The past, present, and future of selection history

Anderson, Brian A.; Kim, Haena; Kim, Andy; Liao, Ming-Ray; Mrkonja, Lana; Clement, Andrew; Grégoire, Laurent

doi:10.1016/j.neubiorev.2021.09.004

Cited by 120 publications

(134 citation statements)

References 492 publications

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“…Even though it is well known that intertrial repetition priming is a prime example of how selection history affects visual search (e.g., Anderson et al, 2021; Failing & Theeuwes, 2018), it is important to note that the current target-to-target RT benefits are not related to what is known as intertrial priming. Several forms of intertrial priming are recognized; for example, there are intertrial RT benefits when a target appears at the same location on consecutive trials (Maljkovic & Nakayama, 1996), or there are benefits when the target has the same defining features across trials (Maljkovic & Nakayama, 1994).…”

Section: Discussionmentioning

confidence: 99%

“…It is also known that participants can learn statistical regularities present in the environment, which in turn affects attentional selection. Through a process known as visual statistical learning (VSL) participants extract the spatial and/or temporal regularities present in the visual input, affecting attention and perception (e.g., Anderson et al, 2021; Frost et al, 2019; Perruchet & Pacton, 2006). Following the seminal work on statistical learning by Saffran, Aslin, and Newport (1996), who demonstrated that infants can extract trisyllabic patterns from continuous speech, a large number of studies have shown that individuals of all ages possess remarkable abilities in successful tracking patterns of co-occurrence of temporally adjacent elements in input streams with different types of visual stimuli (e.g., Dennis et al, 2006; Fiser & Aslin, 2002; Howard et al, 2008; Olson & Chun, 2001; Thomas et al, 2018; Turk-Browne et al, 2005; Turk-Browne & Scholl, 2009).…”

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confidence: 99%

“…For instance, participants can learn that a certain location in a search display has a higher probability of containing targets (e.g., Addleman et al, 2018; Ferrante et al, 2018; Geng & Behrmann, 2002, 2005) or distractors (e.g., Goschy et al, 2014; Wang & Theeuwes, 2018; Won et al, 2019; Zhang et al, 2019), respectively, accelerating target detection or enhancing distractor suppression, which both result in more efficient search. It is generally assumed that VSL modulates the allocation of visual attention via dynamic weight adjustments within the spatial priority map (e.g., Anderson et al, 2021; Fecteau & Munoz, 2006; Itti & Koch, 2001; Zelinsky & Bisley, 2015), even though others have challenged this view (see Allenmark et al, 2019; Liesefeld & Müller, 2021; Sauter et al, 2018). Taking the study by Geng and Behrmann (2002) as an example, each spatial location is initially weighed equally due to the same physical saliency and features (i.e., similar letters).…”

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confidence: 99%

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Statistical learning of across-trial regularities during serial search.

Bogaerts

Theeuwes

2022

Journal of Experimental Psychology: Human Perception and Perfor

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Previous studies have shown that attention becomes biased toward those locations that frequently contain a target and is biased away from locations that have a high probability to contain a distractor. A recent study showed that participants also learned regularities that exist across trials: Participants were faster to find the singleton when its location was predicted by the location of the target singleton on the previous trial. Note, however, that this across-trial statistical learning was only demonstrated for parallel search involving “pop-out” singleton targets. The current study investigated whether there is also learning of across-trial regularities when search is serial, using a T-among-Ls task. In Experiment 1, using search displays with a gray T-target among gray Ls, we found that participants did not learn the existing across-trial regularities. In Experiment 2 we used the same display and same regularities except that during the first half of the experiment the targets were colored red, allowing feature search. Critically, now participants did learn the across-trial regularities during pop-out feature search and the learned biases persisted when search was serial again. Participants were not aware of these regularities suggesting that learning was automatic and implicit. We propose that across-trial target-target associations learned during feature search shape a flexible priority map whereby the selection of the predicting location results in up-weighting of the predicted location on the next trial. This flexible priority map remained active even when search task changed dramatically from parallel to serial search.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Statistical learning of across-trial regularities during serial search.

