Statistical learning in a serial reaction time task: access to separable statistical cues by individual learners

Assessing the impact of attention fluctuations on statistical learning

Attention fluctuates between optimal and suboptimal states. However, whether these fluctuations affect how we learn visual regularities remains untested. Using web-based real-time triggering, we investigated the impact of sustained attentional state on statistical learning using online and offline measures of learning. In three experiments (N = 450), participants performed a continuous performance task (CPT) with shape stimuli. Unbeknownst to participants, we measured response times (RTs) preceding each trial in real time and inserted distinct shape triplets in the trial stream when RTs indicated that a participant was attentive or inattentive. We measured online statistical learning using changes in RTs to regular triplets relative to random triplets encountered in the same attentional states. We measured offline statistical learning with a target detection task in which participants responded to target shapes selected from the regular triplets and with tasks in which participants explicitly re-created the regular triplets or selected regular shapes from foils. Online learning evidence was greater in high vs. low attentional states when combining data from all three experiments, although this was not evident in any experiment alone. On the other hand, we saw no evidence of impacts of attention fluctuations on measures of statistical learning collected offline, after initial exposure in the CPT. These results suggest that attention fluctuations may impact statistical learning while regularities are being extracted online, but that these effects do not persist to subsequent tests of learning about regularities.

Response times are affected by mispredictions in a stochastic game

Acting as a goalkeeper in a video-game, a participant is asked to predict the successive choices of the penalty taker. The sequence of choices of the penalty taker is generated by a stochastic chain with memory of variable length. It has been conjectured that the probability distribution of the response times is a function of the specific sequence of past choices governing the algorithm used by the penalty taker to make his choice at each step. We found empirical evidence that besides this dependence, the distribution of the response times depends also on the success or failure of the previous prediction made by the participant. Moreover, we found statistical evidence that this dependence propagates up to two steps forward after the prediction failure.

Specificity of Motor Contributions to Auditory Statistical Learning

Statistical learning is the ability to extract patterned information from continuous sensory signals. Recent evidence suggests that auditory-motor mechanisms play an important role in auditory statistical learning from speech signals. The question remains whether auditory-motor mechanisms support such learning generally or in a domain-specific manner. In Experiment 1, we tested the specificity of motor processes contributing to learning patterns from speech sequences. Participants either whispered or clapped their hands while listening to structured speech. In Experiment 2, we focused on auditory specificity, testing whether whispering equally affects learning patterns from speech and non-speech sequences. Finally, in Experiment 3, we examined whether learning patterns from speech and non-speech sequences are correlated. Whispering had a stronger effect than clapping on learning patterns from speech sequences in Experiment 1. Moreover, whispering impaired statistical learning more strongly from speech than non-speech sequences in Experiment 2. Interestingly, while participants in the non-speech tasks spontaneously synchronized their motor movements with the auditory stream more than participants in the speech tasks, the effect of the motor movements on learning was stronger in the speech domain. Finally, no correlation between speech and non-speech learning was observed. Overall, our findings support the idea that learning statistical patterns from speech versus non-speech relies on segregated mechanisms, and that the speech motor system contributes to auditory statistical learning in a highly specific manner.

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.

Generalization of cognitive maps across space and time

Prominent theories posit that associative memory structures, known as cognitive maps, support flexible generalization of knowledge across cognitive domains. Here, we evince a representational account of cognitive map flexibility by quantifying how spatial knowledge formed one day was used predictively in a temporal sequence task 24 hours later, biasing both behavior and neural response. Participants learned novel object locations in distinct virtual environments. After learning, hippocampus and ventromedial prefrontal cortex (vmPFC) represented a cognitive map, wherein neural patterns became more similar for same-environment objects and more discriminable for different-environment objects. Twenty-four hours later, participants rated their preference for objects from spatial learning; objects were presented in sequential triplets from either the same or different environments. We found that preference response times were slower when participants transitioned between same- and different-environment triplets. Furthermore, hippocampal spatial map coherence tracked behavioral slowing at the implicit sequence transitions. At transitions, predictive reinstatement of virtual environments decreased in anterior parahippocampal cortex. In the absence of such predictive reinstatement after sequence transitions, hippocampus and vmPFC responses increased, accompanied by hippocampal-vmPFC functional decoupling that predicted individuals' behavioral slowing after a transition. Collectively, these findings reveal how expectations derived from spatial experience generalize to support temporal prediction.

No need to forget, just keep the balance: Hebbian neural networks for statistical learning

Language processing in humans has long been proposed to rely on sophisticated learning abilities including statistical learning. Endress and Johnson (E&J, 2021) recently presented a neural network model for statistical learning based on Hebbian learning principles. This model accounts for word segmentation tasks, one primary paradigm in statistical learning. In this discussion paper we review this model and compare it with the Hebbian model previously presented by Tovar and Westermann (T&W, 2017a; 2017b; 2018) that has accounted for serial reaction time tasks, cross-situational learning, and categorization paradigms, all relevant in the study of statistical learning. We discuss the similarities and differences between both models, and their key findings. From our analysis, we question the concept of "forgetting" in the model of E&J and their suggestion of considering forgetting as the critical ingredient for successful statistical learning. We instead suggest that a set of simple but well-balanced mechanisms including spreading activation, activation persistence, and synaptic weight decay, all based on biologically grounded principles, allow modeling statistical learning in Hebbian neural networks, as demonstrated in the T&W model which successfully covers learning of nonadjacent dependencies and accounts for differences between typical and atypical populations, both aspects that have not been fully demonstrated in the E&J model. We outline the main computational and theoretical differences between the E&J and T&W approaches, present new simulation results, and discuss implications for the development of a computational cognitive theory of statistical learning.

