Statistical learning: a powerful mechanism that operates by mere exposure

The order of stimuli matters when learning second-order transitional probabilities

The order of stimuli within sequences and the transitional probabilities (TPs) it generates are central information in sequence processing. However, less is known about what type of information and how it is extracted by general learning mechanisms. The present study focused on statistical learning of second-order TPs. Second-order TPs are involved when only the combination of two stimuli predicts the third. In a first experiment, TPs depended crucially on the order of presentation of a pair A - B , which led to different predictions depending on the order of the stimuli (i.e., ABC vs. BAF). Eight visuomotor sequences governed by second-order TPs were used and response times (RTs) were recorded for each transition. The task included a learning phase followed by a switch phase during which the second-order TP were reversed (e.g., the sequences ABC and BAF became respectively ABF and BAC). A decrease of RTs between the second and the third stimulus during the learning phase and an increase of RTs during the switch phase suggested that variations of orders within second-order TPs could be learned. Further analyses, however, indicated that such learning was difficult for most participants. A second experiment showed that the difficulty of learning was not solely due to the difficulty to pick up the effect of order of presentation, but that learning second-order transitional probabilities in addition to order would be the main obstacle. These experiments suggest that statistical learning is capable of learning complex associations, even if this remains a challenge for human cognition.

Mind Wandering during Implicit Learning Is Associated with Increased Periodic EEG Activity and Improved Extraction of Hidden Probabilistic Patterns

Mind wandering, occupying 30-50% of our waking time, remains an enigmatic phenomenon in cognitive neuroscience. A large number of studies showed a negative association between mind wandering and attention-demanding (model-based) tasks in both natural settings and laboratory conditions. Mind wandering, however, does not seem to be detrimental for all cognitive domains and was observed to benefit creativity and problem-solving. We examined if mind wandering may facilitate model-free processes, such as probabilistic learning, which relies on the automatic acquisition of statistical regularities with minimal attentional demands. We administered a well-established implicit probabilistic learning task combined with thought probes in healthy adults (N = 37, 30 females). To explore the neural correlates of mind wandering and probabilistic learning, participants were fitted with high-density electroencephalography. Our findings indicate that probabilistic learning was not only immune to periods of mind wandering but was positively associated with it. Spontaneous, as opposed to deliberate mind wandering, was particularly beneficial for extracting the probabilistic patterns hidden in the visual stream. Cortical oscillatory activity in the low-frequency (slow and delta) range, indicative of covert sleep-like states, was associated with both mind wandering and improved probabilistic learning, particularly in the early stages of the task. Given the importance of probabilistic implicit learning in predictive processing, our findings provide novel insights into the potential cognitive benefits of task-unrelated thoughts in addition to shedding light on its neural mechanisms.

Visual Statistical Learning Alters Low-Dimensional Cortical Architecture

Our brains are in a constant state of generating predictions, implicitly extracting environmental regularities to support later cognition and behavior, a process known as statistical learning (SL). While prior work investigating the neural basis of SL has focused on the activity of single brain regions in isolation, much less is known about how distributed brain areas coordinate their activity to support such learning. Using fMRI and a classic visual SL task, we investigated changes in whole-brain functional architecture as human female and male participants implicitly learned to associate pairs of images, and later, when predictions generated from learning were violated. By projecting individuals' patterns of cortical and subcortical functional connectivity onto a low-dimensional manifold space, we found that SL was associated with changes along a single neural dimension describing covariance across the visual-parietal and perirhinal cortex (PRC). During learning, we found regions within the visual cortex expanded along this dimension, reflecting their decreased communication with other networks, whereas regions within the dorsal attention network (DAN) contracted, reflecting their increased connectivity with higher-order cortex. Notably, when SL was interrupted, we found the PRC and entorhinal cortex, which did not initially show learning-related effects, now contracted along this dimension, reflecting their increased connectivity with the default mode and DAN, and decreased covariance with visual cortex. While prior research has linked SL to either broad cortical or medial temporal lobe changes, our findings suggest an integrative view, whereby cortical regions reorganize during association formation, while medial temporal lobe regions respond to their violation.

Individual differences in probabilistic learning and updating predictive representations in individuals with obsessive-compulsive tendencies

BACKGROUND

Obsessive-compulsive (OC) tendencies involve intrusive thoughts and rigid, repetitive behaviours that also manifest at the subclinical level in the general population. The neurocognitive factors driving the development and persistence of the excessive presence of these tendencies remain highly elusive, though emerging theories emphasize the role of implicit information processing. Despite various empirical studies on distinct neurocognitive processes, the incidental retrieval of environmental structures in dynamic and noisy environments, such as probabilistic learning, has received relatively little attention.

METHODS

In this study, we aimed to unravel potential individual differences in implicit probabilistic learning and the updating of predictive representations related to OC tendencies in a non-clinical population. We conducted two independent online experiments (NStudy1 = 164, NStudy2 = 256) with university students. Probabilistic learning was assessed using an implicit visuomotor probabilistic learning task, involving sequences with second-order non-adjacent dependencies.

RESULTS

Our findings revealed that implicit probabilistic learning remained remarkably robust among OC tendencies within a non-clinical population. Furthermore, the results highlighted effective updating capabilities of predictive representations, which were not influenced by OC tendencies.

CONCLUSIONS

These results offer new insights into individual differences in probabilistic learning and updating in relation to OC tendencies, contributing to theoretical, methodological, and practical approaches for understanding the maladaptive behavioural manifestations of OC disorder and subclinical tendencies.

Sensitivity to Graphotactic Regularities in Elementary School: Development and Contributing Variables

PURPOSE

Graphotactic regularities are statistical regularities governing orthographic systems that children are sensitive to from the start of their literacy learning. The current study observed changes in children's sensitivity to a set of graphotactic patterns across different grades in elementary school and measured the contribution of skills such as expressive spelling, reading fluency, nonverbal reasoning, and receptive vocabulary to children's sensitivity of these graphotactic regularities.

METHOD

One thousand one hundred one French-speaking children in Grades 1-5 completed a writing under a dictation task, a text reading fluency task, and a pseudo-orthographic choice task involving different graphotactic regularities. These regularities fell into two categories: legal versus illegal, which defines the legality of letter strings in French, and frequent versus less frequent, which refers to acceptable letter strings that vary in frequency of occurrence either at the beginning or end of a word.

RESULTS

The results of a repeated-measures analysis of variance showed a developmental difference between graphotactic regularity categories. The frequent versus infrequent patterns developed faster than the legal versus illegal patterns until reaching a point of equivalence in Grade 3. At Grades 4 and 5, legal versus illegal graphotactic regularities progressed more quickly while frequent versus less frequent regularities progressed more slowly. Furthermore, generalized linear mixed-model analyses for both types of graphotactic regularities revealed that they were affected by grade, expressive spelling, reading fluency level, and nonverbal reasoning.

