Evidence map›Paper›PMID 35998295›Full record

ArticleeNeuro

Evidence for Independent Processing of Shape by Vision and Touch.

Ryan L Miller, David L Sheinberg

Abstract read
In one paragraph

Article in eNeuro. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

2 authors.

Ryan L MillerDepartment of Neuroscience, Brown University, Providence, Rhode Island 02912 ryan.miller@brown.edu.ORCID https://orcid.org/0000-0003-4994-7345
David L SheinbergDepartment of Neuroscience, Brown University, Providence, Rhode Island 02912.ORCID https://orcid.org/0000-0002-6706-8574

Funding

The neural basis of sequential control in human and non-human primatesP20GM103645 · NIGMS · BROWN UNIVERSITY · PI WORDEN, MICHAEL S · 2013 to 2022
$22.9M
Closing the loop on markerless object trackingR03MH123990 · NIMH · BROWN UNIVERSITY · PI SHEINBERG, DAVID L · 2020 to 2020
$163k
NIGMS NIH HHS P20 GM103645NIMH NIH HHS R03 MH123990
6 · The paper itself

Abstract

Although visual object recognition is well studied and relatively well understood, much less is known about how shapes are recognized by touch and how such haptic stimuli might be compared with visual shapes. One might expect that the processes of visual and haptic object recognition engage similar brain structures given the advantages of avoiding redundant brain circuitry and indeed there is some evidence that this is the case. A potentially fruitful approach to understanding the differences in how shapes might be neurally represented is to find an algorithmic method of comparing shapes, which agrees with human behavior and determines whether that method differs between different modality conditions. If not, it would provide further evidence for a shared representation of shape. We recruited human participants to perform a one-back same-different visual and haptic shape comparison task both within (i.e., comparing two visual shapes or two haptic shapes) and across (i.e., comparing visual with haptic shapes) modalities. We then used various shape metrics to predict performance based on the shape, orientation, and modality of the two stimuli that were being compared on each trial. We found that the metrics that best predict shape comparison behavior heavily depended on the modality of the two shapes, suggesting differences in which features are used for comparing shapes depending on modality and that object recognition is not necessarily performed in a single, modality-agnostic region.

Indexed as

TouchTouch PerceptionBrainHumansVision, OcularVisual Perceptioncross-modalhapticmultisensoryobject recognitionsupramodalvisual

Identifiers

PMID35998295
PMCPMC9215689

What OpenQuestion holds

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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.