Literature DB >> 27388875

The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology.

Julian Jara-Ettinger1, Hyowon Gweon2, Laura E Schulz3, Joshua B Tenenbaum4.   

Abstract

We propose that human social cognition is structured around a basic understanding of ourselves and others as intuitive utility maximizers: from a young age, humans implicitly assume that agents choose goals and actions to maximize the rewards they expect to obtain relative to the costs they expect to incur. This 'naïve utility calculus' allows both children and adults observe the behavior of others and infer their beliefs and desires, their longer-term knowledge and preferences, and even their character: who is knowledgeable or competent, who is praiseworthy or blameworthy, who is friendly, indifferent, or an enemy. We review studies providing support for the naïve utility calculus, and we show how it captures much of the rich social reasoning humans engage in from infancy.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Mesh:

Year:  2016        PMID: 27388875     DOI: 10.1016/j.tics.2016.05.011

Source DB:  PubMed          Journal:  Trends Cogn Sci        ISSN: 1364-6613            Impact factor:   20.229


  25 in total

1.  Young children consider the expected utility of others' learning to decide what to teach.

Authors:  Sophie Bridgers; Julian Jara-Ettinger; Hyowon Gweon
Journal:  Nat Hum Behav       Date:  2019-10-14

2.  People learn other people's preferences through inverse decision-making.

Authors:  Alan Jern; Christopher G Lucas; Charles Kemp
Journal:  Cognition       Date:  2017-06-26

3.  Toward an Integration of Deep Learning and Neuroscience.

Authors:  Adam H Marblestone; Greg Wayne; Konrad P Kording
Journal:  Front Comput Neurosci       Date:  2016-09-14       Impact factor: 2.380

Review 4.  Formalizing emotion concepts within a Bayesian model of theory of mind.

Authors:  Rebecca Saxe; Sean Dae Houlihan
Journal:  Curr Opin Psychol       Date:  2017-04-27

5.  Origins of the concepts cause, cost, and goal in prereaching infants.

Authors:  Shari Liu; Neon B Brooks; Elizabeth S Spelke
Journal:  Proc Natl Acad Sci U S A       Date:  2019-08-20       Impact factor: 11.205

6.  Behavioral and neural representations en route to intuitive action understanding.

Authors:  Leyla Tarhan; Julian De Freitas; Talia Konkle
Journal:  Neuropsychologia       Date:  2021-10-12       Impact factor: 3.139

7.  Learning from other minds: An optimistic critique of reinforcement learning models of social learning.

Authors:  Natalia Vélez; Hyowon Gweon
Journal:  Curr Opin Behav Sci       Date:  2021-03-23

8.  Reading between the lines: Listener's vmPFC simulates speaker cooperative choices in communication games.

Authors:  Qingtian Mi; Cong Wang; Colin F Camerer; Lusha Zhu
Journal:  Sci Adv       Date:  2021-03-03       Impact factor: 14.136

9.  Applying Probabilistic Programming to Affective Computing.

Authors:  Desmond C Ong; Harold Soh; Jamil Zaki; Noah D Goodman
Journal:  IEEE Trans Affect Comput       Date:  2019-03-15       Impact factor: 10.506

10.  Machine Teaching for Human Inverse Reinforcement Learning.

Authors:  Michael S Lee; Henny Admoni; Reid Simmons
Journal:  Front Robot AI       Date:  2021-06-30
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.