentially correct responses, such as generating verbs associated with

particular nouns (Crosson et al., 1999; Petersen, Fox, Posner, Mintun, & Raichle, 1989), or

producing random sequences of finger movements (Frith, Friston, Liddle, & Frackowiak, 1991).

Importantly, ACC activity in these tasks increases when the response is less constrained by the

stimulus, for example when verbs are generated for nouns with many possible actions (e.g., a

“ball” can be thrown, kicked, bounced, etc.) compared with nouns with one main associate (e.g.,

a “bell” tends to be rung) (Barch, Braver, Sabb, & Noll, 2000; Thompson-Schill, D'Esposito,

Aguirre, & Farah, 1997). Thus, ACC activity varies in relation to the level of conflict between

responses even when there is no defined correct response.

However, ACC also strongly activates when subjects make errors. Error-related ACC

activity was first found using electrophysiological recordings in non-human primates (Gemba,

Sasaki, & Brooks, 1986) and scalp EEG studies in humans (Falkenstein, Hohnsbein, Hoorman,

& Blanke, 1990; Gehring, Goss, Coles, Meyer, & Donchin, 1993). In human EEG, a component

labeled the error-related negativity (ERN) is robustly observed within 100 ms of incorrect

responses in choice decision tasks such as the Stroop and flanker tasks. Dipole modeling of the

ERN suggests ACC as its likely neural source (Dehaene, Posner, & Tucker, 1994), a supposition

confirmed using methods with more precise spatial localization (Carter et al., 1998; Debener et

al., 2005; Wang, Ulbert, Schomer, Marinkovic, & Halgren, 2005). These converging findings

suggest that ACC is active following errors, when conflict would be expected to occur between

correct and incorrect responses.

Evidence that conflict-related ACC activity reflects its role in the recruitment of control

has come from studies of sequential adaptation effects. In the Stroop task, for example,

interference from an incongruent word is reduced when the immediately preceding stimulus was

also incongruent, relative to trials following a congruent stimulus (Kerns et al., 2004). The

conflict theory explains this finding in terms of conflict detection on one trial leading to

increased top-down control, and hence reduced conflict, on the subsequent trial. Consistent with

this hypothesis, neuroimaging evidence indicates that lateral PFC activity increases after high

conflict trials (indicating increased top-down control, Egner, Delano, & Hirsch, 2007; Egner &

Hirsch, 2005) whereas ACC activity is reduced (reflecting the reduction in conflict, Botvinick et

al., 1999). Indeed, conflict-related ACC activity on one trial is predictive of lateral PFC activity

and concomitant performance improvements on the next (Kerns et al., 2004). Collectively, these

findings support the hypothesis that ACC and lateral PFC have dissociable but complementary

functional roles, with the former involved in recruiting control and the latter involved in the

execution of control.

(h2) Computational models of conflict monitoring

The two central claims of the conflict monitoring theory—that ACC monitors for the

occurrence of response conflict, and that it uses this information to signal the need for increased

top-down control—have been formalized in computational models (Botvinick et al., 2001). One

such model (Figure 3a) simulates behavior in the flanker task, in which subjects indicate the

direction of a central arrow while ignoring task-irrelevant flankers. The model comprises a task

network (black) and conflict-control feedback loop (blue). In the task network, units representing

possible stimuli activate corresponding left- and right-hand responses under the influence of top-

down attention. Incongruent flankers (e.g., in the stimulus < > <) activate the incorrect response,

producing conflict with the correct response and, hence, slower and less accurate responding

than congruent flankers (e.g., in the stimulus > > >) (Cohen, Servan-Schreiber, & McClelland,

1992; Spencer & Coles, 1999). [Figure 3 here]

Importantly, the level of conflict in the response layer of the network accurately predicts

empirically observed patterns of ACC activity in neuroimaging studies of the flanker task

(Botvinick et al., 1999), and provides a computational basis for recruiting appropriate levels of

top-down attentional control (Botvinick et al., 2001). Hopfield energy (Hopfield, 1982) provides

a simple formulation of the conflict signal:

(1)

€

Conflict = − ai ∗ a j ∗wijj=1

N

∑i=1

N

∑

where a denotes unit activity, w the connection weight between pairs of units, and i and j are

indexed over competing network units. Thus defined, conflict increases when there is co-

activation of units that share a mutually inhibitory connection. This product measure of conflict

is not the only possible formulation (Botvinick et al., 2001; Yu, Dayan, & Cohen, 2009) but it

formalizes conflict in an intuitive and straightforward way: On incongruent trials, the target and

flankers activate different responses such that the product of response activation levels is large

and conflict is high. On congruent trials, incorrect response activity is low or zero, the product is

correspondingly low, and there is little or no conflict.

Closing the conflict-control loop, detected conflict can be used to adjust the level of

attentional control according to the following equation (Botvinick et al., 2001):

(2)

€

C(t +1) = λC(t) + (1− λ)(αE(t) + β)

where C indicates the strength of top-down control, adjusted on a trial-by-trial basis (t) as a

function of previously experienced conflict (energy, E), scaled by a learning rate parameter λ

within limits imposed by parameters α and β. According to this equation, control increases when

high levels of conflict are experienced, leading to enhancement of task-relevant stimulus and

response representations on subsequent trials, thus enabling the model to account for sequential

adaptation effects observed in experimental studies (Botvinick et al., 2001): Strengthening of

control after a high-conflict incongruent trial leads to reduced conflict on the next trial (cf.

Gratton, Coles, & Donchin, 1992). Equations 1 and 2 together therefore provide a

computationally straightforward framework for dynamic and flexible recruitment of control.

As well as the flanker task model described above, this computational approach has been

applied to Stroop and underdetermined word generation tasks (Botvinick et al., 2001), and used

to simulate both conflict- and error-related adjustments in control (Rabbitt, 1966). In each case,

the proposed model implements flexible control without a controlling homunculus: Conflict

monitoring uses readily available information about activation levels of competing responses,

and the conflict signal is converted algorithmically to set the level of top-down control.

Crucially, these models have generated novel predictions that have spurred subsequent empirical

work. For example, a core prediction is that increased conflict-related ACC activity on one trial

should lead to increased cognitive control and improved performance on the subsequent trial.

This prediction has been borne out in fMRI studies (Kerns et al., 2004; Liston, Matalon, Hare,

Davidson, & Casey, 2006). Meanwhile, EEG methods have confirmed key model predictions

regarding the timing of ACC activity: Whereas ACC activity is predicted to occur after incorrect

responses are produced, as reflected in the ERN component of the EEG, conflict on trials with

correct responses is predicted occur primarily before the response (Figure 3b), a prediction

confirmed in EEG studies of a second component, the N2 (Figure 3c; Nieuwenhuis et al., 2003;

van Veen & Carter, 2002; Yeung, Botvinick, & Cohen, 2004). Predictions derived from

computational models of conflict monitoring have therefore been borne out in studies of both the

level (using fMRI) and timing (using EEG) of activity in ACC during task performance.

