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Cue predictiveness and uncertainty determine cue representation during visual statistical learning.

ABSTRACT: This study investigated how humans process probabilistic-associated information when encountering varying levels of uncertainty during implicit visual statistical learning. A novel probabilistic cueing validation paradigm was developed to probe the representation of cues with high (75% probability), medium (50%), low (25%), or zero levels of predictiveness in response to preceding targets that appeared with high (75%), medium (50%), or low (25%) transitional probabilities (TPs). Experiments 1 and 2 demonstrated a significant negative association between cue probe identification accuracy and cue predictiveness when these cues appeared after high-TP but not medium-TP or low-TP targets, establishing exploration-like cue processing triggered by lower-uncertainty rather than high-uncertainty inputs. Experiment 3 ruled out the confounding factor of probe repetition and extended this finding by demonstrating (1) enhanced representation of low-predictive and zero-predictive but not high-predictive cues across blocks after high-TP targets and (2) enhanced representation of high-predictive but not low-predictive and zero-predictive cues across blocks after low-TP targets for learners who exhibited above-chance awareness of cue-target transition. These results suggest that during implicit statistical learning, input characteristics alter cue-processing mechanisms, such that exploration-like and exploitation-like mechanisms are triggered by lower-uncertainty and higher-uncertainty cue-target sequences, respectively.

SUBMITTER: Zhang P

PROVIDER: S-EPMC10631146 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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Cue predictiveness and uncertainty determine cue representation during visual statistical learning.

Zhang Puyuan P Chen Hui H Tong Shelley Xiuli SX

Learning & memory (Cold Spring Harbor, N.Y.) 20231103 11

This study investigated how humans process probabilistic-associated information when encountering varying levels of uncertainty during implicit visual statistical learning. A novel probabilistic cueing validation paradigm was developed to probe the representation of cues with high (75% probability), medium (50%), low (25%), or zero levels of predictiveness in response to preceding targets that appeared with high (75%), medium (50%), or low (25%) transitional probabilities (TPs). Experiments 1 an ...[more]

PMID: 37923354

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Project description:[Correction Notice: An Erratum for this article was reported online in Journal of Experimental Psychology: General on Jan 14 2021 (see record 2021-07705-001). In the article, formatting for UK Research Councils funding was omitted. The author note and copyright line now reflect the standard acknowledgment of and formatting for the funding received for this article. All versions of this article have been corrected.] Attention determines which cues receive processing and are learned about. Learning, however, leads to attentional biases. In the study of animal learning, in some circumstances, cues that have been previously predictive of their consequences are subsequently learned about more than are nonpredictive cues, suggesting that they receive more attention. In other circumstances, cues that have previously led to uncertain consequences are learned about more than are predictive cues. In human learning, there is a clear role for predictiveness, but a role for uncertainty has been less clear. Here, in a human learning task, we show that cues that led to uncertain outcomes were subsequently learned about more than were cues that were previously predictive of their outcomes. This effect occurred when there were few uncertain cues. When the number of uncertain cues was increased, attention switched to predictive cues. This pattern of results was found for cues (1) that were uncertain because they led to 2 different outcomes equally often in a nonpredictable manner and (2) that were used in a nonlinear discrimination and were not predictive individually but were predictive in combination with other cues. This suggests that both the opposing predictiveness and uncertainty effects were determined by the relationship between individual cues and outcomes rather than the predictive strength of combined cues. These results demonstrate that learning affects attention; however, the precise nature of the effect on attention depends on the level of task complexity, which reflects a potential switch between exploration and exploitation of cues. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Project description:Statistical learning (SL) is an innate mechanism by which the brain automatically encodes the n-th order transition probability (TP) of a sequence and grasps the uncertainty of the TP distribution. Through SL, the brain predicts a subsequent event (e n+1 ) based on the preceding events (e n ) that have a length of "n". 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.

Dataset Information

Cue predictiveness and uncertainty determine cue representation during visual statistical learning.

Publications

Cue predictiveness and uncertainty determine cue representation during visual statistical learning.

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