CONCLUSIONS

This study provides evidence of developmental differences in sensitivity to graphotactic regularities according to the type of regularity studied. Reading fluency and expressive spelling skills contribute to graphotactic regularity sensitivity. Higher scores in expressive spelling, reading fluency, nonverbal reasoning, and an older age were related to higher skills in identifying legal versus illegal graphotactic regularities. Such findings can contribute to educational, clinical, and research applications.

The size and shape of sound: The role of articulation and acoustics in iconicity and crossmodal correspondencesa)

Onomatopoeias like hiss and peep are iconic because their forms resemble their meanings. Iconicity can also involve forms and meanings in different modalities, such as when people match the nonce words bouba and kiki to round and angular objects, and mil and mal to small and large ones, also known as "sound symbolism." This paper focuses on what specific analogies motivate such correspondences in spoken language: do people associate shapes and size with how phonemes sound (auditory), or how they are produced (articulatory)? Based on a synthesis of empirical evidence probing the cognitive mechanisms underlying different types of sound symbolism, this paper argues that analogies based on acoustics alone are often sufficient, rendering extant articulatory explanations for many iconic phenomena superfluous. This paper further suggests that different types of crossmodal iconicity in spoken language can fruitfully be understood as an extension of onomatopoeia: when speakers iconically depict such perceptual characteristics as size and shape, they mimic the acoustics that are correlated with these characteristics in the natural world.

Frontal Deactivation and the Efficacy of Statistical Learning: Neural Mechanisms Accompanying Exposure to Visual Statistical Sequences

Statistical learning is the cognitive ability to rapidly identify structure and meaning in unfamiliar streams of sensory experience, even in the absence of feedback. Despite extensive studies, the neurocognitive mechanisms underlying this phenomenon still require further clarification under varying cognitive conditions. Here, we examined neural mechanisms during the first exposure to visually presented sequences in 47 healthy participants. We used two types of visual objects: abstract symbols and pictures of cartoon-like animals. This allowed us to compare informational processing mechanisms with defined distinguishing features. Participants achieved better performance for sequences with easy-to-name than difficult-to-name abstract stimuli. fMRI results revealed greater activation in widespread brain regions in response to random versus statistical sequences for all stimuli types. Behavioral accuracy was associated with increased deactivation of the ventromedial PFC for easy-to-name statistical versus random sequences. For difficult-to-name statistical versus random sequences, performance correlated with dorsomedial prefrontal cortex deactivation. ROI analysis showed a generally positive involvement of the caudate head in sequence processing with significantly stronger activity during the first run of performing the task. Functional connectivity analysis of prefrontal deactivation regions revealed significant connectivity with nodes of the salience network for both object types and inverse connectivity with the caudate head only for easy-to-name objects. The results indicated that distinct subregions of PFC modulate task performance depending on the visual stimulus characteristic. They also showed that among striatal regions, only the head of the caudate was sensitive to initial exposure to visual statistical information.

Pupillometric signature of implicit learning of statistical regularities

Animals learn about the statistical regularities of their environment by a process of implicit learning, a powerful mechanism that may operate by mere exposure.1 Implicit learning supports processes such as speech acquisition but also learning about the spatial and temporal structure of the world more generally, which is essential for effective interaction.2 Here, we used a frequency-tagging technique to demonstrate a pupillometric signature of the learning of the temporal structure (pairing of numerosities) of sequential arrays. Although the numerosity pairings were unnoticed by all participants, the pupil responded clearly to their repetition frequency (1 Hz). Pupillometry allowed us to track the learning as it unfolded (the response became significant after less than 3 min of passive viewing), without ever directing attention to the temporal structure of the stimuli. Diverting attention away from the numerosity feature did not prevent learning, but it did affect the dynamics of the response acquisition. A clear pupillometric response was also elicited by pairing dyads of digits. In all our stimuli, the local features were randomized, implying that learning successfully generalized across stimuli that were locally different and only acquired a temporal structure once their global statistics (overall shape or numerosity) were extracted.

How do multiple meanings affect word learning and remapping?

Many words have multiple meanings. It is currently unclear how a word with more than one meaning is acquired or stored long-term. One possibility is that each word's meaning is less robustly encoded; this, in turn, may make it easier to acquire additional meanings. We investigated this hypothesis across three cross-situational statistical learning experiments (N1 = 62 (in-lab), N2 = 61 (online), N3 = 60 (online)), using the same general procedure: During Learning Phase 1 (LP1), participants acquired both 1:1 (each word has one meaning) and 1:2 (each word has two meanings) mappings. In Learning Phase 2 (LP2), each word received one new meaning. Across experiments, we manipulated the frequency of 1:1 and 1:2 mappings. As expected, 1:2 mappings were always harder to acquire in LP1. However, in the LP2 of Experiment 1, former 1:1 mappings were remapped more easily than former 1:2 ones, while 1:2 mappings were remapped more easily in Experiments 2 and 3. These data suggest that words with multiple meanings are more easily remapped, indicating that each meaning is less robustly associated with the word, which in turn may facilitate the acquisition of an additional meaning. However, the observed inconsistencies in the remapping results between in-lab and online experiments suggest that CSWL may differ across these two settings.

Age-related contextual cueing features are more evident in reaction variability than in reaction time

Visual-spatial contextual cueing learning underpins the daily lives of older adults, enabling them to navigate their surroundings, perform daily activities, and maintain cognitive function. While the contextual cueing effect has received increasing attention from researchers, the relationship between this cognitive ability and healthy ageing remains controversial. To investigate whether visual-spatial contextual cueing learning declines with age, we examined the contextual learning patterns of older (60-71 years old) and younger adults (18-26 years old) using a contextual-guided visual search paradigm and response variability measurements. We observed significant contextual learning effects in both age groups, impacting response speed and variability, with these effects persisting for at least 24 days. However, older adults required more repetitions and memorised fewer repeated stimuli during initial learning. Interestingly, their long-term memory maintenance appeared stronger, as their contextual facilitation persisted in both response speed and variability, while younger adults only persisted in response speed but not variability. Overall, our results suggest an age-related complex and diverse contextual cueing pattern, with older adults showing weaker learning but stronger long-term memory maintenance compared with younger adults.

