Finding Pattern in the Noise: Persistent Implicit Statistical Knowledge Impacts the Processing of Unpredictable Stimuli.

Humans can extract statistical regularities of the environment to predict upcoming events. Previous research recognized that implicitly acquired statistical knowledge remained persistent and continued to influence behavior even when the regularities were no longer present in the environment. Here, in an fMRI experiment, we investigated how the persistence of statistical knowledge is represented in the brain. Participants (n = 32) completed a visual, four-choice, RT task consisting of statistical regularities. Two types of blocks constantly alternated with one another throughout the task: predictable statistical regularities in one block type and unpredictable ones in the other. Participants were unaware of the statistical regularities and their changing distribution across the blocks. Yet, they acquired the statistical regularities and showed significant statistical knowledge at the behavioral level not only in the predictable blocks but also in the unpredictable ones, albeit to a smaller extent. Brain activity in a range of cortical and subcortical areas, including early visual cortex, the insula, the right inferior frontal gyrus, and the right globus pallidus/putamen contributed to the acquisition of statistical regularities. The right insula, inferior frontal gyrus, and hippocampus as well as the bilateral angular gyrus seemed to play a role in maintaining this statistical knowledge. The results altogether suggest that statistical knowledge could be exploited in a relevant, predictable context as well as transmitted to and retrieved in an irrelevant context without a predictable structure.

The relationship between frontal alpha asymmetry and behavioral and brain activity indices of reactive inhibitory control.

Reactive inhibitory control plays an important role in phenotype of different diseases/different phases of a disease. One candidate electrophysiological marker of inhibitory control is frontal alpha asymmetry (FAA). FAA reflects the relative difference in contralateral frontal brain activity. However, the relationship between FAA and potential behavioral/brain activity indices of reactive inhibitory control is not yet clear. We assessed the relationship between resting-state FAA and indicators of reactive inhibitory control. Additionally, we investigated the effect of modulation of FAA via transcranial direct current stimulation (tDCS). We implemented a randomized sham-controlled design with 65 healthy humans (Mage = 23.93, SDage = 6.08; 46 female). Before and after 2-mA anodal tDCS of the right frontal site (with the cathode at the contralateral site) for 20 min, we collected EEG data and reactive inhibitory performance in neutral and food-reward conditions, using the stop signal task (SST). There was no support for the effect of tDCS on FAA or any indices of reactive inhibitory control. Our correlation analysis revealed an association between inhibitory brain activity in the food-reward condition and (pre-tDCS) asymmetry. Higher right relative to left frontal brain activity was correlated with reduced early-onset inhibitory activity and, in contrast, linked with higher late-onset inhibitory control in the food-reward condition. Similarly, event-related potential analyses showed reduced early-onset and enhanced late-onset inhibitory brain activity over time, particularly in the food-reward condition. These results suggest that there can be a dissociation regarding the lateralization of frontal brain activity and early- and late-onset inhibitory brain activity.NEW & NOTEWORTHY This research reveals dissociation between baseline frontal alpha asymmetry and the timing of reactive inhibitory brain activities in food-reward contexts. Whereas inhibitory control performance decreases over time in a stop signal task, electrophysiological indices show reduced early- and heightened late-onset inhibitory brain activity, especially in the reward condition. Additionally, greater right frontal activity correlates with reduced early-onset and increased late-onset inhibitory brain activity.

Neural representations of statistical and rule-based predictions in Gilles de la Tourette syndrome.

Gilles de la Tourette syndrome (GTS) is a disorder characterised by motor and vocal tics, which may represent habitual actions as a result of enhanced learning of associations between stimuli and responses (S-R). In this study, we investigated how adults with GTS and healthy controls (HC) learn two types of regularities in a sequence: statistics (non-adjacent probabilities) and rules (predefined order). Participants completed a visuomotor sequence learning task while EEG was recorded. To understand the neurophysiological underpinnings of these regularities in GTS, multivariate pattern analyses on the temporally decomposed EEG signal as well as sLORETA source localisation method were conducted. We found that people with GTS showed superior statistical learning but comparable rule-based learning compared to HC participants. Adults with GTS had different neural representations for both statistics and rules than HC adults; specifically, adults with GTS maintained the regularity representations longer and had more overlap between them than HCs. Moreover, over different time scales, distinct fronto-parietal structures contribute to statistical learning in the GTS and HC groups. We propose that hyper-learning in GTS is a consequence of the altered sensitivity to encode complex statistics, which might lead to habitual actions.

