{"title":"视觉感知中长期和短期先验冲突对加权先验整合的影响。","authors":"","doi":"10.1016/j.cognition.2024.106006","DOIUrl":null,"url":null,"abstract":"<div><div>The prior distribution of values for a specific feature can be categorized as long- or short-term priors based on their respective learning durations. Studies have demonstrated that the visual system can integrate both priors through weighted averaging and then utilize the integrated prior to efficiently encode stimuli. It is unclear what determines the two priors' relative weights. To address this question, we arranged the orientations according to three distributions: natural, anti-natural, and natural with increased-amplitude distributions. The natural distribution mirrors the distribution of orientations in the natural world, so it does not conflict with the long-term prior; according to the Kullback-Leibler divergence analysis, the natural distribution had a higher conflict with the anti-natural distribution than with the natural distribution with increased amplitude. It was found that the cardinal bias — the orientation estimates are biased away from the cardinal orientations — was strongest in the natural distribution with increased amplitude but weakest in the anti-natural distribution. These results were accurately predicted by an efficient Bayesian observer model in which the prior is the weighted integration of the long- and short-term priors. Importantly, the weight of the short-term prior in the new prior decreased as the level of the conflict between the long- and short-term priors increased. Therefore, this study reveals that the visual system integrates the long- and short-term priors through weighted averaging, with the conflicting level between the two priors determining their relative weights in the integration prior. The integrated prior was used by visual systems to efficiently encode stimuli.</div></div>","PeriodicalId":48455,"journal":{"name":"Cognition","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Impact of conflicts between long- and short-term priors on the weighted prior integration in visual perception\",\"authors\":\"\",\"doi\":\"10.1016/j.cognition.2024.106006\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The prior distribution of values for a specific feature can be categorized as long- or short-term priors based on their respective learning durations. Studies have demonstrated that the visual system can integrate both priors through weighted averaging and then utilize the integrated prior to efficiently encode stimuli. It is unclear what determines the two priors' relative weights. To address this question, we arranged the orientations according to three distributions: natural, anti-natural, and natural with increased-amplitude distributions. The natural distribution mirrors the distribution of orientations in the natural world, so it does not conflict with the long-term prior; according to the Kullback-Leibler divergence analysis, the natural distribution had a higher conflict with the anti-natural distribution than with the natural distribution with increased amplitude. It was found that the cardinal bias — the orientation estimates are biased away from the cardinal orientations — was strongest in the natural distribution with increased amplitude but weakest in the anti-natural distribution. These results were accurately predicted by an efficient Bayesian observer model in which the prior is the weighted integration of the long- and short-term priors. Importantly, the weight of the short-term prior in the new prior decreased as the level of the conflict between the long- and short-term priors increased. Therefore, this study reveals that the visual system integrates the long- and short-term priors through weighted averaging, with the conflicting level between the two priors determining their relative weights in the integration prior. The integrated prior was used by visual systems to efficiently encode stimuli.</div></div>\",\"PeriodicalId\":48455,\"journal\":{\"name\":\"Cognition\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2024-11-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cognition\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0010027724002920\",\"RegionNum\":1,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHOLOGY, EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cognition","FirstCategoryId":"102","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0010027724002920","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, EXPERIMENTAL","Score":null,"Total":0}
Impact of conflicts between long- and short-term priors on the weighted prior integration in visual perception
The prior distribution of values for a specific feature can be categorized as long- or short-term priors based on their respective learning durations. Studies have demonstrated that the visual system can integrate both priors through weighted averaging and then utilize the integrated prior to efficiently encode stimuli. It is unclear what determines the two priors' relative weights. To address this question, we arranged the orientations according to three distributions: natural, anti-natural, and natural with increased-amplitude distributions. The natural distribution mirrors the distribution of orientations in the natural world, so it does not conflict with the long-term prior; according to the Kullback-Leibler divergence analysis, the natural distribution had a higher conflict with the anti-natural distribution than with the natural distribution with increased amplitude. It was found that the cardinal bias — the orientation estimates are biased away from the cardinal orientations — was strongest in the natural distribution with increased amplitude but weakest in the anti-natural distribution. These results were accurately predicted by an efficient Bayesian observer model in which the prior is the weighted integration of the long- and short-term priors. Importantly, the weight of the short-term prior in the new prior decreased as the level of the conflict between the long- and short-term priors increased. Therefore, this study reveals that the visual system integrates the long- and short-term priors through weighted averaging, with the conflicting level between the two priors determining their relative weights in the integration prior. The integrated prior was used by visual systems to efficiently encode stimuli.
期刊介绍:
Cognition is an international journal that publishes theoretical and experimental papers on the study of the mind. It covers a wide variety of subjects concerning all the different aspects of cognition, ranging from biological and experimental studies to formal analysis. Contributions from the fields of psychology, neuroscience, linguistics, computer science, mathematics, ethology and philosophy are welcome in this journal provided that they have some bearing on the functioning of the mind. In addition, the journal serves as a forum for discussion of social and political aspects of cognitive science.