Taking time: Auditory statistical learning benefits from distributed exposure

In an auditory statistical learning paradigm, listeners learn to partition a continuous stream of syllables by discovering the repeating syllable patterns that constitute the speech stream. Here, we ask whether auditory statistical learning benefits from spaced exposure compared with massed exposure. In a longitudinal online study on Prolific, we exposed 100 participants to the regularities in a spaced way (i.e., with exposure blocks spread out over 3 days) and another 100 in a massed way (i.e., with all exposure blocks lumped together on a single day). In the exposure phase, participants listened to streams composed of pairs while responding to a target syllable. The spaced and massed groups exhibited equal learning during exposure, as indicated by a comparable response-time advantage for predictable target syllables. However, in terms of resulting long-term knowledge, we observed a benefit from spaced exposure. Following a 2-week delay period, we tested participants' knowledge of the pairs in a forced-choice test. While both groups performed above chance, the spaced group had higher accuracy. Our findings speak to the importance of the timing of exposure to structured input and also for statistical learning outside of the laboratory (e.g., in language development), and imply that current investigations of auditory statistical learning likely underestimate human statistical learning abilities.

Early Caregiver Predictability Shapes Neural Indices of Statistical Learning Later in Infancy

Caregivers play an outsized role in shaping early life experiences and development, but we often lack mechanistic insight into how exactly caregiver behavior scaffolds the neurodevelopment of specific learning processes. Here, we capitalized on the fact that caregivers differ in how predictable their behavior is to ask if infants' early environmental input shapes their brains' later ability to learn about predictable information. As part of an ongoing longitudinal study in South Africa, we recorded naturalistic, dyadic interactions between 103 (46 females and 57 males) infants and their primary caregivers at 3-6 months of age, from which we calculated the predictability of caregivers' behavior, following caregiver vocalization and overall. When the same infants were 6-12-months-old they participated in an auditory statistical learning task during EEG. We found evidence of learning-related change in infants' neural responses to predictable information during the statistical learning task. The magnitude of statistical learning-related change in infants' EEG responses was associated with the predictability of their caregiver's vocalizations several months earlier, such that infants with more predictable caregiver vocalization patterns showed more evidence of statistical learning later in the first year of life. These results suggest that early experiences with caregiver predictability influence learning, providing support for the hypothesis that the neurodevelopment of core learning and memory systems is closely tied to infants' experiences during key developmental windows.

From behavioral synchrony to language and beyond

Decades of research on joint attention, coordinated joint engagement, and social contingency identify caregiver-child interaction in infancy as a foundation for language. These patterns of early behavioral synchrony contribute to the structure and connectivity of the brain in the temporoparietal regions typically associated with language skills. Thus, children attune to their communication partner and subsequently build cognitive skills directly relating to comprehension and production of language, literacy skills, and beyond. This has yielded marked interest in measuring this contingent, synchronous social behavior neurally. Neurological measures of early social interactions between caregiver and child have become a hotbed for research. In this paper, we review that research and suggest that these early neural couplings between adults and children lay the foundation for a broader cognitive system that includes attention, problem solving, and executive function skills. This review describes the role of behavioral synchrony in language development, asks what the relationship is between neural synchrony and language growth, and how neural synchrony may play a role in the development of a broader cognitive system founded in a socially-gated brain. We address the known neural correlates of these processes with an emphasis on work that examines the tight temporal contingency between communicative partners during these rich social interactions, with a focus on EEG and fNIRS and brief survey of MRI and MEG.

The dynamics of multiword sequence extraction

Being able to process multiword sequences is central for both language comprehension and production. Numerous studies support this claim, but less is known about the way multiword sequences are acquired, and more specifically how associations between their constituents are established over time. Here we adapted the Hebb naming task into a Hebb lexical decision task to study the dynamics of multiword sequence extraction. Participants had to read letter strings presented on a computer screen and were required to classify them as words or pseudowords. Unknown to the participants, a triplet of words or pseudowords systematically appeared in the same order and random words or pseudowords were inserted between two repetitions of the triplet. We found that response times (RTs) for the unpredictable first position in the triplet decreased over repetitions (i.e., indicating the presence of a repetition effect) but more slowly and with a different dynamic compared with items appearing at the predictable second and third positions in the repeated triplet (i.e., showing a slightly different predictability effect). Implicit and explicit learning also varied as a function of the nature of the triplet (i.e., unrelated words, pseudowords, semantically related words, or idioms). Overall, these results provide new empirical evidence about the dynamics of multiword sequence extraction, and more generally about the role of statistical learning in language acquisition.

Perception and Production of Pitch Information in Mandarin-Speaking Children with Autism Spectrum Disorders

This study investigated the categorical perception (CP) of linguistic pitch (lexical tones) and nonlinguistic pitch (pure tones), as well as tonal production in Mandarin-speaking children with autism spectrum disorders (ASD). A total of 26 Mandarin-speaking children with ASD and 29 age-matched typically developing (TD) children were recruited for this study. The Mandarin T2-T3 contrast and corresponding pure tones with identical pitch contours were adopted to assess the nuanced pitch processing abilities of the child participants via the CP paradigm. Accordingly, tonal production was focused on T2 and T3 with analyses of the dynamic pitch contours and tonal differentiation. Mandarin-speaking children with ASD exhibited atypical CP for linguistic pitch in comparison with their TD peers. However, the categorization of linguistic pitch exceeded that of nonlinguistic pitch among the ASD participants, indicating a global over local processing pattern contrary to autistic individuals in non-tonal languages. Additionally, despite atypical pitch contours in producing T2 and T3, the ASD group showed comparable differentiable degrees of the two tones in production to the TD group. Findings of this study served as a foray into contesting current theories' claims of local bias and/or global impairment in the autistic population, prompting further inspections on individuals with different language backgrounds and stimuli processing with various complexities. Additionally, findings of this study underscore the necessity of developing tailored assessments and interventions to enhance the perception and production of complex and confusable tones, thereby improving perceptual robustness and communication skills in Mandarin-speaking children with ASD.

Are Late Talkers Just Late? Neighborhood Density and Word Frequency Properties of Late Talkers' Spoken Vocabularies

PURPOSE

Typically developing toddlers extract patterns from their input to add words to their spoken lexicons, yet some evidence suggests that late talkers leverage the statistical regularities of the ambient language differently than do peers. Using the extended statistical learning account, we sought to compare lexical-level statistical features of spoken vocabularies between late talkers and two typically developing comparison groups.

METHOD

MacArthur-Bates Communicative Developmental Inventories American English Words and Sentences (N = 1,636) were extracted from Wordbank, a database of CDIs. Inventories were divided into three groups: (a) a late talker group (n = 202); (b) a typically developing age-matched group (n = 1,238); and (c) a younger, typically developing group (n = 196) matched to the late talkers on expressive language. Neighborhood density and word frequency were calculated for each word produced by each participant and standardized to z scores. Mixed-effects models were used to evaluate group differences.

