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Prediction error associated with the perceptual segmentation of naturalistic events.

ABSTRACT: Predicting the near future is important for survival and plays a central role in theories of perception, language processing, and learning. Prediction failures may be particularly important for initiating the updating of perceptual and memory systems and, thus, for the subjective experience of events. Here, we asked observers to make predictions about what would happen 5 sec later in a movie of an everyday activity. Those points where prediction was more difficult corresponded with subjective boundaries in the stream of experience. At points of unpredictability, midbrain and striatal regions associated with the phasic release of the neurotransmitter dopamine transiently increased in activity. This activity could provide a global updating signal, cuing other brain systems that a significant new event has begun.

SUBMITTER: Zacks JM

PROVIDER: S-EPMC8653780 | biostudies-literature | 2011 Dec

REPOSITORIES: biostudies-literature

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Prediction error associated with the perceptual segmentation of naturalistic events.

Zacks Jeffrey M JM Kurby Christopher A CA Eisenberg Michelle L ML Haroutunian Nayiri N

Journal of cognitive neuroscience 20110614 12

Predicting the near future is important for survival and plays a central role in theories of perception, language processing, and learning. Prediction failures may be particularly important for initiating the updating of perceptual and memory systems and, thus, for the subjective experience of events. Here, we asked observers to make predictions about what would happen 5 sec later in a movie of an everyday activity. Those points where prediction was more difficult corresponded with subjective bo ...[more]

PMID: 21671745

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Project description:BackgroundThe recent COVID-19 pandemic highlighted the challenges for traditional forecasting. Prediction markets are a promising way to generate collective forecasts and could potentially be enhanced if high-quality crowdsourced inputs were identified and preferentially weighted for likely accuracy in real-time with machine learning.MethodsWe aim to leverage human prediction markets with real-time machine weighting of likely higher accuracy trades to improve performance. The crowd sourced Almanis prediction market longitudinal platform (n = 1822) and Next Generation Social Science (NGS2) platform (n = 103) were utilised.FindingsA 43-feature model predicted accurate forecasters, those with top quintile relative Brier accuracy, with subsequent replication in two out-of-sample datasets (pboth <1 × 10-9). Trades graded by this model as having higher accuracy scores than others produced a greater AUC temporal gain in the overall market after vs before trade. Accuracy score-weighted forecasts had higher accuracy than market forecasts alone, particularly when the two systems disagreed by 5% or more for binary event prediction: the hybrid system demonstrating substantial % AUC gains of 13.2%, p = 1.35 × 10-14 and 13.8%, p = 0.003 in two out-of-sample datasets. When discordant, the hybrid model was correct for COVID-19 event occurrence 72.7% of the time vs 27.3% for market models, p = 0.007. This net classification benefit was replicated in the separate Almanis B dataset, p = 2.4 × 10-7.InterpretationReal-time machine classification followed by weighting human trades according to likely accuracy improves collective forecasting performance. This could provide improved anticipation of and thus response to emerging risks.FundingThis work was supported by an AusIndustry R and D tax incentive program from the Department of Industry, Science, Energy and Resources, Australia, to SlowVoice Pty Ltd. (IR 2101990) and Fellowship (GNT 1110200) and Investigator grant (GNT 1197234) to A-L Ponsonby by the National Health and Medical Research Council of Australia.

Dataset Information

Prediction error associated with the perceptual segmentation of naturalistic events.

Publications

Prediction error associated with the perceptual segmentation of naturalistic events.

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