(h1) Current directions in conflict monitoring research

The preceding section summarizes the core claims of the conflict monitoring theory:

Conflict and competitive interactions place a fundamental constraint on human cognition;

activity in ACC varies with conflict in a manner predictive of changes in the level of top-down

cognitive control; and conflict monitoring provides a computationally tractable mechanism for

the flexible recruitment and allocation of control. Along the way, we have seen a number of

claims—for example about the neural basis of conflict monitoring, its relation to error detection,

and its role in sequential adaptation—that have each become the focus of considerable research

interest in recent years. This section reviews emerging evidence on these topics and others,

emphasizing the fertility of the conflict monitoring theory in stimulating new research while also

considering important challenges to the theory.

(h2) Neural basis of conflict monitoring

The foregoing discussion identifies dorsal-caudal parts of ACC (areas 24c′ and 32′) as

the likely neural basis of conflict monitoring. This region is variously referred to as dorsal ACC

(Botvinick et al., 2004), posterior medial frontal cortex (Ridderinkhof, Ullsperger, Crone, &

Nieuwenhuis, 2004), anterior midcingulate cortex (Vogt, Berger, & Derbyshire, 2003), and the

rostral cingulate zone or rostral cingulate motor area (Debener et al., 2005; Fink, Frackowiak,

Pietrzyk, & Passingham, 1997; Picard & Strick, 1996, 2001). The variability in nomenclature at

least in part reflects disagreement about the precision with which conflict-related activity has

been localized, and about the functional anatomy of the region of interest. There is nevertheless

broad consensus that if conflict monitoring occurs within ACC, then the relevant region lies in

the caudal-dorsal “cognitive” division rather than the rostral-ventral “affective” division (Bush,

Luu, & Posner, 2000; Devinsky et al., 1995).

However, it has been argued that conflict-related activity may localize outside ACC

within the neighboring pre-supplementary motor area (pre-SMA, Rushworth, Walton,

Kennerley, & Bannerman, 2004; Ullsperger & von Cramon, 2004). Evidence for this view has

come from studies of the flanker, go/no-go and Simon tasks in which contrasts between high-

and low-conflict trials identify regions extending dorsally into the pre-SMA, whereas contrasts

between correct and error trials identify regions only in ACC (Garavan, Ross, Kaufman, & Stein,

2003; Ullsperger & von Cramon, 2001, 2004; Wittfoth, Kustermann, Fahle, & Herrmann, 2008).

Pre-SMA activity has also been observed in some tasks that would be expected to produce

conflict, for example when subjects are required to override a preplanned movement (Nachev,

Rees, Parton, Kennard, & Husain, 2005). Correspondingly, single-unit activity in non-human

primates has been found to vary with response conflict in pre-SMA and the supplementary eye

field (Isoda & Hikosaka, 2007; Stuphorn, Taylor, & Schall, 2000) but not in ACC (Emeric et al.,

2008; Nakamura, Roesch, & Olson, 2005).

Attempts to precisely localize conflict-related activity in the medial wall are hindered by

inherent limitations of neuroimaging methods that involve spatial smoothing and group

averaging of data, such that the location of peak activity varies considerably even across studies

with comparable methodologies (Barch et al., 2000; Ridderinkhof et al., 2004), and by the

considerable individual variability in sulcal anatomy in this region, where the dorsal cingulate

areas of interest may lie buried in the cingulate sulcus or may appear in an adjacent paracingulate

region (Paus, Otaky et al., 1996; Paus, Tomaiuolo et al., 1996; Vogt et al., 1995; Yucel et al.,

2001). Adding yet further complication, many functions ascribed to pre-SMA—such as willed

action selection (Lau, Rogers, & Passingham, 2006; Passingham, 1993; Passingham, Bengtsson,

& Lau, 2010) and behavioral switching (Hikosaka & Isoda, 2010; Rushworth, Hadland, Paus, &

Sipila, 2002)—would lead to co-activation with putative conflict monitoring signals in ACC.

Converging evidence nevertheless points to ACC as the consistent source of conflict-

related activity in medial PFC. First, despite variability across individual studies, meta-analyses

consistently localize the center of mass of conflict-related activity around the cingulate sulcus

(Figure 2; Barch et al., 2000; Beckmann et al., 2009; Bush et al., 2000; Paus et al., 1998; Peyron,

Laurent, & Garcia-Larrea, 2000; Ridderinkhof et al., 2004; Roberts & Hall, 2008). Second,

studies that explicitly consider anatomical variability across subjects—using high-resolution

single-subject analyses (Lutcke & Frahm, 2008) or contrasting activation patterns according to

sulcal anatomy (Crosson et al., 1999)—localize conflict-related activity to the paracingulate

sulcus and gyrus when present, and to the cingulate sulcus otherwise, tracking the known

location of ACC areas 24c′ and 32′. Finally, error-related activity is rarely observed in pre-SMA,

yet errors should produce high levels of conflict (Botvinick et al., 2001; Yeung et al., 2004),

suggesting that observed pre-SMA activity reflects its role in action selection rather than conflict

monitoring. In contrast, dorsal ACC is very consistently activated by both conflict and errors

(Braver et al., 2001; Carter et al., 1998; Garavan et al., 2003; Holroyd et al., 2004; Kiehl, Liddle,

& Hopfinger, 2000; Lutcke & Frahm, 2008; Mathalon, Whitfield, & Ford, 2003; Taylor et al.,

2006). Collectively, these considerations converge to identify regions around the cingulate sulcus

as the neural basis of conflict signals in the medial wall.

(h2) Conflict and error processing

According to the conflict monitoring theory, error-related ACC activity can be explained

in terms of response conflict that develops following errors, when continued stimulus processing

leads to activation of the correct response that conflicts with the initial error (Botvinick et al.,

2001). In computational simulations, this post-error conflict signal replicates key properties of

the ERN component of the scalp-recorded EEG, such as its timing and sensitivity to factors

including stimulus frequency and speed-accuracy emphasis (Yeung et al., 2004). Consistent with

the hypothesis that the ERN reflects continuous conflict monitoring rather than explicit error

detection as originally proposed (Falkenstein et al., 1990; Gehring et al., 1993; Scheffers &

Coles, 2000), ERN amplitude is insensitive to whether or not errors are consciously detected

(Klein et al., 2007; Nieuwenhuis, Ridderinkhof, Blom, Band, & Kok, 2001). Subsequent

empirical studies have shown, as predicted, that ERN amplitude varies with levels of response

conflict (Frank, Woroch, & Curran, 2005), for example being greater when correct and incorrect

responses are similar and therefore in more direct competition (Gehring & Fencsik, 2001).

As described above, a key prediction arising from conflict model simulations—that on

correct trials ACC should primarily be active before the response—has been borne out in studies

of a second EEG component, the N2 (Folstein & Van Petten, 2008; Kopp et al., 1996;

Nieuwenhuis et al., 2003; van Veen & Carter, 2002; Yeung et al., 2004; Yeung & Nieuwenhuis,

2009). However, the hypothesis that the ERN and N2 both index conflict monitoring has been

challenged by findings that the two components may be affected in opposite ways by cingulate

lesions (Swick & Turken, 2002) and some psycho-active drugs (Riba, Rodriguez-Fornells,

Morte, Munte, & Barbanoj, 2005; Ridderinkhof et al., 2002). In response, subsequent simulation

studies have shown how such dissociations might arise in a conflict monitoring system, for

example if cingulate lesions caused attentional deficits by disconnecting ACC from lateral PFC

(Yeung & Cohen, 2006). Predictions arising from these simulations—for example, mimicking

the attentional effects of ACC lesions by increasing the salience of irrelevant stimuli in the

flanker task—have since been confirmed in empirical work (Danielmeier, Wessel, Steinhauser,

& Ullsperger, 2009; Yeung, Ralph, & Nieuwenhuis, 2007).