RESULTS

The late talker and younger, language-matched groups' spoken vocabularies consist, on standardized average, of words from denser phonological neighborhoods and words higher in frequency of occurrence in parent-child speech, compared to older, typically developing toddlers.

CONCLUSIONS

These findings provide support for the extended statistical learning account. Late talkers appear to generally be extracting and using similar patterns from their language input as do younger toddlers with similar levels of expressive vocabulary. This suggests that late talkers may be following a delayed, not deviant, trajectory of expressive language growth.

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.

Investigating the Effect of Contextual Cueing with Face Stimuli on Electrophysiological Measures in Younger and Older Adults

Extracting repeated patterns from our surroundings plays a crucial role in contextualizing information, making predictions, and guiding our behavior implicitly. Previous research showed that contextual cueing enhances visual search performance in younger adults. In this study, we investigated whether contextual cueing could also improve older adults' performance and whether age-related differences in the neural processes underlying implicit contextual learning could be detected. Twenty-four younger and 25 older participants performed a visual search task with contextual cueing. Contextual information was generated using repeated face configurations alongside random new configurations. We measured RT difference between new and repeated configurations; ERPs to uncover the neural processes underlying contextual cueing for early (N2pc), intermediate (P3b), and late (r-LRP) processes; and multiscale entropy and spectral power density analyses to examine neural dynamics. Both younger and older adults showed similar contextual cueing benefits in their visual search efficiency at the behavioral level. In addition, they showed similar patterns regarding contextual information processing: Repeated face configurations evoked decreased finer timescale entropy (1-20 msec) and higher frequency band power (13-30 Hz) compared with new configurations. However, we detected age-related differences in ERPs: Younger, but not older adults, had larger N2pc and P3b components for repeated compared with new configurations. These results suggest that contextual cueing remains intact with aging. Although attention- and target-evaluation-related ERPs differed between the age groups, the neural dynamics of contextual learning were preserved with aging, as both age groups increasingly utilized more globally grouped representations for repeated face configurations during the learning process.

Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT

As a disease with high morbidity and high mortality, lung cancer has seriously harmed people's health. Therefore, early diagnosis and treatment are more important. PET/CT is usually used to obtain the early diagnosis, staging, and curative effect evaluation of tumors, especially lung cancer, due to the heterogeneity of tumors and the differences in artificial image interpretation and other reasons, it also fails to entirely reflect the real situation of tumors. Artificial intelligence (AI) has been applied to all aspects of life. Machine learning (ML) is one of the important ways to realize AI. With the help of the ML method used by PET/CT imaging technology, there are many studies in the diagnosis and treatment of lung cancer. This article summarizes the application progress of ML based on PET/CT in lung cancer, in order to better serve the clinical. In this study, we searched PubMed using machine learning, lung cancer, and PET/CT as keywords to find relevant articles in the past 5 years or more. We found that PET/CT-based ML approaches have achieved significant results in the detection, delineation, classification of pathology, molecular subtyping, staging, and response assessment with survival and prognosis of lung cancer, which can provide clinicians a powerful tool to support and assist in critical daily clinical decisions. However, ML has some shortcomings such as slightly poor repeatability and reliability.

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.

Resting network architecture of theta oscillations reflects hyper-learning of sensorimotor information in Gilles de la Tourette syndrome

Gilles de la Tourette syndrome is a neurodevelopmental disorder characterized by motor and vocal tics. It is associated with enhanced processing of stimulus-response associations, including a higher propensity to learn probabilistic stimulus-response contingencies (i.e. statistical learning), the nature of which is still elusive. In this study, we investigated the hypothesis that resting-state theta network organization is a key for the understanding of superior statistical learning in these patients. We investigated the graph-theoretical network architecture of theta oscillations in adult patients with Gilles de la Tourette syndrome and healthy controls during a statistical learning task and in resting states both before and after learning. We found that patients with Gilles de la Tourette syndrome showed a higher statistical learning score than healthy controls, as well as a more optimal (small-world-like) theta network before the task. Thus, patients with Gilles de la Tourette syndrome had a superior facility to integrate and evaluate novel information as a trait-like characteristic. Additionally, the theta network architecture in Gilles de la Tourette syndrome adapted more to the statistical information during the task than in HC. We suggest that hyper-learning in patients with Gilles de la Tourette syndrome is likely a consequence of increased sensitivity to perceive and integrate sensorimotor information leveraged through theta oscillation-based resting-state dynamics. The study delineates the neural basis of a higher propensity in patients with Gilles de la Tourette syndrome to pick up statistical contingencies in their environment. Moreover, the study emphasizes pathophysiologically endowed abilities in patients with Gilles de la Tourette syndrome, which are often not taken into account in the perception of this common disorder but could play an important role in destigmatization.

The Role of Feedback in the Statistical Learning of Language-Like Regularities

In language learning, learners engage with their environment, incorporating cues from different sources. However, in lab-based experiments, using artificial languages, many of the cues and features that are part of real-world language learning are stripped away. In three experiments, we investigated the role of positive, negative, and mixed feedback on the gradual learning of language-like statistical regularities within an active guessing game paradigm. In Experiment 1, participants received deterministic feedback (100%), whereas probabilistic feedback (i.e., 75% or 50%) was introduced in Experiment 2. Finally, Experiment 3 explored the impact of mixed probabilistic feedback (33% positive, 33% negative, 33% no feedback). The results showed that cross-situational learning of words was observed without feedback, but participants were able to learn structural regularities of the miniature language only when feedback was provided. Interestingly, the presence of positive feedback was particularly helpful for the learner, promoting more in-depth learning of the artificial language.

Can Infants Retain Statistically Segmented Words and Mappings Across a Delay?

Infants are sensitive to statistics in spoken language that aid word-form segmentation and immediate mapping to referents. However, it is not clear whether this sensitivity influences the formation and retention of word-referent mappings across a delay, two real-world challenges that learners must overcome. We tested how the timing of referent training, relative to familiarization with transitional probabilities (TPs) in speech, impacts English-learning 23-month-olds' ability to form and retain word-referent mappings. In Experiment 1, we tested infants' ability to retain TP information across a 10-min delay and use it in the service of word learning. Infants successfully mapped high-TP but not low-TP words to referents. In Experiment 2, infants readily mapped the same words even when they were unfamiliar. In Experiment 3, high- and low-TP word-referent mappings were trained immediately after familiarization, and infants readily remembered these associations 10 min later. In sum, although 23-month-old infants do not need strong statistics to map word forms to referents immediately, or to remember those mappings across a delay, infants are nevertheless sensitive to these statistics in the speech stream, and they influence mapping after a delay. These findings suggest that, by 23 months of age, sensitivity to statistics in speech may impact infants' language development by leading word forms with low coherence to be poorly mapped following even a short period of consolidation.