Bogaerts

Theeuwes

2022

Journal of Experimental Psychology: Human Perception and Perfor

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“…It has been well-established that the reward might enhance spatial attention to achieve more efficient goal-directed behavior and to improve target-detection performance (Awh et al, 2012;Chelazzi et al, 2013;Della Libera & Chelazzi, 2006;Engelmann & Pessoa, 2007;Engelmann et al, 2009;Munneke et al, 2015;Pessoa, 2015). Researchers have proposed that past selection episodes (associations with valent outcomes, S-R habit learning, statistical learning) should be labeled as third category of attentional control that is independent of top-down (goal-driven) and bottom-up control (stimulus-driven) (Anderson et al, 2021;Awh et al, 2012;Chelazzi et al, 2013;Failing & Theeuwes, 2018). Some aspects of selection history must be learned over time and the learning experience of reward can shape the attention system (Anderson et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Researchers have proposed that past selection episodes (associations with valent outcomes, S-R habit learning, statistical learning) should be labeled as third category of attentional control that is independent of top-down (goal-driven) and bottom-up control (stimulus-driven) (Anderson et al, 2021;Awh et al, 2012;Chelazzi et al, 2013;Failing & Theeuwes, 2018). Some aspects of selection history must be learned over time and the learning experience of reward can shape the attention system (Anderson et al, 2021). Reward can be described as positive outcomes or events from which individuals can benefit subjectively or objectively (Mohr et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Reward Weakened Inhibition of Return (IOR) in the Near Depth Plane

Wang

Gao

et al. 2022

Perception

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In attentional orienting, researchers have proposed that reward history is a component of attentional control, as the reward value might enhance the spatial attention process to achieve more efficient goal-directed behavior and to improve target-detection performance. Although the effect of reward–induced motivation on attentional orienting has been studied in two–dimensional (2-D) space, the specific mechanisms underlying the influence of reward on inhibition of return (IOR) of attentional orienting in three-dimensional space (3-D) remain unclear. In the present study, by incorporating the Posner spatial-cueing paradigm into a virtual 3-D environment, we aimed to investigate the influence of reward on IOR in 3-D space. The results showed the following: (1) IOR size in the rewarded conditions was smaller than IOR size in the unrewarded condition in the near depth plane, resulting in an IOR difference with or without reward. (2) Reward weakened IOR in the near depth plane because the response to the uncued location was delayed, not because the response to the cued location was accelerated. The present study indicated that the different depth planes of the target location in 3-D space could influence the interaction between reward and IOR, and reward weakened IOR in the near depth plane.

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Attention as a multi‐level system of weights and balances

Narhi-Martinez

Dube

Golomb

2022

WIRES Cognitive Science

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This opinion piece is part of a collection on the topic: "What is attention?" Despite the word's place in the common vernacular, a satisfying definition for "attention" remains elusive. Part of the challenge is there exist many different types of attention, which may or may not share common mechanisms. Here we review this literature and offer an intuitive definition that draws from aspects of prior theories and models of attention but is broad enough to recognize the various types of attention and modalities it acts upon: attention as a multi-level system of weights and balances. While the specific mechanism(s) governing the weighting/balancing may vary across levels, the fundamental role of attention is to dynamically weigh and balance all signals-both externally-generated and internally-generated-such that the highest weighted signals are selected and enhanced. Top-down, bottom-up, and experience-driven factors dynamically

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The past, present, and future of selection history

Cited by 120 publications

References 492 publications

Statistical learning of across-trial regularities during serial search.

Statistical learning of across-trial regularities during serial search.

Reward Weakened Inhibition of Return (IOR) in the Near Depth Plane

Attention as a multi‐level system of weights and balances

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