What to expect where and when: How statistical learning drives visual selection

J Theeuwes, L Bogaerts, D van Moorselaar - Trends in cognitive sciences, 2022 - cell.com
While the visual environment contains massive amounts of information, we should not and
cannot pay attention to all events. Instead, we need to direct attention to those events that …

The past, present, and future of selection history

BA Anderson, H Kim, AJ Kim, MR Liao… - Neuroscience & …, 2021 - Elsevier
The last ten years of attention research have witnessed a revolution, replacing a theoretical
dichotomy (top-down vs. bottom-up control) with a trichotomy (biased by current goals …

Proactive enhancement and suppression elicited by statistical regularities in visual search.

C Huang, M Donk, J Theeuwes - Journal of Experimental …, 2022 - psycnet.apa.org
The present study investigated how attentional selection is affected by simultaneous
statistical learning of target and distractor regularities. Participants performed an additional …

[HTML][HTML] Neural mechanisms underlying distractor inhibition on the basis of feature and/or spatial expectations

D van Moorselaar, N Daneshtalab, HA Slagter - Cortex, 2021 - Elsevier
A rapidly growing body of research indicates that inhibition of distracting information may not
be under flexible, top-down control, but instead heavily relies on expectations derived from …

Modulations of saliency signals at two hierarchical levels of priority computation revealed by spatial statistical distractor learning.

HR Liesefeld, HJ Müller - Journal of Experimental Psychology …, 2021 - psycnet.apa.org
Many attention theories assume that selection is guided by a preattentive, spatial
representation of the scene that combines bottom-up stimulus information with top-down …

Specificity and persistence of statistical learning in distractor suppression.

MK Britton, BA Anderson - Journal of Experimental Psychology …, 2020 - psycnet.apa.org
Statistical regularities in distractor location trigger suppression of high-probability distractor
locations during visual search. The degree to which such suppression reflects …

A theoretical attempt to revive the serial/parallel-search dichotomy

HR Liesefeld, HJ Müller - Attention, Perception, & Psychophysics, 2020 - Springer
A core distinction in Anne Treisman's feature-integration theory (FIT) is in that between
parallel and serial search. We outline this dichotomy and selectively review the reasons why …

Post-capture processes contribute to statistical learning of distractor locations in visual search

M Sauter, NM Hanning, HR Liesefeld, HJ Müller - Cortex, 2021 - Elsevier
People can learn to ignore salient distractors that occur frequently at particular locations,
making them interfere less with task performance. This effect has been attributed to learnt …

Statistical learning of distractor locations is dependent on task context

J De Waard, D Van Moorselaar, L Bogaerts… - Scientific Reports, 2023 - nature.com
Through statistical learning, humans can learn to suppress visual areas that often contain
distractors. Recent findings suggest that this form of learned suppression is insensitive to …

Preparatory control against distraction is not feature-based

HR Liesefeld, AM Liesefeld, HJ Müller - Cerebral Cortex, 2022 - academic.oup.com
Salient-but-irrelevant stimuli (distractors) co-occurring with search targets can capture
attention against the observer's will. Recently, evidence has accumulated that preparatory …