Dampened sensory representations for expected input across the ventral visual stream

Expectations, derived from previous experience, can help in making perception faster, more reliable and informative. A key neural signature of perceptual expectations is expectation suppression, an attenuated neural response to expected compared with unexpected stimuli. While expectation suppression has been reported using a variety of paradigms and recording methods, it remains unclear what neural modulation underlies this response attenuation. Sharpening models propose that neural populations tuned away from an expected stimulus are particularly suppressed by expectations, thereby resulting in an attenuated, but sharper population response. In contrast, dampening models suggest that neural populations tuned toward the expected stimulus are most suppressed, thus resulting in a dampened, less redundant population response. Empirical support is divided, with some studies favoring sharpening, while others support dampening. A key limitation of previous neuroimaging studies is the ability to draw inferences about neural-level modulations based on population (e.g. voxel) level signals. Indeed, recent simulations of repetition suppression showed that opposite neural modulations can lead to comparable population-level modulations. Forward models provide one solution to this inference limitation. Here, we used forward models to implement sharpening and dampening models, mapping neural modulations to voxel-level data. We show that a feature-specific gain modulation, suppressing neurons tuned toward the expected stimulus, best explains the empirical fMRI data. Thus, our results support the dampening account of expectation suppression, suggesting that expectations reduce redundancy in sensory cortex, and thereby promote updating of internal models on the basis of surprising information.

Statistical learning in children with a family risk of dyslexia

The assumption that statistical learning is affected in dyslexia has generally been evaluated in children and adults with diagnosed dyslexia, not in pre-literate children with a family risk (FR) of dyslexia. In this study, four-to-five-year-old FR children (n = 25) and No-FR children (n = 33) completed tasks of emerging literacy (phoneme awareness and RAN). They also performed an online non-adjacent dependency learning (NADL) task, based on the Serial Reaction Time (SRT) task paradigm. Children's accuracy (hits), signal sensitivity (d') and reaction times were measured. The FR group performed marginally more poorly on phoneme awareness and significantly more poorly on RAN than the No-FR group. Regarding NADL outcomes, the results were less straightforward: the data suggested successful statistical learning for both groups, as indicated by the hit and reaction time curves found. However, the FR group was less accurate and slower on the task than the No-FR group. Furthermore, unlike the No-FR group, performance in the FR group varied as a function of the specific stimulus presented. Taken together, these findings fail to show a robust difference in statistical learning between children with and without an FR of dyslexia at preschool age, in line with earlier work on older children and adults with dyslexia.

Individual differences in artificial and natural language statistical learning

Statistical learning (SL) is considered a cornerstone of cognition. While decades of research have unveiled the remarkable breadth of structures that participants can learn from statistical patterns in experimental contexts, how this ability interfaces with real-world cognitive phenomena remains inconclusive. These mixed results may arise from the fact that SL is often treated as a general ability that operates uniformly across all domains, typically assuming that sensitivity to one kind of regularity implies equal sensitivity to others. In a preregistered study, we sought to clarify the link between SL and language by aligning the type of structure being processed in each task. We focused on the learning of trigram patterns using artificial and natural language statistics, to evaluate whether SL predicts sensitivity to comparable structures in natural speech. Adults were trained and tested on an artificial language incorporating statistically-defined syllable trigrams. We then evaluated their sensitivity to similar statistical structures in natural language using a multiword chunking task, which examines serial recall of high-frequency word trigrams-one of the building blocks of language. Participants' aptitude in learning artificial syllable trigrams positively correlated with their sensitivity to high-frequency word trigrams in natural language, suggesting that similar computations span learning across both tasks. Short-term SL taps into key aspects of long-term language acquisition when the statistical structures-and the computations used to process them-are comparable. Better aligning the specific statistical patterning across tasks may therefore provide an important steppingstone toward elucidating the relationship between SL and cognition at large.

Spatial and Temporal Context Jointly Modulate the Sensory Response within the Ventral Visual Stream

Both spatial and temporal context play an important role in visual perception and behavior. Humans can extract statistical regularities from both forms of context to help process the present and to construct expectations about the future. Numerous studies have found reduced neural responses to expected stimuli compared with unexpected stimuli, for both spatial and temporal regularities. However, it is largely unclear whether and how these forms of context interact. In the current fMRI study, 33 human volunteers were exposed to pairs of object stimuli that could be expected or surprising in terms of their spatial and temporal context. We found reliable independent contributions of both spatial and temporal context in modulating the neural response. Specifically, neural responses to stimuli in expected compared with unexpected contexts were suppressed throughout the ventral visual stream. These results suggest that both spatial and temporal context may aid sensory processing in a similar fashion, providing evidence on how different types of context jointly modulate perceptual processing.

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.

Implicit sequence learning using auditory cues leads to modality-specific representations

The implicit acquisition of statistical information from the environment is considered a fundamental type of human learning. Paradigms using visually cued sequences have been frequently used to study implicit learning. However, learning sequences of auditory cues is likely to be important in domains such as language or music. In three experiments, we established a novel auditorily cued implicit perceptual-motor sequence learning paradigm to compare to traditional visually cued sequence learning and identify whether this type of learning generalizes across cue modality. Participants exhibited reliable sequence-specific learning to auditory cues in all three experiments (Experiments 1-3), which was generally not influenced by explicit knowledge (Experiment 2). In Experiment 3, a large drop in knowledge expression in the novel cue modality was observed, suggesting that the acquired implicit sequence knowledge depended largely on sensory-specific representations. Overall, auditorily cued learning was similar to, though proceeded faster than, learning in comparable visually cued sequence learning paradigms. Similarity between learning from cues in different sensory modalities suggests that there may be a common process for the automatic extraction of sequential statistical structure. However, the lack of robust transfer sequence knowledge across modalities argues against a purely domain-general learning mechanism for all kinds of sequences. By expanding quantitative methodologies to characterize sequence learning in the auditory domain, these findings illustrate the possibility of bridging research in sequence and statistical learning domains to identify common mechanisms of complex cognitive skill and language learning.