Also important to address are findings of anatomical dissociations between conflict and

errors: Whereas conflict primarily activates dorsal ACC, error-related activity typically extends

into the affective rostral-ventral subdivision of ACC (Kiehl et al., 2000; Mathalon et al., 2003;

Menon, Adleman, White, Glover, & Reiss, 2001; Taylor et al., 2006; Wittfoth et al., 2008). This

anatomical dissociation is not directly predicted by the conflict monitoring theory, but nor is it

unexpected given that conflicts and errors may require different compensatory reactions. Thus,

whereas conflict detection should primarily drive adjustments in attentional control (Fan, Hof,

Guise, Fossella, & Posner, 2008; Kerns et al., 2004), errors may elicit affective responses

(Pourtois et al., 2010; Taylor et al., 2006) and strategic speed-accuracy trade-offs (Botvinick et

al., 2001; Rabbitt, 1966) that could impose additional cognitive demands (Jentzsch & Dudschig,

2009; Notebaert et al., 2009).

Nevertheless, substantial challenges remain to the conflict account of error-related ACC

activity. First, the theory cannot explain evidence of ACC activity following the presentation of

feedback indicating that an error has occurred (Gehring & Willoughby, 2002; Holroyd & Coles,

2002; Miltner, Braun, & Coles, 1997), as discussed below in relation to reinforcement learning

theories of ACC function. Second, the theory predicts that ACC activity on correct trials should

primarily occur before the response, yet a correct-response negativity (CRN) is sometimes

observed with timing comparable to the ERN (Gehring & Knight, 2000; Pailing & Segalowitz,

2004; Vidal, Hasbroucq, Grapperon, & Bonnet, 2000) although arguably reflecting different

neural mechanisms (Coles, Scheffers, & Holroyd, 2001; Roger, Benar, Vidal, Hasbroucq, &

Burle, 2010; Vidal, Burle, Bonnet, Grapperon, & Hasbroucq, 2003; Yordanova, Falkenstein,

Hohnsbein, & Kolev, 2004). Finally, some predictions from conflict model simulations have

been disconfirmed (Burle, Roger, Allain, Vidal, & Hasbroucq, 2008; Carbonnell & Falkenstein,

2006; Steinhauser, Maier, & Hubner, 2008). For example, the theory predicts greater conflict

when errors are quickly corrected, a prediction confirmed in some studies (Rodriguez-Fornells,

Kurzbuch, & Muente, 2002) but not others (Burle et al., 2008). Thus, although the conflict

theory successfully explains and predicts a range of findings regarding conflict and errors, debate

continues as to whether it provides a comprehensive account of error-related ACC activity.

(h2) Conflict adaptation

The conflict monitoring theory claims that detection of conflict should lead to increased

recruitment of cognitive control (Botvinick et al., 2001), reflected in increased activation of high-

level task representations in lateral PFC (Kerns et al., 2004) and, subsequently, enhancement of

task-relevant information in sensory and motor cortices (Egner & Hirsch, 2005). Experimental

tests of this claim have focused on trial-to-trial sequential adaptation effects observed in the

Stroop (Kerns et al., 2004), flanker (Gratton et al., 1992), and Simon (Sturmer, Leuthold,

Soetens, Schroter, & Sommer, 2002) tasks, among others, in which the effects of task-irrelevant

information—interference on incongruent trials and facilitation on congruent trials—are reduced

following high-conflict incongruent trials (Figure 4) [Figure 4 here]. The theory explains these

findings in terms of conflict detected on trial N-1 leading to increased control on trial N that

attenuates the effects of task-irrelevant information on behavior.

It has been argued, however, that sequential adaptation effects are problematically

confounded with low-level repetition and priming effects (e.g., Hommel, 2004; Mayr, Awh, &

Laurey, 2003). For example, performance may improve on successive incongruent trials simply

because this trial sequence includes many exact repetitions of the stimulus and required response

(Mayr et al., 2003). Excluding exact repetitions has been found to abolish sequential adaptation

effects in some studies (Fernandez-Duque & Knight, 2008; Mayr et al., 2003; Nieuwenhuis et

al., 2006). Moreover, sequential adaptation effects have sometimes been found only during the

early stages of testing (Mayr & Awh, 2009) and to be somewhat independent of conflict

experienced on trial N-1 as measured through EMG activity corresponding to the competing

responses (Burle, Allain, Vidal, & Hasbroucq, 2005).

Nevertheless, it seems unlikely that repetition and priming fully account for observed

sequential adaptation effects, which may be evident even when repetition effects are factored out

via multiple regression (Notebaert & Verguts, 2007) or excluded from analysis (Kunde & Wuhr,

2006; Notebaert, Gevers, Verbruggen, & Liefooghe, 2006; Ullsperger, Bylsma, & Botvinick,

2005). Critically, repetition trials were excluded in neuroimaging studies reporting that increased

ACC activity on high-conflict trials is predictive of increased PFC activity and reduced conflict

on subsequent trials (Egner & Hirsch, 2005; Kerns, 2006; Kerns et al., 2004; Larson, Kaufman,

& Perlstein, 2009). Feature repetition provides no ready account of these findings, nor of why

sequential adaptation should be lost following ACC lesions (di Pellegrino, Ciaramelli, &

Ladavas, 2007), transcranial magnetic stimulation of lateral PFC (Sturmer, Redlich, Irlbacher, &

Brandt, 2007), the presentation of positive feedback to counteract aversive effects of conflict

(van Steenbergen, Band, & Hommel, 2009), or reducing the time between trials (Notebaert et al.,

2006). Moreover, experiments favoring the feature repetition account may have overlooked other

sequential factors such as negative priming (Tipper, 1985) that could mask conflict adaptation

(Bugg, 2008; Davelaar & Stevens, 2009). Taken together, these combined findings provide

strong support for the existence of conflict adaptation independent of feature repetition.

Recent studies have extended this conclusion to evaluate the specificity of conflict

adaptation, asking whether conflict detection in one task can lead to subsequent control

adjustments in another. Although cross-task adaptation is sometimes observed, for example

when flanker task conflict on trial N-1 leads to reduced Stroop interference on trial N (Freitas,

Bahar, Yang, & Banai, 2007; Kunde & Wuhr, 2006), many studies report that conflict adaptation

is task-specific (Brown, Reynolds, & Braver, 2007; Egner et al., 2007; Fernandez-Duque &

Knight, 2008; Funes, Lupianez, & Humphreys, 2010a; Kiesel, Kunde, & Hoffmann, 2006).

