A computational model of risk, conflict, and individual difference effects in the anterior cingulate cortex

Abstract

The error likelihood effect in anterior cingulate cortex (ACC) has recently been shown to be a special case of an even more general risk prediction effect, which signals both the likelihood of an error and the potential severity of its consequences. Surprisingly, these error likelihood and anticipated consequence effects are strikingly absent in risk-taking individuals. Conversely, conflict effects in ACC were found to be stronger in these same individuals. Here we show that the error likelihood computational model can account for individual differences in error likelihood, predicted error consequence, and conflict effects in ACC with no changes from the published version of the model. In particular, the model accounts for the counterintuitive inverse relationship between conflict and error likelihood effects as a function of the ACC learning rate in response to errors. As the learning rate increases, ACC learns more effectively from mistakes, which increases risk prediction effects at the expense of conflict effects. Thus, the model predicts that individuals with faster error-based learning in ACC will be more risk-averse and shows greater ACC error likelihood effects but smaller ACC conflict effects. Furthermore, the model suggests that apparent response conflict effects in ACC may actually consist of two related effects: increased error likelihood and a greater number of simultaneously cued responses, whether or not the responses are mutually incompatible. The results clarify the basic computational mechanisms of learned risk aversion and may have broad implications for predicting and managing risky behavior in healthy and clinical populations.