scholarly journals Dorsal striatum mediates deliberate decision making, not late-stage, stimulus-response learning

2017 ◽  
Vol 38 (12) ◽  
pp. 6133-6156 ◽  
Author(s):  
Nole M. Hiebert ◽  
Adrian M. Owen ◽  
Ken N. Seergobin ◽  
Penny A. MacDonald
Keyword(s):  
Decision Making ◽  
Late Stage ◽  
Dorsal Striatum ◽  
Response Learning ◽  
Stimulus Response

scholarly journals Striatum-Mediated Deficits in Stimulus-Response Learning and Decision-Making in OCD

2020 ◽  
Vol 11 ◽  
Author(s):  
Nole M. Hiebert ◽  
Marc R. Lawrence ◽  
Hooman Ganjavi ◽  
Mark Watling ◽  
Adrian M. Owen ◽  
...  
Keyword(s):  
Decision Making ◽  
Response Learning ◽  
Stimulus Response

scholarly journals A general model of hippocampal and dorsal striatal learning and decision making

2020 ◽  
Vol 117 (49) ◽  
pp. 31427-31437
Author(s):  
Jesse P. Geerts ◽  
Fabian Chersi ◽  
Kimberly L. Stachenfeld ◽  
Neil Burgess
Keyword(s):  
Decision Making ◽  
Reference Frame ◽  
Place Cells ◽  
Cognitive Map ◽  
Relational Structure ◽  
Place Learning ◽  
Response Learning ◽  
Adaptive Combination ◽  
Stimulus Response ◽  
Model Free

Humans and other animals use multiple strategies for making decisions. Reinforcement-learning theory distinguishes between stimulus–response (model-free; MF) learning and deliberative (model-based; MB) planning. The spatial-navigation literature presents a parallel dichotomy between navigation strategies. In “response learning,” associated with the dorsolateral striatum (DLS), decisions are anchored to an egocentric reference frame. In “place learning,” associated with the hippocampus, decisions are anchored to an allocentric reference frame. Emerging evidence suggests that the contribution of hippocampus to place learning may also underlie its contribution to MB learning by representing relational structure in a cognitive map. Here, we introduce a computational model in which hippocampus subserves place and MB learning by learning a “successor representation” of relational structure between states; DLS implements model-free response learning by learning associations between actions and egocentric representations of landmarks; and action values from either system are weighted by the reliability of its predictions. We show that this model reproduces a range of seemingly disparate behavioral findings in spatial and nonspatial decision tasks and explains the effects of lesions to DLS and hippocampus on these tasks. Furthermore, modeling place cells as driven by boundaries explains the observation that, unlike navigation guided by landmarks, navigation guided by boundaries is robust to “blocking” by prior state–reward associations due to learned associations between place cells. Our model, originally shaped by detailed constraints in the spatial literature, successfully characterizes the hippocampal–striatal system as a general system for decision making via adaptive combination of stimulus–response learning and the use of a cognitive map.

scholarly journals Striatum in stimulus–response learning via feedback and in decision making

NeuroImage ◽  
2014 ◽  
Vol 101 ◽  
pp. 448-457 ◽  
Author(s):  
Nole M. Hiebert ◽  
Andrew Vo ◽  
Adam Hampshire ◽  
Adrian M. Owen ◽  
Ken N. Seergobin ◽  
...  
Keyword(s):  
Decision Making ◽  
Response Learning ◽  
Stimulus Response

scholarly journals Rule adherence warps decision-making

2019 ◽  
Author(s):  
R. Becket Ebitz ◽  
Jiaxin Cindy Tu ◽  
Benjamin Y. Hayden
Keyword(s):  
Decision Making ◽  
Dorsal Striatum ◽  
Neuronal Firing ◽  
Energetic Cost ◽  
Processing Efficiency ◽  
Rule Based ◽  
Stimulus Response ◽  
Neural Representations ◽  
Sensorimotor Information ◽  
The Brain

