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. Author manuscript; available in PMC: 2023 Feb 1.
Published in final edited form as: Neurosci Biobehav Rev. 2021 Dec 12;133:104493. doi: 10.1016/j.neubiorev.2021.12.016

Fig. 7.

Fig. 7.

Dissociable Influences of Reinforcement and Punishment on Cognitive Control Allocation a) A core assumption of the Expected Value of Control (EVC) model is that individuals will adjust control allocation to maximize their expected reward rate and minimize their expected costs for exerting control. Expected outcomes are determined by considering the likelihood of an error (ER), the reinforcement for responding correctly (R), and the punishment for responding incorrectly (P). These expected outcomes are normalized by the expected response time RT (which is a combination of both decision-related and decision-unrelated factors, i.e., decision time [DT] and non-decision time [NDT]) to determine the expected reward rate. EVC is determined by subtracting from this reward rate a cost function (e.g., here represented by a parameter E that scales the square of drift rate), reflecting the non-linear effort cost associated with increased attention on a given trial (for discussions of alternate forms of effort functions, see Leng et al., 2020; Ritz et al., 2021). This formulation allows for a distinction between positive reinforcement and negative reinforcement. Critically, this enables us to delineate whether an aversive incentive should be treated as negative reinforcement or punishment. b) The EVC model predicts that individuals seek to configure drift rate and threshold to maximize their EVC and adjust this configuration as task incentives vary. Specifically, the model predicts that rewards for correct responses (e.g., positive reinforcement) will bias strategic adjusting in attention (drift rate). In contrast, penalties for incorrect responses (e.g., punishment) will bias a strategic adjustment in response caution (threshold). c) Task performance from our behavioral study using the task in Fig. 4b (Leng et al., 2020) was consistent with these normative predictions. The upright triangles indicate a higher value (e.g., high reward, high penalty), while the inverted triangles indicate a lower value (e.g., low reward, low penalty).