Tracking statistical learning online: Word segmentation in a target detection task

Despite the essential role of statistical learning in shaping human behavior, there are still controversies concerning its measurement. In this paper, we present a novel online target-detection task in an acoustic word segmentation paradigm, which is able to track the process of learning and does not build on deliberation and decision making. Beside testing the novel online task, we also examined its relationship with two offline measures: the traditional two-alternative forced choice (2AFC) task, and the statistically-induced chunking recall (SICR) task (Isbilen et al., 2017). Participants showed a significant learning effect on the online task, reflected in the decrease of reaction times during training and in the differences between reaction times to predictable versus unpredictable targets. Online learning scores correlated with the 2AFC scores, but this association was only present when participants did not have explicit knowledge about stimuli. SICR scores were not associated with any of the other measures. The internal consistency was higher for online learning measures than for the other two tasks. These findings show that the online target detection task is a good tool for assessing statistical learning, and invite further research on its psychometric properties.

Convergent and Distinct Effects of Multisensory Combination on Statistical Learning Using a Computer Glove

Learning to play a musical instrument involves mapping visual + auditory cues to motor movements and anticipating transitions. Inspired by the serial reaction time task and artificial grammar learning, we investigated explicit and implicit knowledge of statistical learning in a sensorimotor task. Using a between-subjects design with four groups, one group of participants were provided with visual cues and followed along by tapping the corresponding fingertip to their thumb, while using a computer glove. Another group additionally received accompanying auditory tones; the final two groups received sensory (visual or visual + auditory) cues but did not provide a motor response—all together following a 2 × 2 design. Implicit knowledge was measured by response time, whereas explicit knowledge was assessed using probe tests. Findings indicate that explicit knowledge was best with only the single modality, but implicit knowledge was best when all three modalities were involved.

Potential and efficiency of statistical learning closely intertwined with individuals' executive functions: a mathematical modeling study

Statistical learning (SL) is essential in enabling humans to extract probabilistic regularities from the world. The ability to accomplish ultimate learning performance with training (i.e., the potential of learning) has been known to be dissociated with performance improvement per amount of learning time (i.e., the efficiency of learning). Here, we quantified the potential and efficiency of SL separately through mathematical modeling and scrutinized how they were affected by various executive functions. Our results showed that a high potential of SL was associated with poor inhibition and good visuo-spatial working memory, whereas high efficiency of SL was closely related to good inhibition and good set-shifting. We unveiled the distinct characteristics of SL in relation to potential and efficiency and their interaction with executive functions.

Implicit learning of two artificial grammars

This study investigated the implicit learning of two artificial systems. Two finite-state grammars were implemented with the same tone set (leading to short melodies) and played by the same timbre in exposure and test phases. The grammars were presented in separate exposure phases, and potentially acquired knowledge was tested with two experimental tasks: a grammar categorization task (Experiment 1) and a grammatical error detection task (Experiment 2). Results showed that participants were able to categorize new items as belonging to one or the other grammar (Experiment 1) and detect grammatical errors in new sequences of each grammar (Experiment 2). Our findings suggest the capacity of intra-modal learning of regularities in the auditory modality and based on stimuli that share the same perceptual properties.

Statistical prediction of the future impairs episodic encoding of the present

Memory is typically thought of as enabling reminiscence about past experiences. However, memory also informs and guides processing of future experiences. These two functions of memory are often at odds: Remembering specific experiences from the past requires storing idiosyncratic properties that define particular moments in space and time, but by definition such properties will not be shared with similar situations in the future and thus may not be applicable to future situations. We discovered that, when faced with this conflict, the brain prioritizes prediction over encoding. Behavioral tests of recognition and source recall showed that items allowing for prediction of what will appear next based on learned regularities were less likely to be encoded into memory. Brain imaging revealed that the hippocampus was responsible for this interference between statistical learning and episodic memory. The more that the hippocampus predicted the category of an upcoming item, the worse the current item was encoded. This competition may serve an adaptive purpose, focusing encoding on experiences for which we do not yet have a predictive model.

How sequence learning unfolds: Insights from anticipatory eye movements

The acquisition of sequential knowledge is pivotal in forming skilled behavior. Despite extensive research of sequence learning, much remains unknown regarding what knowledge participants learn in such studies, and how that knowledge takes form over time. By tracking eye-movements made before stimuli appear on screen during a serial reaction time (SRT) task, we devised a method for assessing learning at the individual participant level in an item-based resolution. Our method enables uncovering what participants actually learn about the sequence presented to them, and when. Results demonstrate that learning is more heterogeneous than previously thought, driven by learning both of chunks and of statistics embedded in the sequence. Also, learning develops rapidly, but in a fragmented and non-sequential manner, eventually encompassing only a subset of available regularities. The tools developed in this work may aid in further dissociating processes and mechanisms underlying sequence learning and its impairments, in normal and in clinical populations.