Chunking as a function of sequence length.

Chunking mechanisms are central to several cognitive processes. During the acquisition of visuo-motor sequences, it is commonly reported that these sequences are segmented into chunks leading to more fluid, rapid, and accurate performances. The question of a chunk's storage capacity has been often investigated but little is known about the dynamics of chunk size evolution relative to sequence length. In two experiments, we studied the dynamics and the evolution of a sequence's chunking pattern as a function of sequence length in a non-human primate species (Guinea baboons, Papio papio). Using an operant conditioning device, baboons had to point on a touch screen to a moving target. In Experiment 1, they had to produce repeatedly the same sequence of 4 movements during 2000 trials. In Experiment 2, the sequence was composed of 5 movements and was repeated 4000 times. For both lengths, baboons initially produced small chunks that became fewer and longer with practice. Moreover, the dynamics and the evolution of the chunking pattern varied as a function of sequence length. Finally, with extended practice (i.e., more than 2000 trials), we observed that the mean chunk size reached a plateau indicating that there are fundamental limits to chunking processes that also depend on sequence length. These data therefore provide new empirical evidence for understanding the general properties of chunking mechanisms in sequence learning.

Modality-specific and modality-independent neural representations work in concert in predictive processes during sequence learning.

Probabilistic sequence learning supports the development of skills and enables predictive processing. It remains contentious whether visuomotor sequence learning is driven by the representation of the visual sequence (perceptual coding) or by the representation of the response sequence (motor coding). Neurotypical adults performed a visuomotor sequence learning task. Learning occurred incidentally as it was evidenced by faster responses to high-probability than to low-probability targets. To uncover the neurophysiology of the learning process, we conducted both univariate analyses and multivariate pattern analyses (MVPAs) on the temporally decomposed EEG signal. Univariate analyses showed that sequence learning modulated the amplitudes of the motor code of the decomposed signal but not in the perceptual and perceptual-motor signals. However, MVPA revealed that all 3 codes of the decomposed EEG contribute to the neurophysiological representation of the learnt probabilities. Source localization revealed the involvement of a wider network of frontal and parietal activations that were distinctive across coding levels. These findings suggest that perceptual and motor coding both contribute to the learning of sequential regularities rather than to a neither-nor distinction. Moreover, modality-specific encoding worked in concert with modality-independent representations, which suggests that probabilistic sequence learning is nonunitary and encompasses a set of encoding principles.

Autistic and schizotypal traits exhibit similarities in their impact on mentalization and adult attachment impairments: a cross-sectional study.

BACKGROUND: Deficits in mentalizing and attachment occur in the autism and schizophrenia spectrum, and their extended traits in the general population. Parental attachment and the broader social environment highly influence the development of mentalizing. Given the similarities in the symptomatology and neurodevelopmental correlates of autism spectrum disorder (ASD) and schizophrenia (SCH), it is crucial to identify their overlaps and differences to support screening, differential diagnosis, and intervention. METHODS: This cross-sectional study utilized questionnaire data from 2203 adults (65.1% female, mean age[SD] = 37.98[9.66]), including participants diagnosed with ASD, SCH, and those exhibiting subclinical traits to investigate the associations between mentalizing, attachment, and perceived social support during adolescence across the autistic and schizotypy spectrum. RESULTS: It was revealed that both autistic and schizotypal traits have comparable effects on insecure adult attachment, primarily through challenges in mentalizing. The impact of mentalizing deficits on adult attachment slightly varies between autistic and schizotypal traits. Conversely, perceived social support during adolescence relates to improved mentalizing and secure adult attachment as a protective factor during development. CONCLUSIONS: These outcomes highlight the significance of supportive therapeutic relationships and community care while suggesting directions for further research and collaborative treatments addressing ASD and SCH, considering the differential impact of mentalizing on adult attachment.

