Domain generality versus modality specificity: the paradox of statistical learning

Statistically Induced Chunking Recall: A Memory-Based Approach to Statistical Learning

The computations involved in statistical learning have long been debated. Here, we build on work suggesting that a basic memory process, chunking, may account for the processing of statistical regularities into larger units. Drawing on methods from the memory literature, we developed a novel paradigm to test statistical learning by leveraging a robust phenomenon observed in serial recall tasks: that short-term memory is fundamentally shaped by long-term distributional learning. In the statistically induced chunking recall (SICR) task, participants are exposed to an artificial language, using a standard statistical learning exposure phase. Afterward, they recall strings of syllables that either follow the statistics of the artificial language or comprise the same syllables presented in a random order. We hypothesized that if individuals had chunked the artificial language into word-like units, then the statistically structured items would be more accurately recalled relative to the random controls. Our results demonstrate that SICR effectively captures learning in both the auditory and visual modalities, with participants displaying significantly improved recall of the statistically structured items, and even recall specific trigram chunks from the input. SICR also exhibits greater test-retest reliability in the auditory modality and sensitivity to individual differences in both modalities than the standard two-alternative forced-choice task. These results thereby provide key empirical support to the chunking account of statistical learning and contribute a valuable new tool to the literature.

Neural Encoding of Auditory Statistics

The human brain extracts statistical regularities embedded in real-world scenes to sift through the complexity stemming from changing dynamics and entwined uncertainty along multiple perceptual dimensions (e.g., pitch, timbre, location). While there is evidence that sensory dynamics along different auditory dimensions are tracked independently by separate cortical networks, how these statistics are integrated to give rise to unified objects remains unknown, particularly in dynamic scenes that lack conspicuous coupling between features. Using tone sequences with stochastic regularities along spectral and spatial dimensions, this study examines behavioral and electrophysiological responses from human listeners (male and female) to changing statistics in auditory sequences and uses a computational model of predictive Bayesian inference to formulate multiple hypotheses for statistical integration across features. Neural responses reveal multiplexed brain responses reflecting both local statistics along individual features in frontocentral networks, together with global (object-level) processing in centroparietal networks. Independent tracking of local surprisal along each acoustic feature reveals linear modulation of neural responses, while global melody-level statistics follow a nonlinear integration of statistical beliefs across features to guide perception. Near identical results are obtained in separate experiments along spectral and spatial acoustic dimensions, suggesting a common mechanism for statistical inference in the brain. Potential variations in statistical integration strategies and memory deployment shed light on individual variability between listeners in terms of behavioral efficacy and fidelity of neural encoding of stochastic change in acoustic sequences.SIGNIFICANCE STATEMENT The world around us is complex and ever changing: in everyday listening, sound sources evolve along multiple dimensions, such as pitch, timbre, and spatial location, and they exhibit emergent statistical properties that change over time. In the face of this complexity, the brain builds an internal representation of the external world by collecting statistics from the sensory input along multiple dimensions. Using a Bayesian predictive inference model, this work considers alternative hypotheses for how statistics are combined across sensory dimensions. Behavioral and neural responses from human listeners show the brain multiplexes two representations, where local statistics along each feature linearly affect neural responses, and global statistics nonlinearly combine statistical beliefs across dimensions to shape perception of stochastic auditory sequences.

The measurement, evolution, and neural representation of action grammars of human behavior

Human behaviors from toolmaking to language are thought to rely on a uniquely evolved capacity for hierarchical action sequencing. Testing this idea will require objective, generalizable methods for measuring the structural complexity of real-world behavior. Here we present a data-driven approach for extracting action grammars from basic ethograms, exemplified with respect to the evolutionarily relevant behavior of stone toolmaking. We analyzed sequences from the experimental replication of ~ 2.5 Mya Oldowan vs. ~ 0.5 Mya Acheulean tools, finding that, while using the same "alphabet" of elementary actions, Acheulean sequences are quantifiably more complex and Oldowan grammars are a subset of Acheulean grammars. We illustrate the utility of our complexity measures by re-analyzing data from an fMRI study of stone toolmaking to identify brain responses to structural complexity. Beyond specific implications regarding the co-evolution of language and technology, this exercise illustrates the general applicability of our method to investigate naturalistic human behavior and cognition.

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

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

Across-trial spatial suppression in visual search

In order to focus on objects of interest, humans must be able to avoid distraction by salient stimuli that are not relevant to the task at hand. Many recent studies have shown that through statistical learning we are able to suppress the location that is most likely to contain a salient distractor. Here we demonstrate a remarkable flexibility in attentional suppression. Participants had to search for a shape singleton while a color distractor singleton was present. Unbeknown to the participant, the color distractor was presented according to a consistent pattern across trials. Our findings show that participants learn this distractor sequence as they proactively suppressed the anticipated location of the distractor on the next trial. Critically, none of the participants were aware of these hidden sequences. We conclude that the spatial priority map is highly flexible, operating at a subconscious level preparing the attentional system for what will happen next.

