Statistical learning in the absence of explicit top-down attention

Duncan, Dock; Theeuwes, Jan

doi:10.1016/j.cortex.2020.07.006

Cited by 49 publications

(81 citation statements)

References 42 publications

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“…Similarly consistent with our findings, some previous results indeed found that regularities that were implicitly learned remained in place even when the task changed or when the regularities were no longer present (e.g., Duncan & Theeuwes, 2020;Sauter et al, 2019). For example, showed that regularities regarding the location of the target learned during a training session generalized to a similar search tasks with different stimuli (i.e., from the T-among-Ls task to the 2-among-5s task) and with different difficulty (i.e., different offsets between two segments of nontarget "L" in different phases).…”

Section: Discussionsupporting

confidence: 93%

“…For example, Jiang et al (2015) showed that regularities regarding the location of the target learned during a training session generalized to a similar search tasks with different stimuli (i.e., from the T-among-Ls task to the 2-among-5s task) and with different difficulty (i.e., different offsets between two segments of nontarget “L” in different phases). Duncan and Theeuwes (2020) showed that when participants performed two different tasks involving the same display, regularities that were present during one task stayed in place while performing another task. The learned spatial biases could persist more than one day even if when the spatial distribution was even (Jiang et al, 2013; Sauter et al, 2019).…”

Section: Discussionmentioning

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: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Statistical learning of across-trial regularities during serial search.

Bogaerts

Theeuwes

2022

Journal of Experimental Psychology: Human Perception and Perfor

Self Cite

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show abstract

“…Our results are also consistent with those reported in related visual learning paradigms. For instance, Duncan and Theeuwes (2020) explored the persistence of attentional biases in a task where participants had to avoid searching in certain locations of the display because distractors appeared more frequently in that location. They found that this attentional bias disappeared after approximately 120 trials in which the spatial distribution of distractors was unbiased.…”

Section: Discussionmentioning

confidence: 99%

Is probabilistic cuing of visual search an inflexible attentional habit? A meta-analytic review

Giménez‐Fernández¹,

Luque²,

Shanks³

et al. 2021

Preprint

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In studies on probabilistic cuing of visual search, participants search for a target among several distractors and report some feature of the target. In a biased stage the target appears more frequently in one specific area of the search display. Eventually, participants become faster at finding the target in that rich region compared to the sparse region. In some experiments, this stage is followed by an unbiased stage, where the target is evenly located across all regions of the display. Despite this change in the spatial distribution of targets, search speed usually remains faster when the target is located in the previously rich region. The persistence of the bias even when it is no longer advantageous underpins the claim that this phenomenon is a habit-like process. The aim of this meta-analysis was to test whether the magnitude of probabilistic cuing decreases from the biased to the unbiased stage. A multi-level random-effects meta-analysis of 42 studies confirmed that probabilistic cuing during the unbiased stage was roughly half the size of cuing during the biased stage, and this decrease persisted even after correcting for publication bias. We propose that probabilistic cuing is better understood as evidence of top-down attentional control than as an attentional habit.

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“…Critically, contingence between performance and outcomes was very large even when assuming that participants were insensitive to contingency changes (see Supplemental Material). It seems, however, unlikely that participants were unable to align expectations with changing outcome contingencies at least to some degree: People can implicitly track undisclosed contingencies (Duncan & Theeuwes, 2020; Kóbor et al., 2019), including those that depend on own response speed (Hwang & Kim, 2016). In our task, differences between incentive conditions were pointed out during instructions and condition‐specific feedback was presented in unique font colors (see Figure 1).…”

Section: Methodsmentioning

confidence: 99%

Comparing the motivational value of rewards and losses in an EEG‐pupillometry study

Carsten

Kostandyan

Boehler

et al. 2020

Eur J of Neuroscience

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]. Here, we further tested the hypothesis that motivation to gain rewards is higher than to avoid losses, even when incentive values are matched. As implicit markers of motivation, we assessed electroencephalography (EEG) focusing on the P3 after target and feedback onset, and the Feedback-Related Negativity (FRN), as well as simultaneously recorded pupil size. Comparing only reward and loss prospect trials in Experiment 1, we found no consistent differences in behavior and electrophysiological markers of motivation, although pupil data suggested higher arousal after feedback in potential-loss trials. Including additional noincentive trials in Experiment 2, we found consistent evidence that motivation to gain rewards was higher than to avoid losses: In line with behavior, the target-P3 was most pronounced for reward-related stimuli, followed by loss and no-incentive ones. This same ranking was found in the P3 and the FRN after positive outcomes (i.e., reward, avoided loss, and correct feedback in no-incentive trials). Negative outcomes featured a different pattern in line with the pupil response, which suggests that losses are emotionally salient events, without invigorating behavior proportionally. In sum, these findings suggest that the motivation to gain rewards is more pronounced than motivation to avoid equivalent losses, at least in tasks promoting transient increases in attention triggered by incentive prospect. These motivational differences may arise as avoided losses are not profitable in the long term, in contrast to gained rewards.

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Statistical learning in the absence of explicit top-down attention

Cited by 49 publications

References 42 publications

Statistical learning of across-trial regularities during serial search.

Statistical learning of across-trial regularities during serial search.

Is probabilistic cuing of visual search an inflexible attentional habit? A meta-analytic review

Comparing the motivational value of rewards and losses in an EEG‐pupillometry study

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