Divided attention does not affect the acquisition and consolidation of transitional probabilities

Horváth, Klára; Török, Cs.; Pesthy, Orsolya; Németh, Dezső; Janacsek, Karolina

doi:10.1038/s41598-020-79232-y

Cited by 21 publications

(13 citation statements)

References 79 publications

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“…In contrast, high-order rule learning could need more attentional resources and consequently occurs only between practice periods. It has indeed been shown during sequence learning that simple transitions 33 , 35 – 37 , but not more complex structures 38 , could be learned under attentional distraction.…”

Section: Discussionmentioning

confidence: 98%

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Statistical learning occurs during practice while high-order rule learning during rest period

et al. 2021

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Knowing when the brain learns is crucial for both the comprehension of memory formation and consolidation and for developing new training and neurorehabilitation strategies in healthy and patient populations. Recently, a rapid form of offline learning developing during short rest periods has been shown to account for most of procedural learning, leading to the hypothesis that the brain mainly learns during rest between practice periods. Nonetheless, procedural learning has several subcomponents not disentangled in previous studies investigating learning dynamics, such as acquiring the statistical regularities of the task, or else the high-order rules that regulate its organization. Here we analyzed 506 behavioral sessions of implicit visuomotor deterministic and probabilistic sequence learning tasks, allowing the distinction between general skill learning, statistical learning, and high-order rule learning. Our results show that the temporal dynamics of apparently simultaneous learning processes differ. While high-order rule learning is acquired offline, statistical learning is evidenced online. These findings open new avenues on the short-scale temporal dynamics of learning and memory consolidation and reveal a fundamental distinction between statistical and high-order rule learning, the former benefiting from online evidence accumulation and the latter requiring short rest periods for rapid consolidation.

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Section: Discussionmentioning

confidence: 98%

“…These results suggest that statistical learning benefits from evidence accumulation developing during practice and does not consolidate but decays during rest periods. This observation may explain why no evidence for offline consolidation of statistical learning was found during 12-h sleep or awake periods 30 – 33 .…”

Section: Discussionmentioning

confidence: 99%

Statistical learning occurs during practice while high-order rule learning during rest period

et al. 2021

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“…These RT effects could further increase in ASRT tasks where the acquisition of probabilities is intentional (Nemeth, Janacsek, & Fiser, 2013;Simor et al, 2019;Tóth-Fáber, Janacsek, Szőllősi, Kéri, & Németh, 2020). Meanwhile, in fixed-paced tasks (Kóbor et al, 2020;Tóth et al, 2017) and in those with slow stimulus presentation rate (i.e., long ITI, see Kiss, Nemeth, & Janacsek, 2019;Kóbor et al, 2019), RT effects have been found to be reduced (but see Horváth, Török, Pesthy, Nemeth, & Janacsek, 2020). The present task was fixed-paced, the length of the ITI was longer than previously yielding a slow rate of stimulus presentation, and acquisition occurred incidentally.…”

Section: Interpretation Of Behavioral Resultsmentioning

confidence: 52%

Implicit anticipation of probabilistic regularities: Larger CNV emerges for unpredictable events

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Horváth

et al. 2020

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Anticipation of upcoming events plays a crucial role in automatic behaviors. It is, however, still unclear whether the event-related brain potential (ERP) markers of anticipation could track the implicit acquisition of probabilistic regularities that can be considered as building blocks of automatic behaviors. Therefore, in a four-choice reaction time (RT) task performed by young adults (N = 36), the contingent negative variation (CNV) as an ERP marker of anticipation was measured from the onset of an uninformative warning signal until the presentation of the target stimulus. Due to the probability structure of the task, target stimuli were either predictable or unpredictable, but this was unknown to participants. Results showed that the CNV amplitude during response preparation was larger before the unpredictable than before the predictable stimuli. In addition, although RTs increased, the P3 amplitude decreased for the unpredictable as compared with the predictable stimuli, possibly due to the stronger response preparation that preceded stimulus presentation. These results suggest that enhanced attentional resources are allocated to the implicit anticipation and processing of unpredictable events. This possibly results from the formation of internal models on the probabilistic regularities of the stimulus stream, favoring predictable events. Overall, we provide ERP evidence for the implicit anticipation of probabilistic regularities, confirming the role of predictive processes in learning and memory.

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“…To control for the differences in average RTs, we transformed the data in the following way. We divided each participants’ raw RT values of each trial type and each epoch by their own average performance of the first epoch of the task [for a similar approach, see 56 , 57 ], then we multiplied these values by 100 for easier presentation and interpretability. Participants’ performance was around 100 at the beginning of the task and changed as the task progressed.…”

Section: Resultsmentioning

confidence: 99%

“…As typical response times are under 500 ms, the fast pace of the task makes it unlikely that such explicit knowledge would have an impact on participants’ RTs. Therefore, the consciously accessible knowledge of the deterministic serial order of the pattern stimuli (as measured by the post-block sequence report score) and learning of serial-order information (as measured by the RTs) seem to be dissociable measures [ 57 ]. The post-block sequence report may be considered as a more explicit measure of the knowledge on the deterministic serial order of pattern stimuli, while the learning RT scores may serve as an implicit measure of serial-order learning, even in an intentional learning condition.…”

Section: Discussionmentioning

confidence: 99%

Regularity detection under stress: Faster extraction of probability-based regularities

et al. 2021

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Acute stress can crucially influence learning and memory processes. One of the key processes underlying human learning and memory is the ability of our brain to rapidly detect and extract regularities from sensory input across time and space leading to effective predictive processing. Here, we aimed to get an in-depth look into the effect of stress on the acquisition of two aspects of regularity extraction. We examined whether and how stress affects the learning (1) of probability-based regularities and (2) of serial order-based regularities in the same experimental design, and (3) the explicit access to the acquired information. Considering that the acquisition of probability-based regularities is a relatively rapid process, we primarily focused on the early phase of the task. We induced stress with the Socially Evaluated Cold Pressor Test in 27 young adults, while 26 participants were enrolled in the control group. Salivary cortisol levels and subjective ratings of affective states showed successful stress induction. After the stress induction, we measured regularity extraction with the cued Alternating Serial Reaction Time task. We found that stress promoted the extraction of probability-based regularities measured by the learning performance in the early phase of the task and did not alter the learning of serial order-based regularities. Post-block reports showed weaker explicit access to the serial order-based regularities in the stress group. Our results can contribute to a process-level understanding on how stress alters learning and memory functions related to predictive processes.

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Divided attention does not affect the acquisition and consolidation of transitional probabilities

Cited by 21 publications

References 79 publications

Statistical learning occurs during practice while high-order rule learning during rest period

Statistical learning occurs during practice while high-order rule learning during rest period

Implicit anticipation of probabilistic regularities: Larger CNV emerges for unpredictable events

Regularity detection under stress: Faster extraction of probability-based regularities

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