Regularity detection under stress: Faster extraction of probability-based regularities

Acute stress can crucially influence learning and memory processes. One of the key processes underlying human learning and memory is the ability of our brain to rapidly detect and extract regularities from sensory input across time and space leading to effective predictive processing. Here, we aimed to get an in-depth look into the effect of stress on the acquisition of two aspects of regularity extraction. We examined whether and how stress affects the learning (1) of probability-based regularities and (2) of serial order-based regularities in the same experimental design, and (3) the explicit access to the acquired information. Considering that the acquisition of probability-based regularities is a relatively rapid process, we primarily focused on the early phase of the task. We induced stress with the Socially Evaluated Cold Pressor Test in 27 young adults, while 26 participants were enrolled in the control group. Salivary cortisol levels and subjective ratings of affective states showed successful stress induction. After the stress induction, we measured regularity extraction with the cued Alternating Serial Reaction Time task. We found that stress promoted the extraction of probability-based regularities measured by the learning performance in the early phase of the task and did not alter the learning of serial order-based regularities. Post-block reports showed weaker explicit access to the serial order-based regularities in the stress group. Our results can contribute to a process-level understanding on how stress alters learning and memory functions related to predictive processes.

Statistical and sequence learning lead to persistent memory in children after a one-year offline period

Extraction of environmental patterns underlies human learning throughout the lifespan and plays a crucial role not only in cognitive but also perceptual, motor, and social skills. At least two types of regularities contribute to acquiring skills: (1) statistical, probability-based regularities, and (2) serial order-based regularities. Memory performance of probability-based and/or serial order-based regularities over short periods (from minutes to weeks) has been widely investigated across the lifespan. However, long-term (months or year-long) memory performance of such knowledge has received relatively less attention and has not been assessed in children yet. Here, we aimed to test the long-term memory performance of probability-based and serial order-based regularities over a 1-year offline period in neurotypical children between the age of 9 and 15. Participants performed a visuomotor four-choice reaction time task designed to measure the acquisition of probability-based and serial order-based regularities simultaneously. Short-term consolidation effects were controlled by retesting their performance after a 5-h delay. They were then retested on the same task 1 year later without any practice between the sessions. Participants successfully acquired both probability-based and serial order-based regularities and retained both types of knowledge over the 1-year period. The successful retention was independent of age. Our study demonstrates that the representation of probability-based and serial order-based regularities remains stable over a long period of time. These findings offer indirect evidence for the developmental invariance model of skill consolidation.

Statistical Learning and the Effect of Starting Small in Developmental Dyslexia

Purpose Impairments in statistical learning abilities of individuals with developmental dyslexia (DD) have been demonstrated in word segmentation and in visual artificial grammar learning (AGL) tasks, but so far, little attention has been devoted to the AGL abilities of this population in the acoustic verbal domain. This study aimed to test whether adolescents with dyslexia have difficulties in extracting abstract patterns from auditory sequences of nonsense syllables based on a finite state grammar relative to typically developing (TD) peers. We also tested whether incremental presentation of stimuli of different lengths (starting small) has a facilitating effect on learning complex structures in dyslexia (and in TD) as opposed to presenting strings in random order. Method Thirty-one adolescents with DD and 31 age-matched control participants completed an AGL task. Participants passively listened to acoustic sequences of nonsense syllables generated by an artificial grammar in the training phase. In the test phase, they were presented with pairs of novel grammatical and nongrammatical sequences and were required to decide which member of a sequence pair was more similar to the material heard during training. Results Performance levels and the proportion of learners were smaller in participants with DD than in the control group. While the starting small effect was nominally present both in performance levels and in the number of learners in participants with DD, but not in the group with TD, the presentation of strings in incremental order did not statistically improve learning performance in either group. Conclusion Our results suggest that (a) statistical learning of abstract sequences in the acoustic domain is less efficient in people with dyslexia than in TD controls and (b) while incremental presentation of stimuli of different length did not improve learning in our study, the observed pattern of results suggests that the effects of different training designs should be explored further in developmental disorders.

Categorization of Vocal Emotion Cues Depends on Distributions of Input

Learners use the distributional properties of stimuli to identify environmentally relevant categories in a range of perceptual domains, including words, shapes, faces, and colors. We examined whether similar processes may also operate on affective information conveyed through the voice. In Experiment 1, we tested how adults (18–22-year-olds) and children (8–10-year-olds) categorized affective states communicated by vocalizations varying continuously from “calm” to “upset.” We found that the threshold for categorizing both verbal (i.e., spoken word) and nonverbal (i.e., a yell) vocalizations as “upset” depended on the statistical distribution of the stimuli participants encountered. In Experiment 2, we replicated and extended these findings in adults using vocalizations that conveyed multiple negative affect states. These results suggest perceivers’ flexibly and rapidly update their interpretation of affective vocal cues based upon context.

