A computational model of risk, conflict, and individual difference effects in the anterior cingulate cortex - PubMed (original) (raw)

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

Joshua W Brown et al. Brain Res. 2008.

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.

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Figures

Figure 1

Figure 1

Error Likelihood computational model. Adapted with permission from (Brown and Braver, 2005). Go and Change response cues may be presented in the cue colors associated with either high or low error likelihoods. Each of these signals provides a separate input to the model. As errors occur more frequently in response to color cues associated with a higher error likelihood, more model ACC cells learn to respond preferentially to the inputs associated with more frequent errors. The model ACC in turn activates a control signal that generally slows responding.

Figure 2

Figure 2

Human fMRI and computational model results. Adapted from (Brown and Braver, Submitted) A) Error likelihood computational model shows greater error likelihood effects but weaker RI effects in high learning rate runs (which simulate low gambling likelihood subjects) B) Error likelihood effects found in ACC of gambling averse (low gambling) but not gambling tolerant (high gambling) individuals. RI effects found in human subjects were numerically greater in high gambling individuals but not significantly so in this region of ACC. Nonetheless, neighboring ACC regions did show significantly greater RI effects in high vs. low gambling individuals (Brown and Braver, Submitted), C) Model shows greater predicted error magnitude effects in high learning rate runs (simulated gambling averse) than in low learning rate runs. D) human fMRI data are consistent with model predictions in C.

Figure 3

Figure 3

(A) Model ACC effect trajectory as a function of learning throughout a simulated session. Results shown for learning rate = 10. Initially, RI effects dominate. As learning occurs with experience of the task, RI effects weaken, and error likelihood effects dominate. (B) Model error likelihood and RI effects as a function of ACC learning rate. Higher learning rates correspond to lower gambling likelihood. As the learning rate increases, the error likelihood effect in the model ACC increases, and the RI effect decreases, in agreement with effects found in human data (Brown and Braver, Submitted). (C) Conventions as in A, but for potential error consequence magnitude. (D) Conventions as in B, but for potential error consequence magnitude.

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