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.

Evidence for a competitive relationship between executive functions and statistical learning.

The ability of the brain to extract patterns from the environment and predict future events, known as statistical learning, has been proposed to interact in a competitive manner with prefrontal lobe-related networks and their characteristic cognitive or executive functions. However, it remains unclear whether these cognitive functions also possess a competitive relationship with implicit statistical learning across individuals and at the level of latent executive function components. In order to address this currently unknown aspect, we investigated, in two independent experiments (NStudy1 = 186, NStudy2 = 157), the relationship between implicit statistical learning, measured by the Alternating Serial Reaction Time task, and executive functions, measured by multiple neuropsychological tests. In both studies, a modest, but consistent negative correlation between implicit statistical learning and most executive function measures was observed. Factor analysis further revealed that a factor representing verbal fluency and complex working memory seemed to drive these negative correlations. Thus, the antagonistic relationship between implicit statistical learning and executive functions might specifically be mediated by the updating component of executive functions or/and long-term memory access.

Interpersonal Distance Theory of Autism and Its Implication for Cognitive Assessment, Therapy, and Daily Life.

The interpersonal distance (IPD) theory provides a novel approach to studying autism spectrum disorder (ASD). In this article, we present recent findings on the neurobiological underpinnings of IPD regulation that are distinct in individuals with ASD. We also discuss the potential influence of environmental factors on IPD. We suggest that different IPD regulation may have implications for cognitive performance in experimental and diagnostic settings, may influence the effectiveness of training and therapy, and may play a role in the typical forms of social communication and leisure activities chosen by autistic individuals. We argue that reconsidering the results of ASD research through the lens of IPD would lead to a different interpretation of previous findings. Finally, we propose a methodological approach to study this phenomenon systematically.

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.

Intact predictive processing in autistic adults: evidence from statistical learning.

Impairment in predictive processes gained a lot of attention in recent years as an explanation for autistic symptoms. However, empirical evidence does not always underpin this framework. Thus, it is unclear what aspects of predictive processing are affected in autism spectrum disorder. In this study, we tested autistic adults on a task in which participants acquire probability-based regularities (that is, a statistical learning task). Twenty neurotypical and 22 autistic adults learned a probabilistic, temporally distributed regularity for about 40 min. Using frequentist and Bayesian methods, we found that autistic adults performed comparably to neurotypical adults, and the dynamics of learning did not differ between groups either. Thus, our study provides evidence for intact statistical learning in autistic adults. Furthermore, we discuss potential ways this result can extend the scope of the predictive processing framework, noting that atypical processing might not always mean a deficit in performance.

Lifespan developmental invariance in memory consolidation: evidence from procedural memory.

Characterizing ontogenetic changes across the lifespan is a crucial tool in understanding neurocognitive functions. While age-related changes in learning and memory functions have been extensively characterized in the past decades, the lifespan trajectory of memory consolidation, a critical function that supports the stabilization and long-term retention of memories, is still poorly understood. Here we focus on this fundamental cognitive function and probe the consolidation of procedural memories that underlie cognitive, motor, and social skills and automatic behaviors. We used a lifespan approach: 255 participants aged between 7 and 76 years performed a well-established procedural memory task in the same experimental design across the whole sample. This task enabled us to disentangle two critical processes in the procedural domain: statistical learning and general skill learning. The former is the ability to extract and learn predictable patterns of the environment, while the latter captures a general speed-up as learning progresses due to improved visuomotor coordination and other cognitive processes, independent of acquisition of the predictable patterns. To measure the consolidation of statistical and general skill knowledge, the task was administered in two sessions with a 24-h delay between them. Here, we report successful retention of statistical knowledge with no differences across age groups. For general skill knowledge, offline improvement was observed over the delay period, and the degree of this improvement was also comparable across the age groups. Overall, our findings reveal age invariance in these two key aspects of procedural memory consolidation across the human lifespan.

Altered interpersonal distance regulation in autism spectrum disorder.

Interpersonal distance regulation is an essential element of social communication. Its impairment in autism spectrum disorder (ASD) is widely acknowledged among practitioners, but only a handful of studies reported empirical research in real-life settings, focusing mainly on children. Interpersonal distance in adults with ASD and related autonomic functions received less attention. Here, we measured interpersonal distance along with heart rate variability (HRV) in adults with ASD, and tested the modulatory effects of eye-contact and attribution. Twenty-two adults diagnosed with ASD and 21 matched neurotypical controls participated in our study from October 2019 to February 2020. Our experimental design combined the modified version of the stop distance paradigm with HRV measurement controlling for eye contact between the experimenter and the participant to measure interpersonal distance. Still, we did not detect significant modulatory effect of eye contact and attribution. Our results showed a greater preferred distance in ASD. Moreover, we found lower baseline HRV and reduced HRV reactivity in ASD; however, these autonomic measurements could not predict preferred interpersonal distance. Our study highlights the importance of interpersonal space regulation in ASD: it might be considered that people with ASD need individually variable, presumably greater interpersonal distance. In addition, regardless of the distance they may have reduced autonomic regulatory capacity in social situations. Our results could help shape future experiments with sophisticated designs to grasp the complexity and underlying factors of distance regulation in typical and atypical populations.