The successor representation subserves hierarchical abstraction for goal-directed behavior

Humans have the ability to craft abstract, temporally extended and hierarchically organized plans. For instance, when considering how to make spaghetti for dinner, we typically concern ourselves with useful "subgoals" in the task, such as cutting onions, boiling pasta, and cooking a sauce, rather than particulars such as how many cuts to make to the onion, or exactly which muscles to contract. A core question is how such decomposition of a more abstract task into logical subtasks happens in the first place. Previous research has shown that humans are sensitive to a form of higher-order statistical learning named "community structure". Community structure is a common feature of abstract tasks characterized by a logical ordering of subtasks. This structure can be captured by a model where humans learn predictions of upcoming events multiple steps into the future, discounting predictions of events further away in time. One such model is the "successor representation", which has been argued to be useful for hierarchical abstraction. As of yet, no study has convincingly shown that this hierarchical abstraction can be put to use for goal-directed behavior. Here, we investigate whether participants utilize learned community structure to craft hierarchically informed action plans for goal-directed behavior. Participants were asked to search for paintings in a virtual museum, where the paintings were grouped together in "wings" representing community structure in the museum. We find that participants' choices accord with the hierarchical structure of the museum and that their response times are best predicted by a successor representation. The degree to which the response times reflect the community structure of the museum correlates with several measures of performance, including the ability to craft temporally abstract action plans. These results suggest that successor representation learning subserves hierarchical abstractions relevant for goal-directed behavior.

Rhythmically Modulating Neural Entrainment during Exposure to Regularities Influences Statistical Learning

The ability to discover regularities in the environment, such as syllable patterns in speech, is known as statistical learning. Previous studies have shown that statistical learning is accompanied by neural entrainment, in which neural activity temporally aligns with repeating patterns over time. However, it is unclear whether these rhythmic neural dynamics play a functional role in statistical learning or whether they largely reflect the downstream consequences of learning, such as the enhanced perception of learned words in speech. To better understand this issue, we manipulated participants' neural entrainment during statistical learning using continuous rhythmic visual stimulation. Participants were exposed to a speech stream of repeating nonsense words while viewing either (1) a visual stimulus with a "congruent" rhythm that aligned with the word structure, (2) a visual stimulus with an incongruent rhythm, or (3) a static visual stimulus. Statistical learning was subsequently measured using both an explicit and implicit test. Participants in the congruent condition showed a significant increase in neural entrainment over auditory regions at the relevant word frequency, over and above effects of passive volume conduction, indicating that visual stimulation successfully altered neural entrainment within relevant neural substrates. Critically, during the subsequent implicit test, participants in the congruent condition showed an enhanced ability to predict upcoming syllables and stronger neural phase synchronization to component words, suggesting that they had gained greater sensitivity to the statistical structure of the speech stream relative to the incongruent and static groups. This learning benefit could not be attributed to strategic processes, as participants were largely unaware of the contingencies between the visual stimulation and embedded words. These results indicate that manipulating neural entrainment during exposure to regularities influences statistical learning outcomes, suggesting that neural entrainment may functionally contribute to statistical learning. Our findings encourage future studies using non-invasive brain stimulation methods to further understand the role of entrainment in statistical learning.

Implicit auditory memory in older listeners: From encoding to 6-month retention

Any listening task, from sound recognition to sound-based communication, rests on auditory memory which is known to decline in healthy ageing. However, how this decline maps onto multiple components and stages of auditory memory remains poorly characterised. In an online unsupervised longitudinal study, we tested ageing effects on implicit auditory memory for rapid tone patterns. The test required participants (younger, aged 20-30, and older adults aged 60-70) to quickly respond to rapid regularly repeating patterns emerging from random sequences. Patterns were novel in most trials (REGn), but unbeknownst to the participants, a few distinct patterns reoccurred identically throughout the sessions (REGr). After correcting for processing speed, the response times (RT) to REGn should reflect the information held in echoic and short-term memory before detecting the pattern; long-term memory formation and retention should be reflected by the RT advantage (RTA) to REGr vs REGn which is expected to grow with exposure. Older participants were slower than younger adults in detecting REGn and exhibited a smaller RTA to REGr. Computational simulations using a model of auditory sequence memory indicated that these effects reflect age-related limitations both in early and long-term memory stages. In contrast to ageing-related accelerated forgetting of verbal material, here older adults maintained stable memory traces for REGr patterns up to 6 months after the first exposure. The results demonstrate that ageing is associated with reduced short-term memory and long-term memory formation for tone patterns, but not with forgetting, even over surprisingly long timescales.

Prerequisites of language acquisition in the newborn brain

Learning to decode and produce speech is one of the most demanding tasks faced by infants. Nevertheless, infants typically utter their first words within a year, and phrases soon follow. Here we review cognitive abilities of newborn infants that promote language acquisition, focusing primarily on studies tapping neural activity. The results of these studies indicate that infants possess core adult auditory abilities already at birth, including statistical learning and rule extraction from variable speech input. Thus, the neonatal brain is ready to categorize sounds, detect word boundaries, learn words, and separate speech streams: in short, to acquire language quickly and efficiently from everyday linguistic input.

Dynamic encoding of phonetic categories in zebra finch auditory forebrain

Vocal communication requires the formation of acoustic categories to enable invariant representations of sounds despite superficial variations. Humans form acoustic categories for speech phonemes, enabling the listener to recognize words independent of speakers; animals can also discriminate speech phonemes. We investigated the neural mechanisms of this process using electrophysiological recordings from the zebra finch secondary auditory area, caudomedial nidopallium (NCM), during passive exposure to human speech stimuli consisting of two naturally spoken words produced by multiple speakers. Analysis of neural distance and decoding accuracy showed improvements in neural discrimination between word categories over the course of exposure, and this improved representation transferred to the same words by novel speakers. We conclude that NCM neurons formed generalized representations of word categories independent of speaker-specific variations that became more refined over the course of passive exposure. The discovery of this dynamic encoding process in NCM suggests a general processing mechanism for forming categorical representations of complex acoustic signals that humans share with other animals.

