Biology subjects

Janacsek, K.

Publications and source records attributed to Janacsek, K..

6 recordsLinked to original sources

bioRxiv2018

Explicit instruction differentially affects subcomponents of procedural learning and consolidation

Procedural memory facilitates the efficient processing of complex environmental stimuli and contributes to the acquisition of automatic behaviours and habits. Learning can occur intentionally or incidentally, yet, how the mode of learning affects procedural memory is still poorly understood. Importantly, procedural memory is a complex cognitive function composed of different subprocesses, including the acquisition and consolidation of statistical, frequency-based and sequential, order-based knowledge. Therefore, we tested how statistical and sequence knowledge develops during incidental versus intentional procedural memory formation and during consolidation. Seventy-four young adults performed either the uncued, incidental (N = 37) or the cued, intentional (N = 37) version of a probabilistic sequence learning task. Performance was retested after a 12-hour offline period, enabling us to test the effect of sleep on consolidation; therefore, half of the participants slept during the delay, while the other half had normal daily activity (PM-AM versus AM-PM design). The mode of learning (incidental versus intentional) had no effect on the acquisition of statistical knowledge, while intention to learn increased sequence learning performance. Consolidation was not affected by intention to learn: Both statistical and sequence knowledge was retained over the 12-hour delay, irrespective of the mode of learning and whether the delay included sleep or wake activity. These results suggest a time-dependent instead of sleep-dependent consolidation of both statistical and sequence knowledge. Our findings could contribute to a better understanding of how the mode of learning (intentional or incidental) affects procedural memory formation and consolidation.

neuroscience↗

bioRxiv2018

Does subjective sleep quality predict cognitive performance? - Evidence from three empirical studies

The role of sleep in cognitive performance has gained increasing attention in neuroscience and sleep research in the recent decades, however, the relationship between subjective (self-reported) sleep quality and cognitive performance has not yet been comprehensively characterized. In this paper, our aim was to test the relationship between subjective sleep quality and a wide range of cognitive functions in a healthy young adult sample combined across three studies. Sleep quality was assessed by Pittsburgh Sleep Quality Index, Athens Insomnia Scale, and a sleep diary to capture general subjective sleep quality, and Groningen Sleep Quality Scale to capture prior nights sleep quality. Within cognitive functions, we tested working memory, executive functions, and several sub-processes of procedural learning. To provide more reliable results, we included robust frequentist and Bayesian statistical analyses as well. Unequivocally across all analyses, we showed that there is no association between subjective sleep quality and cognitive performance in the domain of working memory, executive functions and procedural learning in healthy young adults. Our paper can contribute to a deeper understanding of subjective sleep quality and its measures, and we discuss various factors that may affect whether associations can be observed between subjective sleep quality and cognitive performance.

neuroscience↗

bioRxiv2017

Deconstructing Procedural Memory: Different Learning Trajectories and Consolidation of Sequence and Statistical Learning

Procedural learning is a fundamental cognitive function that facilitates efficient processing of and automatic responses to complex environmental stimuli. Here, we examined training-dependent and off-line changes of two sub-processes of procedural learning: namely, sequence learning and statistical learning. Whereas sequence learning requires the acquisition of order-based relationships between the elements of a sequence, statistical learning is based on the acquisition of probabilistic associations between elements. Seventy-eight healthy young adults (58 females and 20 males) completed the modified version of the Alternating Serial Reaction Time task that was designed to measure Sequence and Statistical Learning simultaneously. After training, participants were randomly assigned to one of three conditions: active wakefulness, quiet rest, or daytime sleep. We examined off-line changes in Sequence and Statistical Learning as well as further improvements after extended practice. Performance in Sequence Learning increased during training, while Statistical Learning plateaued relatively rapidly. After the off-line period, both the acquired sequence and statistical knowledge was preserved, irrespective of the vigilance state (awake, quiet rest or sleep). Sequence Learning further improved during extended practice, while Statistical Learning did not. Moreover, within the sleep group, cortical oscillations and sleep spindle parameters showed differential associations with Sequence and Statistical Learning. Our findings can contribute to a deeper understanding of the dynamic changes of multiple parallel learning and consolidation processes that occur during procedural memory formation.

