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

Theeuwes, Jan; Bogaerts, Louisa; Moorselaar, Dirk van

doi:10.1016/j.tics.2022.06.001

Cited by 81 publications

(130 citation statements)

References 150 publications

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“…The current evidence suggests that updating of local priorities across visual searches accrues extremely fast (Valsecchi & Turatto, 2021), which makes spatial probability learning very flexible (Theeuwes et al, 2022). Indeed, in the current study where the high probability location was not static but systematically varied across time, learned priority was quickly adjusted in response to a location change.…”

Section: Discussionmentioning

confidence: 98%

“…A number of influential new theories of attention have highlighted the need to integrate selection history into models of dynamic attentional priority (Awh et al, 2012; Chelazzi et al, 2019; Liesefeld & Müller, 2019; Pearson et al, 2022; Theeuwes et al, 2022). The current results advance this project by providing a novel method of visualizing the history-mediated layer of the attentional priority map while also suggesting the neural mechanisms underlying such latent biases.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The influence of past experience on current behavior in attention is known as selection history - a category which encompasses statistical learning processes as well as other history based effects such as value-driven attentional capture or intertrial priming (Anderson et al, 2021; Awh et al, 2012; Theeuwes, 2018; Theeuwes et al, 2022). Together with top-down (goal driven) and bottom-up (saliency driven) attentional mechanisms, selection history is thought to converge in what is known as the attentional priority map – a real time representation of the behavioral relevance and saliency of the stimulus present in the visual field (Fecteau & Munoz, 2006; Ferrante et al, 2018; Theeuwes, 2018; Theeuwes et al, 2022). While these maps can exist in any feature space, they are often studied in the context of spatial features, where attentional priority is coded as weights on a topographic representation of physical space, and attentional selection is ultimately awarded in a winner-take-all fashion to the region with the highest activity (Itti & Koch, 2001; Zelinsky & Bisley, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…While these maps can exist in any feature space, they are often studied in the context of spatial features, where attentional priority is coded as weights on a topographic representation of physical space, and attentional selection is ultimately awarded in a winner-take-all fashion to the region with the highest activity (Itti & Koch, 2001; Zelinsky & Bisley, 2015). Selection history can then be thought of as a layer in this system, continuously up-regulating weights to locations which contained relevant information in the past, and down-regulating locations that frequently contain distracting information (Theeuwes et al, 2022; Theeuwes & Failing, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Pinging the brain to reveal the hidden attentional priority map using encephalography

Duncan

Moorselaar

Theeuwes

2022

Preprint

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Priority maps are winner-take-all neural mechanisms that are thought to guide the allocation of attention. Within these maps, attentional priority is coded as weights on a topographic representation of physical space. Research has shown that these weights are not just influenced by the current stimuli presented but also by selection episodes that occurred in the past – called selection history. The current study demonstrates, for the first time, that these latent, implicitly-learned spatial biases can be visualized by ‘pinging’ the brain (a technique previously used to study activity-silent working memory). By briefly flashing high-contrast, task-irrelevant visual stimuli in the inter-stimulus interval between searches, participants’ current history-trained latent attentional priority could be decoded via scalp encephalography. Our findings not only offer a novel method of visualizing this latent attentional priority map, but also shed light on the underlying mechanisms allowing our past experiences to influence future behaviour.

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Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pinging the brain to reveal the hidden attentional priority map using encephalography

Duncan

Moorselaar

Theeuwes

2022

Preprint

Self Cite

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How can we apply decision‐making theories to wild animal behavior? Predictions arising from dual process theory and Bayesian decision theory

Teichroeb,

Smeltzer,

Mathur

et al. 2023

American J Primatol

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Our understanding of decision‐making processes and cognitive biases is ever increasing, thanks to an accumulation of testable models and a large body of research over the last several decades. The vast majority of this work has been done in humans and laboratory animals because these study subjects and situations allow for tightly controlled experiments. However, it raises questions about how this knowledge can be applied to wild animals in their complex environments. Here, we review two prominent decision‐making theories, dual process theory and Bayesian decision theory, to assess the similarities in these approaches and consider how they may apply to wild animals living in heterogenous environments within complicated social groupings. In particular, we wanted to assess when wild animals are likely to respond to a situation with a quick heuristic decision and when they are likely to spend more time and energy on the decision‐making process. Based on the literature and evidence from our multi‐destination routing experiments on primates, we find that individuals are likely to make quick, heuristic decisions when they encounter routine situations, or signals/cues that accurately predict a certain outcome, or easy problems that experience or evolutionary history has prepared them for. Conversely, effortful decision‐making is likely in novel or surprising situations, when signals and cues have unpredictable or uncertain relationships to an outcome, and when problems are computationally complex. Though if problems are overly complex, satisficing via heuristics is likely, to avoid costly mental effort. We present hypotheses for how animals with different socio‐ecologies may have to distribute their cognitive effort. Finally, we examine the conservation implications and potential cognitive overload for animals experiencing increasingly novel situations caused by current human‐induced rapid environmental change.

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A proposed attention-based model for spatial memory formation and retrieval

Soyer¹

2022

Cogn Process

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

Cited by 81 publications

References 150 publications

Pinging the brain to reveal the hidden attentional priority map using encephalography

Pinging the brain to reveal the hidden attentional priority map using encephalography

How can we apply decision‐making theories to wild animal behavior? Predictions arising from dual process theory and Bayesian decision theory

A proposed attention-based model for spatial memory formation and retrieval

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