The complexity of measuring reliability in learning tasks: An illustration using the Alternating Serial Reaction Time Task.

Despite the fact that reliability estimation is crucial for robust inference, it is underutilized in neuroscience and cognitive psychology. Appreciating reliability can help researchers increase statistical power, effect sizes, and reproducibility, decrease the impact of measurement error, and inform methodological choices. However, accurately calculating reliability for many experimental learning tasks is challenging. In this study, we highlight a number of these issues, and estimate multiple metrics of internal consistency and split-half reliability of a widely used learning task on a large sample of 180 subjects. We show how pre-processing choices, task length, and sample size can affect reliability and its estimation. Our results show that the Alternating Serial Reaction Time Task has respectable reliability, especially when learning scores are calculated based on reaction times and two-stage averaging. We also show that a task length of 25 blocks can be sufficient to meet the usual thresholds for minimally acceptable reliability. We further illustrate how relying on a single point estimate of reliability can be misleading, and the calculation of multiple metrics, along with their uncertainties, can lead to a more complete characterization of the psychometric properties of tasks.

Nomen est omen: Serial reaction time task is not a motor but a visuomotor learning task.

The serial reaction time task is a widely used task in behavioural and cognitive neuroscience to assess human and animal learning. Many publications refer to this task as a 'motor learning task', but it is also a perceptual learning task. We emphasize here that the incorrect use of the term 'motor learning' misleads researchers and medical doctors by emphasizing the motor cortex's exclusive role. It has the potential to lead to the misinterpretation of neuroscientific, neuroimaging and clinical studies. The domino effect has the potential to generate more flawed hypotheses and theories.

Tracking human skill learning with a hierarchical Bayesian sequence model.

Humans can implicitly learn complex perceptuo-motor skills over the course of large numbers of trials. This likely depends on our becoming better able to take advantage of ever richer and temporally deeper predictive relationships in the environment. Here, we offer a novel characterization of this process, fitting a non-parametric, hierarchical Bayesian sequence model to the reaction times of human participants' responses over ten sessions, each comprising thousands of trials, in a serial reaction time task involving higher-order dependencies. The model, adapted from the domain of language, forgetfully updates trial-by-trial, and seamlessly combines predictive information from shorter and longer windows onto past events, weighing the windows proportionally to their predictive power. As the model implies a posterior over window depths, we were able to determine how, and how many, previous sequence elements influenced individual participants' internal predictions, and how this changed with practice. Already in the first session, the model showed that participants had begun to rely on two previous elements (i.e., trigrams), thereby successfully adapting to the most prominent higher-order structure in the task. The extent to which local statistical fluctuations in trigram frequency influenced participants' responses waned over subsequent sessions, as participants forgot the trigrams less and evidenced skilled performance. By the eighth session, a subset of participants shifted their prior further to consider a context deeper than two previous elements. Finally, participants showed resistance to interference and slow forgetting of the old sequence when it was changed in the final sessions. Model parameters for individual participants covaried appropriately with independent measures of working memory and error characteristics. In sum, the model offers the first principled account of the adaptive complexity and nuanced dynamics of humans' internal sequence representations during long-term implicit skill learning.

Tracking the contribution of inductive bias to individualised internal models.

Internal models capture the regularities of the environment and are central to understanding how humans adapt to environmental statistics. In general, the correct internal model is unknown to observers, instead they rely on an approximate model that is continually adapted throughout learning. However, experimenters assume an ideal observer model, which captures stimulus structure but ignores the diverging hypotheses that humans form during learning. We combine non-parametric Bayesian methods and probabilistic programming to infer rich and dynamic individualised internal models from response times. We demonstrate that the approach is capable of characterizing the discrepancy between the internal model maintained by individuals and the ideal observer model and to track the evolution of the contribution of the ideal observer model to the internal model throughout training. In particular, in an implicit visuomotor sequence learning task the identified discrepancy revealed an inductive bias that was consistent across individuals but varied in strength and persistence.

