Evidence map›Paper›PMID 33034850›Full record

ArticleAttention, perception & psychophysics2021

Incentive value and spatial certainty combine additively to determine visual priorities.

K G Garner, H Bowman, J E Raymond

Abstract read
In one paragraph

Article in Attention, perception & psychophysics, 2021. 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
–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

1 citing paper in PubMed.

  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.

K G GarnerSchool of Psychology, University of Birmingham, Birmingham, UK. getkellygarner@gmail.com.ORCID http://orcid.org/0000-0002-0690-6941
H BowmanSchool of Psychology, University of Birmingham, Birmingham, UK.
J E RaymondSchool of Psychology, University of Birmingham, Birmingham, UK.

Funding

Economic and Social Research Council ES/L000210/1
6 · The paper itself

Abstract

How does the brain combine information predictive of the value of a visually guided task (incentive value) with information predictive of where task-relevant stimuli may occur (spatial certainty)? Human behavioural evidence indicates that these two predictions may be combined additively to bias visual selection (Additive Hypothesis), whereas neuroeconomic studies posit that they may be multiplicatively combined (Expected Value Hypothesis). We sought to adjudicate between these two alternatives. Participants viewed two coloured placeholders that specified the potential value of correctly identifying an imminent letter target if it appeared in that placeholder. Then, prior to the target's presentation, an endogenous spatial cue was presented indicating the target's more likely location. Spatial cues were parametrically manipulated with regard to the information gained (in bits). Across two experiments, performance was better for targets appearing in high versus low value placeholders and better when targets appeared in validly cued locations. Interestingly, as shown with a Bayesian model selection approach, these effects did not interact, clearly supporting the Additive Hypothesis. Even when conditions were adjusted to increase the optimality of a multiplicative operation, support for it remained. These findings refute recent theories that expected value computations are the singular mechanism driving the deployment of endogenous spatial attention. Instead, incentive value and spatial certainty seem to act independently to influence visual selection.

Indexed as

CuesMotivationAttentionBayes TheoremHumansReaction TimeAttentionExpectationIncentivePredictionReward

Identifiers

PMID33034850
PMCPMC7875944

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.