Short-Term Memory for Serial Order Moderates Aspects of Language Acquisition in Children With Developmental Language Disorder: Findings From the HelSLI Study

Previous studies of verbal short-term memory (STM) indicate that STM for serial order may be linked to language development and developmental language disorder (DLD). To clarify whether a domain-general mechanism is impaired in DLD, we studied the relations between age, non-verbal serial STM, and language competence (expressive language, receptive language, and language reasoning). We hypothesized that non-verbal serial STM differences between groups of children with DLD and typically developing (TD) children are linked to their language acquisition differences. Fifty-one children with DLD and sixty-six TD children participated as part of the HelSLI project in this cross-sectional study. The children were 4-6-year-old monolingual native Finnish speakers. They completed several tests of language and cognitive functioning, as well as new game-like tests of visual and auditory non-verbal serial STM. We used regression analyses to examine how serial STM moderates the effect of age on language. A non-verbal composite measure of serial visual and auditory STM moderated cross-sectional development of receptive language in the children with DLD. This moderation was not observed in the TD children. However, we found more rapid cross-sectional development of non-verbal serial STM in the TD children than in the children with DLD. The results suggest that children with DLD may be more likely to have compromised general serial STM processing and that superior non-verbal serial STM may be associated with better language acquisition in children with DLD.

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.

Oculomotor anticipation reveals a multitude of learning processes underlying the serial reaction time task

Sequence learning is the cognitive faculty enabling everyday skill acquisition. In the lab, it is typically measured in speed of response to sequential stimuli, whereby faster responses are taken to indicate improved anticipation. However, response speed is an indirect measure of anticipation, that can provide only limited information on underlying processes. As a result, little is known about what is learned during sequence learning, and how that unfolds over time. In this work, eye movements that occurred before targets appeared on screen in an ocular serial reaction time (O-SRT) task provided an online indication of where participants anticipated upcoming targets. When analyzed in the context of the stimuli preceding them, oculomotor anticipations revealed several simultaneous learning processes. These processes influenced each other, as learning the task grammar facilitated acquisition of the target sequence. However, they were dissociable, as the grammar was similarly learned whether a repeating sequence inhabited the task or not. Individual differences were found in how the different learning processes progressed, allowing for similar performance to be produced for different latent reasons. This study provides new insights into the processes subserving sequence learning, and a new method for high-resolution study of it.

Evidence of ordinal position encoding of sequences extracted from continuous speech

Infants' capacity to extract statistical regularities from sequential information is impressive and well documented. However, statistical learning's underlying mechanism remains mostly unknown, and its role in language acquisition is still under debate. To shed light on these issues, here we address the question of which information human subjects extract and encode after familiarisation with a continuous sequence of stimuli and its dependence on the type of segmentation cues and on the stimuli modality. Specifically, we investigate whether adults and 5-month-old infants learn the syllables' co-occurrence in the stream or generate a representation of the Words that include syllables' ordinal position. We test if subtle pauses signalling word boundaries change the encoding and, in adults, if it varies across modalities. In six behavioural experiments, we show that: (i) Adults and infants learn the streams' statistical structure. (ii) Ordinal encoding emerges in the auditory modality, and pauses enhanced it. However, (iii) ordinal encoding seems to depend on the learning stage and not on pauses marking Words' edges. Interestingly, (iv) for visual presentation of orthographic syllables, we do not find evidence of ordinal encoding in adults. Our results support the emergence, in the auditory modality, of a Word representation where its constituents are associated with an ordinal position at play already early in life, bringing new insights into speech processing and language acquisition. Additionally, we successfully use for the first time pupillometry in an infant segmentation task.

Self-explaining roads: What does visual cognition tell us about designing safer roads?

In 1995, Theeuwes and Godthelp published a paper called "self-explaining roads," in which they argued for the development of a new concept for approaching safe road design. Since this publication, self-explaining roads (SER) became one of the leading principles in road design worldwide. The underlying notion is that roads should be designed in such a way that road users immediately know how to behave and what to expect on these roads. In other words, the environment should be designed such that it elicits adequate and safe behavior. The present paper describes in detail the theoretical basis for the idea of SER and explains why this has such a large effect on human behavior. It is argued that the notion is firmly rooted in the theoretical framework of statistical learning, subjective road categorization and the associated expectations. The paper illustrates some successful implementation and describes recent developments worldwide.

Stimulus variation-based training enhances artificial grammar learning

The current study was designed to explore whether statistical learning ability is affected by the diversity of the stimulus set used in the training phase. The effect of stimulus diversity was assessed by controlling and manipulating the number of exposures to a given set and the number of unique strings presented to the learner during the training phase. 147 students participated in two studies. In the unvaried stimulus study, 71 participants learned the same basic set of 15 exemplars, once(15 × 1 exposure), twice (15 × 2 exposures = 30 total strings) and 3 times (15 × 3 exposures = 45 total strings). In the varied stimulus study, 75 participants learned 15, 30 and 45, all of which were unique, unrepeated exemplars. All groups were asked to classify test strings for their grammaticality following training. Results of the d' measures in the unvaried stimulus study indicate similar performance across the groups. Conversely, the results of the varied stimulus study show that the group presented with 45 unique strings performed significantly better than the baseline group (15 strings). Analysis of the differences across the equivalent groups in the two studies (15 × 2 exposures vs. 30 unique strings and 15 × 3 exposures vs. 45 unique strings) indicates differences in performance only between the group who was presented with the same 15 strings three times and the group presented with 45 unrepeated strings. Taken together, our results shed additional light on the central role of stimulus variation in Artificial Grammar Learning.

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.

