Evidence map›Paper›PMID 33247803›Full record

ReviewScience China. Life sciences2021

Neural mechanisms of social learning and decision-making.

Yinmei Ni, Jian Li

Abstract readReview
PubMed Publisher
In one paragraph

Review in Science China. Life sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Yinmei NiSchool of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, 100871, China. niyinmei@pku.edu.cn.
Jian LiSchool of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, 100871, China. li.jian@pku.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

One of the hallmarks of human society is the ubiquitous interactions among individuals. Indeed, a significant portion of human daily routine decision making is socially related. Normative economic theory, namely game theory, has prescribed the canonical decision strategy when "rational" social agents have full information about the decision environment. In reality, however, social decision is often influenced by the trait and state parameters of selves and others. Therefore, understanding the cognitive and neural processes of inferring the decision parameters is pivotal for social decision making. Recently, both correlational and causal non-invasive neuroimaging studies have started to reveal the critical neural computations underlying social learning and decision-making, and highlighted the unique roles of "social" brain structures such as temporal-parietal junction (TPJ) and dorsomedial prefrontal cortex (dmPFC). Here we review recent advances in social decision neuroscience and maintain the focus on how the inference about others is dynamically acquired during social learning, as well as how the prosocial (altruistic) behavior results from orchestrated interactions of different brain regions specified under the social utility framework. We conclude by emphasizing the importance of combining computational decision theory with the identification of neural mechanisms that represent, evaluate and integrate value related social information and generate decision variables guiding behavioral output in the complex social environment.

Indexed as

BrainDecision MakingHumansSocial Learningaltruismdecision-makingreinforcement learningsocial cognitionvalue

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.