Hierarchical Structure in Sequence Processing: How to Measure It and Determine Its Neural Implementation

In many domains of human cognition, hierarchically structured representations are thought to play a key role. In this paper, we start with some foundational definitions of key phenomena like "sequence" and "hierarchy," and then outline potential signatures of hierarchical structure that can be observed in behavioral and neuroimaging data. Appropriate behavioral methods include classic ones from psycholinguistics along with some from the more recent artificial grammar learning and sentence processing literature. We then turn to neuroimaging evidence for hierarchical structure with a focus on the functional MRI literature. We conclude that, although a broad consensus exists about a role for a neural circuit incorporating the inferior frontal gyrus, the superior temporal sulcus, and the arcuate fasciculus, considerable uncertainty remains about the precise computational function(s) of this circuitry. An explicit theoretical framework, combined with an empirical approach focusing on distinguishing between plausible alternative hypotheses, will be necessary for further progress.

Proactively location-based suppression elicited by statistical learning

Recently, Wang and Theeuwes used the additional singleton task and showed that attentional capture was reduced for the location that was likely to contain a distractor [1]. It is argued that due to statistical learning, the location that was likely to contain a distractor was suppressed relative to all other locations. The current study replicated these findings and by adding a search-probe condition, we were able to determine the initial distribution of attentional resources across the visual field. Consistent with a space-based resource allocation ("biased competition") model, it was shown that the representation of a probe presented at the location that was likely to contain a distractor was suppressed relative to other locations. Critically, the suppression of this location resulted in more attention being allocated to the target location relative to a condition in which the distractor was not suppressed. This suggests that less capture by the distractor results in more attention being allocated to the target. The results are consistent with the view that the location that is likely to contain a distractor is suppressed before display onset, modulating the first feed-forward sweep of information input into the spatial priority map.

Baboons (Papio papio) Process a Context-Free but Not a Context-Sensitive Grammar

Language processing involves the ability to master supra-regular grammars, that go beyond the level of complexity of regular grammars. This ability has been hypothesized to be a uniquely human capacity. Our study probed baboons' capacity to learn two supra-regular grammars of different levels of complexity: a context-free grammar generating sequences following a mirror structure (e.g., AB | BA, ABC | CBA) and a context-sensitive grammar generating sequences following a repeat structure (e.g., AB | AB, ABC | ABC), the latter requiring greater computational power to be processed. Fourteen baboons were tested in a prediction task, requiring them to track a moving target on a touchscreen. In distinct experiments, sequences of target locations followed one of the above two grammars, with rare violations. Baboons showed slower response times when violations occurred in mirror sequences, but did not react to violations in repeat sequences, suggesting that they learned the context-free (mirror) but not the context-sensitive (repeat) grammar. By contrast, humans tested with the same task learned both grammars. These data suggest a difference in sensitivity in baboons between a context-free and a context-sensitive grammar.

Exploring and Exploiting Uncertainty: Statistical Learning Ability Affects How We Learn to Process Language Along Multiple Dimensions of Experience

While the effects of pattern learning on language processing are well known, the way in which pattern learning shapes exploratory behavior has long gone unnoticed. We report on the way in which individual differences in statistical pattern learning affect performance in the domain of language along multiple dimensions. Analyzing data from healthy monolingual adults' performance on a serial reaction time task and a self-paced reading task, we show how individual differences in statistical pattern learning are reflected in readers' knowledge of linguistic co-occurrence patterns and in their exploration and exploitation of content-specific and task-general information. First, we investigated the extent to which an individual's pattern learning correlates with his or her sensitivity to systematic morphological and syntactic co-occurrences, as evidenced while reading authentic sentences. We found that the stream of morphological and syntactic information has a more pronounced effect on the reading speed of, as we will label them, content-sensitive learners in that the more probable the co-occurrence pattern, the faster their reading of that pattern will be. Next, we investigated how differences in pattern learning are reflected in the ways in which individuals approach the reading task itself and adapt to it. Casting this relation in terms of exploration/exploitation strategies, known from Reinforcement Learning, we conclude that content-sensitive learners are also more likely to initially probe (explore) a wider range of directly relevant patterns, which they can later use (exploit) to optimize their reading performance further. By affecting exploratory behavior, pattern learning influences the information that is gathered and becomes available for exploitation, thereby increasing the effect pattern learning has on language cognition.

Working memory affects anticipatory behavior during implicit pattern learning

We investigated the relation between implicit sequence learning and individual differences in working memory (WM) capacity. Participants performed an oculomotor version of the serial reaction time (SRT) task and three computerized WM tasks. Implicit learning was measured using anticipation measures only, as they represent strong indicators of learning. Our results demonstrate that anticipatory behavior in the SRT task changes as a function of WM capacity, such that it increases with decreased WM capacity. On the other hand, WM capacity did not affect the overall number of correct anticipations in the task. In addition, we report a positive relation between WM capacity and the number of consecutive correct anticipations (or chunks), and a negative relation between WM capacity and the overall number of errors, indicating different learning strategies during implicit sequence learning. The results of the current study are theoretically important, because they demonstrate that individual differences in WM capacity could account for differences in learning processes, and ultimately change individuals' anticipatory behavior, even when learning is implicit, without intention and awareness.

English only? Monolinguals in linguistically diverse contexts have an edge in language learning

Accumulating evidence shows how language context shapes bilingual language use and its cognitive consequences. However, few studies have considered the impact of language context for monolinguals. Although monolinguals' language processing is assumed to be relatively stable and homogeneous, some research has shown novel learning through exposure alone. Monolinguals living in linguistically diverse contexts regularly overhear languages they do not understand, and may absorb information about those languages in ways that shape their language networks. The current study used behavioral and ERP measures to compare monolinguals living in a linguistically diverse environment and a unilingual environment in their ability to learn vowel harmony in Finnish. Monolinguals in both contexts demonstrated similar learning of studied words; however, their ERPs differed for generalization. Monolinguals in the diverse context revealed an anterior late positivity, whereas monolinguals in the unilingual context showed no effect. The results suggest that linguistic diversity promotes new language learning.

