TY - JOUR
T1 - Cognitive Science as a Source of Forward and Inverse Models of Human Decisions for Robotics and Control
AU - Ho, Mark K.
AU - Griffiths, Thomas L.
N1 - Publisher Copyright:
Copyright © 2022 by Annual Reviews.
PY - 2022
Y1 - 2022
N2 - Those designing autonomous systems that interact with humans will invariably face questions about how humans think and make decisions. Fortunately, computational cognitive science offers insight into human decision-making using tools that will be familiar to those with backgrounds in optimization and control (e.g., probability theory, statistical machine learning, and reinforcement learning). Here, we review some of this work, focusing on how cognitive science can provide forward models of human decision-making and inverse models of how humans think about others rsquo decision-making. We highlight relevant recent developments, including approaches that synthesize black box and theory-driven modeling, accounts that recast heuristics and biases as forms of bounded optimality, and models that characterize human theory of mind and communication in decision-Theoretic terms. In doing so, we aim to provide readers with a glimpse of the range of frameworks, methodologies, and actionable insights that lie at the intersection of cognitive science and control research.
AB - Those designing autonomous systems that interact with humans will invariably face questions about how humans think and make decisions. Fortunately, computational cognitive science offers insight into human decision-making using tools that will be familiar to those with backgrounds in optimization and control (e.g., probability theory, statistical machine learning, and reinforcement learning). Here, we review some of this work, focusing on how cognitive science can provide forward models of human decision-making and inverse models of how humans think about others rsquo decision-making. We highlight relevant recent developments, including approaches that synthesize black box and theory-driven modeling, accounts that recast heuristics and biases as forms of bounded optimality, and models that characterize human theory of mind and communication in decision-Theoretic terms. In doing so, we aim to provide readers with a glimpse of the range of frameworks, methodologies, and actionable insights that lie at the intersection of cognitive science and control research.
KW - Cognitive science
KW - Decision-making
KW - Psychology
KW - Robotics
KW - Theory of mind
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U2 - 10.1146/annurev-control-042920-015547
DO - 10.1146/annurev-control-042920-015547
M3 - Review article
AN - SCOPUS:85129831031
VL - 5
SP - 33
EP - 53
JO - Annual Review of Control, Robotics, and Autonomous Systems
JF - Annual Review of Control, Robotics, and Autonomous Systems
ER -