The world seems different in a social context: A neural network analysis of human experimental data.
Human perception and behavior are affected by the situational context, in particular during social interactions. A recent study demonstrated that humans perceive visual stimuli differently depending on whether they do the task by themselves or together with a robot. Specifically, it was found that t...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0273643 |
| _version_ | 1798033049505497088 |
|---|---|
| author | Maria Tsfasman Anja Philippsen Carlo Mazzola Serge Thill Alessandra Sciutti Yukie Nagai |
| author_facet | Maria Tsfasman Anja Philippsen Carlo Mazzola Serge Thill Alessandra Sciutti Yukie Nagai |
| author_sort | Maria Tsfasman |
| collection | DOAJ |
| description | Human perception and behavior are affected by the situational context, in particular during social interactions. A recent study demonstrated that humans perceive visual stimuli differently depending on whether they do the task by themselves or together with a robot. Specifically, it was found that the central tendency effect is stronger in social than in non-social task settings. The particular nature of such behavioral changes induced by social interaction, and their underlying cognitive processes in the human brain are, however, still not well understood. In this paper, we address this question by training an artificial neural network inspired by the predictive coding theory on the above behavioral data set. Using this computational model, we investigate whether the change in behavior that was caused by the situational context in the human experiment could be explained by continuous modifications of a parameter expressing how strongly sensory and prior information affect perception. We demonstrate that it is possible to replicate human behavioral data in both individual and social task settings by modifying the precision of prior and sensory signals, indicating that social and non-social task settings might in fact exist on a continuum. At the same time, an analysis of the neural activation traces of the trained networks provides evidence that information is coded in fundamentally different ways in the network in the individual and in the social conditions. Our results emphasize the importance of computational replications of behavioral data for generating hypotheses on the underlying cognitive mechanisms of shared perception and may provide inspiration for follow-up studies in the field of neuroscience. |
| first_indexed | 2024-04-11T20:24:02Z |
| format | Article |
| id | doaj.art-accea7064881404099896142d46ca4b1 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-11T20:24:02Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-accea7064881404099896142d46ca4b12022-12-22T04:04:43ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01178e027364310.1371/journal.pone.0273643The world seems different in a social context: A neural network analysis of human experimental data.Maria TsfasmanAnja PhilippsenCarlo MazzolaSerge ThillAlessandra SciuttiYukie NagaiHuman perception and behavior are affected by the situational context, in particular during social interactions. A recent study demonstrated that humans perceive visual stimuli differently depending on whether they do the task by themselves or together with a robot. Specifically, it was found that the central tendency effect is stronger in social than in non-social task settings. The particular nature of such behavioral changes induced by social interaction, and their underlying cognitive processes in the human brain are, however, still not well understood. In this paper, we address this question by training an artificial neural network inspired by the predictive coding theory on the above behavioral data set. Using this computational model, we investigate whether the change in behavior that was caused by the situational context in the human experiment could be explained by continuous modifications of a parameter expressing how strongly sensory and prior information affect perception. We demonstrate that it is possible to replicate human behavioral data in both individual and social task settings by modifying the precision of prior and sensory signals, indicating that social and non-social task settings might in fact exist on a continuum. At the same time, an analysis of the neural activation traces of the trained networks provides evidence that information is coded in fundamentally different ways in the network in the individual and in the social conditions. Our results emphasize the importance of computational replications of behavioral data for generating hypotheses on the underlying cognitive mechanisms of shared perception and may provide inspiration for follow-up studies in the field of neuroscience.https://doi.org/10.1371/journal.pone.0273643 |
| spellingShingle | Maria Tsfasman Anja Philippsen Carlo Mazzola Serge Thill Alessandra Sciutti Yukie Nagai The world seems different in a social context: A neural network analysis of human experimental data. PLoS ONE |
| title | The world seems different in a social context: A neural network analysis of human experimental data. |
| title_full | The world seems different in a social context: A neural network analysis of human experimental data. |
| title_fullStr | The world seems different in a social context: A neural network analysis of human experimental data. |
| title_full_unstemmed | The world seems different in a social context: A neural network analysis of human experimental data. |
| title_short | The world seems different in a social context: A neural network analysis of human experimental data. |
| title_sort | world seems different in a social context a neural network analysis of human experimental data |
| url | https://doi.org/10.1371/journal.pone.0273643 |
| work_keys_str_mv | AT mariatsfasman theworldseemsdifferentinasocialcontextaneuralnetworkanalysisofhumanexperimentaldata AT anjaphilippsen theworldseemsdifferentinasocialcontextaneuralnetworkanalysisofhumanexperimentaldata AT carlomazzola theworldseemsdifferentinasocialcontextaneuralnetworkanalysisofhumanexperimentaldata AT sergethill theworldseemsdifferentinasocialcontextaneuralnetworkanalysisofhumanexperimentaldata AT alessandrasciutti theworldseemsdifferentinasocialcontextaneuralnetworkanalysisofhumanexperimentaldata AT yukienagai theworldseemsdifferentinasocialcontextaneuralnetworkanalysisofhumanexperimentaldata AT mariatsfasman worldseemsdifferentinasocialcontextaneuralnetworkanalysisofhumanexperimentaldata AT anjaphilippsen worldseemsdifferentinasocialcontextaneuralnetworkanalysisofhumanexperimentaldata AT carlomazzola worldseemsdifferentinasocialcontextaneuralnetworkanalysisofhumanexperimentaldata AT sergethill worldseemsdifferentinasocialcontextaneuralnetworkanalysisofhumanexperimentaldata AT alessandrasciutti worldseemsdifferentinasocialcontextaneuralnetworkanalysisofhumanexperimentaldata AT yukienagai worldseemsdifferentinasocialcontextaneuralnetworkanalysisofhumanexperimentaldata |