Statistical learning attenuates visual activity only for attended stimuli

Perception and behavior can be guided by predictions, which are often based on learned statistical regularities. Neural responses to expected stimuli are frequently found to be attenuated after statistical learning. However, whether this sensory attenuation following statistical learning occurs automatically or depends on attention remains unknown. In the present fMRI study, we exposed human volunteers to sequentially presented object stimuli, in which the first object predicted the identity of the second object. We observed a reliable attenuation of neural activity for expected compared to unexpected stimuli in the ventral visual stream. Crucially, this sensory attenuation was only apparent when stimuli were attended, and vanished when attention was directed away from the predictable objects. These results put important constraints on neurocomputational theories that cast perception as a process of probabilistic integration of prior knowledge and sensory information.

Neural Organization of Hierarchical Motor Sequence Representations in the Human Neocortex

Although it is widely accepted that the brain represents movement sequences hierarchically, the neural implementation of this organization is still poorly understood. To address this issue, we experimentally manipulated how participants represented sequences of finger presses at the levels of individual movements, chunks, and entire sequences. Using representational fMRI analyses, we then examined how this hierarchical structure was reflected in the fine-grained brain activity patterns of the participants while they performed the 8 trained sequences. We found clear evidence of each level of the movement hierarchy at the representational level. However, anatomically, chunk and sequence representations substantially overlapped in the premotor and parietal cortices, whereas individual movements were uniquely represented in the primary motor cortex. The findings challenge the common hypothesis of an orderly anatomical separation of different levels of an action hierarchy and argue for a special status of the distinction between individual movements and sequential context.

A New Method for Studying Statistical Learning in Young Children

Purpose The purpose of this study was to determine whether a new oculomotor serial reaction time (RT) task revealed statistical sequence learning in young children. Method We used eye tracking to measure typically developing children's oculomotor RTs in response to cartoon-like creatures that appeared successively in quadrants of a monitor during 200 trials: an initial patterned phase (120 trials) in which the creature's location reflected 15 repetitions of an 8-element sequence, a pseudorandom phase (40 trials) in which the location was not predictable, and a final patterned phase (40 trials). In an auditory-visual version of the task, spoken nonwords linked to quadrants preceded the creature's appearance. In Study 1, we administered either the visual or the auditory-visual version to 5- and 6-year-old children; in Study 2, we examined the performance of 4-year-olds on the auditory-visual version. Results In both studies, group mean RT z scores were significantly shorter ( p < .05) during patterned than pseudorandom phases, with large effect sizes (Cohen's dz values = 1.17-1.79). Conclusion The new oculomotor serial RT task detected statistical sequence learning in typically developing children.

Unimodal statistical learning produces multimodal object-like representations

The concept of objects is fundamental to cognition and is defined by a consistent set of sensory properties and physical affordances. Although it is unknown how the abstract concept of an object emerges, most accounts assume that visual or haptic boundaries are crucial in this process. Here, we tested an alternative hypothesis that boundaries are not essential but simply reflect a more fundamental principle: consistent visual or haptic statistical properties. Using a novel visuo-haptic statistical learning paradigm, we familiarised participants with objects defined solely by across-scene statistics provided either visually or through physical interactions. We then tested them on both a visual familiarity and a haptic pulling task, thus measuring both within-modality learning and across-modality generalisation. Participants showed strong within-modality learning and ‘zero-shot’ across-modality generalisation which were highly correlated. Our results demonstrate that humans can segment scenes into objects, without any explicit boundary cues, using purely statistical information.

Skill in discrete keying sequences is execution rate specific

The present study tested the hypothesis that in motor sequences, the interval between successive movements is critical for the type of representation that develops. Participants practiced two 7-key sequences in the context of a discrete sequence production (DSP) task. The 0-RSI group practiced these sequences with response stimulus intervals (RSIs) of 0, which is typical for the DSP task, while the long-RSI group practiced the same sequences with unpredictable RSIs between 500 and 2000 ms. The ensuing test phase examined performance of these familiar and of unfamiliar sequences for both groups under both RSI regimes. The results support our hypothesis that the motor chunks that 0-RSI participants developed could not be used with long RSIs, whereas the long-RSI participants developed sequence representations that cannot be used with 0 RSIs. A new, computerized, sequence awareness task showed that long-RSI participants had limited sequence knowledge. The sequencing skill developed by long-RSI participants can, therefore, not have been based on explicit knowledge.

Training Motor Sequences: Effects of Speed and Accuracy Instructions

Participants practiced a fixed 3- and a fixed 6-key press sequence for 144 times each. In the speed group, they were instructed to execute their sequences fast without bothering much about errors while the accurate group was instructed to be careful and prevent errors. In the test phase, participants executed series of 3 and 6 responses (a) when all element-specific stimuli were displayed in the familiar order, (b) in response to just the familiar first stimulus, and (c) by responding to random stimuli. The speed instruction yielded stronger sequencing skill while the accuracy instruction developed stronger reaction skill.

Network constraints on learnability of probabilistic motor sequences

Human learners are adept at grasping the complex relationships underlying incoming sequential input¹. In the present work, we formalize complex relationships as graph structures² derived from temporal associations^3,4 in motor sequences. Next, we explore the extent to which learners are sensitive to key variations in the topological properties⁵ inherent to those graph structures. Participants performed a probabilistic motor sequence task in which the order of button presses was determined by the traversal of graphs with modular, lattice-like, or random organization. Graph nodes each represented a unique button press and edges represented a transition between button presses. Results indicate that learning, indexed here by participants’ response times, was strongly mediated by the graph’s meso-scale organization, with modular graphs being associated with shorter response times than random and lattice graphs. Moreover, variations in a node’s number of connections (degree) and a node’s role in mediating long-distance communication (betweenness centrality) impacted graph learning, even after accounting for level of practice on that node. These results demonstrate that the graph architecture underlying temporal sequences of stimuli fundamentally constrains learning, and moreover that tools from network science provide a valuable framework for assessing how learners encode complex, temporally structured information.

