The computational challenge of social learning

被引:29
|
作者
FeldmanHall, Oriel [1 ,2 ]
Nassar, Matthew R. [2 ,3 ]
机构
[1] Brown Univ, Dept Cognit Linguist & Psychol Sci, Providence, RI 02912 USA
[2] Brown Univ, Carney Inst Brain Sci, Providence, RI 02912 USA
[3] Brown Univ, Dept Neurosci, Providence, RI 02912 USA
关键词
PREDICTION ERRORS; NEURAL MECHANISMS; DECISION-MAKING; REINFORCEMENT; PREFERENCES; MODELS; NEUROSCIENCE; OTHERS; REPRESENTATIONS; EMULATION;
D O I
10.1016/j.tics.2021.09.002
中图分类号
B84 [心理学]; C [社会科学总论]; Q98 [人类学];
学科分类号
03 ; 0303 ; 030303 ; 04 ; 0402 ;
摘要
The complex reward structure of the social world and the uncertainty endemic to social contexts poses a challenge for modeling. For example, during social interactions, the actions of one person influence the internal states of another. These social dependencies make it difficult to formalize social learning problems in a mathematically tractable way. While it is tempting to dispense with these complexities, they are a defining feature of social life. Because the structure of social interactions challenges the simplifying assumptions often made in models, they make an ideal testbed for computational models of cognition. By adopting a framework that embeds existing social knowledge into the model, we can go beyond explaining behaviors in laboratory tasks to explaining those observed in the wild.
引用
收藏
页码:1045 / 1057
页数:13
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