Similarity between two tasks appears to be the critical factor: Cross-task adaptation is observed

when tasks share similar stimuli, responses, or stimulus-response mappings (Akcay & Hazeltine,

2008; Cho, Orr, Cohen, & Carter, 2009; Notebaert & Verguts, 2008), but not otherwise. These

findings suggest that conflict adaptation is process-specific: Adaptation generalizes when tasks

share processes such as attending to particular stimulus features or selecting specific responses.

Neuroimaging dissociations between conflict-control mechanisms in the Stroop and Simon tasks

are consistent with this hypothesis (Egner et al., 2007). A question for future research is whether

such dissociations reflect the existence of dissociable process-specific conflict monitoring

systems in ACC, or if instead a common conflict monitoring system interacts with dissociable

control systems depending on task structure (Akcay & Hazeltine, 2008).

(h2) Uses of conflict signals

Most research to date has focused on trial-to-trial conflict adaptation, but conflict-control

feedback may operate over both shorter and longer time-scales, recruiting control online within a

trial and setting levels of control over extended periods. Relevant to the latter, conflict is reduced

when incongruent trials occur frequently across blocks of the Stroop and flanker tasks (Gratton et

al., 1992; Tzelgov, Henik, & Berger, 1992), and ACC activity covaries with this effect (Carter et

al., 2000). Computational simulations show how accumulated trial-to-trial sequential adjustments

in control might account for these block-level adjustments (Blais, Robidoux, Risko, & Besner,

2007; Botvinick et al., 2001; Verguts & Notebaert, 2008). However, block-level and sequential

adjustments seem to differ in their generalization across tasks (Funes, Lupianez, & Humphreys,

2010b), persistence over training (Mayr & Awh, 2009), and influence on specific processing

stages (Purmann, Badde, & Wendt, 2009). One intriguing hypothesis is that these dissociations

reflect the operation of a unitary conflict monitoring system as it interacts with cognitive control

systems operating over different time-scales (cf. Braver, Reynolds, & Donaldson, 2003). This

issue is ripe for investigation, for example, using neuroimaging designs sensitive to both

transient and sustained neural activity (Donaldson, 2004).

Still less is known about within-trial conflict adaptation. fMRI and EEG studies indicate

that functional connectivity between ACC and frontoparietal regions dynamically increases on

high-conflict trials (Cavanagh, Cohen, & Allen, 2009; Fan et al., 2008; Hanslmayr et al., 2008;

Wang et al., 2010), but these interactions might serve primarily to influence future conflict

adaptation. Perhaps more conclusive is recent evidence that trials with increased ACC activity

are associated with reduced activation of incorrect responses (Pastotter, Hanslmayr, & Bauml,

2010), suggestive of ACC-mediated control adjustments within trials. Future research might

usefully study such within-trial effects in other paradigms in which online recruitment of control

has been inferred, such as suppression of direct response activation in the Simon and Stroop

tasks (Davelaar, 2008; Ridderinkhof, 2002; Yu et al., 2009), reduction of conflict when the

presented stimulus has previously been associated with high levels of conflict (Crump, Gong, &

Wmiken, 2006; Jacoby, Lindsay, & Hessels, 2003; Wendt, Kluwe, & Vietze, 2008), and reduced

sensitivity to task-irrelevant information on trials in which a high level of conflict is experienced

(Goschke & Dreisbach, 2008). It currently remains open whether ACC conflict monitoring might

contribute to these effects.

Recent computational modeling studies have meanwhile suggested uses of conflict

signals beyond simple recruitment of control. First, conflict may act as an online “braking

signal” that prompts selective or cautious responding. This idea has been incorporated in models

of cognitive control in task switching (Brown et al., 2007), as well as models of optimal decision

making by the basal ganglia (Bogacz & Gurney, 2007) in which conflict signals from ACC

prompt response inhibition by the subthalamic nucleus (Bogacz, Wagenmakers, Forstmann, &

Nieuwenhuis, 2010; Frank, 2006). Second, conflict has been proposed as a useful signal driving

learning (Blais et al., 2007; Botvinick, 2007; Verguts & Notebaert, 2009). For example, conflict

adaptation effects have been simulated using models in which conflict modulates Hebbian

learning between top-down control and bottom-up stimulus representations (Blais et al., 2007;

Verguts & Notebaert, 2008). Conflict might also serve as an aversive signal to drive

reinforcement learning of behavioral strategies to minimize cognitive effort (Botvinick, 2007).

Specifically, whereas transient conflict indicates the need for increased control, conflict over an

extended period might indicate that alternative strategies should be sought (Aston-Jones &

Cohen, 2005). Thus, conflict monitoring may provide a valuable source of information that can

be used in various ways to optimize behavioral performance, although a coherent and

comprehensive account of these varied uses remains to be developed.

(h2) Dissociable forms of conflict

Comparisons between conflict tasks have typically identified overlapping activation

patterns in ACC (Fan, Flombaum, McCandliss, Thomas, & Posner, 2003; Rubia et al., 2001;

Wager et al., 2005), regardless of the stimuli inducing the conflict (Roberts & Hall, 2008) and

the response modality in which the conflict occurs (Barch et al., 2001). Collectively, these

findings suggest that a common ACC system monitors for conflicts among representations of

very different kinds. However, one dissociation now well established is between stimulus and

response conflict (Davelaar, 2008; Liston et al., 2006; Milham et al., 2001; Schulte et al., 2009;

van Veen & Carter, 2002; van Veen, Cohen, Botvinick, Stenger, & Carter, 2001; Wendelken,

Ditterich, Bunge, & Carter, 2009). Stimulus and response conflict can be dissociated using

flanker tasks in which two stimuli require left-hand responses (e.g., H and P) and two require

right-hand responses (e.g., S and M), so that response conflict trials (e.g., HHSHH) can be

compared with trials in which the stimuli conflict but their associated responses do not (e.g.,

HHPHH). Stimulus conflict has been found to activate lateral frontal and parietal cortex (Liston

et al., 2006; Wendelken et al., 2009) but produces little activity in ACC (van Veen et al., 2001),

as one might expect given that ACC receives little direct sensory input (Beckmann et al., 2009;

Margulies et al., 2007; van Hoesen et al., 1993).

A proposed dissociation between cognitive and emotional conflict has a similarly strong

basis in the differential connectivity of the dorsal-caudal and rostral-ventral subdivisions of ACC

(Bush et al., 2000; Devinsky et al., 1995). Rostral ACC activity is observed in conflict tasks in

which task-irrelevant information is affectively valenced (Bishop, Duncan, & Lawrence, 2004;

Vuilleumier, Armony, Driver, & Dolan, 2001; Whalen et al., 1998), for example when subjects

judge whether faces are happy or fearful while ignoring an irrelevant superimposed word

(HAPPY or FEAR) (Etkin, Egner, Peraza, Kandel, & Hirsch, 2006). This activity is functionally

and anatomically dissociable from response conflict-related activity in dorsal ACC (Mohanty et

al., 2007). However, dorsal and rostral ACC do not appear to independently monitor response

and emotional conflicts, but rather interact during emotional conflict in a way that mirrors the

interaction between dorsal ACC and lateral PFC in resolving response conflict (Egner, Etkin,

Gale, & Hirsch, 2008). Thus, dorsal ACC activity is also observed in emotional conflict tasks

(Davis et al., 2005; Egner et al., 2008; Mohanty et al., 2007; Wittfoth et al., 2010) and, in

emotional conflict adaptation, rostral ACC activity increases rather than reduces following high-

conflict trials (Etkin et al., 2006).