Expectation affects learning and modulates memory experience at retrieval

Our ability to make predictions and monitor regularities has a profound impact on the way we perceive the environment, but the effect this mechanism has on memory is not well understood. In four experiments, we explored the effects on memory of the expectation status of information at encoding or at retrieval. In a rule-learning task participants learned a contingency relationship between 6 different symbols and the type of stimulus that followed each one. Either at encoding (Experiments 1a and 1b) or at retrieval (Experiments 2a and 2b), the established relationship was violated for a subset of stimuli resulting in the presentation of both expected and unexpected stimuli. The expectation status of the stimuli was found to have opposite effects on familiarity and recollection performance, the two kinds of memory that support recognition memory. At encoding (Experiments 1a and 1b), the presentation of expected stimuli selectively enhanced subsequent familiarity performance, while unexpected stimuli selectively enhanced subsequent recollection. Similarly, at retrieval (Experiments 2a and 2b), expected stimuli were more likely to be deemed familiar than unexpected stimuli, whereas unexpected stimuli were more likely to be recollected than were expected stimuli. These findings suggest that two separate memory enhancement mechanisms exist; one sensitive and modulating the accuracy of memory for the contextually distinctive or unexpected, and the other sensitive to and modulating the accuracy of memory for the expected. Therefore, the degree to which information fits with expectation has critical implications for the type of computational mechanism that will be engaged to support memory.

Symbolic flexibility during unsupervised word learning in children and adults

Considerable debate in language acquisition concerns whether word learning is driven by domain-general (symbolically flexible) or domain-specific learning mechanisms. Prior work has shown that very young children can map objects to either words or nonlinguistic sounds, but by 20 months of age this ability narrows to only words. This suggests that although symbolically flexible mechanisms are operative early, they become more specified over development. However, such research has been conducted only with young children in ostensive teaching contexts. Thus, we investigated symbolic flexibility at later ages in more referentially ambiguous learning situations. In Experiment 1, 47 6- to 8-year-olds acquired eight symbol-object mappings in a cross-situational word learning paradigm where multiple mappings are learned based only on co-occurrence. In the word condition participants learned with novel pseudowords, whereas in the sound condition participants learned with nonlinguistic sounds (e.g., beeps). Children acquired the mappings, but performance did not differ across conditions, suggesting broad symbolic flexibility. In Experiment 2, 41 adults learned 16 mappings in a comparable design. They learned with ease in both conditions but showed a significant advantage for words. Thus, symbolic flexibility decreases with age, potentially due to repeated experiences with linguistic materials. Moreover, trial-by-trial analyses of the microstructure of both children's and adults' performance did not reveal any substantial differences due to condition, consistent with the hypothesis that learning mechanisms are generally employed similarly with both words and nonlinguistic sounds.

Representation of spatial sequences using nested rules in human prefrontal cortex

Memory for spatial sequences does not depend solely on the number of locations to be stored, but also on the presence of spatial regularities. Here, we show that the human brain quickly stores spatial sequences by detecting geometrical regularities at multiple time scales and encoding them in a format akin to a programming language. We measured gaze-anticipation behavior while spatial sequences of variable regularity were repeated. Participants' behavior suggested that they quickly discovered the most compact description of each sequence in a language comprising nested rules, and used these rules to compress the sequence in memory and predict the next items. Activity in dorsal inferior prefrontal cortex correlated with the amount of compression, while right dorsolateral prefrontal cortex encoded the presence of embedded structures. Sequence learning was accompanied by a progressive differentiation of multi-voxel activity patterns in these regions. We propose that humans are endowed with a simple "language of geometry" which recruits a dorsal prefrontal circuit for geometrical rules, distinct from but close to areas involved in natural language processing.

Prefrontal and Hippocampal Structure Predict Statistical Learning Ability in Early Childhood

Statistical learning can be used to gain sensitivity to many important regularities in our environment, including structure that is foundational to language and visual perception. As yet, little is known about how statistical learning takes place in the human brain, especially in children's developing brains and with regard to the broader neurobiology of learning and memory. We therefore explored the relationship between statistical learning and the thickness and volume of structures that are traditionally implicated in declarative and procedural memory, focusing specifically on the left inferior PFC, the hippocampus, and the caudate during early childhood (ages 5-8.5 years). We found that the thickness of the left inferior frontal cortex and volume of the right hippocampus predicted statistical learning ability in young children. Importantly, these regions did not change in thickness or volume with age, but the relationship between learning and the right hippocampus interacted with age such that older children's hippocampal structure more strongly predicted performance. Overall, the data show that children's statistical learning is supported by multiple neural structures that are more broadly implicated in learning and memory, especially declarative memory (hippocampus) and attention/top-down control (the PFC).