Working memory performance in disordered gambling and gaming: A systematic review.

BACKGROUND: Converging evidence supports that gaming and gambling disorders are associated with executive dysfunction. The involvement of different components of executive functions (EF) in these forms of behavioural addiction is unclear. AIM: In a systematic review, we aim to uncover the association between working memory (WM), a crucial component of EF, and disordered gaming and gambling. Note that, in the context of this review, gaming has been used synonymously with video gaming. METHODS: Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), we systematically searched for studies published from 2012 onwards. RESULTS: The search yielded 6081 records after removing duplicates, from which 17 peer-reviewed journal articles were eligible for inclusion. The association between WM and problem or disordered gaming and gambling have been categorized separately to observe possible differences. Essentially, problem gaming or gambling, compared to disorder, presents lesser severity and clinical significance. The results demonstrate reduced auditory-verbal WM in individuals with gambling disorder. Decreased WM capacity was also associated with problem gambling, with a correlation between problem gambling severity and decreased WM capacity. Similarly, gaming disorder was associated with decreased WM. Specifically, gaming disorder patients had lower WM capacity than the healthy controls. CONCLUSION: Working memory seems to be a significant predictor of gambling and gaming disorders. Therefore, holistic treatment approaches that incorporate cognitive techniques that could enhance working memory may significantly boost gambling and gaming disorders treatment success.

Reduced functional connectivity supports statistical learning of temporally distributed regularities.

Statistical learning is a powerful ability that extracts regularities from our environment and makes predictions about future events. Using functional magnetic resonance imaging, we aimed to probe how a wide range of brain areas are intertwined to support statistical learning, characterising its architecture in the whole-brain functional connectivity (FC). Participants performed a statistical learning task of temporally distributed regularities. We used refined behavioural learning scores to associate individuals' learning performances with the FC changed by statistical learning. As a result, the learning performance was mediated by the activation strength in the lateral occipital cortex, angular gyrus, precuneus, anterior cingulate cortex, and superior frontal gyrus. Through a group independent component analysis, activations of the superior frontal network showed the largest correlation with the statistical learning performances. Seed-to-voxel whole-brain and seed-to-ROI FC analyses revealed that the FC between the superior frontal gyrus and the salience, language, and dorsal attention networks were reduced during statistical learning. We suggest that the weakened functional connections between the superior frontal gyrus and brain regions involved in top-down control processes serve a pivotal role in statistical learning, supporting better processing of novel information such as the extraction of new patterns from the environment.

Lower-level associations in Gilles de la Tourette syndrome: Convergence between hyperbinding of stimulus and response features and procedural hyperfunctioning theories.

Gilles de la Tourette syndrome (GTS) can be characterized by enhanced cognitive functions related to creating, modifying and maintaining connections between stimuli and responses (S-R links). Specifically, two areas, procedural sequence learning and, as a novel finding, also event file binding, show converging evidence of hyperfunctioning in GTS. In this review, we describe how these two enhanced functions can be considered as cognitive mechanisms behind habitual behaviour, such as tics in GTS. Moreover, the presence of both procedural sequence learning and event file binding hyperfunctioning in the same disorder can be treated as evidence for their functional connections, even beyond GTS. Importantly though, we argue that hyperfunctioning of event file binding and procedural learning are not interchangeable: they have different time scales, different sensitivities to potential impairment in action sequencing and distinguishable contributions to the cognitive profile of GTS. An integrated theoretical account of hyperbinding and hyperlearning in GTS allows to formulate predictions for the emergence, activation and long-term persistence of tics in GTS.

Neurophysiological and functional neuroanatomical coding of statistical and deterministic rule information during sequence learning.

