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Reinforcement learning and its connections with neuroscience and psychology
Neural Networks, 2020 Reinforcement learning methods have recently been very successful at performing complex sequential tasks like playing Atari games, Go and Poker. These algorithms have outperformed humans in several tasks by learning from scratch, using only scalar ...
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Toward a Psychology of Deep Reinforcement Learning Agents Using a Cognitive Architecture
Topics in Cognitive Science, 2021We argue that cognitive models can provide a common ground between human users and deep reinforcement learning (Deep RL) algorithms for purposes of explainable artificial intelligence (AI).
Konstantinos Mitsopoulos +5 more
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ICN‐driven group psychology visualization analysis mechanism using reinforcement learning
Internet Technology Letters, 2021Reinforcement Learning (RL) has been widely considered as a robust method to complete the large‐scale data analysis with high computation efficiency, learning ability, and stability, and it has been applied into many fields, such as transaction detection,
Yewen Qin
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Two‐stage reinforcement learning task predicts psychological traits
PsyCh Journal, 2023AbstractExternal sources of information influence human actions. However, psychological traits (PTs), considered internal variables, also play a crucial role in decision making. PTs are stable across time and contexts and define the set of behavioral repertoires that individuals express. Here, we explored how multiple metrics of adaptive behavior under
Mario Treviño +5 more
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Intelligent problem-solving as integrated hierarchical reinforcement learning
Nature Machine Intelligence, 2022According to cognitive psychology and related disciplines, the development of complex problem-solving behaviour in biological agents depends on hierarchical cognitive mechanisms.
Manfred Eppe +5 more
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Reinforcement Learning with Fast and Forgetful Memory
Neural Information Processing Systems, 2023Nearly all real world tasks are inherently partially observable, necessitating the use of memory in Reinforcement Learning (RL). Most model-free approaches summarize the trajectory into a latent Markov state using memory models borrowed from Supervised ...
Steven D. Morad +3 more
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Reinforcement Principles in an Introductory Educational Psychology Course
The Journal of Educational Research, 1972An experimental teaching procedure was applied to the classroom behavior of 143 students enrolled in an undergraduate Educational Psychology class. Primary questions concerned: (a) the efficacy of the experimental procedure in promoting student achievement, and (b) student perceptions regarding the relative value of the course.
Oary L. Sapp +2 more
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REINFORCEMENT OF PERFORMANCE ESTIMATION IN INTRODUCTORY PSYCHOLOGY
Psychological Reports, 199864 undergraduates enrolled in two sections of introductory psychology estimated their performance on each of four upcoming classroom tests. One section ( n = 30, 14 men and 16 women) received extra credit for accurate predictions of their test scores and the other section ( n = 34, 15 men and 19 women) received no reinforcement.
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Dynamic Inverse Reinforcement Learning for Characterizing Animal Behavior
Neural Information Processing Systems, 2022Understanding decision-making is a core objective in both neuroscience and psychology, and computational models have often been helpful in the pursuit of this goal.
Zoe C. Ashwood +2 more
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