Learning hierarchical sequence representations across human cortex and hippocampus

Sensory input arrives in continuous sequences that humans experience as segmented units, e.g., words and events. The brain's ability to discover regularities is called statistical learning. Structure can be represented at multiple levels, including transitional probabilities, ordinal position, and identity of units. To investigate sequence encoding in cortex and hippocampus, we recorded from intracranial electrodes in human subjects as they were exposed to auditory and visual sequences containing temporal regularities. We find neural tracking of regularities within minutes, with characteristic profiles across brain areas. Early processing tracked lower-level features (e.g., syllables) and learned units (e.g., words), while later processing tracked only learned units. Learning rapidly shaped neural representations, with a gradient of complexity from early brain areas encoding transitional probability, to associative regions and hippocampus encoding ordinal position and identity of units. These findings indicate the existence of multiple, parallel computational systems for sequence learning across hierarchically organized cortico-hippocampal circuits.

How statistical learning interacts with the socioeconomic environment to shape children's language development

Language is acquired in part through statistical learning abilities that encode environmental regularities. Language development is also heavily influenced by social environmental factors such as socioeconomic status. However, it is unknown to what extent statistical learning interacts with SES to affect language outcomes. We measured event-related potentials in 26 children aged 8–12 while they performed a visual statistical learning task. Regression analyses indicated that children’s learning performance moderated the relationship between socioeconomic status and both syntactic and vocabulary language comprehension scores. For children demonstrating high learning, socioeconomic status had a weaker effect on language compared to children showing low learning. These results suggest that high statistical learning ability can provide a buffer against the disadvantages associated with being raised in a lower socioeconomic status household.

Why Does Dual-Tasking Hamper Implicit Sequence Learning?

Research on the limitations of dual-tasking might profit from using setups with a predictable sequence of stimuli and responses and assessing the acquisition of this sequence. Detrimental effects of dual-tasking on implicit sequence learning in the serial reaction time task (SRTT; Nissen & Bullemer, 1987) - when paired with an uncorrelated task - have been attributed to participants' lack of separating the streams of events in either task. Assuming that co-occurring events are automatically integrated, we reasoned that participants could need to first learn which events co-occur, before they can acquire sequence knowledge. In the training phase, we paired an 8-element visual-manual SRTT with an auditory-vocal task. Afterwards, we tested under single-tasking conditions whether SRTT sequence knowledge had been acquired. By applying different variants of probabilistic SRTT-tone pairings across three experiments, we tested what type of predictive relationship was needed to preserve sequence learning. In Experiment 1, where half of the SRTT-elements were paired to 100% with one specific tone and the other half randomly, only the fixedly paired elements were learned. Yet, no sequence learning was found when each of the eight SRTT-elements was paired with tone identity in a 75%-25% ratio (Experiment 2). Sequence learning was, however, intact when the 75%-25% ratio was applied to the four SRTT target locations instead (Experiment 3). The results suggest that participants (when lacking a separation of the task representations while dual-tasking) can learn a sequence inherent in one of two tasks to the extent that across-task contingencies can be learned first.

Divided attention does not affect the acquisition and consolidation of transitional probabilities

Statistical learning facilitates the efficient processing and prediction of environmental events and contributes to the acquisition of automatic behaviors. Whereas a minimal level of attention seems to be required for learning to occur, it is still unclear how acquisition and consolidation of statistical knowledge are affected when attention is divided during learning. To test the effect of divided attention on statistical learning and consolidation, ninety-six healthy young adults performed the Alternating Serial Reaction Time task in which they incidentally acquired second-order transitional probabilities. Half of the participants completed the task with a concurrent secondary intentional sequence learning task that was applied to the same stimulus stream. The other half of the participants performed the task without any attention manipulation. Performance was retested after a 12-h post-learning offline period. Half of each group slept during the delay, while the other half had normal daily activity, enabling us to test the effect of delay activity (sleep vs. wake) on the consolidation of statistical knowledge. Divided attention had no effect on statistical learning: The acquisition of second-order transitional probabilities was comparable with and without the secondary task. Consolidation was neither affected by divided attention: Statistical knowledge was similarly retained over the 12-h delay, irrespective of the delay activity. Our findings can contribute to a better understanding of the role of attentional processes in and the robustness of visuomotor statistical learning and consolidation.

Children automatically abstract categorical regularities during statistical learning

Statistical learning allows us to discover myriad structures in our environment, which is saturated with information at many different levels-from items to categories. How do children learn different levels of information-about regularities that pertain to items and the categories they come from-and how does this differ from adults? Studies on category learning and memory have suggested that children may be more focused on items than adults. If this is also the case for statistical learning, children may not extract and learn the multi-level regularities that adults can. We report three experiments showing that children and adults extract both item- and category-level regularities in statistical learning. In Experiments 1 and 2, we show that both children and adults can learn structure at the item and category levels when they are measured independently. In Experiment 3, we show that both children and adults learn about categories even when exposure does not require this: both are able to generalize their learning from the item to the category level. Results indicate that statistical learning operates across multi-level structure in children and adults alike, enabling generalization of learning from specific items to exemplars from categories of those items that observers have never seen. Even though children may be more focused on items during other forms of learning, they learn about categories from item-level input during statistical learning.