Humans are capable of acquiring multiple types of information presented in the same information stream. It has been suggested that at least two parallel learning processes are important during learning of sequential patterns-statistical learning and rule-based learning. Yet, the neurophysiological underpinnings of these parallel learning processes are not fully understood. To differentiate between the simultaneous mechanisms at the single trial level, we apply a temporal EEG signal decomposition approach together with sLORETA source localization method to delineate whether distinct statistical and rule-based learning codes can be distinguished in EEG data and can be related to distinct functional neuroanatomical structures. We demonstrate that concomitant but distinct aspects of information coded in the N2 time window play a role in these mechanisms: mismatch detection and response control underlie statistical learning and rule-based learning, respectively, albeit with different levels of time-sensitivity. Moreover, the effects of the two learning mechanisms in the different temporally decomposed clusters of neural activity also differed from each other in neural sources. Importantly, the right inferior frontal cortex (BA44) was specifically implicated in visuomotor statistical learning, confirming its role in the acquisition of transitional probabilities. In contrast, visuomotor rule-based learning was associated with the prefrontal gyrus (BA6). The results show how simultaneous learning mechanisms operate at the neurophysiological level and are orchestrated by distinct prefrontal cortical areas. The current findings deepen our understanding on the mechanisms of how humans are capable of learning multiple types of information from the same stimulus stream in a parallel fashion.

[Transcranial direct current stimulation in neuroscientific researches - pitfalls and solutions]. / Transcranialis egyenáram-stimuláció a kognitív idegtudományban ­ buktatók és megoldások.

Transcranial direct current stimulation (tDCS) is a promising brain stimulation tool which is non-invasive, easy to use and relatively cheap. Since it can change brain activity in a temporal manner, it can contribute to both clinical practice and neuroscientific research. However, the effectiveness of tDCS has been questioned considering the lack of full understanding of its mechanism of action and the seemingly contradictory results. In this review, we aim to provide a summary of potential problems and possible solutions. Our main focus is on the inter-individual differences in the effect of tDCS which can explain the noisy data, thus, controlling for them is important in order to show reliable results. This review is hoped to contribute to maximizing the potential of tDCS by helping future researchers to design replicable studies.

[Cognitive advantage in atypical development: enhanced procedural learning in Tourette syndrome].

Tourette syndrome is a neurodevelopmental disorder that is characterized by motor and vocal tics and by alterations in the cortico-basal ganglia-thalamo-cortical (CBGTC) circuitry. The CBGTC circuitry plays an important role in procedural learning, in the acquisition of skills and habits. Tics and habits are similar phenomenologically since tics can be described as overlearned habits. Based on these characteristics, prior studies proposed enhanced pro - ce dural learning, i.e., procedural hyperfunctioning in Tourette syndrome. A growing body of evidence supports this notion. The focus of the present review article is to discuss procedural hyperfunctioning in Tourette syndrome. We aim to shed light on a cognitive advantage in Tourette syndrome and to draw attention to the notion that pathologies and developmental disorder can be characterized not only with impairments and cognitive dysfunctions but with enhanced functions as well.

Tracking the implicit acquisition of nonadjacent transitional probabilities by ERPs.

The implicit acquisition of complex probabilistic regularities has been found to be crucial in numerous automatized cognitive abilities, including language processing and associative learning. However, it has not been completely elucidated how the implicit extraction of second-order nonadjacent transitional probabilities is reflected by neurophysiological processes. Therefore, this study investigated the sensitivity of event-related brain potentials (ERPs) to these probabilistic regularities embedded in a sequence of visual stimuli without providing explicit information on the structure of the stimulus stream. Healthy young adults (N = 32) performed a four-choice RT task that included a sequential regularity between nonadjacent trials yielding a complex transitional probability structure. ERPs were measured relative to both stimulus and response onset. RTs indicated the rapid acquisition of the sequential regularity and the transitional probabilities. The acquisition process was also tracked by the stimulus-locked and response-locked P3 component: The P3 peak was larger for the sequence than for the random stimuli, while the late P3 was larger for less probable than for more probable short-range relations among the random stimuli. According to the RT and P3 effects, sensitivity to the sequential regularity is assumed to be supported by the initial sensitivity to the transitional probabilities. These results suggest that stimulus-response contingencies on the probabilistic regularities of the ongoing stimulus context are implicitly mapped and constantly revised. Overall, this study (1) highlights the role of predictive processes during implicit memory formation, and (2) delineates a potential to gain further insight into the dynamics of implicit acquisition processes.

