Evidence map›Paper›PMID 37227714›Full record

ArticleJournal of vision2023

Stronger serial dependence in the depth plane than the fronto-parallel plane between realistic objects: Evidence from virtual reality.

Ömer Daglar Tanrikulu, David Pascucci, Árni Kristjánsson

Abstract read
In one paragraph

Article in Journal of vision, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 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

3 authors.

Ömer Daglar TanrikuluDepartment of Psychology, University of New Hampshire, Durham, NH, USA.
David PascucciLaboratory of Psychophysics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Árni KristjánssonFaculty of Psychology, School of Health Sciences, University of Iceland, Reykjavik, Iceland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Visual estimates of stimulus features are systematically biased toward the features of previously encountered stimuli. Such serial dependencies have often been linked to how the brain maintains perceptual continuity. However, serial dependence has mostly been studied for simple two-dimensional stimuli. Here, we present the first attempt at examining serial dependence in three dimensions with natural objects, using virtual reality (VR). In Experiment 1, observers were presented with 3D virtually rendered objects commonly encountered in daily life and were asked to reproduce their orientation. The rotation plane of the object and its distance from the observer were manipulated. Large positive serial dependence effects were observed, but most notably, larger biases were observed when the object was rotated in depth, and when the object was rendered as being further away from the observer. In Experiment 2, we tested the object specificity of serial dependence by varying object identity from trial to trial. Similar serial dependence was observed irrespective of whether the test item was the same object, a different exemplar from the same object category, or a different object from a separate category. In Experiment 3, we manipulated the retinal size of the stimulus in conjunction with its distance. Serial dependence was most strongly modulated by retinal size, rather than VR depth cues. Our results suggest that the increased uncertainty added by the third dimension in VR increases serial dependence. We argue that investigating serial dependence in VR will provide potentially more accurate insights into the nature and mechanisms behind these biases.

Indexed as

Virtual RealityBrainCuesHeadHumansUncertainty

Identifiers

PMID37227714
PMCPMC10214883

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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.