Deterministic and probabilistic regularities underlying risky choices are acquired in a changing decision context.

Predictions supporting risky decisions could become unreliable when outcome probabilities temporarily change, making adaptation more challenging. Therefore, this study investigated whether sensitivity to the temporal structure in outcome probabilities can develop and remain persistent in a changing decision environment. In a variant of the Balloon Analogue Risk Task with 90 balloons, outcomes (rewards or balloon bursts) were predictable in the task's first and final 30 balloons and unpredictable in the middle 30 balloons. The temporal regularity underlying the predictable outcomes differed across three experimental conditions. In the deterministic condition, a repeating three-element sequence dictated the maximum number of pumps before a balloon burst. In the probabilistic condition, a single probabilistic regularity ensured that burst probability increased as a function of pumps. In the hybrid condition, a repeating sequence of three different probabilistic regularities increased burst probabilities. In every condition, the regularity was absent in the middle 30 balloons. Participants were not informed about the presence or absence of the regularity. Sensitivity to both the deterministic and hybrid regularities emerged and influenced risk taking. Unpredictable outcomes of the middle phase did not deteriorate this sensitivity. In conclusion, humans can adapt their risky choices in a changing decision environment by exploiting the statistical structure that controls how the environment changes.

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.

Do temporal factors affect whether our performance accurately reflects our underlying knowledge? The effects of stimulus presentation rates on the performance versus competence dissociation.

Ample evidence shows that the momentary performance can dissociate from the underlying knowledge (competence). Under what circumstances such dissociation occurs, however, remains unclear. Here we tested how temporal factors, and more specifically, the elapsed time between subsequent events affects the dissociation between performance and competence by systematically manipulating the stimulus presentation rates during and after learning. Participants completed a probabilistic sequence learning task with a fast (120 msec) or a slow (850 msec) response-to-stimulus-interval (RSI) during the Learning phase and they were tested with both RSIs 24 h later (Testing phase). We also tested whether they gained explicit knowledge about the sequence or their knowledge remained implicit. Our results revealed higher reaction time learning scores when tested with the fast RSI, irrespective of the RSI during learning, suggesting that faster presentation rates can help better express the acquired knowledge, leading to increased performance measures. For accuracy, participants showed higher learning scores when tested with the same presentation rate as the one that they encountered during learning. The acquired knowledge remained implicit in both groups, suggesting that the observed findings were not confounded by differences in awareness gained in the two groups. Overall, our study highlights that the momentary performance does not always accurately reflect the underlying knowledge, and temporal factors seem to influence this dissociation. Our findings have theoretical, methodological, and translational implications that likely extend beyond learning and memory to other functions and domains as well, including aspects of decision-making, perception, theory of mind, and language.

Speed and accuracy instructions affect two aspects of skill learning differently.

Procedural learning is key to optimal skill learning and is essential for functioning in everyday life. The findings of previous studies are contradictory regarding whether procedural learning can be modified by prioritizing speed or accuracy during learning. The conflicting results may be due to the fact that procedural learning is a multifaceted cognitive function. The purpose of our study is to determine whether and how speed and accuracy instructions affect two aspects of procedural learning: the learning of probability-based and serial-order-based regularities. Two groups of healthy individuals were instructed to practice on a cued probabilistic sequence learning task: one group focused on being fast and the other on being accurate during the learning phase. The speed instruction resulted in enhanced expression of probability-based but not serial-order-based knowledge. After a retention period, we instructed the participants to focus on speed and accuracy equally, and we tested their acquired knowledge. The acquired knowledge was comparable between groups in both types of learning. These findings suggest that different aspects of procedural learning can be affected differently by instructions. However, only momentary performance might be boosted by speed instruction; the acquired knowledge remains intact. In addition, as the accuracy instruction resulted in accuracy near ceiling level, the results illustrate that response errors are not needed for humans to learn in the procedural domain and draw attention to the fact that different instructions can separate competence from performance.

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.

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.

Inhibitory control hinders habit change.

Our habits constantly influence the environment, often in negative ways that amplify global environmental and health risks. Hence, change is urgent. To facilitate habit change, inhibiting unwanted behaviors appears to be a natural human reaction. Here, we use a novel experimental design to test how inhibitory control affects two key components of changing (rewiring) habit-like behaviors in healthy humans: the acquisition of new habit-like behavior and the simultaneous unlearning of an old one. We found that, while the new behavior was acquired, the old behavior persisted and coexisted with the new. Critically, inhibition hindered both overcoming the old behavior and establishing the new one. Our findings highlight that suppressing unwanted behaviors is not only ineffective but may even further strengthen them. Meanwhile, actively engaging in a preferred behavior appears indispensable for its successful acquisition. Our design could be used to uncover how new approaches affect the cognitive basis of changing habit-like behaviors.

