Evidence map›Paper›PMID 41425258›Full record

ArticleImaging neuroscience (Cambridge, Mass.)2025

Mapping object space dimensions: New insights from temporal dynamics.

Alexis Kidder, Genevieve L Quek, Tijl Grootswagers

Abstract read
In one paragraph

Article in Imaging neuroscience (Cambridge, Mass.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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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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

3 authors.

Alexis KidderDepartment of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United Sates.ORCID https://orcid.org/0000-0002-2198-231X
Genevieve L QuekThe MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Sydney, NSW Australia.ORCID https://orcid.org/0000-0002-5905-8405
Tijl GrootswagersThe MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Sydney, NSW Australia.ORCID https://orcid.org/0000-0002-7961-5002

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

How is object information organized in high-level visual cortex? A recent comprehensive model of object space in macaques defines object space via orthogonal axes of animacy and aspect ratio (i.e., stubby vs. spiky) (Bao et al., 2020). However, when using object stimuli that dissociated category, animacy, and aspect ratio in human fMRI, object space appeared to be principally defined by category and animacy, with limited tuning of aspect ratio in object-selective regions (Yargholi & Op de Beeck, 2023). Here, we aimed to further clarify the contribution of aspect ratio during object processing by using whole-brain electroencephalography (EEG) to systematically investigate the time course underlying aspect ratio, animacy, and category information during visual object processing. Participants (N = 20) viewed the stimulus set used by Yargholi and Op de Beeck (2023), as well as silhouette versions of the stimuli that lacked internal object details (thus increasing reliance on shape information). Stimuli appeared in 5 Hz rapid serial visual presentation streams, with intact and silhouette stimuli sets shown in separate streams. Using standard multivariate decoding pipelines and representational similarity analysis, we found that information about aspect ratio, category, and animacy was represented during visual object processing. The dominant dimension was modulated by stimulus type, demonstrating that the observable dimensions of object space depend on the nature of the stimuli presented. Taken together, these findings demonstrate that aspect ratio is indeed represented during object processing, but earlier and more transiently than categorical dimensions, such as animacy. By focusing on underlying temporal dynamics, our results provide a more nuanced understanding of how object space evolves over time that can speak of how extant findings on this topic might be reconciled.

Indexed as

EEGobject processingobject spacetemporal dynamics

Identifiers

PMID41425258
PMCPMC12713111

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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.