In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 18, No. 12 ( 2022-12-5), p. e1010747-
Abstract:
When judging the average value of sample stimuli (e.g., numbers) people tend to either over- or underweight extreme sample values, depending on task context. In a context of overweighting, recent work has shown that extreme sample values were overly represented also in neural signals, in terms of an anti-compressed geometry of number samples in multivariate electroencephalography (EEG) patterns. Here, we asked whether neural representational geometries may also reflect a relative underweighting of extreme values (i.e., compression) which has been observed behaviorally in a great variety of tasks. We used a simple experimental manipulation (instructions to average a single-stream or to compare dual-streams of samples) to induce compression or anti-compression in behavior when participants judged rapid number sequences. Model-based representational similarity analysis (RSA) replicated the previous finding of neural anti-compression in the dual-stream task, but failed to provide evidence for neural compression in the single-stream task, despite the evidence for compression in behavior. Instead, the results indicated enhanced neural processing of extreme values in either task, regardless of whether extremes were over- or underweighted in subsequent behavioral choice. We further observed more general differences in the neural representation of the sample information between the two tasks. Together, our results indicate a mismatch between sample-level EEG geometries and behavior, which raises new questions about the origin of common psychometric distortions, such as diminishing sensitivity for larger values.
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1010747
DOI:
10.1371/journal.pcbi.1010747.g001
DOI:
10.1371/journal.pcbi.1010747.g002
DOI:
10.1371/journal.pcbi.1010747.g003
DOI:
10.1371/journal.pcbi.1010747.g004
DOI:
10.1371/journal.pcbi.1010747.s001
DOI:
10.1371/journal.pcbi.1010747.s002
DOI:
10.1371/journal.pcbi.1010747.r001
DOI:
10.1371/journal.pcbi.1010747.r002
DOI:
10.1371/journal.pcbi.1010747.r003
DOI:
10.1371/journal.pcbi.1010747.r004
DOI:
10.1371/journal.pcbi.1010747.r005
DOI:
10.1371/journal.pcbi.1010747.r006
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2193340-6
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