2017
DOI: 10.3758/s13414-017-1435-1
|View full text |Cite
|
Sign up to set email alerts
|

Temporal ambiguity of onsets in a cueing task prevents facilitation but not inhibition of return

Abstract: Cueing effects, i.e., early facilitation of reaction time and inhibition of return (IOR), are well-established and robust phenomena characterizing exogenous orienting and are widely observed in experiments with a traditional Posner cueing paradigm. Krüger, MacInnes, and Hunt (2014) proposed that facilitatory effects of peripheral cues are the result of a cue-target perceptual merging due to re-entrant visual processing. To test the role and timing of these feedback mechanisms in peripheral cueing effects, we m… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2017
2017
2021
2021

Publication Types

Select...
5

Relationship

1
4

Authors

Journals

citations
Cited by 5 publications
(2 citation statements)
references
References 45 publications
0
2
0
Order By: Relevance
“…2000; Lupiáñez et al 2006;Malevich et al 2018;Posner 1980;Posner and Cohen 1984;Posner et al 1985;Tian et al 2016). We found that the cueing effect was most negative~200 ms after cue onset (Fig.…”
Section: Express Stimulus-induced Microsaccadesmentioning
confidence: 64%
“…2000; Lupiáñez et al 2006;Malevich et al 2018;Posner 1980;Posner and Cohen 1984;Posner et al 1985;Tian et al 2016). We found that the cueing effect was most negative~200 ms after cue onset (Fig.…”
Section: Express Stimulus-induced Microsaccadesmentioning
confidence: 64%
“…Linear mixed-effect models have been intensely in use in various publications instead of the traditional ANOVA approach in the last decades (cf. Baayen et al, 2008 ; Bell et al, 2019 ; Schad et al, 2020 ), and have been applied (amongst others) to assess attentional cueing effects (e.g., Kliegl et al, 2011 ; Chauhan et al, 2017 ; MacInnes and Bhatnagar, 2018 ; Malevich et al, 2018 ). One of the big advantages of linear mixed models is that one can make use of the full data set, i.e., one does not lose information due to averaging, they allow for the estimation of random effects, missing data points cause no problem, and we can model random slopes, i.e., different effects for each level of the grouping variable.…”
Section: Methodsmentioning
confidence: 99%