Rules warp feature encoding in decision-making circuits

• Published in: PLoS biology Vol. 18; no. 11; p. e3000951
• Main Authors: Ebitz, R. Becket, Tu, Jiaxin Cindy, Hayden, Benjamin Y.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.11.2020; Public Library of Science (PLoS)
• Subjects: Animals; Biology and Life Sciences; Choice Behavior - physiology; Cognition & reasoning; Cognitive ability; Core making; Corpus Striatum - physiology; Decision making; Decision Making - physiology; Hypotheses; Macaca mulatta - physiology; Macaca mulatta - psychology; Male; Medicine and Health Sciences; Motor skill learning; Motor task performance; Nerve Net - physiology; Nervous System Physiological Phenomena; Neurons - physiology; Physical sciences; Prefrontal Cortex - physiology; Reinforcement; Reward; Social Sciences; Task Performance and Analysis; Ventral Striatum - physiology
• ISSN: 1545-7885; 1544-9173; 1545-7885
• DOI: 10.1371/journal.pbio.3000951

We have the capacity to follow arbitrary stimulus–response rules, meaning simple policies that guide our behavior. Rule identity is broadly encoded across decision-making circuits, but there are less data on how rules shape the computations that lead to choices. One idea is that rules could simplify these computations. When we follow a rule, there is no need to encode or compute information that is irrelevant to the current rule, which could reduce the metabolic or energetic demands of decision-making. However, it is not clear if the brain can actually take advantage of this computational simplicity. To test this idea, we recorded from neurons in 3 regions linked to decision-making, the orbitofrontal cortex (OFC), ventral striatum (VS), and dorsal striatum (DS), while macaques performed a rule-based decision-making task. Rule-based decisions were identified via modeling rules as the latent causes of decisions. This left us with a set of physically identical choices that maximized reward and information, but could not be explained by simple stimulus–response rules. Contrasting rule-based choices with these residual choices revealed that following rules (1) decreased the energetic cost of decision-making; and (2) expanded rule-relevant coding dimensions and compressed rule-irrelevant ones. Together, these results suggest that we use rules, in part, because they reduce the costs of decision-making through a distributed representational warping in decision-making circuits.