journal homepage: www.elsevier.com/locate/COGNIT A Bayesian formulation of behavioral control

Associative theory postulates that learning the consequences of our actions in a given context is represented in the brain as stimulus-response--outcome associations that evolve according to predictionerror signals (the discrepancy between the observed and predicted outcome). We tested the theory on brain functional magnetic resonance imaging data acquired from human participants learning arbitrary visuomotor associations. We developed a novel task that systematically manipulated learning and induced highly reproducible performances. This granted the validation of the model-based results and an in-depth analysis of the brain signals in representative single trials. Consistent with the Rescorla--Wagner model, prediction-error signals are computed in the human brain and selectively engage the ventral striatum. In addition, we found evidence of computations not formally predicted by the Rescorla--Wagner model. The dorsal fronto-parietal network, the dorsal striatum, and the ventrolateral prefrontal cortex are activated both on the incorrect and first correct trials and may reflect the processing of relevant visuomotor mappings during the early phases of learning. The left dorsolateral prefrontal cortex is selectively activated on the first correct outcome. The results provide quantitative evidence of the neural computations mediating arbitrary visuomotor learning and suggest new directions for future computational models.

How motivation influences habitual behavior is unclear, since only motivational decrements have been considered. Here, in two experiments, we investigated the effects of motivational up-shifts and side-shifts on instrumental behavior which was extensively trained using a protocol known to promote habitual responding. In Experiment 1, hungry rats were trained to lever press for sucrose solution. Following a side-shift from food- to water-deprivation, rats showed less lever pressing in extinction compared to non-shifted controls, although a subsequent consumption test found no differences in sucrose consumption between thirsty and hungry groups. In Experiment 2, undeprived rats were trained to lever press for either sucrose solution or sucrose pellets. A post-training up-shift from satiety to water-deprivation did not affect lever pressing in extinction, regardless of outcome identity, although free consumption of sucrose solution, but not of pellets, was enhanced. Together, these results suggest that motivation affects extensively-trained instrumental behavior through a combination of general drive and generalization decrement, but not through determining the value 1 of the outcome. This is in stark contrast to the known effects of motivational states on moderately-trained (goal directed) behavior. The absence of an outcome-specific effect is in line with theories arguing for stimulusresponse rather than response-outcome control of habitual behavior.

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Subjective and affective elements are well-known to influence human decision making. This dissertation presents a theoretical and empirical framework on how human decision makers’ subjective experience and affective prediction influence their choice behavior under uncertainty, frames and emotions. The framework extends and integrates existing theories of prospect theory (PT) and reinforcement learning (RL), drawing on a growing literature illuminating the role of affect in decision making and the neural underpinnings of human decision behavior. The proposed Affective-Cognitive (AC) model extends Prospect Theory (PT)-based subjective value functions to model human experienced-utility and predictedutility functions. The AC model assumes that the shapes (or parameters) of these subjective value functions dynamically vary with the decision maker’s affective states in sequential decision making. Human decision-making experiments were conducted to infer how people adjust the parameters (i.e., shape and reference point) of their experienced-utility and predicted-utility functions in sequential decision-making situations involving incidental affective states (e.g., anger, fear, economic fear) and task-related confidence. I constructed

Associative accounts of goal-directed action, developed in the fields of human ideomotor action and that of animal learning, can capture cognitive belief-desire psychology of human decision-making. Whereas outcome-response accounts can account for the fact that the thought of a goal can call to mind the action that has previously procured this goal, response-outcome accounts capture decision-making processes that start out with the consideration of possible response alternatives followed only in the second instance by evaluation of their consequences. We argue that while the outcome-response mechanism plays a crucial role in response priming effects, the response-outcome mechanism is particularly important for action selection on the basis of current needs and desires. We therefore develop an integrative account that encapsulates these two routes of action selection within the framework of the associative cybernetic model. This model has the additional benefit of providing mechanisms for the incentive modulation of goal-directed action and for the development of behavioural autonomy, and therefore provides a promising account of the multi-faceted process of animal as well as human instrumental decision-making.

statistics and the trade-off between

Impaired associative fear learning in mice with complete loss