Learned states of preparatory attentional control.

Sali, Anthony W.; Anderson, Brian A.; Yantis, Steven

doi:10.1037/xlm0000146

Cited by 22 publications

(33 citation statements)

References 82 publications

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“…One line of work (e.g., Brady & Oliva, 2008;Kidd, Piantadosi, & Aslin, 2012;Turk-Browne et al, 2005), studied adaptation to the regularity formed by their order (e.g., stimulus 'B' always follows stimulus 'A') and not by interval duration (e.g., 'B' reappears after a predictable interval, yet can be followed by either 'A' or 'C'). Other studies have shown that regular intervals can be implicitly learned: infants (Lewkowicz, 2003) and adults van Rijn, 2017, Olson andChun, 2001, Experiment 1a and b) can implicitly detect rhythmic interval patterns; temporal regularities influence the learning of language (Hay & Saffran, 2012) and artificial pitch grammar (Selchenkova, Jones, & Tillmann, 2014); temporal structure in a rapid serial visual presentation task can bias the probability of shifting the focus of attention (Sali, Anderson, & Yantis, 2015); and temporal information can be integrated in the learning of rhythmic perceptual-motor action sequences (Gobel, Sanchez, & Reber, 2011;O'Reilly, McCarthy, Capizzi, & Nobre, 2008;Schultz, Stevens, Keller, & Tillmann, 2013;Shin & Ivry, 2002). However, it is unclear whether implicit knowledge about regular IMPLICIT LEARNING OF TEMPORAL BEHAVIOR 5 intervals is used to anticipate when to act.…”

Section: Implicit Learning Of Temporal Behavior 3 Implicit Learning Omentioning

confidence: 97%

Implicit Learning of Temporal Behavior in Complex Dynamic Environments

Salet

Kruijne

Rijn

2020

Preprint

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To anticipate future events humans exploit predictive patterns in the environment. Such statistical learning is often outside of awareness. Timing research suggests that humans also adapt to temporal regularities. However, in these experiments, the intervals to be timed are isolated and explicitly cued, contrasting with everyday life where intervals are often unnoticed. In the present study, we found implicit adaptation to temporal regularities in an ecological setting. Ninety-eight participants played a game in which they responded to sudden-onset targets. While two targets appeared at random times, one target appeared every three seconds. In two experiments, we found adaptation to the regularity: Response times were lower, hit rates higher, and mouse cursor trajectories revealed anticipatory movements. Crucially, this was observed when participants were informed about the regularity, but also when they were unaware of it. Here, we for the first time, show implicit learning of temporal behavior in a complex environment.

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Section: Implicit Learning Of Temporal Behavior 3 Implicit Learning Omentioning

confidence: 97%

Implicit Learning of Temporal Behavior in Complex Dynamic Environments

Salet

Kruijne

Rijn

2020

Preprint

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“…The proposed framework posits a specific role for subcortical reward signals in the shaping of attentional priority. In this regard, value‐driven attention might differ from other influences of selection history on attention, such as intertrial priming, statistical regularities, and status as a former target (under conditions without explicit reward) . However, to the extent that successful identification of a target generates an internal reward signal, similar learning principles might apply in these circumstances.…”

Section: Some Limitations and Outstanding Issuesmentioning

confidence: 99%

The attention habit: how reward learning shapes attentional selection

Anderson

2015

Ann. N.Y. Acad. Sci.

Self Cite

344

337

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There is growing consensus that reward plays an important role in the control of attention. Until recently, reward was thought to influence attention indirectly by modulating task-specific motivation and its effects on voluntary control over selection. Such an account was consistent with the goal-directed (endogenous) versus stimulus-driven (exogenous) framework that had long dominated the field of attention research. Now, a different perspective is emerging. Demonstrations that previously reward-associated stimuli can automatically capture attention even when physically inconspicuous and task-irrelevant challenge previously held assumptions about attentional control. The idea that attentional selection can be value driven, reflecting a distinct and previously unrecognized control mechanism, has gained traction. Since these early demonstrations, the influence of reward learning on attention has rapidly become an area of intense investigation, sparking many new insights. The result is an emerging picture of how the reward system of the brain automatically biases information processing. Here, I review the progress that has been made in this area, synthesizing a wealth of recent evidence to provide an integrated, up-to-date account of value-driven attention and some of its broader implications.

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“…Importantly, recent studies have extended these findings of “context-control learning” to the case of cognitive flexibility. For example, it has been shown that switch costs can be reduced for stimuli that are presented at a screen location associated with a higher likelihood of task switches (relative to repetitions), even when people are unaware of this contingency (Crump & Logan, 2010; for a similar observation in attention shifting, see Sali, Anderson, & Yantis, 2015). In a similar vein, Farooqui and Manly (2015) demonstrated that subliminally presented (i.e., not consciously perceived) cues signaling a higher likelihood of task switches were followed by smaller task switch costs.…”

Section: Cognitive Flexibility Can Be Triggered By Contextual Cuesmentioning

confidence: 99%

Getting a Grip on Cognitive Flexibility

Braem

Egner

2018

Curr Dir Psychol Sci

188

129

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Cognitive flexibility refers to the ability to quickly reconfigure our mind, like when we switch between different tasks. This review highlights recent evidence showing that cognitive flexibility can be conditioned by simple incentives typically known to drive lower-level learning, such as stimulus-response associations. Cognitive flexibility can also become associated with, and triggered by, bottom-up contextual cues in our environment, including subliminal cues. Therefore, we suggest that the control functions that mediate cognitive flexibility are grounded in, and guided by, basic associative learning mechanisms, and abide by the same learning principles as more low-level forms of behavior. Such a learning perspective on cognitive flexibility offers new directions and important implications for further research, theory, and applications.

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Learned states of preparatory attentional control.

Cited by 22 publications

References 82 publications

Implicit Learning of Temporal Behavior in Complex Dynamic Environments

Implicit Learning of Temporal Behavior in Complex Dynamic Environments

The attention habit: how reward learning shapes attentional selection

Getting a Grip on Cognitive Flexibility

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