Human Inferences about Sequences: A Minimal Transition Probability Model.
The brain constantly infers the causes of the inputs it receives and uses these inferences to generate statistical expectations about future observations. Experimental evidence for these expectations and their violations include explicit reports, sequential effects on reaction times, and mismatch or...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2016-12-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1005260&type=printable |
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| _version_ | 1849467441332092928 |
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| author | Florent Meyniel Maxime Maheu Stanislas Dehaene |
| author_facet | Florent Meyniel Maxime Maheu Stanislas Dehaene |
| author_sort | Florent Meyniel |
| collection | DOAJ |
| description | The brain constantly infers the causes of the inputs it receives and uses these inferences to generate statistical expectations about future observations. Experimental evidence for these expectations and their violations include explicit reports, sequential effects on reaction times, and mismatch or surprise signals recorded in electrophysiology and functional MRI. Here, we explore the hypothesis that the brain acts as a near-optimal inference device that constantly attempts to infer the time-varying matrix of transition probabilities between the stimuli it receives, even when those stimuli are in fact fully unpredictable. This parsimonious Bayesian model, with a single free parameter, accounts for a broad range of findings on surprise signals, sequential effects and the perception of randomness. Notably, it explains the pervasive asymmetry between repetitions and alternations encountered in those studies. Our analysis suggests that a neural machinery for inferring transition probabilities lies at the core of human sequence knowledge. |
| format | Article |
| id | doaj-art-550fd6dfbcc541f58df77eec0cd0b7e8 |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2016-12-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-550fd6dfbcc541f58df77eec0cd0b7e82025-08-20T03:26:15ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582016-12-011212e100526010.1371/journal.pcbi.1005260Human Inferences about Sequences: A Minimal Transition Probability Model.Florent MeynielMaxime MaheuStanislas DehaeneThe brain constantly infers the causes of the inputs it receives and uses these inferences to generate statistical expectations about future observations. Experimental evidence for these expectations and their violations include explicit reports, sequential effects on reaction times, and mismatch or surprise signals recorded in electrophysiology and functional MRI. Here, we explore the hypothesis that the brain acts as a near-optimal inference device that constantly attempts to infer the time-varying matrix of transition probabilities between the stimuli it receives, even when those stimuli are in fact fully unpredictable. This parsimonious Bayesian model, with a single free parameter, accounts for a broad range of findings on surprise signals, sequential effects and the perception of randomness. Notably, it explains the pervasive asymmetry between repetitions and alternations encountered in those studies. Our analysis suggests that a neural machinery for inferring transition probabilities lies at the core of human sequence knowledge.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1005260&type=printable |
| spellingShingle | Florent Meyniel Maxime Maheu Stanislas Dehaene Human Inferences about Sequences: A Minimal Transition Probability Model. PLoS Computational Biology |
| title | Human Inferences about Sequences: A Minimal Transition Probability Model. |
| title_full | Human Inferences about Sequences: A Minimal Transition Probability Model. |
| title_fullStr | Human Inferences about Sequences: A Minimal Transition Probability Model. |
| title_full_unstemmed | Human Inferences about Sequences: A Minimal Transition Probability Model. |
| title_short | Human Inferences about Sequences: A Minimal Transition Probability Model. |
| title_sort | human inferences about sequences a minimal transition probability model |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1005260&type=printable |
| work_keys_str_mv | AT florentmeyniel humaninferencesaboutsequencesaminimaltransitionprobabilitymodel AT maximemaheu humaninferencesaboutsequencesaminimaltransitionprobabilitymodel AT stanislasdehaene humaninferencesaboutsequencesaminimaltransitionprobabilitymodel |