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
PY - 2024
Y1 - 2024
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.
UR - https://www.scopus.com/pages/publications/85184117511
U2 - 10.1007/978-3-031-51777-8_6
DO - 10.1007/978-3-031-51777-8_6
M3 - Conference contribution
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
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 -