Crossmodal interactions in human learning and memory

Most studies of memory and perceptual learning in humans have employed unisensory settings to simplify the study paradigm. However, in daily life we are often surrounded by complex and cluttered scenes made up of many objects and sources of sensory stimulation. Our experiences are, therefore, highly multisensory both when passively observing the world and when acting and navigating. We argue that human learning and memory systems are evolved to operate under these multisensory and dynamic conditions. The nervous system exploits the rich array of sensory inputs in this process, is sensitive to the relationship between the sensory inputs, and continuously updates sensory representations, and encodes memory traces based on the relationship between the senses. We review some recent findings that demonstrate a range of human learning and memory phenomena in which the interactions between visual and auditory modalities play an important role, and suggest possible neural mechanisms that can underlie some surprising recent findings. We outline open questions as well as directions of future research to unravel human perceptual learning and memory.

Examining the Relation Between Exercise and Word Learning in Preschool-Age Children

PURPOSE

There is evidence suggesting that aerobic exercise immediately after vocabulary training can improve word recall in school-age children. This work examined whether the previously identified word-learning benefits associated with exercise can be extended to preschoolers. Additionally, we evaluated whether the effects of physical activity on vocabulary learning may be influenced by existing language skills that the child possesses.

METHOD

Children ages 3-6 years completed the study (N = 42). Data were collected via a virtual testing session in which participants completed a word-learning task that included two experimental conditions (exercise and resting). In the resting measure, children were taught names of novel objects and then sat down and colored for 3 min before being tested on their ability to identify the trained words. The exercise condition was identical, except that participants engaged in 3 min of guided aerobic exercise before testing. Additionally, at the end of the visit, participants completed the Quick Interactive Language Screener (QUILS), which measured general language skills.

RESULTS

Accuracy of word recognition was significantly higher after the exercise condition compared to the resting condition. Furthermore, this pattern of results was not related to children's existing language skills, as measured by the QUILS.

CONCLUSIONS

This study is one of the first to closely examine the relation between physical activity and word-learning abilities in children as young as 3-6 years of age. Results align with previous findings stating that aerobic exercise can boost vocabulary learning and suggest that this is the case regardless of existing language skills.

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.

Statistical learning of target location and distractor location rely on different mechanisms during visual search

More studies have demonstrated that people have the capacity to learn and make use of environmental regularities. This capacity is known as statistical learning (SL). Despite rich empirical findings, it is not clear how the two forms of SL (SL of target location and SL of distractor location) influence visual search and whether they rely on the shared cognitive mechanism. In Experiment 1 and Experiment 2, we manipulated the probability of target location and the probability of distractor location, respectively. The results suggest that attentional guidance (they referred to overt attention) may mainly contribute to the SL effect of the target location and the distractor location, which is in line with the notion of priority mapping. To a small extent, facilitation of response selection may also contribute to the SL effect of the target location but does not contribute to the SL effect of the distractor location. However, the main difference between the two kinds of SL occurred in the early stage (it involved covert attention). Together, our findings indicate that the two forms of SL reflect partly shared and partly independent cognitive mechanisms.

Updating Contextual Sensory Expectations for Adaptive Behavior

The brain has the extraordinary capacity to construct predictive models of the environment by internalizing statistical regularities in the sensory inputs. The resulting sensory expectations shape how we perceive and react to the world; at the neural level, this relates to decreased neural responses to expected than unexpected stimuli ("expectation suppression"). Crucially, expectations may need revision as context changes. However, existing research has often neglected this issue. Further, it is unclear whether contextual revisions apply selectively to expectations relevant to the task at hand, hence serving adaptive behavior. The present fMRI study examined how contextual visual expectations spread throughout the cortical hierarchy as we update our beliefs. We created a volatile environment: two alternating contexts contained different sequences of object images, thereby producing context-dependent expectations that needed revision when the context changed. Human participants of both sexes attended a training session before scanning to learn the contextual sequences. The fMRI experiment then tested for the emergence of contextual expectation suppression in two separate tasks, respectively, with task-relevant and task-irrelevant expectations. Effects of contextual expectation emerged progressively across the cortical hierarchy as participants attuned themselves to the context: expectation suppression appeared first in the insula, inferior frontal gyrus, and posterior parietal cortex, followed by the ventral visual stream, up to early visual cortex. This applied selectively to task-relevant expectations. Together, the present results suggest that an insular and frontoparietal executive control network may guide the flexible deployment of contextual sensory expectations for adaptive behavior in our complex and dynamic world.SIGNIFICANCE STATEMENT The world is structured by statistical regularities, which we use to predict the future. This is often accompanied by suppressed neural responses to expected compared with unexpected events ("expectation suppression"). Crucially, the world is also highly volatile and context-dependent: expected events may become unexpected when the context changes, thus raising the crucial need for belief updating. However, this issue has generally been neglected. By setting up a volatile environment, we show that expectation suppression emerges first in executive control regions, followed by relevant sensory areas, only when observers use their expectations to optimize behavior. This provides surprising yet clear evidence on how the brain controls the updating of sensory expectations for adaptive behavior in our ever-changing world.

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.

The relation between rhythm processing and cognitive abilities during child development: The role of prediction

Rhythm and meter are central elements of music. From the very beginning, children are responsive to rhythms and acquire increasingly complex rhythmic skills over the course of development. Previous research has shown that the processing of musical rhythm is not only related to children’s music-specific responses but also to their cognitive abilities outside the domain of music. However, despite a lot of research on that topic, the connections and underlying mechanisms involved in such relation are still unclear in some respects. In this article, we aim at analyzing the relation between rhythmic and cognitive-motor abilities during childhood and at providing a new hypothesis about this relation. We consider whether predictive processing may be involved in the relation between rhythmic and various cognitive abilities and hypothesize that prediction as a cross-domain process is a central mechanism building a bridge between rhythm processing and cognitive-motor abilities. Further empirical studies focusing on rhythm processing and cognitive-motor abilities are needed to precisely investigate the links between rhythmic, predictive, and cognitive processes.

Probability, Dependency, and Frequency Are Not All Equally Involved in Statistical Learning

The ability to learn sequences depends on different factors governing sequence structure, such as transitional probability (TP, probability of a stimulus given a previous stimulus), adjacent or nonadjacent dependency, and frequency. Current evidence indicates that adjacent and nonadjacent pairs are not equally learnable; the same applies to second-order and first-order TPs and to the frequency of the sequences. However, the relative importance of these factors and interactive effects on learning remain poorly understood. The first experiment tested the effects of TPs and dependency separately on the learning of nonlinguistic visual sequences, and the second experiment used the factors of the first experiment and added a frequency factor to test their interactive effects with verbal sequences of stimuli (pseudo-words). The results of both experiments showed higher performance during online learning for first-order TPs in adjacent pairs. Moreover, Experiment 2 indicated poorer performance during offline recall for nonadjacent dependencies and low-frequency sequences. We discuss the results that different factors are not used equally in prediction and memorization.

