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When, What, and How Much to Reward in Reinforcement Learning-Based Models of Cognition

Christian P. Janssen & Wayne D. Gray

Cognitive Science 36 (2):333-358 (2012)

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A Biologically Plausible Action Selection System for Cognitive Architectures: Implications of Basal Ganglia Anatomy for Learning and Decision‐Making Models.Andrea Stocco - 2018 - Cognitive Science 42 (2):457-490.details Several attempts have been made previously to provide a biological grounding for cognitive architectures by relating their components to the computations of specific brain circuits. Often, the architecture's action selection system is identified with the basal ganglia. However, this identification overlooks one of the most important features of the basal ganglia—the existence of a direct and an indirect pathway that compete against each other. This characteristic has important consequences in decision-making tasks, which are brought to light by Parkinson's disease as (...) Download Export citation Bookmark 6 citations
Predicting Short‐Term Remembering as Boundedly Optimal Strategy Choice.Andrew Howes, Geoffrey B. Duggan, Kiran Kalidindi, Yuan-Chi Tseng & Richard L. Lewis - 2016 - Cognitive Science 40 (5):1192-1223.details It is known that, on average, people adapt their choice of memory strategy to the subjective utility of interaction. What is not known is whether an individual's choices are boundedly optimal. Two experiments are reported that test the hypothesis that an individual's decisions about the distribution of remembering between internal and external resources are boundedly optimal where optimality is defined relative to experience, cognitive constraints, and reward. The theory makes predictions that are tested against data, not fitted to it. The (...) Download Export citation Bookmark 4 citations
Dividing Attention Between Tasks: Testing Whether Explicit Payoff Functions Elicit Optimal Dual-Task Performance.George D. Farmer, Christian P. Janssen, Anh T. Nguyen & Duncan P. Brumby - 2018 - Cognitive Science 42 (3):820-849.details We test people's ability to optimize performance across two concurrent tasks. Participants performed a number entry task while controlling a randomly moving cursor with a joystick. Participants received explicit feedback on their performance on these tasks in the form of a single combined score. This payoff function was varied between conditions to change the value of one task relative to the other. We found that participants adapted their strategy for interleaving the two tasks, by varying how long they spent on (...) Download Export citation Bookmark
Interrogating Feature Learning Models to Discover Insights Into the Development of Human Expertise in a Real‐Time, Dynamic Decision‐Making Task.Catherine Sibert, Wayne D. Gray & John K. Lindstedt - 2017 - Topics in Cognitive Science 9 (2):374-394.details Tetris provides a difficult, dynamic task environment within which some people are novices and others, after years of work and practice, become extreme experts. Here we study two core skills; namely, choosing the goal or objective function that will maximize performance and a feature-based analysis of the current game board to determine where to place the currently falling zoid so as to maximize the goal. In Study 1, we build cross-entropy reinforcement learning models to determine whether different goals result in (...) Download Export citation Bookmark 7 citations
Interrogating Feature Learning Models to Discover Insights Into the Development of Human Expertise in a Real‐Time, Dynamic Decision‐Making Task.Catherine Sibert, Wayne D. Gray & John K. Lindstedt - 2016 - Topics in Cognitive Science 8 (4).details Tetris provides a difficult, dynamic task environment within which some people are novices and others, after years of work and practice, become extreme experts. Here we study two core skills; namely, choosing the goal or objective function that will maximize performance and a feature-based analysis of the current game board to determine where to place the currently falling zoid so as to maximize the goal. In Study 1, we build cross-entropy reinforcement learning models to determine whether different goals result in (...) Download Export citation Bookmark 7 citations

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