neuroscience↗

bioRxiv2017

Do adolescents take more risks? Not when facing a novel uncertain situation

In real-life decision-making, sub-optimal risk-taking seems characteristic of adolescents. Such behavior increases the chance of serious negative, and at times, irreversible outcomes for this population (e.g., road traffic accidents, addictions). We are still lacking conclusive evidence, however, for an inverted U-shaped developmental trajectory for risk-taking. This raises the question whether adolescents are really more risk-prone or when facing a novel risky situation, they behave just as children and adults do. To answer this question, we used the Balloon Analogue Risk Task (BART) to assess the risky decision making of 188 individuals ranging in age from 7 to 30. The BART provided useful data for characterizing multiple aspects of risk-taking. Surprisingly, we found that adolescents were not more inclined to take risks than children or young adults. Participants in all age groups were able to adapt their learning processes to the probabilistic environment and improve their performance during the sequential risky choice. There were no age-related differences in risk-taking at any stage of the task. Likewise, neither negative feedback reactivity nor overall task performance distinguished adolescents from the younger and older age groups. Our findings prompt 1) methodological considerations about the validity of the BART and 2) theoretical debate whether the amount of experience on its own may account for age-related changes in real-life risk-taking, since risk-taking in a novel and uncertain situation was invariant across developmental stages.

neuroscience↗

bioRxiv2017

When Less is More: Enhanced Statistical Learning After Disruption of Bilateral DLPFC

Brain networks related to human learning can interact in cooperative but also competitive ways to optimize performance. The investigation of such interactive processes is rare in research on learning and memory. Previous studies have shown that manipulations reducing the engagement of prefrontal cortical areas could lead to improved statistical learning performance. However, no study has investigated how disruption of the dorsolateral prefrontal cortex (DLPFC) affects the acquisition and consolidation of non-adjacent second-order dependencies. The present study aimed to test the role of the DLPFC, more specifically, the Brodmann 9 area in implicit temporal statistical learning of non-adjacent dependencies. We applied 1 Hz inhibitory transcranial magnetic stimulation or sham stimulation over both the left and right DLPFC intermittently during the learning. The DLPFC-stimulated group showed better performance compared to the sham group after a 24-hour consolidation period. This finding suggests that the disruption of DLPFC during learning induces qualitative changes in the consolidation of non-adjacent statistical regularities. A possible mechanism behind this result is that the stimulation of the DLPFC promotes a shift to model-free learning by weakening the access to model-based processes.

neuroscience↗

bioRxiv2017

Sensitivity to sequential regularities in risky decision making

Probabilistic sequence learning involves a set of robust mechanisms that enable the extraction of statistical patterns embedded in the environment. It contributes to different perceptual and cognitive processes as well as to effective behavior adaptation, which is a crucial aspect of decision making. Although previous research attempted to model reinforcement learning and reward sensitivity in different risky decision-making paradigms, the basic mechanism of the sensitivity to statistical regularities has not been anchored to external tasks. Therefore, the present study aimed to investigate the statistical learning mechanism underlying individual differences in risky decision making. To reach this goal, we tested whether implicit probabilistic sequence learning and risky decision making share common variance. To have a more complex characterization of individual differences in risky decision making, hierarchical cluster analysis was conducted on performance data obtained in the Balloon Analogue Risk Task (BART) in a large sample of healthy young adults. Implicit probabilistic sequence learning was measured by the Alternating Serial Reaction Time (ASRT) task. According to the results, a four-cluster structure was identified involving average risk-taking, slowly responding, risk-taker, and risk-averse groups of participants, respectively. While the entire sample showed significant learning on the ASRT task, we found greater sensitivity to statistical regularities in the risk-taker and risk-averse groups than in participants with average risk-taking. These findings revealed common mechanisms in risky decision making and implicit probabilistic sequence learning and an adaptive aspect of higher risk taking on the BART. Our results could help to clarify the neurocognitive complexity of decision making and its individual differences.

neuroscience↗