Problem-solving strategies used in anatomical multiple-choice questions

BACKGROUND AND AIMS

Multiple-choice questions (MCQ) in the anatomical sciences are often perceived to be targeting recall of facts and regurgitation of trivial details. Moving away from this assumption requires the design of purposeful multiple-choice questions that focus on higher-order cognitive functions as opposed to rote memorization. In order to develop such questions, it was important to first understand the strategies that students use in solving multiple-choice questions. Using the think-aloud protocol, this study seeks to understand strategies students use in solving multiple-choice questions. Specifically, it seeks to uncover patterns in the reasoning process and tactics used when solving higher and lower order MCQ in anatomy. The research also provides insights onto how these strategies influence the student's probability of answering questions correctly.

METHODS

Multiple-choice questions were created at three levels of cognitive functioning based on the ideas, connections, extensions (ICE) learning framework. The think-aloud protocol was used to unravel problem-solving strategies used by 92 undergraduate anatomy students as they solved multiple-choice questions.

RESULTS

Sixteen strategies were identified through the oral and written think-alouds that students used to solve MCQ. Eleven of these have been described and supported by the literature, while the rest were utilized by our students when solving MCQ in anatomy. Domain-specific strategies of visualizing and recalling had the highest use. Personal connection was a strategy that allowed students to achieve success in all ICE levels in the oral think-alouds and in the I and E levels in the written think-alouds.

CONCLUSIONS

This research argues that it is upon us as educators to make learning visible to our students, specifically through the use of think-alouds. It also raises awareness that when educators facilitate the process of students making personal connections, it aids students in new knowledge being integrated effectively and retrieved accurately.

Visual statistical learning is facilitated in Zipfian distributions

Humans can extract co-occurrence regularities from their environment, and use them for learning. This statistical learning ability (SL) has been studied extensively as a way to explain how we learn the structure of our environment. These investigations have illustrated the impact of various distributional properties on learning. However, almost all SL studies present the regularities to be learned in uniform frequency distributions where each unit (e.g., image triplet) appears the same number of times: While the regularities themselves are informative, the appearance of the units cannot be predicted. In contrast, real-world learning environments, including the words children hear and the objects they see, are not uniform. Recent research shows that word segmentation is facilitated in a skewed (Zipfian) distribution. Here, we examine the domain-generality of the effect and ask if visual SL is also facilitated in a Zipfian distribution. We use an existing database to show that object combinations have a skewed distribution in children's environment. We then show that children and adults showed better learning in a Zipfian distribution compared to a uniform one, overall, and for low-frequency triplets. These results illustrate the facilitative impact of skewed distributions on learning across modality and age; suggest that the use of uniform distributions may underestimate performance; and point to the possible learnability advantage of such distributions in the real-world.

Statistical Learning and Language Impairments: Toward More Precise Theoretical Accounts

Statistical-learning (SL) theory offers an experience-based account of typical and atypical spoken and written language acquisition. Recent work has provided initial support for this view, tying individual differences in SL abilities to linguistic skills, including language impairments. In the current article, we provide a critical review of studies testing SL abilities in participants with and without developmental dyslexia and specific language impairment and discuss the directions that this field of research has taken so far. We identify substantial vagueness in the demarcation lines between different theoretical constructs (e.g., “statistical learning,” “implicit learning,” and “procedural learning”) as well as in the mappings between experimental tasks and these theoretical constructs. Moreover, we argue that current studies are not designed to contrast different theoretical approaches but rather test singular confirmatory predictions without including control tasks showing normal performance. We end by providing concrete suggestions for how to advance research on SL deficits in language impairments.

Independent effects of statistical learning and top-down attention

It is well known that spatial attention can be directed in a top-down way to task-relevant locations in space. In addition, through visual statistical learning (VSL), attention can be biased towards relevant (target) locations and away from irrelevant (distractor) locations. The present study investigates the interaction between the explicit task-relevant, top-down attention and the lingering attentional biases due to VSL. We wanted to determine the contribution of each of these two processes to attentional selection. In the current study, participants performed a search task while keeping a location in spatial working memory. In Experiment 1, the target appeared more often in one location, and appeared less often in other location. In Experiment 2, a color singleton distractor was presented more often in location than in all other locations. The results show that when the search target matched the location that was kept in working memory, participants were much faster at responding to the search target than when it did not match, signifying top-down attentional selection. Independent of this top-down effect, we found a clear effect of VSL as responses were even faster when target (Experiment 1) or the distractor (Experiment 2) was presented at a more likely location in visual field. We conclude that attentional selection is driven by implicit biases due to statistical learning and by explicit top-down processing, each process individually and independently modulating the neural activity within the spatial priority map.

