Evidence map›Paper›PMID 40938760›Full record

ArticleeLife2025

The spatial frequency representation predicts category coding in the inferior temporal cortex.

Ramin Toosi, Behnam Karami, Roxana Koushki, Farideh Shakerian, Jalaledin Noroozi, Ehsan Rezayat, Abdol-Hossein Vahabie, Mohammad Ali Akhaee, Mohammad-Reza A Dehaqani

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In one paragraph

Article in eLife, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

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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

9 authors.

Ramin ToosiSchool of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Islamic Republic of Iran.ORCID https://orcid.org/0000-0002-7099-9353
Behnam KaramiSchool of Cognitive Sciences, Institute for Research in Fundamental Sciences, Tehran, Iran.ORCID https://orcid.org/0000-0001-6594-6516
Roxana KoushkiSchool of Cognitive Sciences, Institute for Research in Fundamental Sciences, Tehran, Iran.
Farideh ShakerianDepartment of Brain and Cognitive Sciences, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran.ORCID https://orcid.org/0009-0001-8426-5879
Jalaledin NorooziSchool of Cognitive Sciences, Institute for Research in Fundamental Sciences, Tehran, Iran.
Ehsan RezayatDepartment of Cognitive Sciences, Faculty of Psychology and Education, University of Tehran, Tehran, Iran.ORCID https://orcid.org/0000-0003-3808-1283
Abdol-Hossein VahabieSchool of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Islamic Republic of Iran.ORCID https://orcid.org/0000-0003-1603-8866
Mohammad Ali AkhaeeSchool of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Islamic Republic of Iran.ORCID https://orcid.org/0000-0003-3753-5618
Mohammad-Reza A DehaqaniSchool of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Islamic Republic of Iran.ORCID https://orcid.org/0000-0003-4365-4365

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding the neural representation of spatial frequency (SF) in the primate cortex is vital for unraveling visual processing in object recognition. While many studies focus on SF in the primary visual cortex, the characteristics and interaction of SF with category representation remain unclear. To explore SF representation in the inferior temporal (IT) cortex of macaques, we conducted extracellular recordings with complex stimuli systematically filtered by SF. Our findings reveal explicit SF coding at both single-neuron and population levels. Temporal dynamics analysis of SF representation reveals that low SF (LSF) is decoded faster than high SF (HSF), and the SF preference dynamically shifts from LSF to HSF over time. Additionally, the SF representation for each neuron forms a profile that predicts category selectivity at the population level. IT neurons cluster into four groups based on SF preference, each with distinct category coding behaviors. Notably, HSF-preferring neurons show the highest category decoding for faces. Despite the existing connection between SF and category coding, we have identified uncorrelated representations of SF and category. Unlike category coding, SF is more sparsely represented and depends more on individual neurons. These findings dissociate SF and category representations, underscoring SF's pivotal role in object recognition.

Indexed as

NeuronsPattern Recognition, VisualTemporal LobeVisual PerceptionAnimalsMacaca mulattaMalePhotic Stimulationcoarse-to-fine processinginferior temporal cortexneuroscienceobject recognitionrhesus macaquespatial frequency

Identifiers

PMID40938760
PMCPMC12431771

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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.