ABSTRACTWe have the capacity to follow arbitrary stimulus-response rules, meaning policies that determine how we will behave across circumstances. Yet, it is not clear how rules guide sensorimotor decision-making in the brain. Here, we recorded from neurons in three regions linked to decision-making, the orbitofrontal cortex, ventral striatum, and dorsal striatum, while macaques performed a rule-based decision-making task. We found that different rules warped the neural representations of chosen options by expanding rule-relevant coding dimensions relative to rule-irrelevant ones. Some cognitive theories suggest that warping could increase processing efficiency by facilitating rule-relevant computations at the expense of irrelevant ones. To test this idea, we modeled rules as the latent causes of decisions and identified a set of “rule-free” choices that could not be explained by simple rules. Contrasting these with rule-based choices revealed that following rules decreased the energetic cost of decision-making while warping the representational geometry of choice.SIGNIFICANCE STATEMENTOne important part of our ability to adapt flexibly to the world around us is our ability to implement arbitrary stimulus-response mappings, known as “rules”. Many studies have shown that when we follow a rule, its identity is encoded in neuronal firing rates. However, it remains unclear how rules regulate behavior. Here, we report that rules warp the way that sensorimotor information is represented in decision-making circuits: enhancing information that is relevant to the current rule at the expense of information that is irrelevant. These results imply that rules are implemented as a kind of attentional gate on what information is available for decision-making.

scholarly journals Rules warp feature encoding in decision-making circuits

PLoS Biology ◽  
2020 ◽  
Vol 18 (11) ◽  
pp. e3000951 ◽  
Author(s):  
R. Becket Ebitz ◽  
Jiaxin Cindy Tu ◽  
Benjamin Y. Hayden
Keyword(s):  
Decision Making ◽  
Dorsal Striatum ◽  
Energetic Cost ◽  
Rule Based ◽  
Stimulus Response ◽  
Feature Encoding ◽  
Computational Simplicity ◽  
Physically Identical ◽  
The Brain ◽  
Current Rule

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.

Neural activity in the dorsal striatum during cognitive decision making

2010 ◽  
Vol 68 ◽  
pp. e299
Author(s):  
Satoshi Nonomura ◽  
Kazuyuki Samejima ◽  
Kenji Doya ◽  
Jun Tanji
Keyword(s):  
Decision Making ◽  
Neural Activity ◽  
Dorsal Striatum ◽  
Cognitive Decision

scholarly journals Hierarchical decision processes that operate over distinct timescales underlie choice and changes in strategy

2016 ◽  
Vol 113 (31) ◽  
pp. E4531-E4540 ◽  
Author(s):  
Braden A. Purcell ◽  
Roozbeh Kiani
Keyword(s):  
Decision Making ◽  
Neural Mechanism ◽  
Decision Processes ◽  
Response Strategy ◽  
Limited Information ◽  
Decision Strategy ◽  
Stimulus Response ◽  
Hierarchical Decision ◽  
The Right ◽  
High Level

Decision-making in a natural environment depends on a hierarchy of interacting decision processes. A high-level strategy guides ongoing choices, and the outcomes of those choices determine whether or not the strategy should change. When the right decision strategy is uncertain, as in most natural settings, feedback becomes ambiguous because negative outcomes may be due to limited information or bad strategy. Disambiguating the cause of feedback requires active inference and is key to updating the strategy. We hypothesize that the expected accuracy of a choice plays a crucial rule in this inference, and setting the strategy depends on integration of outcome and expectations across choices. We test this hypothesis with a task in which subjects report the net direction of random dot kinematograms with varying difficulty while the correct stimulus−response association undergoes invisible and unpredictable switches every few trials. We show that subjects treat negative feedback as evidence for a switch but weigh it with their expected accuracy. Subjects accumulate switch evidence (in units of log-likelihood ratio) across trials and update their response strategy when accumulated evidence reaches a bound. A computational framework based on these principles quantitatively explains all aspects of the behavior, providing a plausible neural mechanism for the implementation of hierarchical multiscale decision processes. We suggest that a similar neural computation—bounded accumulation of evidence—underlies both the choice and switches in the strategy that govern the choice, and that expected accuracy of a choice represents a key link between the levels of the decision-making hierarchy.

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