Effects of tDCS on visual statistical learning

Visual statistical learning describes the encoding of structure in sensory input, and it has important consequences for cognition and behaviour. Higher-order brain regions in the prefrontal and posterior parietal cortices have been associated with statistical learning behaviours. Yet causal evidence of a cortical contribution remains limited. In a recent study, the modulation of cortical activity by transcranial direct current stimulation (tDCS) disrupted statistical learning in a spatial contextual cueing phenomenon; supporting a cortical role. Here, we examined whether the same tDCS protocol would influence statistical learning assessed by the Visual Statistical Learning phenomenon (i.e., Fiser and Aslin, 2001), which uses identity-based regularities while controlling for spatial location. In Experiment 1, we employed the popular exposure-test design to tap the learning of structure after passive viewing. Using a large sample (N = 150), we found no effect of the tDCS protocol when compared to a sham control nor to an active control region. In Experiment 2 (N = 80), we developed an online task that was sensitive to the timecourse of learning. Under these task conditions, we did observe a stimulation effect on learning, consistent with the previous work. The way tDCS affected learning appeared to be task-specific; expediting statistical learning in this case. Together with the existing evidence, these findings support the hypothesis that cortical areas are involved in the visual statistical learning process, and suggest the mechanisms of cortical involvement may be task-dependent and dynamic across time.

An evolutionary account of intermodality differences in statistical learning

The cognitive mechanisms underlying statistical learning are engaged for the purposes of speech processing and language acquisition. However, these mechanisms are shared by a wide variety of species that do not possess the language faculty. Moreover, statistical learning operates across domains, including nonlinguistic material. Ancient mechanisms for segmenting continuous sensory input into discrete constituents have evolved for general-purpose segmentation of the environment and been readopted for processing linguistic input. Linguistic input provides a rich set of cues for the boundaries between sequential constituents. Such input engages a wider variety of more specialized mechanisms operating on these language-specific cues, thus potentially reducing the role of conditional statistics in tokenizing a continuous linguistic stream. We provide an explicit within-subject comparison of the utility of statistical learning in language versus nonlanguage domains across the visual and auditory modalities. The results showed that in the auditory modality statistical learning is more efficient with speech-like input, while in the visual modality efficiency is higher with nonlanguage input. We suggest that the speech faculty has been important for individual fitness for an extended period, leading to the adaptation of statistical learning mechanisms for speech processing. This is not the case in the visual modality, in which linguistic material presents a less ecological type of sensory input.

Linguistic and non-linguistic non-adjacent dependency learning in early development

Non-adjacent dependencies (NADs) are important building blocks for language and extracting them from the input is a fundamental part of language acquisition. Prior event-related potential (ERP) studies revealed changes in the neural signature of NAD learning between infancy and adulthood, suggesting a developmental shift in the learning route for NADs. The present study aimed to specify which brain regions are involved in this developmental shift and whether this shift extends to NAD learning in the non-linguistic domain. In two experiments, 2- and 3-year-old German-learning children were familiarized with either Italian sentences or tone sequences containing NADs and subsequently tested with NAD violations, while functional near-infrared spectroscopy (fNIRS) data were recorded. Results showed increased hemodynamic responses related to the detection of linguistic NAD violations in the left temporal, inferior frontal, and parietal regions in 2-year-old children, but not in 3-year-old children. A different developmental trajectory was found for non-linguistic NADs, where 3-year-old, but not 2-year-old children showed evidence for the detection of non-linguistic NAD violations. These results confirm a developmental shift in the NAD learning route and point to distinct mechanisms underlying NAD learning in the linguistic and the non-linguistic domain.

Individual differences in learning the regularities between orthography, phonology and semantics predict early reading skills

Statistical views of literacy development maintain that proficient reading requires the assimilation of myriad statistical regularities present in the writing system. Indeed, previous studies have tied statistical learning (SL) abilities to reading skills, establishing the existence of a link between the two. However, some issues are currently left unanswered, including questions regarding the underlying bases for these associations as well as the types of statistical regularities actually assimilated by developing readers. Here we present an alternative approach to study the role of SL in literacy development, focusing on individual differences among beginning readers. Instead of using an artificial task to estimate SL abilities, our approach identifies individual differences in children's reliance on statistical regularities as reflected by actual reading behavior. We specifically focus on individuals' reliance on regularities in the mapping between print and speech versus associations between print and meaning in a word naming task. We present data from 399 children, showing that those whose oral naming performance is impacted more by print-speech regularities and less by associations between print and meaning have better reading skills. These findings suggest that a key route by which SL mechanisms impact developing reading abilities is via their role in the assimilation of sub-lexical regularities between printed and spoken language -and more generally, in detecting regularities that are more reliable than others. We discuss the implications of our findings to both SL and reading theories.

Nonadjacent dependency processing in monkeys, apes, and humans

The ability to track syntactic relationships between words, particularly over distances ("nonadjacent dependencies"), is a critical faculty underpinning human language, although its evolutionary origins remain poorly understood. While some monkey species are reported to process auditory nonadjacent dependencies, comparative data from apes are missing, complicating inferences regarding shared ancestry. Here, we examined nonadjacent dependency processing in common marmosets, chimpanzees, and humans using "artificial grammars": strings of arbitrary acoustic stimuli composed of adjacent (nonhumans) or nonadjacent (all species) dependencies. Individuals from each species (i) generalized the grammars to novel stimuli and (ii) detected grammatical violations, indicating that they processed the dependencies between constituent elements. Furthermore, there was no difference between marmosets and chimpanzees in their sensitivity to nonadjacent dependencies. These notable similarities between monkeys, apes, and humans indicate that nonadjacent dependency processing, a crucial cognitive facilitator of language, is an ancestral trait that evolved at least ~40 million years before language itself.