Subcortical Cognition: The Fruit Below the Rind.

Cognitive neuroscience has highlighted the cerebral cortex while often overlooking subcortical structures. This cortical proclivity is found in basic and translational research on many aspects of cognition, especially higher cognitive domains such as language, reading, music, and math. We suggest that, for both anatomical and evolutionary reasons, multiple subcortical structures play substantial roles across higher and lower cognition. We present a comprehensive review of existing evidence, which indeed reveals extensive subcortical contributions in multiple cognitive domains. We argue that the findings are overall both real and important. Next, we advance a theoretical framework to capture the nature of (sub)cortical contributions to cognition. Finally, we propose how new subcortical cognitive roles can be identified by leveraging anatomical and evolutionary principles, and we describe specific methods that can be used to reveal subcortical cognition. Altogether, this review aims to advance cognitive neuroscience by highlighting subcortical cognition and facilitating its future investigation.

Cautious or causal? Key implicit sequence learning paradigms should not be overlooked when assessing the role of DLPFC (Commentary on Prutean et al.).

The role of the dorsolateral prefrontal cortex (DLPFC) in implicit sequence/statistical learning has received considerable attention in recent cognitive neuroscience research. Studies have used non-invasive brain stimulation methods to test whether the DLPFC plays a role in the incidental acquisition and expression of implicit sequence learning. In a recent study, Prutean et al. has concluded that stimulating the left or the right DLPFC might not affect the expression of implicit sequence learning measured by the Serial Reaction Time (SRT) task. The authors speculated that the previous results revealing improved implicit sequence learning following DLPFC stimulation might have been found because explicit awareness accumulated with the use of Alternating Serial Reaction Time (ASRT) task. Our response presents solid evidence that the ASRT task measures implicit sequence learning that remains unconscious both at the judgment and structural level. Therefore, contrary to the conclusion of Prutean et al., we argue that the DLPFC could have a crucial effect on implicit sequence learning that may be task-dependent. We suggest that future research should focus on the specific cognitive processes that may be differentially involved in the SRT versus ASRT tasks, and test what the role of the DLPFC is in those specific cognitive processes.

A Process-Oriented View of Procedural Memory Can Help Better Understand Tourette's Syndrome.

Tourette's syndrome (TS) is a neurodevelopmental disorder characterized by repetitive movements and vocalizations, also known as tics. The phenomenology of tics and the underlying neurobiology of the disorder have suggested that the altered functioning of the procedural memory system might contribute to its etiology. However, contrary to the robust findings of impaired procedural memory in neurodevelopmental disorders of language, results from TS have been somewhat mixed. We review the previous studies in the field and note that they have reported normal, impaired, and even enhanced procedural performance. These mixed findings may be at least partially be explained by the diversity of the samples in both age and tic severity, the vast array of tasks used, the low sample sizes, and the possible confounding effects of other cognitive functions, such as executive functions, working memory or attention. However, we propose that another often overlooked factor could also contribute to the mixed findings, namely the multiprocess nature of the procedural system itself. We propose that a process-oriented view of procedural memory functions could serve as a theoretical framework to help integrate these varied findings. We discuss evidence suggesting heterogeneity in the neural regions and their functional contributions to procedural memory. Our process-oriented framework can help to deepen our understanding of the complex profile of procedural functioning in TS and atypical development in general.

Access to Procedural Memories After One Year: Evidence for Robust Memory Consolidation in Tourette Syndrome.

Tourette syndrome is a childhood-onset neurodevelopmental disorder characterized by motor and vocal tics. On the neural level, tics are thought to be related to the disturbances of the cortico-basal ganglia-thalamo-cortical loops, which also play an important role in procedural learning. Several studies have investigated the acquisition of procedural information and the access to established procedural information in TS. Based on these, the notion of procedural hyperfunctioning, i.e., enhanced procedural learning, has been proposed. However, one neglected area is the retention of acquired procedural information, especially following a long-term offline period. Here, we investigated the 5-hour and 1-year consolidation of two aspects of procedural memory, namely serial-order and probability-based information. Nineteen children with TS between the ages of 10 and 15 as well as 19 typically developing gender- and age-matched controls were tested on a visuomotor four-choice reaction time task that enables the simultaneous assessment of the two aspects. They were retested on the same task 5 hours and 1 year later without any practice in the offline periods. Both groups successfully acquired and retained the probability-based information both when tested 5 hours and then 1 year later, with comparable performance between the TS and control groups. Children with TS did not acquire the serial-order information during the learning phase; hence, retention could not be reliably tested. Our study showed evidence for short-term and long-term retention of one aspect of procedural memory, namely probability-based information in TS, whereas learning of serial-order information might be impaired in this disorder.

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

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.