Emotional Faces Facilitate Statistical Learning

Detecting regularities and extracting patterns is a vital skill to organize complex information in our environments. Statistical learning, a process where we detect regularities by attending to relationships between cues in our environment, contributes to knowledge acquisition across myriad domains. However, less is known about how emotional cues-specifically facial configurations of emotion-influence statistical learning. Here, we tested two pre-registered aims to advance knowledge about emotional signals and statistical learning: (1) we examined statistical learning in the context of emotional compared to non-emotional information, and (2) we assessed how emotional congruency (i.e., whether facial stimuli conveyed the same, or different emotions) influenced regularity extraction. We demonstrated statistical learning in the context of emotional signals. Further, we showed that statistical learning occurs more efficiently in the context of emotional faces. We also established that congruent cues benefited an online measure of statistical learning, but had varied effects when statistical learning was assessed via post-exposure recognition test. The results shed light on how affective signals influence well-studied cognitive skills and address a knowledge gap about how cue congruency impacts statistical learning, including how emotional cues might guide predictions in our social world.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42761-022-00130-9.

The Effects of Cooperative and Competitive Situations on Statistical Learning

Devising cooperative or competitive situations is an important teaching strategy in educational practices. Nevertheless, there is still controversy regarding which situation is better for learning. This study was conducted to explore the effects of cooperative and competitive situations on statistical learning, through the alternating serial reaction time (ASRT) task. Individual cooperative and competitive situations were devised in this study, in which individual situation served as the control condition. Ninety recruited participants were randomly assigned to a cooperative, competitive, or individual group to perform the ASRT task. For general learning, cooperative and competitive situations could indeed make learners respond faster, and there was no significant difference in the RT between the cooperative and competitive groups. Moreover, statistical learning was observed in all three groups. An additional analysis of the early stage of the experiment showed that the learning effect of the competitive group was greater than those of the cooperative and individual groups, in terms of statistical learning. However, the final learning effect was not significantly different among the three groups. Overall, the cooperative and competitive situations had a positive impact on learning and enabled the students to acquire approximately the same learning effect in a shorter time period, compared with the individual situation. Specifically, the competitive situation accelerated the statistical learning process but not the general learning process.

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.

Statistical Learning in Vision

Vision and learning have long been considered to be two areas of research linked only distantly. However, recent developments in vision research have changed the conceptual definition of vision from a signal-evaluating process to a goal-oriented interpreting process, and this shift binds learning, together with the resulting internal representations, intimately to vision. In this review, we consider various types of learning (perceptual, statistical, and rule/abstract) associated with vision in the past decades and argue that they represent differently specialized versions of the fundamental learning process, which must be captured in its entirety when applied to complex visual processes. We show why the generalized version of statistical learning can provide the appropriate setup for such a unified treatment of learning in vision, what computational framework best accommodates this kind of statistical learning, and what plausible neural scheme could feasibly implement this framework. Finally, we list the challenges that the field of statistical learning faces in fulfilling the promise of being the right vehicle for advancing our understanding of vision in its entirety. Expected final online publication date for the Annual Review of Vision Science, Volume 8 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

The Evolution of Chunks in Sequence Learning

Chunking mechanisms are central to several cognitive processes and notably to the acquisition of visuo-motor sequences. Individuals segment sequences into chunks of items to perform visuo-motor tasks more fluidly, rapidly, and accurately. However, the exact dynamics of chunking processes in the case of extended practice remain unclear. Using an operant conditioning device, 18 Guinea baboons (Papio papio) produced a fixed sequence of nine movements during 1000 trials by pointing to a moving target on a touch screen. Response times analyses revealed a specific chunking pattern of the sequence for each baboon. More importantly, we found that these patterns evolved during the course of the experiment, with chunks becoming progressively fewer and longer. We identified two chunk reorganization mechanisms: the recombination of preexisting chunks and the concatenation of two distinct chunks into a single one. These results provide new evidence on chunking mechanisms in sequence learning and challenge current models of associative and statistical learning.

A Dynamical, Radically Embodied, and Ecological Theory of Rhythm Development

Musical rhythm abilities-the perception of and coordinated action to the rhythmic structure of music-undergo remarkable change over human development. In the current paper, we introduce a theoretical framework for modeling the development of musical rhythm. The framework, based on Neural Resonance Theory (NRT), explains rhythm development in terms of resonance and attunement, which are formalized using a general theory that includes non-linear resonance and Hebbian plasticity. First, we review the developmental literature on musical rhythm, highlighting several developmental processes related to rhythm perception and action. Next, we offer an exposition of Neural Resonance Theory and argue that elements of the theory are consistent with dynamical, radically embodied (i.e., non-representational) and ecological approaches to cognition and development. We then discuss how dynamical models, implemented as self-organizing networks of neural oscillations with Hebbian plasticity, predict key features of music development. We conclude by illustrating how the notions of dynamical embodiment, resonance, and attunement provide a conceptual language for characterizing musical rhythm development, and, when formalized in physiologically informed dynamical models, provide a theoretical framework for generating testable empirical predictions about musical rhythm development, such as the kinds of native and non-native rhythmic structures infants and children can learn, steady-state evoked potentials to native and non-native musical rhythms, and the effects of short-term (e.g., infant bouncing, infant music classes), long-term (e.g., perceptual narrowing to musical rhythm), and very-long term (e.g., music enculturation, musical training) learning on music perception-action.

Effects of healthy aging and left hemisphere stroke on statistical language learning

Spoken sentences are continuous streams of sound, without reliable acoustic cues to word boundaries. We have previously proposed that language learners identify words via an implicit statistical learning mechanism that computes transitional probabilities between syllables. Neuroimaging studies in healthy young adults associate this learning with left inferior frontal gyrus, left arcuate fasciculus, and bilateral striatum. Here, we test the effects of healthy aging and left hemisphere (LH) injury on statistical learning. Following 10-minute exposure to an artificial language, participants rated familiarity of Words, Part-words (sequences spanning word boundaries), and Non-words (unfamiliar sequences). Young controls (N=14) showed robust learning, rating Words>Part-words>Non-words. Older controls (N=28) showed this pattern to a weaker degree. Stroke survivors (N=24) as a group showed no learning. A lesion comparison examining individual differences revealed that "non-learners" are more likely to have anterior lesions. Together, these findings demonstrate that word segmentation is sensitive to healthy aging and LH injury.

