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Order of statistical learning depends on perceptive uncertainty

Tatsuya Daikoku, Masato Yumoto

2023Current Research in Neurobiology12 citationsDOIOpen Access PDF

Abstract

. It is now known that uncertainty modulates prediction in top-down processing by the human predictive brain. However, the manner in which the human brain modulates the order of SL strategies based on the degree of uncertainty remains an open question. The present study examined how uncertainty modulates the neural effects of SL and whether differences in uncertainty alter the order of SL strategies. It used auditory sequences in which the uncertainty of sequential information is manipulated based on the conditional entropy. Three sequences with different TP ratios of 90:10, 80:20, and 67:33 were prepared as low-, intermediate, and high-uncertainty sequences, respectively (conditional entropy: 0.47, 0.72, and 0.92 bit, respectively). Neural responses were recorded when the participants listened to the three sequences. The results showed that stimuli with lower TPs elicited a stronger neural response than those with higher TPs, as demonstrated by a number of previous studies. Furthermore, we found that participants adopted higher-order SL strategies in the high uncertainty sequence. These results may indicate that the human brain has an ability to flexibly alter the order based on the uncertainty. This uncertainty may be an important factor that determines the order of SL strategies. Particularly, considering that a higher-order SL strategy mathematically allows the reduction of uncertainty in information, we assumed that the brain may take higher-order SL strategies when encountering high uncertain information in order to reduce the uncertainty. The present study may shed new light on understanding individual differences in SL performance across different uncertain situations.

Topics & Concepts

Entropy (arrow of time)Sequence (biology)Artificial neural networkOrder (exchange)Artificial intelligenceProbability distributionComputer sciencePsychologyMathematicsStatisticsBiologyPhysicsGeneticsEconomicsFinanceQuantum mechanicsNeural dynamics and brain functionNeuroscience and Music PerceptionNeural Networks and Applications
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