Within dorsal ACC, dissociable activity patterns have been reported as a function of the

nature of conflicting representations. Most research and theorizing to date has focused on

conflict between simple actions, typically key-presses made with different fingers, but efforts

have been made to study conflict between more abstract response representations. Thus, decision

conflict, which occurs when subjects choose between options that are not (or not yet) associated

with particular actions, has been found to activate more anterior portions of ACC than conflict

between defined actions (Pochon, Riis, Sanfey, Nystrom, & Cohen, 2008; Venkatraman, Rosati,

Taren, & Huettel, 2009). The opposite organization is suggested by studies of conflict between

high-level task and semantic representations (Aarts, Roelofs, & van Turennout, 2009; Melcher &

Gruber, 2009; Milham & Banich, 2005; Ruff, Woodward, Laurens, & Liddle, 2001; van Veen &

Carter, 2005). Task-level Stroop conflict can be identified, for example, by contrasting congruent

(e.g., RED in red ink) and neutral stimuli (e.g., XXX in red ink). Response conflict should be low

in both cases, but the irrelevant word in congruent stimuli should lead to conflict between the

word reading and color naming tasks. Task conflict has been found to activate posterior regions

of ACC and pre-SMA, contrasting with anterior activation seen to decision conflict (Melcher &

Gruber, 2009; Milham & Banich, 2005; but see Roelofs, van Turennout, & Coles, 2006). Thus,

although findings from decision, semantic and task-level conflict suggest subdivisions within

dorsal ACC, the precise functional architecture of the region remains unknown.

Finally, recent studies have investigated whether ACC activates solely during the

experience of conflict—our focus thus far—or also during anticipation of conflict, using pre-cues

that indicate the expected level of conflict prior to stimulus presentation. Despite some evidence

to the contrary (Luks, Simpson, Dale, & Hough, 2007; MacDonald, Cohen, Stenger, & Carter,

2000; Nieuwenhuis, Schweizer, Mars, Botvinick, & Hajcak, 2007), it now seems clear that

anticipatory activity occurs in ACC (Aarts, Roelofs, & Van Turennout, 2008; Dosenbach et al.,

2006; Fan et al., 2007; Luks, Simpson, Feiwell, & Miller, 2002), and more so for cues indicating

high conflict to come (Barber & Carter, 2005; Sohn, Albert, Jung, Carter, & Anderson, 2007;

Weissman, Gopalakrishnan, Hazlett, & Woldorff, 2005). One recent study found that

anticipatory activity dissociates from conflict-related activity in ACC (Orr & Weissman, 2009),

with the former lying more posterior and extending into pre-SMA. There is notable overlap

between this anticipatory activity and task-level conflict effect discussed above. In both cases, it

remains open whether the focus of activity lies within ACC or pre-SMA, and whether the

activity is conflict-related or rather reflects processes related to attention and task preparation

(Brass & von Cramon, 2002; Orr & Weissman, 2009).

(h2) Neuropsychology of conflict monitoring

The vast majority of conflict monitoring studies have used correlational neuroimaging

methods, but there is obvious value in establishing more causal evidence. However, although

ACC lesions are consistently associated with deficits in executive function (Devinsky et al.,

1995), evidence specific to conflict monitoring has proven controversial. Several studies have

found that Stroop interference is not markedly increased in patients with cingulate lesions (Baird

et al., 2006; Fellows & Farah, 2005; Stuss, Floden, Alexander, Levine, & Katz, 2001), and these

patients—including some with extensive bilateral damage—may show intact conflict adaptation

(Fellows & Farah, 2005) even if their subjective experience of conflict is markedly reduced

(Naccache et al., 2005). However, there are also several reports of impaired performance in a

variety of conflict tasks in patients with ACC lesions (Modirrousta & Fellows, 2008; Picton et

al., 2007), including some with very focal damage (Cohen, Kaplan, Moser, Jenkins, &

Wilkinson, 1999; Cohen, Kaplan, Zuffante et al., 1999; Ochsner et al., 2001; Swick & Turken,

2002; Turken & Swick, 1999; Yen et al., 2009), and with crucial lesion sites mapping on well to

the localization of conflict-related activity in neuroimaging studies (Swick & Jovanovic, 2002).

Impaired conflict adaptation and error monitoring have also been observed following damage to

dorsal ACC (Modirrousta & Fellows, 2008; Swick & Jovanovic, 2002) as well as in patients with

damage affecting more rostral ACC regions (di Pellegrino et al., 2007).

These discrepancies may at least partly reflect methodological issues: Many patient

studies use blocked designs in which conflict is predictable, yet ACC is primarily important

when adapting to varying levels of conflict (Floden, Vallesi, & Stuss, 2010). Nevertheless,

whereas cingulate patients only inconsistently show increased sensitivity to response conflict, the

same patients very consistently exhibit general slowing of responding across tasks irrespective of

conflict (Alexander, Stuss, Shallice, Picton, & Gillingham, 2005; Picton et al., 2007; Stuss et al.,

2005). Such findings have suggested a role for ACC in “energizing” responding (Stuss et al.,

2005), but this hypothesis is challenged by evidence that ACC is sensitive to tasks requiring

response inhibition (Nieuwenhuis et al., 2003; Rubia et al., 2001; Tsuchida & Fellows, 2009).

Instead, the combined findings are intriguing when taken with the recent proposal that conflict

signals from ACC may be used to set an appropriate response threshold to optimize decision

making (Bogacz et al., 2010; Frank, 2006). If correct, this interpretation would support the view

that the role of ACC extends beyond conflict-control regulation to include more general

functions relevant to setting appropriate response strategies.

Conflict monitoring has also been investigated in psychiatric patient populations.

Abnormalities in conflict-related activity are inconsistent in mood disorder and attention-deficit

hyperactivity disorder, but are well-established in patients with schizophrenia and obsessive-

compulsive disorder (Melcher, Falkai, & Gruber, 2008; Ullsperger, 2006). As part of a broader

pattern of hypofrontality (Benes, 2000; Cohen & Servan-Schreiber, 1992) and performance

monitoring deficits (Malenka, Angel, Hampton, & Berger, 1982), patients with schizophrenia

exhibit reduced conflict-related ACC activity (Carter, Mintun, Nichols, & Cohen, 1997;

McNeely, West, Christensen, & Alain, 2003) and reduced conflict adaptation (Kerns et al.,

2005). These findings are suggestive of conflict monitoring deficits that might contribute to both

the positive (psychotic) symptoms and the cognitive deficits evident in these patients. In contrast,

ACC tends to be hyperactive in patients with obsessive-compulsive disorder, both in association

with errors (Gehring, Himle, & Nisenson, 2000; but see Nieuwenhuis, Nielen, Mol, Hajcak, &

Veltman, 2005) and response conflict (Maltby, Tolin, Worhunsky, O'Keefe, & Kiehl, 2005;

Ursu, Stenger, Shear, Jones, & Carter, 2003; Yucel et al., 2007), with the degree of ACC

hyperactivity predictive of symptom severity. There is obvious intuitive appeal to the notion that

patients with obsessive-compulsive disorder have an overactive action monitoring system.

