Evidence map›Paper›PMID 40881166›Full record

ArticleVisual cognition2023

Incidental Learning of Temporal and Spatial Associations in Hybrid Search.

Iris Wiegand, Jeremy M Wolfe, Joseph H R Maes, Roy P C Kessels

Abstract read
In one paragraph

Article in Visual cognition, 2023. 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Iris WiegandDonders Institute for Brain, Behavior and Cognition, Department of Neuropsychology and Rehabilitation Psychology, Radboud University, Nijmegen, The Netherlands.
Jeremy M WolfeVisual Attention Lab, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Joseph H R MaesDonders Institute for Brain, Behavior and Cognition, Department of Neuropsychology and Rehabilitation Psychology, Radboud University, Nijmegen, The Netherlands.
Roy P C KesselsDonders Institute for Brain, Behavior and Cognition, Department of Neuropsychology and Rehabilitation Psychology, Radboud University, Nijmegen, The Netherlands.

Funding

Improving Perception in Digital Breast TomographyR01CA207490 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI WOLFE, JEREMY M · 2016 to 2025
$4.6M
NCI NIH HHS R01 CA207490
6 · The paper itself

Abstract

In a visual environment, objects are encoded within a spatial and temporal context. The present study investigated whether incidental learning of spatial and temporal associations in hybrid visual and memory search enable observers to predict targets in space and time. In three experiments, observers looked for four, previously memorized, target items across many trials. We examined effects of learning target item sequences (e.g., the butterfly always follows the paint box), target item-location associations (the butterfly is always in the right corner), and target item-location sequences (the butterfly in the right corner always follows the paint box in the lower middle-left). We found only weak incidental learning for the sequences of target items alone. By contrast, we found good learning of target item-location associations. Furthermore, we did find a reliable effect of sequence learning for target item-location associations. These findings suggest that spatiotemporal learning in hybrid search is hierarchical: Only if spatial and non-spatial target features are bound, temporal associations can bias attention dynamically to the task-relevant features expected to occur next.

Indexed as

Associative LearningMemory SearchObject-location bindingSequence LearningSpatiotemporal bindingVisual Search

Identifiers

PMID40881166
PMCPMC12382420

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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.