The conflict and error-likelihood theories provide contrasting accounts of perhaps the most replicated neuroimaging finding regarding ACC function: its sensitivity to task difficulty. According to the conflict theory, this sensitivity reflects the role of ACC in monitoring for conflict between competing responses when decision making is difficult or uncertain. According to the error-likelihood theory, ACC activity reflects prior learning about the frequency of errors in these situations. Conflict and error likelihood are confounded in most experimental contrasts because errors are likely in conditions of response conflict. However, our computational simulations demonstrate that conflict and error likelihood dissociate as a function of response speed. Our empirical EEG data demonstrate that when response conflict and error likelihood are dissociated in this way, ACC activity tracks the level of conflict, not error likelihood.
The conflict-monitoring theory predicts that ACC activity should be greatest on trials with the longest RTs—which are the trials with the lowest error rates—and should be reduced on faster, less accurate trials. It may seem somewhat counterintuitive that trials with the highest conflict should produce the fewest errors. In our simulations, this feature follows from the fact that activity in the target stimulus unit and correct response unit tend to increase over time, as stimulus processing progresses under the influence of attention. As a consequence, although trials with long RTs tend to have high levels of conflict, the responses ultimately made tend to be correct. In contrast, very fast RTs occur when processing noise causes “fast guess” responses. Fast guesses tend to be inaccurate, by definition, and are associated with little conflict because responses occur before significant conflict develops. Thus, conflict is negatively correlated with error rate.
In contrast, the error-likelihood theory predicts that ACC activity should increase with error rate. In our simulations, the model develops accurate estimates of error likelihood to the extent that there is stability in performance accuracy over time, such that accuracy on the current trial is correlated with past performance. Such stability is introduced, for example, through speed-accuracy trade-offs whereby periods of fast responding are associated with increased error rates. Any systematic variability of this kind will reinforce the positive correlation between actual and perceived error rate. Given that systematic variability is a ubiquitous feature of human performance (Gilden, 2001
) and is evident in the present data—for example, as a block-wise speed-accuracy tradeoff—the error-likelihood theory should predict a positive correlation between ACC activity and observed error rate.
Conflict and error likelihood therefore vary in opposing ways as a function of response speed. Our empirical data provided clear evidence that ACC activity follows the predictions of the conflict-monitoring theory: N2 amplitude was markedly larger on trials with long RTs (for which conflict is high and error likelihood is low) than on trials with short RTs (for which conflict is low and error likelihood is high). These findings suggest strongly that ACC activity in the flanker task reflects the current level of cognitive demand—increasing with the degree of response conflict (Botvinick et al., 2001
; Yeung et al., 2004
)—rather than retrospectively coding past performance on the basis of gradual reinforcement learning (Brown and Braver, 2005
). Inspection of detailed patterns in the EEG data () emphasizes this point. Thus, although errors were consistently more likely on incongruent trials than on congruent trials, N2 amplitude on some congruent trials clearly exceeded N2 amplitude on some incongruent trials. Indeed, strikingly, the N2 was larger on slow congruent trials—the condition with highest response accuracy—than on fast incongruent trials—the condition with lowest accuracy.
The observed negative correlation between ACC activity and error rate presents a clear challenge to the error-likelihood theory. It nonetheless remains possible that error likelihood is coded in some regions of medial prefrontal cortex, but that this coding is not reflected in the broad, spatially summated activity measured in the scalp-recorded EEG. According to this interpretation, sub-regions in ACC or neighboring cortex may show sensitivity to error likelihood as well as response conflict, but the former regions might only be visible to methods such as fMRI with finer-grained spatial resolution (Brown and Braver, 2007
; Brown, 2009
). In the context of findings of multiple sub-regions of activity within ACC, the present research suggests a simple discriminative test for identifying whether activated regions are sensitive to conflict or error likelihood: As we have demonstrated, conflict-sensitive regions should show increased activity as a function of RT within conditions, whereas regions sensitive to error likelihood should show the opposite pattern.
More broadly, the present research illustrates the value of combining computational and neuroimaging approaches in the development of theories of cognitive function. In the present research, computational simulations played a crucial role in identifying contrasting predictions of the competing theories. Future research using this combined approach might profitably address the question of how we might reconcile the findings of the present study—which suggest that ACC activity in speeded decision tasks reflects current cognitive demands rather than past performance—with an emerging consensus that ACC plays an important role in value-based decision making (e.g., Rushworth et al., 2004
). These differing interpretations may at least in part reflect the distinct methodological approaches used to study ACC function in different contexts: speeded decisions in which the correct response is known (as in the present study) versus reward-guided decisions in which the correct response has to be learned. In this regard, we concur with recent suggestions that apparent discrepancies regarding ACC function are interpretable within a common underlying framework, in which ACC contributes broadly to the optimization of decision processes in the context of cognitive and environmental demands (Botvinick, 2007
). On this view, monitoring for conflict during decision making, as observed in the present study, would represent one valuable source of information utilized by ACC in this optimization process.