Such findings suggest that performance monitoring deficits may contribute to the

symptoms apparent in patient groups and may provide useful diagnostic measures in clinical

settings (Melcher et al., 2008; Ullsperger, 2006). Such inferences must be drawn with caution,

however, because disruptions in ACC performance monitoring might be secondary symptoms

rather than a primary cause of observed deficits (Yeung & Cohen, 2006): ACC function is

known to be sensitive to damage to a widespread network of regions, including lateral PFC and

the basal ganglia (Gehring & Knight, 2000; Ullsperger & von Cramon, 2006). Existing findings

nevertheless provide grounds for optimism that conflict monitoring might provide a useful

framework for investigating cognitive deficits associated with ACC dysfunction. A crucial open

question is whether therapeutic interventions targeted at conflict monitoring can ameliorate the

cognitive deficits in these patient populations (cf. Edwards, Barch, & Braver, 2010).

(h1) Conflict monitoring in context

The research reviewed above has been conducted within the framework of the conflict

monitoring theory, providing extensions and challenges to parts of that framework. This final

section considers the theory in the context of wider research on ACC function. As will become

apparent, consideration of evidence gathered via other methodologies, in other species, and

involving wider conceptions of the functional role of ACC, brings into question the degree to

which conflict monitoring provides a comprehensive account of the role of this region in control

and in cognition more widely.

(h2) Conflict monitoring in non-human primates

Non-human primate studies have to date provided little support for the conflict

monitoring theory. Whereas recordings of spiking activity and local field potentials in human

ACC reveal evidence of conflict-related activity (Davis, Hutchison, Lozano, Tasker, &

Dostrovsky, 2000; Davis et al., 2005; Wang et al., 2005), no such effects have been observed in

monkey ACC (Emeric et al., 2008; Ito, Stuphorn, Brown, & Schall, 2003; Nakamura et al.,

2005). Moreover, ACC lesions in primates have been found to have no effect on conflict

adaptation, which is instead primarily sensitive to lateral PFC damage (Mansouri, Buckley, &

Tanaka, 2007). More promising evidence of conflict-related activity in primates has been

observed in the pre-SMA and supplementary eye field (Isoda & Hikosaka, 2007; Nakamura et

al., 2005; Stuphorn et al., 2000). However, conflict effects in these regions typically reflect

modulations of movement-selective task-related activity, rather than pure signals of conflict

independent of the responses selected (Isoda & Hikosaka, 2007; Nakamura et al., 2005). Indeed,

these findings have led to the suggestion that conflict-related activity in human neuroimaging

studies may be an artifact of coarse-grained neuroimaging methods averaging activity across

populations of neurons that selectively code for conflicting responses (Nakamura et al., 2005).

Given the serious implications this conclusion would have for all of the research

considered in this chapter, it is unsurprising that alternative accounts of the between-species

discrepancy have been proposed (Botvinick et al., 2004; Cole, Yeung, Freiwald, & Botvinick,

2009). However, several potential explanations can be immediately ruled out. First, the

discrepancy is unlikely to reflect the fact that human studies typically use manual responses

whereas primate work mostly uses saccadic tasks, because conflict-related activity is observed in

human ACC when saccadic tasks are used (Curtis, Cole, Rao, & D'Esposito, 2005; Paus et al.,

1993). Nor is it likely that primate studies have simply failed to find conflict-signaling neurons

that are nonetheless present, given that these studies sampled widely in ACC and had little

trouble identifying error-related activity, suggesting that the regions under scrutiny are

homologous (Nakamura et al., 2005). Moreover, although human ACC contains cell types not

found in monkeys (Nimchinsky et al., 1999), these large spindle neurons are restricted to the

cingulate gyrus rather than the sulcal regions that are the focus of conflict-related activity.

Several possible accounts of the between-species discrepancy remain after excluding

these alternatives. First, and perhaps most strikingly, human conflict-related activity localizes to

area 32′—an area of cingulofrontal transition cortex interposed between the cingulate motor

areas and the pre-SMA and SMA—which has no direct homolog in dorsal ACC in monkeys

(Vogt et al., 1995). As such, conflict monitoring functions may be substantially more developed

in humans compared with non-human primates (Cole et al., 2009). Second, conflict-related

activity may be less evident in single-unit spiking activity than in neuroimaging recordings that

are sensitive to synchronization and synaptic activity within local neural circuits (Botvinick et

al., 2004). Consistent with this interpretation, a recent fMRI study of awake behaving monkeys

found strong ACC activity in a conflict-inducing antisaccade task (Ford, Gati, Menon, &

Everling, 2009). Third, it may be misguided to expect conflict-related activity in single-unit

recordings to occur independent of the particular responses selected: Recent computational

modeling suggests that conflict may be detected in a response-specific manner (Brown et al.,

2007), and may lead to modulation of control that is similarly stimulus- and response-specific

(Blais et al., 2007; Verguts & Notebaert, 2008).

Each of these possibilities identifies important avenues for future research. Thus, if

conflict monitoring is uniquely developed in humans, it becomes important to explain why

primate behavior is nonetheless sensitive to conflict (Mansouri et al., 2007; Nakamura et al.,

2005) and to identify cognitive abilities that might depend critically on this supposedly unique

function. Conversely, if conflict signals are apparent in both humans and monkeys but only in

regional activity, it becomes vital to identify the functional role of these signals given that

spiking activity is required if conflict signals are to be conveyed to other cortical regions

(Nakamura et al., 2005). One hypothesis could be that local activity in ACC varies with conflict,

as envisioned by the theory, but its spiking output may convey much more than a simple scalar

signal of conflict, for example indicating specific changes in control strategy. According to this

hypothesis, activation patterns in ACC should correlate with specific behavioral strategies, a

prediction ripe for evaluation using multivariate fMRI analysis approaches. Finally, if conflict is

detected in a response-specific manner, as envisioned in recent computational models, the

behavior of units in these models should be meaningfully related to single-unit activity recorded

in monkey ACC. Thus, although studies in non-human primates have so far provided little

support for the conflict monitoring theory, they suggest a number of intriguing avenues for its

development in future research.

