{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:53:08Z","timestamp":1792788788018,"version":"4.5.0"},"reference-count":86,"publisher":"Society for Neuroscience","issue":"16","license":[{"start":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T00:00:00Z","timestamp":1634774400000},"content-version":"vor","delay-in-days":230,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31871101"],"award-info":[{"award-number":["31871101"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31571117"],"award-info":[{"award-number":["31571117"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31930052"],"award-info":[{"award-number":["31930052"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing Municipal Commission of Science and Technology","award":["Z181100001518002"],"award-info":[{"award-number":["Z181100001518002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Neurosci."],"published-print":{"date-parts":[[2021,4,21]]},"abstract":"<jats:p>Humans do not have an accurate representation of probability information in the environment but distort it in a surprisingly stereotyped way (\u201cprobability distortion\u201d), as shown in a wide range of judgment and decision-making tasks. Many theories hypothesize that humans automatically compensate for the uncertainty inherent in probability information (\u201crepresentational uncertainty\u201d) and probability distortion is a consequence of uncertainty compensation. Here we examined whether and how the representational uncertainty of probability is quantified in the human brain and its relevance to probability distortion behavior. Human subjects (13 female and 9 male) kept tracking the relative frequency of one color of dot in a sequence of dot arrays while their brain activity was recorded by MEG. We found converging evidence from both neural entrainment and time-resolved decoding analysis that a mathematically derived measure of representational uncertainty is automatically computed in the brain, despite it is not explicitly required by the task. In particular, the encodings of relative frequency and its representational uncertainty, respectively, occur at latencies of \u223c300 and 400 ms. The relative strength of the brain responses to these two quantities correlates with the probability distortion behavior. The automatic and fast encoding of the representational uncertainty provides neural basis for the uncertainty compensation hypothesis of probability distortion. More generally, since representational uncertainty is closely related to confidence estimation, our findings exemplify how confidence might emerge before perceptual judgment.<\/jats:p>\n                  <jats:p>\n                    <jats:bold>SIGNIFICANCE STATEMENT<\/jats:bold>\n                    Human perception of probabilities and relative frequencies can be markedly distorted, which is a potential source of disastrous decisions. But the brain is not just ignorant of probability; probability distortions are highly patterned and similar across different tasks. Recent theoretical work suggests that probability distortions arise from the brain's compensation of its own uncertainty in representing probability. Is such representational uncertainty really computed in the brain? To answer this question, we asked human subjects to track an ongoing stimulus sequence of relative frequencies and recorded their brain responses using MEG. Indeed, we found that the neural encoding of representational uncertainty accompanies that of relative frequency, although the former is not explicitly required by the task.\n                  <\/jats:p>","DOI":"10.1523\/jneurosci.2006-20.2021","type":"journal-article","created":{"date-parts":[[2021,3,5]],"date-time":"2021-03-05T14:48:08Z","timestamp":1614955688000},"page":"3692-3706","source":"Crossref","is-referenced-by-count":3,"title":["Automatic and Fast Encoding of Representational Uncertainty Underlies the Distortion of Relative Frequency"],"prefix":"10.1523","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9428-1023","authenticated-orcid":false,"given":"Xiangjuan","family":"Ren","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8349-9796","authenticated-orcid":false,"given":"Huan","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9771-0660","authenticated-orcid":false,"given":"Hang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"393","published-online":{"date-parts":[[2021,3,5]]},"reference":[{"key":"2023041803461557000_41.16.3692.1","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.1974.1100705"},{"key":"2023041803461557000_41.16.3692.2","doi-asserted-by":"crossref","first-page":"966","DOI":"10.5897\/SRE2014.6121","article-title":"Modification of the adaptive Nadaraya-Watson kernel regression estimator","volume":"9","author":"Aljuhani","year":"2014","journal-title":"Sci Res Essays"},{"key":"2023041803461557000_41.16.3692.3","doi-asserted-by":"publisher","DOI":"10.1214\/09-SS054"},{"key":"2023041803461557000_41.16.3692.4","doi-asserted-by":"publisher","DOI":"10.1037\/h0057955"},{"key":"2023041803461557000_41.16.3692.5","doi-asserted-by":"publisher","DOI":"10.1126\/science.1185718"},{"key":"2023041803461557000_41.16.3692.6","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.2725-10.2011"},{"key":"2023041803461557000_41.16.3692.7","doi-asserted-by":"publisher","DOI":"10.1016\/j.jml.2012.11.001"},{"key":"2023041803461557000_41.16.3692.8","unstructured":"Bates DM , Kliegl R , Vasishth S , Baayen H (2015) Parsimonious mixed models. arXiv 1506.04967."},{"key":"2023041803461557000_41.16.3692.9","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1995.tb02031.x"},{"key":"2023041803461557000_41.16.3692.10","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1013699998"},{"key":"2023041803461557000_41.16.3692.11","doi-asserted-by":"publisher","DOI":"10.1163\/156856897X00357"},{"key":"2023041803461557000_41.16.3692.12","unstructured":"Cochran WG (1977). 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