Why Language Processing Recruits Modality Specific Brain Regions: It Is Not About Understanding Words, but About Modelling Situations

Whether language comprehension requires the participation of brain structures that evolved for perception and action has been a subject of intense debate. While brain-imaging evidence for the involvement of such modality-specific regions has grown, the fact that lesions to these structures do not necessarily erase word knowledge has invited the conclusion that language-induced activity in these structures might not be essential for word recognition. Why language processing recruits these structures remains unanswered, however. Here, we examine the original findings from a slightly different perspective. We first consider the 'original' function of structures in modality-specific brain regions that are recruited by language activity. We propose that these structures help elaborate 'internal forward models' in motor control (c.f. emulators). Emulators are brain systems that capture the relationship between an action and its sensory consequences. During language processing emulators could thus allow accessing associative memories. We further postulate the existence of a linguistic system that exploits, in a rule-based manner, emulators and other nonlinguistic brain systems, to gain complementary (and redundant) information during language processing. Emulators are therefore just one of several sources of information. We emphasize that whether a given word-form triggers activity in modality-specific brain regions depends on the linguistic context and not on the word-form as such. The role of modality-specific systems in language processing is thus not to help understanding words but to model the verbally depicted situation by supplying memorized context information. We present a model derived from these assumptions and provide predictions and perspectives for future research.

Beta-Band Activity Is a Signature of Statistical Learning

Through statistical learning (SL), cognitive systems may discover the underlying regularities in the environment. Testing human adults (n = 35, 21 females), we document, in the context of a classical visual SL task, divergent rhythmic EEG activity in the interstimulus delay periods within patterns versus between patterns (i.e., pattern transitions). Our findings reveal increased oscillatory activity in the beta band (∼20 Hz) at triplet transitions that indexes learning: it emerges with increased pattern repetitions; and importantly, it is highly correlated with behavioral learning outcomes. These findings hold the promise of converging on an online measure of learning regularities and provide important theoretical insights regarding the mechanisms of SL and prediction.SIGNIFICANCE STATEMENT Statistical learning has become a major theoretical construct in cognitive science, providing the primary means by which organisms learn about regularities in the environment. As such, it is a critical building block for basic and higher-order cognitive functions. Here we identify, for the first time, a spectral neural index in the time window before stimulus presentation, which evolves with increased pattern exposure, and is predictive of learning performance. The manifestation of learning that is revealed, not in stimulus processing but in the blank interval between stimuli, makes a direct link between the fields of statistical learning on the one hand and either prediction or consolidation on the other hand, suggesting a possible mechanistic account of visual statistical learning.

Not All Words Are Equally Acquired: Transitional Probabilities and Instructions Affect the Electrophysiological Correlates of Statistical Learning

Statistical learning (SL), the process of extracting regularities from the environment, is a fundamental skill of our cognitive system to structure the world regularly and predictably. SL has been studied using mainly behavioral tasks under implicit conditions and with triplets presenting the same level of difficulty, i.e., a mean transitional probability (TP) of 1.00. Yet, the neural mechanisms underlying SL under other learning conditions remain largely unknown. Here, we investigated the neurofunctional correlates of SL using triplets (i.e., three-syllable nonsense words) with a mean TP of 1.00 (easy "words") and 0.50 (hard "words") in an SL task performed under incidental (implicit) and intentional (explicit) conditions, to determine whether the same core mechanisms were recruited to assist learning. Event-related potentials (ERPs) were recorded while participants listened firstly to a continuous auditory stream made of the concatenation of four easy and four hard "words" under implicit instructions, and subsequently to another auditory stream made of the concatenation of four easy and four hard "words" drawn from another artificial language under explicit instructions. The stream in each of the SL tasks was presented in two consecutive blocks of ~3.5-min each (~7-min in total) to further examine how ERP components might change over time. Behavioral measures of SL were collected after the familiarization phase of each SL task by asking participants to perform a two-alternative forced-choice (2-AFC) task. Results from the 2-AFC tasks revealed a moderate but reliable level of SL, with no differences between conditions. ERPs were, nevertheless, sensitive to the effect of TPs, showing larger amplitudes of N400 for easy "words," as well as to the effect of instructions, with a reduced N250 for "words" presented under explicit conditions. Also, significant differences in the N100 were found as a result of the interaction between TPs, instructions, and the amount of exposure to the auditory stream. Taken together, our findings suggest that triplets' predictability impacts the emergence of "words" representations in the brain both for statistical regularities extracted under incidental and intentional instructions, although the prior knowledge of the "words" seems to favor the recruitment of different SL mechanisms.

Implicit-statistical learning in aphasia and its relation to lesion location

BACKGROUND

Implicit-statistical learning (ISL) research investigates whether domain-general mechanisms are recruited in the linguistic processes that require manipulation of patterned regularities (e.g. syntax). Aphasia is a language disorder caused by focal brain damage in the left fronto-temporal-parietal network. Research shows that people with aphasia (PWA) with frontal lobe lesions manifest convergent deficits in syntax and ISL mechanisms. So far, ISL mechanisms in PWA with temporal or parietal lobe lesions have not been systematically investigated.