(h2) Reinforcement and reward-guided decision making

Conflict monitoring research typically focuses on ACC activity in tasks in which the

required response is clearly defined: The challenge for subjects is to produce this response in the

face of conflict from a prepotent or habitual alternative (Miller & Cohen, 2001). However, ACC

activity has also been widely studied using tasks in which the correct response is not instructed,

but must be learnt by trial-and-error using feedback. In such tasks, robust ACC activity is not

only observed as subjects choose their response (Blair et al., 2006; Holroyd et al., 2004; Marsh,

Blair, Vythilingam, Busis, & Blair, 2007; Walton, Devlin, & Rushworth, 2004)—a finding that

might reflect conflict between the different options—but also as subjects anticipate or receive

feedback about their choices (Bush et al., 2002; Gehring & Willoughby, 2002; Holroyd et al.,

2004; Knutson, Taylor, Kaufman, Peterson, & Glover, 2005; Miltner et al., 1997; Yeung &

Sanfey, 2004). This evidence supports the view that ACC plays a critical role in reward-guided

decision making and learning, serving to guide behavior by associating actions with their

outcomes (Alexander & Brown, 2011; Holroyd & Coles, 2002; Rushworth, Behrens, Rudebeck,

& Walton, 2007). Consistent with this hypothesis, feedback-related ACC activity increases with

the unpredictability (Holroyd, Nieuwenhuis, Yeung, & Cohen, 2003) and volatility (Behrens,

Woolrich, Walton, & Rushworth, 2007) of experienced outcomes, and is predictive of whether

subjects will avoid repeating incorrect responses in the future (Hester, Barre, Murphy, Silk, &

Mattingley, 2008).

This view of ACC function is supported by single-unit recordings in non-human primates

where, in contrast to the apparent absence of conflict-sensitive neurons, coding of reward and

reinforcement is well-established (Amiez, Joseph, & Procyk, 2006; Shidara & Richmond, 2002).

Indeed, when comparable tasks are used in humans and non-human primates, close

correspondences are observed between ACC activity across species (Bush et al., 2002; Shima &

Tanji, 1998; Williams, Bush, Rauch, Cosgrove, & Eskandar, 2004). ACC neuron activity

specifically appears to code the value of experienced outcomes in relation to selected actions

(Hayden & Platt, 2010; Luk & Wallis, 2009), for example increasing when unexpected

reductions in reward lead to changes in behavior (Shima & Tanji, 1998; Williams et al., 2004).

This activity appears to play a causal role in learning: Focal lesions in ACC impair animals’

ability to use reinforcement to learn to select appropriate actions (Amiez et al., 2006; Hadland,

Rushworth, Gaffan, & Passingham, 2003; Kennerley, Walton, Behrens, Buckley, & Rushworth,

2006; Shima & Tanji, 1998).

Given these successes, an important question concerns the relation between reward-

guided decision making and conflict monitoring. It could be that they are dissociable functions of

separate ACC subregions (Marsh et al., 2007; Rogers et al., 2004). However, meta-analyses

indicate substantial overlap in conflict- and feedback-related activity within ACC (Beckmann et

al., 2009; Ridderinkhof et al., 2004). Moreover, reinforcement learning and conflict monitoring

in certain cases provide contrasting accounts of the same phenomena (Alexander & Brown,

2011; Holroyd & Coles, 2002; Yeung et al., 2004), suggesting the importance of reconciling the

theories. One suggestion has been that conflict-related activity in ACC is artifactual: a by-

product of coding the likelihood of errors or anticipation of negative outcomes that correlate with

conflict (Brown & Braver, 2005). However, this hypothesis struggles to explain ACC activity

seen in tasks in which errors are rare (e.g., in the Stroop task; Carter et al., 2000) or absent (e.g.,

in word generation tasks in which the conflict occurs between multiple correct responses; Barch

et al., 2000; Thompson-Schill et al., 1997). Moreover, when conflict and error likelihood are

dissociated based on response speed—because slow responses produce high conflict but low

error likelihood—ACC activity varies with the level of conflict (Yeung & Nieuwenhuis, 2009).

Recent computational modeling work has attempted to address these criticisms of reinforcement

learning approaches, using more complex algorithms that are capable of learning vector

representations of expected response outcomes rather than simple scalar predictions of expected

reward (Alexander & Brown, 2011). However, this model fails to account for benchmark

features of ACC activity seen during reinforcement learning, such as its specific sensitivity to

negative outcomes even when those outcomes are as likely as positive outcomes (Holroyd et al.,

2003; Yeung & Sanfey, 2004). As such, a reductive model of conflict- and learning-related

activity in ACC remains elusive.

Instead, conflict monitoring and reinforcement learning seem to provide complementary

rather than competing accounts of ACC function (Botvinick, 2007). In particular, ACC might

contribute broadly to the optimization of behavior through its sensitivity to signals indicating the

efficiency and effectiveness of actions, both internal (in the case of conflict) and environmental

(in the case of reinforcement learning). One specific proposal is that conflict registers as a cost to

drive avoidance learning against effortful or error-prone options (Botvinick, 2007). This

interpretation is consistent with evidence from animal studies that ACC plays a crucial role in

evaluating the effort associated with actions (Rudebeck, Walton, Smyth, Bannerman, &

Rushworth, 2006; Walton, Bannerman, Alterescu, & Rushworth, 2003), and extends this notion

to encompass mental as well as physical effort. More generally, these ideas begin to situate the

conflict monitoring theory within the broader context of ACC function as it is understood in a

range of task contexts and in a variety of species.

(h2) Contrasting theories of ACC function

A broader conception of ACC function might also help relate the conflict monitoring

theory to other perspectives on the role of ACC. One influential hypothesis is that ACC supports

action selection (Dum & Strick, 1991; Picard & Strick, 1996), particularly when responding is

voluntary rather than cued by stimuli in the environment (Paus, 2001). Thus, patients with

cingulate lesions produce fewer spontaneous verbalizations and movements than controls, and

demonstrate corresponding deficits in unstructured tasks (e.g., when asked simply to draw as

many pictures or designs as possible over a fixed period; Cohen, Kaplan, Zuffante et al., 1999).

Deficits in voluntary action are seen in extreme form in akinetic mutism, in which patients

become immobile and unresponsive to external stimuli following bilateral damage to ACC and

nearby cortex (Nemeth, Hegedus, & Molnar, 1988). Neuroimaging studies report greater ACC

activity during self-initiated movement—for example, when movements are produced in an

irregular rhythm—than when the same movements are made in response to external stimuli

(Deiber, Honda, Ibanez, Sadato, & Hallett, 1999; Krieghoff, Brass, Prinz, & Waszak, 2009).

Corresponding single-unit recordings in non-human primates demonstrate that this activity may

precede movement initiation by more than a second (Shima et al., 1991).

Conflict monitoring provides no ready account of these findings: Although the

neuroimaging findings might reflect increased conflict when actions are self-initiated (Barch et

al., 2000; Botvinick et al., 2001), it is unclear why a lesion-induced conflict monitoring deficit

should reduce spontaneous speech and action. Instead, a more productive reconciliation might be

in relation to the view that conflict monitoring represents one facet of the role of ACC in

evaluating the costs and benefits of actions (Botvinick, 2007). Such evaluation would be vital in

voluntary action selection, which must normally depend on a cost-benefit analysis of action

choices (Holroyd & Yeung, 2012). Within this framework, an important open question is

whether ACC itself directly selects or initiates actions (Paus, 2001), or rather supports action

selection indirectly via its connections with premotor and prefrontal cortex (Kouneiher, Charron,

& Koechlin, 2009). Recent neuroimaging evidence has been taken to favor the former view

(Banich, 2009; Dosenbach, Fair, Cohen, Schlaggar, & Petersen, 2008).