The Value of Statistical Learning to Cognitive Network Science

To study the human mind is to consider the nature of associations-how are they learned, what are their constituent parts, and how can they be severed or adjusted? The manipulation of associations stands as a pillar of statistical learning (SL) research, which strongly suggests that processes as diverse as word segmentation, learning of grammatical patterns, and event perception can be explained by the learner's sensitivity to simple temporal dependencies (among other regularities). Used to determine the edges of a network, associations are similarly crucial to consider when quantifying the graph-theoretical properties of various cognitive systems. With this point of convergence in mind, the present work reaffirms the unique value of network science in illuminating the broad-level architectures of complex cognitive systems. However, I also describe how insights from the SL literature, coupled with insights from psycholinguistics more broadly, offer a strong theoretical backbone upon which we can develop and study networks that reflect, as closely as possible, the psychological realities of learning.

Is there such a thing as a 'good statistical learner'?

A growing body of research investigates individual differences in the learning of statistical structure, tying them to variability in cognitive (dis)abilities. This approach views statistical learning (SL) as a general individual ability that underlies performance across a range of cognitive domains. But is there a general SL capacity that can sort individuals from 'bad' to 'good' statistical learners? Explicating the suppositions underlying this approach, we suggest that current evidence supporting it is meager. We outline an alternative perspective that considers the variability of statistical environments within different cognitive domains. Once we focus on learning that is tuned to the statistics of real-world sensory inputs, an alternative view of SL computations emerges with a radically different outlook for SL research.

Quantifying Everyday Ecologies: Principles for Manual Annotation of Many Hours of Infants' Lives

Everyday experiences are the experiences available to shape developmental change. Remarkable advances in devices used to record infants' and toddlers' everyday experiences, as well as in repositories to aggregate and share such recordings across teams of theorists, have yielded a potential gold mine of insights to spur next-generation theories of experience-dependent change. Making full use of these advances, however, currently requires manual annotation. Manually annotating many hours of everyday life is a dedicated pursuit requiring significant time and resources, and in many domains is an endeavor currently lacking foundational facts to guide potentially consequential implementation decisions. These realities make manual annotation a frequent barrier to discoveries, as theorists instead opt for narrower scoped activities. Here, we provide theorists with a framework for manually annotating many hours of everyday life designed to reduce both theoretical and practical overwhelm. We share insights based on our team's recent adventures in the previously uncharted territory of everyday music. We identify principles, and share implementation examples and tools, to help theorists achieve scalable solutions to challenges that are especially fierce when annotating extended timescales. These principles for quantifying everyday ecologies will help theorists collectively maximize return on investment in databases of everyday recordings and will enable a broad community of scholars—across institutions, skillsets, experiences, and working environments—to make discoveries about the experiences upon which development may depend.

Everyday music in infancy

Infants enculturate to their soundscape over the first year of life, yet theories of how they do so rarely make contact with details about the sounds available in everyday life. Here, we report on properties of a ubiquitous early ecology in which foundational skills get built: music. We captured daylong recordings from 35 infants ages 6–12 months at home and fully double‐coded 467 h of everyday sounds for music and its features, tunes, and voices. Analyses of this first‐of‐its‐kind corpus revealed two distributional properties of infants’ everyday musical ecology. First, infants encountered vocal music in over half, and instrumental in over three‐quarters, of everyday music. Live sources generated one‐third, and recorded sources three‐quarters, of everyday music. Second, infants did not encounter each individual tune and voice in their day equally often. Instead, the most available identity cumulated to many more seconds of the day than would be expected under a uniform distribution. These properties of everyday music in human infancy are different from what is discoverable in environments highly constrained by context (e.g., laboratories) and time (e.g., minutes rather than hours). Together with recent insights about the everyday motor, language, and visual ecologies of infancy, these findings reinforce an emerging priority to build theories of development that address the opportunities and challenges of real input encountered by real learners.

Examining the Role of Physical Activity on Word Learning in School-Aged Children

Purpose Previous studies show that there is increased brain activity after exercise, leading to improved word recall in adults. The aim of this study was to examine whether different types of exercise (i.e., aerobic vs. anaerobic) may also lead to improved performance during vocabulary learning in children. Method A total of 48 participants (24 in Experiment 1 and 24 in Experiment 2) between the ages of 6 and 12 years completed a word learning task. Training of words took place in a resting and in an exercise condition using a within-subject design. In the resting measure, children were taught names of novel objects and then colored for 3 min before being tested on their ability to recognize the words. In the exercise condition, the same steps were followed, but instead of coloring, children engaged in 3 min of either aerobic exercise (i.e., swimming in Experiment 1) or anaerobic exercise (i.e., a CrossFit-like workout in Experiment 2). Results In Experiment 1, accuracy of word recognition was significantly higher for words that were trained in the aerobic exercise compared to the resting condition. In Experiment 2, there was no significant difference in performance between the anaerobic exercise and resting conditions. Conclusions These findings suggest that previously identified benefits of exercise on language abilities in adults also extend to school-aged children. However, not all types of physical activity lead to this boost in performance, as only aerobic (but not anaerobic) exercise improved children's ability to acquire new word-object relations. Supplemental Material https://doi.org/10.23641/asha.14462187.

The challenge of learning a new language in adulthood: Evidence from a multi-methodological neuroscientific approach

Being proficient in several foreign languages is an essential part of every-day life. In contrast to childhood, learning a new language can be highly challenging for adults. The present study aims at investigating neural mechanisms supporting very initial foreign language learning in adulthood. For this reason, subjects underwent an implicit semantic associative training in which they had to learn new pseudoword-picture pairings. Learning success was measured via a recognition experiment presenting learned versus new pseudoword-picture pairings. Neural correlates were assessed by an innovative multi-methodological approach simultaneously applying electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). Results indicate memory-related processes based on familiarity and mechanisms of cognitive control to be present during initial vocabulary learning. Findings underline the fascinating plasticity of the adult brain during foreign language learning, even after a short semantic training of only 18 minutes as well as the importance of comparing evidence from different neuroscientific methods and behavioral data.

Statistical learning of unbalanced exclusive-or temporal sequences in humans

A pervasive issue in statistical learning has been to determine the parameters of regularity extraction. Our hypothesis was that the extraction of transitional probabilities can prevail over frequency if the task involves prediction. Participants were exposed to four repeated sequences of three stimuli (XYZ) with each stimulus corresponding to the position of a red dot on a touch screen that participants were required to touch sequentially. The temporal and spatial structure of the positions corresponded to a serial version of the exclusive-or (XOR) that allowed testing of the respective effect of frequency and first- and second-order transitional probabilities. The XOR allowed the first-order transitional probability to vary while being not completely related to frequency and to vary while the second-order transitional probability was fixed (p(Z|X, Y) = 1). The findings show that first-order transitional probability prevails over frequency to predict the second stimulus from the first and that it also influences the prediction of the third item despite the presence of second-order transitional probability that could have offered a certain prediction of the third item. These results are particularly informative in light of statistical learning models.