Evidence map›Paper›PMID 36656593›Full record

ArticleJournal of vision2023

Space- and feature-based attention operate both independently and interactively within latent components of perceptual decision making.

Guangsheng Liang, John E Poquiz, Miranda Scolari

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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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.

Guangsheng LiangDepartment of Psychological Sciences, Texas Tech University, Lubbock, TX, USA.
John E PoquizDepartment of Psychological Sciences, Texas Tech University, Lubbock, TX, USA.
Miranda ScolariDepartment of Psychological Sciences, Texas Tech University, Lubbock, TX, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Top-down visual attention filters undesired stimuli while selected information is afforded the lion's share of limited cognitive resources. Multiple selection mechanisms can be deployed simultaneously, but how unique influences of each combine to facilitate behavior remains unclear. Previously, we failed to observe an additive perceptual benefit when both space-based attention (SBA) and feature-based attention (FBA) were cued in a sparse display (Liang & Scolari, 2020): FBA was restricted to higher order decision-making processes when combined with a valid spatial cue, whereas SBA additionally facilitated target enhancement. Here, we introduced a series of design modifications across three experiments to elicit both attention mechanisms within signal enhancement while also investigating the impacts on decision making. First, we found that when highly reliable spatial and feature cues made unique contributions to search (experiment 1), or when each cue component was moderately reliable (experiments 2a and 2b), both mechanisms were deployed independently to resolve the target. However, the same manipulations produced interactive attention effects within other latent decision-making components that depended on the probability of the integrated cueing object. Time spent before evidence accumulation was reduced and responses were more conservative for the most likely pre-cue combination-even when it included an invalid component. These data indicate that selection mechanisms operate on sensory signals invariably in an independent manner, whereas a higher-order dependency occurs outside of signal enhancement.

Indexed as

CuesVisual PerceptionDecision MakingHumansPhotic StimulationProbabilityReaction Time

Identifiers

PMID36656593
PMCPMC9872836

What OpenQuestion holds

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LicenceCC BY
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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.