Evidence map›Paper›PMID 35391983›Full record

ArticleFrontiers in psychology2022

Exploring the Common Mechanisms of Motion-Based Visual Prediction.

Dan Hu, Matias Ison, Alan Johnston

Open access · goldAbstract read
In one paragraph

Article in Frontiers in psychology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 47% of its field
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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. 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

3 authors at 1 institution in 1 country.

Dan HuSchool of Psychology, University of Nottingham, Nottingham, United Kingdom.
Matias IsonSchool of Psychology, University of Nottingham, Nottingham, United Kingdom.
Alan JohnstonSchool of Psychology, University of Nottingham, Nottingham, United Kingdom.
University of Nottingham · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human vision supports prediction for moving stimuli. Here we take an individual differences approach to investigate whether there could be a common processing rate for motion-based visual prediction across diverse motion phenomena. Motion Induced Spatial Conflict (MISC) refers to an incongruity arising from two edges of a combined stimulus, moving rigidly, but with different apparent speeds. This discrepancy induces an illusory jitter that has been attributed to conflict within a motion prediction mechanism. Its apparent frequency has been shown to correlate with the frequency of alpha oscillations in the brain. We asked what other psychophysical measures might correlate positively with MISC frequency. We measured the correlation between MISC jitter frequency and another three measures that might be linked to motion-based spatial prediction. We demonstrate that the illusory jitter frequency in MISC correlates significantly with the accrual rate of the Motion Induced Position Shift (MIPS) effect - the well-established observation that a carrier movement in a static envelope of a Gabor target leads to an apparent position shift of the envelope in the direction of motion. We did not observe significant correlations with the other two measures - the Adaptation Induced Spatial Shift accrual rate (AISS) and the Smooth Motion Threshold (SMT). These results suggest a shared perceptual rate between MISC and MIPS, implying a common periodic mechanism for motion-based visual prediction.

Indexed as

alpha activityindividual differencemotionmotion adaptationspatial illusionvisual prediction

Identifiers

PMID35391983
PMCPMC8981589
OpenAlexW4221004621

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.