Evidence map›Paper›PMID 30443034›Full record

ArticleScientific reports2018

Active Confirmation Bias in the Evaluative Processing of Food Images.

Kajornvut Ounjai, Shunsuke Kobayashi, Muneyoshi Takahashi, Tetsuya Matsuda, Johan Lauwereyns

Abstract read
In one paragraph

Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 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

5 authors.

Kajornvut OunjaiGraduate School of Systems Life Sciences, Kyushu University, Fukuoka, 819-0395, Japan. kajornvut.o@gmail.com.
Shunsuke KobayashiDepartment of Neurology, Fukushima Medical University, Fukushima, 960-1295, Japan.
Muneyoshi TakahashiTamagawa University Brain Science Institute, Tokyo, 194-8610, Japan.
Tetsuya MatsudaTamagawa University Brain Science Institute, Tokyo, 194-8610, Japan.
Johan LauwereynsGraduate School of Systems Life Sciences, Kyushu University, Fukuoka, 819-0395, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Predictive processing is fundamental to many aspects of the human mind, including perception and decision-making. It remains to be elucidated, however, in which way predictive information impacts on evaluative processing, particularly in tasks that employ bivalent stimulus sets. Various accounts, including framing, proactive interference, and cognitive control, appear to imply contradictory proposals on the relation between prediction and preference formation. To disambiguate whether predictive cues produce congruent biases versus opponent mechanisms in evaluative processing, we conducted two experiments in which participants were asked to rate individual food images. The image database included appetitive and aversive items. In each trial, a cue predicted, with varying degrees of reliability, the valence of the impending food image. In both experiments, we found that the ratings exhibited congruent biases as a function of the reliability of the predictive cue, with the highest evaluations following the most reliable positive-valence predictions. Eye prepositioning further showed a selective spatial bias suggestive of response preparation in line with the predictions. The response times also exhibited a pattern of results consistent with selective preparation, producing slow responses following invalid predictions. The data suggested an active form of evaluative processing, implementing a confirmation bias that aims to accommodate the prediction.

Indexed as

BiasFoodImage Processing, Computer-AssistedFemaleFixation, OcularHumansMaleTime FactorsYoung Adult

Identifiers

PMID30443034
PMCPMC6237889

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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.