[Changes of cognitive functions in healthy aging]. / A kognitív képességek változásai idoskorban.

BACKGROUND AND PURPOSE: Introduction - Mental health has crucial role in our life. Cognitive changes or decline can lead to many difficulties in daily routine of older people (e.g. organization of daily activities), which can, consequently, influence their well-being. Therefore it is an important question, which cognitive abilities are affected by age-related decline. METHODS: In our study we aimed to investigate the changes of cognitive abilities in healthy older adults between 61 and 85 years of age compared to the performance of younger adults. Digit span, counting span, listening span, letter fluency, semantic fluency and action fluency tests were used to assess cognitive abilities, namely working memory and executive functions. RESULTS: The results showed that younger adults performed significantly better in all tests than older adults. Importantly, the performance of older adults was better on tests requiring less complex mental computations (e.g. digit span test) than on more complex tests where both storing and mani-pulating information was required (e.g., counting span test). We also showed that within the older age group, cognitive functions' decline was linearly associated with increasing age. CONCLUSION: The present study used several, well-established neuropsychological tests to map the changes of working memory and executive functions in healthy older adults between 61 and 85 years of age compared to younger adults. Our findings can contribute to the development of prevention programs aimed at improving the quality of life of older adults and preventing age-related cognitive decline.

[Age-related and gender differences in conslidation of implicit sequence learning between 7 and 29 years of age].

INTRODUCTION: Extracting and acquiring sequential regularities from the environment is a fundamental human ability that underlies the acquisition of various motor, social and cognitive skills. As psychiatric rehabilitation often depends on the integrity of these skills, it is crucial to understand individual differences in sequence learning. Here we aimed to test age- and gender-related differences in sequence learning as well as in the consolidation of the acquired knowledge. METHODS: In the present study we aimed to characterize age-related and gender differences in the consolidation of implicitly acquired sequential memories between 7 and 29 years of age (N = 261). Participants were clustered into six age groups. The Alternating Serial Reaction Time (ASRT) task was used to measure implicit sequence learning. Participants were retested 24 hours after the learning phase. RESULTS: In the learning phase, implicit sequence learning showed a gradually declining pattern across age groups with children exhibiting the best learning performance, which is consistent with previous studies. Regarding consolidation, we found retention of implicit sequential memories in all age groups, with no age-related differences. We found no gender differences in the acquisition of sequential memories but gender differences emerged after the consolidation: male participants showed somewhat better performance in terms of accuracy compared to the female participants. CONCLUSION: Our study explores implicit sequence learning and consolidation in a relatively wide age range and can contribute to the development and testing of alternative methods in age-specific psychiatric rehabilitation.

Dynamics of EEG functional connectivity during statistical learning.

Statistical learning is a fundamental mechanism of the brain, which extracts and represents regularities of our environment. Statistical learning is crucial in predictive processing, and in the acquisition of perceptual, motor, cognitive, and social skills. Although previous studies have revealed competitive neurocognitive processes underlying statistical learning, the neural communication of the related brain regions (functional connectivity, FC) has not yet been investigated. The present study aimed to fill this gap by investigating FC networks that promote statistical learning in humans. Young adults (N=28) performed a statistical learning task while 128-channels EEG was acquired. The task involved probabilistic sequences, which enabled to measure incidental/implicit learning of conditional probabilities. Phase synchronization in seven frequency bands was used to quantify FC between cortical regions during the first, second, and third periods of the learning task, respectively. Here we show that statistical learning is negatively correlated with FC of the anterior brain regions in slow (theta) and fast (beta) oscillations. These negative correlations increased as the learning progressed. Our findings provide evidence that dynamic antagonist brain networks serve a hallmark of statistical learning.