Suppressed Sensory Response to Predictable Object Stimuli throughout the Ventral Visual Stream

Prediction plays a crucial role in perception, as prominently suggested by predictive coding theories. However, the exact form and mechanism of predictive modulations of sensory processing remain unclear, with some studies reporting a downregulation of the sensory response for predictable input whereas others observed an enhanced response. In a similar vein, downregulation of the sensory response for predictable input has been linked to either sharpening or dampening of the sensory representation, which are opposite in nature. In the present study, we set out to investigate the neural consequences of perceptual expectation of object stimuli throughout the visual hierarchy, using fMRI in human volunteers. Participants of both sexes were exposed to pairs of sequentially presented object images in a statistical learning paradigm, in which the first object predicted the identity of the second object. Image transitions were not task relevant; thus, all learning of statistical regularities was incidental. We found strong suppression of neural responses to expected compared with unexpected stimuli throughout the ventral visual stream, including primary visual cortex, lateral occipital complex, and anterior ventral visual areas. Expectation suppression in lateral occipital complex scaled positively with image preference and voxel selectivity, lending support to the dampening account of expectation suppression in object perception.SIGNIFICANCE STATEMENT It has been suggested that the brain fundamentally relies on predictions and constructs models of the world to make sense of sensory information. Previous research on the neural basis of prediction has documented suppressed neural responses to expected compared with unexpected stimuli. In the present study, we demonstrate robust expectation suppression throughout the entire ventral visual stream, and underlying this suppression a dampening of the sensory representation in object-selective visual cortex, but not in primary visual cortex. Together, our results provide novel evidence in support of theories conceptualizing perception as an active inference process, which selectively dampens cortical representations of predictable objects. This dampening may support our ability to automatically filter out irrelevant, predictable objects.

New insights into statistical learning and chunk learning in implicit sequence acquisition

Implicit sequence learning is ubiquitous in our daily life. However, it is unclear whether the initial acquisition of sequences results from learning to chunk items (i.e., chunk learning) or learning the underlying statistical regularities (i.e., statistical learning). By grouping responses with or without a distinct chunk or statistical structure into segments and comparing these responses, previous studies have demonstrated both chunk and statistical learning. However, few studies have considered the response sequence as a whole and examined the temporal dependency of the entire sequence, where the temporal dependencies could disclose the internal representations of chunk and statistical learning. Participants performed a serial reaction time (SRT) task under different stimulus interval conditions. We found that sequence learning reflected by reaction time (RT) rather than motor improvements represented by movement time (MT). The temporal dependency of RT and MT revealed that both RT and MT displayed recursive patterns caused by biomechanical effects of response locations and foot transitions. Chunking was noticeable only in the presence of the recurring RT or MT but vanished after the recursive component was removed, implying that chunk formation may result from biomechanical constraints rather than learning itself. In addition, we observed notable first-order autocorrelations in RT. This trial-to-trial association enhanced as learning progressed regardless of stimulus intervals, reflecting the internal cognitive representation of the first-order stimulus contingencies. Our results suggest that initial acquisition of implicit sequences may arise from first-order statistical learning rather than chunk learning.

Online incidental statistical learning of audiovisual word sequences in adults: a registered report

Statistical learning has been proposed as a key mechanism in language learning. Our main goal was to examine whether adults are capable of simultaneously extracting statistical dependencies in a task where stimuli include a range of structures amenable to statistical learning within a single paradigm. We devised an online statistical learning task using real word auditory-picture sequences that vary in two dimensions: (i) predictability and (ii) adjacency of dependent elements. This task was followed by an offline recall task to probe learning of each sequence type. We registered three hypotheses with specific predictions. First, adults would extract regular patterns from continuous stream (effect of grammaticality). Second, within grammatical conditions, they would show differential speeding up for each condition as a factor of statistical complexity of the condition and exposure. Third, our novel approach to measure online statistical learning would be reliable in showing individual differences in statistical learning ability. Further, we explored the relation between statistical learning and a measure of verbal short-term memory (STM). Forty-two participants were tested and retested after an interval of at least 3 days on our novel statistical learning task. We analysed the reaction time data using a novel regression discontinuity approach. Consistent with prediction, participants showed a grammaticality effect, agreeing with the predicted order of difficulty for learning different statistical structures. Furthermore, a learning index from the task showed acceptable test-retest reliability (r = 0.67). However, STM did not correlate with statistical learning. We discuss the findings noting the benefits of online measures in tracking the learning process.

Splitting the variance of statistical learning performance: A parametric investigation of exposure duration and transitional probabilities

What determines individuals' efficacy in detecting regularities in visual statistical learning? Our theoretical starting point assumes that the variance in performance of statistical learning (SL) can be split into the variance related to efficiency in encoding representations within a modality and the variance related to the relative computational efficiency of detecting the distributional properties of the encoded representations. Using a novel methodology, we dissociated encoding from higher-order learning factors, by independently manipulating exposure duration and transitional probabilities in a stream of visual shapes. Our results show that the encoding of shapes and the retrieving of their transitional probabilities are not independent and additive processes, but interact to jointly determine SL performance. The theoretical implications of these findings for a mechanistic explanation of SL are discussed.

Mechanisms of the associated nontargets effect: Processes influenced by statistical learning in a simple visual environment

In three visual search experiments participants were asked to make a target response if either of two targets was present and to make a nontarget response if neither target was present. Some target-absent displays included only nontarget features that never occurred in the same displays as target features, whereas other target-absent displays included nontarget features that did sometimes occur with target features. Nontarget responses were reliably faster in the former case than in the latter. This “associated nontargets effect” appears to arise from participants’ ability to learn and to use contingencies between the presence of certain nontargets and the absence of any target.

