TY - GEN
T1 - Predictive Theory of Mind Models Based on Public Announcement Logic
AU - Top, Jakob Dirk
AU - Jonker, Catholijn
AU - Verbrugge, Rineke
AU - de Weerd, Harmen
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2024/1/13
Y1 - 2024/1/13
N2 - Epistemic logic can be used to reason about statements such as ‘I know that you know that I know that φ ’. In this logic, and its extensions, it is commonly assumed that agents can reason about epistemic statements of arbitrary nesting depth. In contrast, empirical findings on Theory of Mind, the ability to (recursively) reason about mental states of others, show that human recursive reasoning capability has an upper bound. In the present paper we work towards resolving this disparity by proposing some elements of a logic of bounded Theory of Mind, built on Public Announcement Logic. Using this logic, and a statistical method called Random-Effects Bayesian Model Selection, we estimate the distribution of Theory of Mind levels in the participant population of a previous behavioral experiment. Despite not modeling stochastic behavior, we find that approximately three-quarters of participants’ decisions can be described using Theory of Mind. In contrast to previous empirical research, our models estimate the majority of participants to be second-order Theory of Mind users.
AB - Epistemic logic can be used to reason about statements such as ‘I know that you know that I know that φ ’. In this logic, and its extensions, it is commonly assumed that agents can reason about epistemic statements of arbitrary nesting depth. In contrast, empirical findings on Theory of Mind, the ability to (recursively) reason about mental states of others, show that human recursive reasoning capability has an upper bound. In the present paper we work towards resolving this disparity by proposing some elements of a logic of bounded Theory of Mind, built on Public Announcement Logic. Using this logic, and a statistical method called Random-Effects Bayesian Model Selection, we estimate the distribution of Theory of Mind levels in the participant population of a previous behavioral experiment. Despite not modeling stochastic behavior, we find that approximately three-quarters of participants’ decisions can be described using Theory of Mind. In contrast to previous empirical research, our models estimate the majority of participants to be second-order Theory of Mind users.
KW - Behavioral Modeling
KW - Cognitive Science
KW - Epistemic Logic
KW - Public Announcement Logic
KW - Random-Effects Bayesian Model Selection
KW - Theory of Mind
UR - http://www.scopus.com/inward/record.url?scp=85184117511&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-51777-8_6
DO - 10.1007/978-3-031-51777-8_6
M3 - Conference contribution
AN - SCOPUS:85184117511
SN - 9783031517761
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 85
EP - 103
BT - Dynamic Logic. New Trends and Applications - 5th International Workshop, DaLi 2023, Revised Selected Papers
A2 - Gierasimczuk, Nina
A2 - Velázquez-Quesada, Fernando R.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 5th International Workshop on Dynamic Logic - New Trends and Applications, DaLi 2023
Y2 - 15 September 2023 through 16 September 2023
ER -