Literature DB >> 26338030

You see what you have learned. Evidence for an interrelation of associative learning and visual selective attention.

Tobias Feldmann-Wüstefeld1, Metin Uengoer1, Anna Schubö1.   

Abstract

Besides visual salience and observers' current intention, prior learning experience may influence deployment of visual attention. Associative learning models postulate that observers pay more attention to stimuli previously experienced as reliable predictors of specific outcomes. To investigate the impact of learning experience on deployment of attention, we combined an associative learning task with a visual search task and measured event-related potentials of the EEG as neural markers of attention deployment. In the learning task, participants categorized stimuli varying in color/shape with only one dimension being predictive of category membership. In the search task, participants searched a shape target while disregarding irrelevant color distractors. Behavioral results showed that color distractors impaired performance to a greater degree when color rather than shape was predictive in the learning task. Neurophysiological results show that the amplified distraction was due to differential attention deployment (N2pc). Experiment 2 showed that when color was predictive for learning, color distractors captured more attention in the search task (ND component) and more suppression of color distractor was required (PD component). The present results thus demonstrate that priority in visual attention is biased toward predictive stimuli, which allows learning experience to shape selection. We also show that learning experience can overrule strong top-down control (blocked tasks, Experiment 3) and that learning experience has a longer-term effect on attention deployment (tasks on two successive days, Experiment 4).
© 2015 Society for Psychophysiological Research.

Entities:  

Keywords:  Associative learning; Attentional capture; N2pc; NT; PD; Visual attention

Mesh:

Year:  2015        PMID: 26338030     DOI: 10.1111/psyp.12514

Source DB:  PubMed          Journal:  Psychophysiology        ISSN: 0048-5772            Impact factor:   4.016


  16 in total

1.  Spatially Guided Distractor Suppression during Visual Search.

Authors:  Tobias Feldmann-Wüstefeld; Marina Weinberger; Edward Awh
Journal:  J Neurosci       Date:  2021-03-02       Impact factor: 6.167

2.  Neural Evidence for the Contribution of Active Suppression During Working Memory Filtering.

Authors:  Tobias Feldmann-Wüstefeld; Edward K Vogel
Journal:  Cereb Cortex       Date:  2019-02-01       Impact factor: 5.357

Review 3.  Inhibition as a potential resolution to the attentional capture debate.

Authors:  Nicholas Gaspelin; Steven J Luck
Journal:  Curr Opin Psychol       Date:  2018-10-29

4.  Context modulation of learned attention deployment.

Authors:  Metin Uengoer; John M Pearce; Harald Lachnit; Stephan Koenig
Journal:  Learn Behav       Date:  2018-03       Impact factor: 1.986

Review 5.  The Role of Inhibition in Avoiding Distraction by Salient Stimuli.

Authors:  Nicholas Gaspelin; Steven J Luck
Journal:  Trends Cogn Sci       Date:  2017-11-27       Impact factor: 20.229

6.  Progress Toward Resolving the Attentional Capture Debate.

Authors:  Steven J Luck; Nicholas Gaspelin; Charles L Folk; Roger W Remington; Jan Theeuwes
Journal:  Vis cogn       Date:  2020-12-01

7.  You prime what you code: The fAIM model of priming of pop-out.

Authors:  Wouter Kruijne; Martijn Meeter
Journal:  PLoS One       Date:  2017-11-22       Impact factor: 3.240

8.  Controlling the Flow of Distracting Information in Working Memory.

Authors:  Nicole Hakim; Tobias Feldmann-Wüstefeld; Edward Awh; Edward K Vogel
Journal:  Cereb Cortex       Date:  2021-06-10       Impact factor: 5.357

9.  Mixed signals: The effect of conflicting reward- and goal-driven biases on selective attention.

Authors:  Daniel Preciado; Jaap Munneke; Jan Theeuwes
Journal:  Atten Percept Psychophys       Date:  2017-07       Impact factor: 2.199

10.  Changes of Attention during Value-Based Reversal Learning Are Tracked by N2pc and Feedback-Related Negativity.

Authors:  Mariann Oemisch; Marcus R Watson; Thilo Womelsdorf; Anna Schubö
Journal:  Front Hum Neurosci       Date:  2017-11-07       Impact factor: 3.169

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.