Infants' Motor Proficiency and Statistical Learning for Actions

Prior research has shown that infants learn statistical regularities in action sequences better than they learn non-action event sequences. This is consistent with current theories claiming that the same mechanism guides action observation and action execution. The current eye-tracking study tested the prediction, based on these theories, that infants' ability to learn statistical regularities in action sequences is modulated by their own motor abilities. Eight- to eleven-month-old infants observed an action sequence containing two deterministic action pairs (i.e., action A always followed by action B) embedded within an otherwise random sequence. One pair was performed with a whole-hand grasp. The second pair was performed with a pincer grasp, a fine motor skill that emerges around 9 months of age. Infants were then categorized into groups according to which grasp was dominant in their motor repertoire. Predictive looks to correct upcoming actions during the deterministic pairs were analyzed to measure whether infants learned and anticipated the sequence regularities. Findings indicate that infants learned the statistical regularities: across motor groups, they made more correct than incorrect predictive fixations to upcoming actions. Overall, learning was not significantly modulated by their dominant grasping abilities. However, infants with a dominant pincer grasp showed an earlier increase in correct predictions for the pincer grasp pair and not the whole-hand grasp. Likewise, infants with a dominant whole-hand grasp showed an early increase in correct predictions for the pair performed with a whole-hand grasp, and not the pincer grasp. Together, these findings suggest that infants' ability to learn action sequences is facilitated when the observed action matches their own action repertoire. However, findings cannot be explained entirely by motor accounts, as infants also learned the actions less congruent with their own abilities. Findings are discussed in terms of the interplay between the motor system and additional non-motor resources during the acquisition of new motor skills in infancy.

Statistical learning in songbirds: from self-tutoring to song culture

At the onset of vocal development, both songbirds and humans produce variable vocal babbling with broadly distributed acoustic features. Over development, these vocalizations differentiate into the well-defined, categorical signals that characterize adult vocal behaviour. A broadly distributed signal is ideal for vocal exploration, that is, for matching vocal production to the statistics of the sensory input. The developmental transition to categorical signals is a gradual process during which the vocal output becomes differentiated and stable. But does it require categorical input? We trained juvenile zebra finches with playbacks of their own developing song, produced just a few moments earlier, updated continuously over development. Although the vocalizations of these self-tutored (ST) birds were initially broadly distributed, birds quickly developed categorical signals, as fast as birds that were trained with a categorical, adult song template. By contrast, siblings of those birds that received no training (isolates) developed phonological categories much more slowly and never reached the same level of category differentiation as their ST brothers. Therefore, instead of simply mirroring the statistical properties of their sensory input, songbirds actively transform it into distinct categories. We suggest that the early self-generation of phonological categories facilitates the establishment of vocal culture by making the song easier to transmit at the micro level, while promoting stability of shared vocabulary at the group level over generations.

This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’.

The impact of cerebellar transcranial direct current stimulation (tDCS) on learning fine-motor sequences

The cerebellum has been shown to be important for skill learning, including the learning of motor sequences. We investigated whether cerebellar transcranial direct current stimulation (tDCS) would enhance learning of fine motor sequences. Because the ability to generalize or transfer to novel task variations or circumstances is a crucial goal of real world training, we also examined the effect of tDCS on performance of novel sequences after training. In Study 1, participants received either anodal, cathodal or sham stimulation while simultaneously practising three eight-element key press sequences in a non-repeating, interleaved order. Immediately after sequence practice with concurrent tDCS, a transfer session was given in which participants practised three interleaved novel sequences. No stimulation was given during transfer. An inhibitory effect of cathodal tDCS was found during practice, such that the rate of learning was slowed in comparison to the anodal and sham groups. In Study 2, participants received anodal or sham stimulation and a 24 h delay was added between the practice and transfer sessions to reduce mental fatigue. Although this consolidation period benefitted subsequent transfer for both tDCS groups, anodal tDCS enhanced transfer performance. Together, these studies demonstrate polarity-specific effects on fine motor sequence learning and generalization.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'.

Towards a theory of individual differences in statistical learning

In recent years, statistical learning (SL) research has seen a growing interest in tracking individual performance in SL tasks, mainly as a predictor of linguistic abilities. We review studies from this line of research and outline three presuppositions underlying the experimental approach they employ: (i) that SL is a unified theoretical construct; (ii) that current SL tasks are interchangeable, and equally valid for assessing SL ability; and (iii) that performance in the standard forced-choice test in the task is a good proxy of SL ability. We argue that these three critical presuppositions are subject to a number of theoretical and empirical issues. First, SL shows patterns of modality- and informational-specificity, suggesting that SL cannot be treated as a unified construct. Second, different SL tasks may tap into separate sub-components of SL that are not necessarily interchangeable. Third, the commonly used forced-choice tests in most SL tasks are subject to inherent limitations and confounds. As a first step, we offer a methodological approach that explicitly spells out a potential set of different SL dimensions, allowing for better transparency in choosing a specific SL task as a predictor of a given linguistic outcome. We then offer possible methodological solutions for better tracking and measuring SL ability. Taken together, these discussions provide a novel theoretical and methodological approach for assessing individual differences in SL, with clear testable predictions.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'.

Redefining "Learning" in Statistical Learning: What Does an Online Measure Reveal About the Assimilation of Visual Regularities?

From a theoretical perspective, most discussions of statistical learning (SL) have focused on the possible "statistical" properties that are the object of learning. Much less attention has been given to defining what "learning" is in the context of "statistical learning." One major difficulty is that SL research has been monitoring participants' performance in laboratory settings with a strikingly narrow set of tasks, where learning is typically assessed offline, through a set of two-alternative-forced-choice questions, which follow a brief visual or auditory familiarization stream. Is that all there is to characterizing SL abilities? Here we adopt a novel perspective for investigating the processing of regularities in the visual modality. By tracking online performance in a self-paced SL paradigm, we focus on the trajectory of learning. In a set of three experiments we show that this paradigm provides a reliable and valid signature of SL performance, and it offers important insights for understanding how statistical regularities are perceived and assimilated in the visual modality. This demonstrates the promise of integrating different operational measures to our theory of SL.