The notion that ACC plays a regulative role, directly exerting control rather than simply

evaluating performance, has been a longstanding alternative view of ACC function (Posner &

Petersen, 1990). Although this hypothesis is challenged by conflict adaptation effects—after

high-conflict trials, ACC activity reduces with the level of conflict rather than increasing with

the level of control (Botvinick et al., 1999)—recent findings have reignited interest in regulative

accounts of ACC function (Banich, 2009; Dosenbach et al., 2008). Evidence that ACC not only

activates transiently during conflict, but also during preparation and in a sustained manner

throughout task performance (Dosenbach et al., 2006), has led to the view that ACC is part of a

core system for maintaining a stable cognitive state over time (Dosenbach et al., 2008).

Meanwhile, evidence that conflict-related changes in ACC activity may disappear when

between-condition differences in reaction time are controlled for (Grinband et al., 2011) has led

to the suggestion that ACC plays a more general role in decision making. Finally, EEG evidence

that ACC activity follows lateral PFC activity in the Stroop task, and modulates its influence on

behavioral performance (Silton et al., 2010), has been taken to show that ACC operates at a

relatively late stage of a control “cascade”, making transient contributions to the selection of

specific actions (Banich, 2009).

These theories present a different view of ACC function to that proposed by the conflict

monitoring theory. However, given disagreement about the supposed nature of control exerted by

ACC—sustained task-level control versus transient response selection—it remains unclear

whether a coherent integration is possible. Moreover, current evidence is far from definitive in

challenging the conflict monitoring theory: Preparatory ACC activity could reflect task-level

conflict (Aarts et al., 2008; Melcher & Gruber, 2009; Milham & Banich, 2005; Ruff et al., 2001;

van Veen & Carter, 2005), sustained activity might reflect the integration of conflict over an

extended period (Aston-Jones & Cohen, 2005; Botvinick et al., 2001), activity related to

variation in reaction time is well explained by existing models of conflict monitoring (Yeung,

Cohen & Botvinick, 2011), and ACC-related modulation of control by lateral PFC might reflect

within-trial conflict adaptation (Cavanagh et al., 2009; Fan et al., 2008; Hanslmayr et al., 2008;

Wang et al., 2010). Thus, ACC involvement in regulative rather than evaluative aspects of

control remains to be determined. It is nevertheless increasingly clear, particularly given

evidence of ACC involvement in voluntary action selection and reinforcement learning, that

conflict monitoring is just one aspect of the role of ACC in the evaluation and control of thought

and action.

(h1) Conclusion

The conflict monitoring theory has been a significant success story of cognitive

neuroscience research. Countering claims that neuroimaging is of limited relevance to theories of

cognition (Coltheart, 2006), the initial impetus for the theory came primarily from imaging

work—specifically, emerging evidence of ACC activity associated with a diverse range of

cognitive demands—but the theory itself is truly cognitive: It proposes a computationally

specified process that explains not only the original neuroimaging evidence but also established

findings in the behavioral literature. In three critical respects, therefore, the theory has proven

highly successful. First, it has given substantial impetus to a research field—on performance

monitoring and the recruitment of control—that had been largely neglected in prior work.

Second, it has provided a powerful set of behavioral, neuroimaging, and computational tools

with which to probe these functions. And third, it has proven immensely productive in generating

testable predictions about behavior and brain activity that have, by and large, been borne out

experimentally: The original empirical (Carter et al., 1998) and conceptual (Botvinick et al.,

2001) expositions of the theory have now been cited over 1000 times each, attesting to the

influence of the theory in cognitive neuroscience. Ongoing efforts to enrich understanding of the

ways conflict might be detected and used (Aston-Jones & Cohen, 2005; Blais et al., 2007;

Botvinick, 2007; Brown et al., 2007; Frank, 2006; Verguts & Notebaert, 2009) bode well for the

continuing vitality of the theory as a source of new research.

These successes notwithstanding, scientific theories are ultimately judged according to

whether or not they are right, and in this respect the legacy of the conflict monitoring theory is

far less assured. Several substantive challenges have been discussed, but two issues in particular

seem fundamental. The first arises from human neuropsychological studies: Conflict monitoring

is proposed as a fundamental mechanism underpinning the recruitment of control, yet patients

with ACC lesions only inconsistently exhibit deficits in conflict monitoring, adaptation, and

control, and are notably spared in overall intellect and executive function. The second challenge

arises from the failure to find evidence of conflict monitoring in non-human primates (and other

animals). Again, if conflict monitoring is a fundamental cognitive function, one would expect it

to be present in our near relatives, or for there to be obvious between-species differences in the

experience or expression of conflict, yet evidence for either possibility is currently lacking.

The continuing success and influence of the conflict monitoring theory suggest that it

would be premature to abandon it in the face of these challenges. Instead, these issues are

perhaps most productively viewed as identifying key questions for future research. As discussed

above, one such question is the relation between conflict monitoring, action selection, and

reinforcement learning. Recent work has begun to reconcile these views (Botvinick, 2007;

Holroyd & Yeung, 2012), but it remains unclear whether these integrative accounts can explain

the wealth of evidence supporting the conflict monitoring theory as originally presented.

Meanwhile, the relative insensitivity of current lab paradigms to lesions in ACC suggests a need

to identify tasks that more effectively index this functioning. On the assumption that cognitive

control crucially supports the structuring of behavior through time (Duncan, 2010) and the rapid,

flexible adaptation to new requirements (Monsell, 2003), the immediate theoretical challenge is

to articulate clearly the role that conflict monitoring might play in these abilities. The

corresponding empirical challenge is to design tasks that precisely probe this contribution, and to

evaluate their sensitivity to hypothesized deficits following ACC lesions and between-species

differences in ACC function. Efforts towards these goals would represent an important step in

the continuing evolution of the conflict monitoring theory: from an account primarily inspired by

findings from constrained laboratory paradigms, towards a mature and detailed theory that makes

direct contact with established principles of cognitive control.

(h1) Future directions

1. Why do recordings of regional activity (e.g., using EEG and fMRI) show evidence of

conflict-related activity in ACC while single-unit recordings generally fail to do so? Is the

regional conflict signal merely an artifact of averaging across populations of neurons whose

spiking activity is affected by, but does not code for, conflict? Or:

a. Is conflict detected in a stimulus/response-specific manner, such that it would be

misguided to search for conflict signals that are independent of the response selected?

b. Does the spiking output of ACC contain more information than a simple scalar signal

of experienced conflict, such as instructions about specific changes in behavioral strategy?

2. Can the conflict monitoring theory explain why performance on conflict tasks is only

inconsistently affected by ACC lesions in humans? Is conflict-related activity seen in

neuroimaging studies epiphenomenal? Or:

a. Do the deficits suffered by these patients provide important clues regarding additional

uses of the ACC conflict signal, for example in setting an appropriate response threshold?

b. Are typical laboratory tasks too simple to capture the contribution of conflict

monitoring to flexible, structured human behavior? If so, how does conflict monitoring by ACC

support rapid behavioral shifting or the structured sequencing of thought and action?

3. How can the conflict monitoring theory be reconciled with the presumed role of ACC

in reinforcement learning and voluntary action selection? Can a reconciled view account for the

large corpus of existing data collected in support of the theory?

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