Evidence map›Paper›PMID 39605701›Full record

ArticlebioRxiv : the preprint server for biology2024

Neural evidence for decision-making underlying attractive serial dependence.

Jiangang Shan, Jasper E Hajonides, Nicholas E Myers

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Jiangang ShanDepartment of Psychology, University of Wisconsin-Madison, USA.ORCID 0000-0001-5588-6011
Jasper E HajonidesOxford Centre for Human Brain Activity & Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK.ORCID 0000-0002-0812-6842
Nicholas E MyersSchool of Psychology, University of Nottingham, UK.ORCID 0000-0001-5599-3044

Funding

Controlling the Contents of Working MemoryR01MH131678 · NIMH · UNIVERSITY OF WISCONSIN-MADISON · PI BRADLEY R POSTLE · 2023 to 2026
$2.5M
NIMH NIH HHS R01 MH131678
6 · The paper itself

Abstract

Recall of stimuli is biased by stimulus history, variously manifested as an attractive bias toward or repulsive bias from previous stimuli (i.e., serial dependence). It is unclear when attractive vs repulsive biases arise and if they share neural mechanisms. A recent model of attractive serial dependence proposes a two-stage process in which adaptation causes a repulsive bias during encoding that is later counteracted by an attractive bias at the decision-making stage in a Bayesian-inference-like manner. Neural evidence exists for a repulsive bias at encoding, but evidence for the attractive bias during the response period has been more elusive. We recently [1] showed that while different stimuli in trial history exerted different (attractive or repulsive) serial biases on behavioral reports, during encoding the neural representation of the current item was always repulsively biased. Here we assessed whether this discrepancy between neural and behavioral effects is resolved during subsequent decision-making. Multivariate decoding of magnetoencephalography data during working memory recall showed a neural distinction between attractive and repulsive biases: an attractive neural bias emerged only late in recall. But stimuli that created a repulsive bias on behavior led to a repulsive neural bias early in the recall phase, suggesting that it had already been incorporated earlier. Our results suggest that attractive (but not repulsive) serial dependence arises during decision-making, and that priors that influence post-perceptual decision-making are updated by the previous trial's target, but not by other stimuli.

Indexed as

Bayesian inferenceMEGmultivariate decodingneural representationserial dependenceworking memory

Identifiers

PMID39605701
PMCPMC11601478

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.