AIMS

We investigated two complementary hypotheses: 1) the anatomical hypothesis, that PWA with frontal lesions display more severely impaired ISL abilities than PWA with posterior lesions and 2) the behavioural hypothesis, that the magnitude of impairment in ISL mechanisms correlates to syntactic deficits in aphasia.

METHODS

We tested 13 PWA, 5 with frontal lesions and 8 with posterior lesions, and 11 non-brain-damaged controls on a visual statistical learning (VSL) task. In addition, all PWA completed several linguistic tasks. Reaction times, obtained in the VSL task, were analyzed using linear mixed-effects model. Correlational statistics were used to assess the relationship between VSL task performance and linguistic measures.

RESULTS AND DISCUSSION

We did not find support for the anatomical hypothesis as patients with spared frontal regions also manifested impaired ISL mechanisms. This is attributed to a) ISL mechanisms being vulnerable to other cognitive dysfunctions and/or b) ISL mechanisms anatomically extending to the posterior brain regions. Notably, ISL mechanisms were impaired, but not absent in aphasia. With regards to the behavioural hypothesis, we provide empirical evidence of correlation between ISL mechanisms and syntactic, but not lexical impairment in aphasia. We discuss both the theoretical contributions to the debate of domain-independence of ISL mechanisms and clinical implications for implicit language therapy.

The role of metacognition in recognition of the content of statistical learning

Despite theoretical debate on the extent to which statistical learning is incidental or modulated by explicit instructions and conscious awareness of the content of statistical learning, no study has ever investigated the metacognition of statistical learning. We used an artificial language-learning paradigm and a segmentation task that required splitting a continuous stream of syllables into discrete recurrent constituents. During this task, statistical learning potentially produces knowledge of discrete constituents as well as about statistical regularities that are embodied in familiarization input. We measured metacognitive sensitivity and efficiency (using hierarchical Bayesian modelling to estimate metacognitive sensitivity and efficiency) to probe the role of conscious awareness in recognition of constituents extracted from the familiarization input and recognition of novel constituents embodying the same statistical regularities as these extracted constituents. Novel constituents are conceptualized to represent recognition of statistical structure rather than recognition of items retrieved from memory as whole constituents. We found that participants are equally sensitive to both types of learning products, yet subject them to varying degrees of conscious processing during the postfamiliarization recognition test. The data point to the contribution of conscious awareness to at least some types of statistical learning content.

The Effect of Repetition on Truth Judgments Across Development

According to numerous research studies, when adults hear a statement twice, they are more likely to think it is true compared with when they have heard it only once. Multiple theoretical explanations exist for this illusory-truth effect. However, none of the current theories fully explains how or why people begin to use repetition as a cue for truth. In this preregistered study, we investigated those developmental origins in twenty-four 5-year-olds, twenty-four 10-year-olds, and 32 adults. If the link between repetition and truth is learned implicitly, then even 5-year-olds should show the effect. Alternatively, realizing this connection may require metacognition and intentional reflection, skills acquired later in development. Repetition increased truth judgments for all three age groups, and prior knowledge did not protect participants from the effects of repetition. These results suggest that the illusory-truth effect is a universal effect learned at a young age.

Statistical learning in the absence of explicit top-down attention

Recently it has been shown that statistical learning of regularities presented in a display can bias attentional selection, such that attentional capture by salient objects is reduced by suppressing the location where these distractors are likely to appear. The role of attention in learning these contingencies is not immediately clear. Specifically, it is not known whether attention needs to be directed to the contingencies present in the display for learning to occur. In the current study we investigated whether participants can learn statistical regularities present in the environment even when these regularities are not relevant for the participant and are not part of their top-down goals. We used the additional singleton paradigm in which a color singleton was presented much more often in one location than in all other locations. We show that after being exposed to these regularities regarding the location of the color singleton during an unrelated task in which there are no targets nor distractors, participants showed a suppression effect from the previously learned contingencies when switching to a task in which they search for a target and suppress a distractor. We conclude that visual statistical learning can occur in the absence of top-down attention.

Speed or Accuracy Instructions During Skill Learning do not Affect the Acquired Knowledge

A crucial question in skill learning research is how instruction affects the performance or the underlying representations. Little is known about the effects of instructions on one critical aspect of skill learning, namely, picking-up statistical regularities. More specifically, the present study tests how prelearning speed or accuracy instructions affect the acquisition of non-adjacent second-order dependencies. We trained 2 groups of participants on an implicit probabilistic sequence learning task: one group focused on being fast and the other on being accurate. As expected, we detected a strong instruction effect: accuracy instruction resulted in a nearly errorless performance, and speed instruction caused short reaction times (RTs). Despite the differences in the average RTs and accuracy scores, we found a similar level of statistical learning performance in the training phase. After the training phase, we tested the 2 groups under the same instruction (focusing on both speed and accuracy), and they showed comparable performance, suggesting a similar level of underlying statistical representations. Our findings support that skill learning can result in robust representations, and they highlight that this form of knowledge may appear with almost errorless performance. Moreover, multiple sessions with different instructions enabled the separation of competence from performance.

Auditory but Not Audiovisual Cues Lead to Higher Neural Sensitivity to the Statistical Regularities of an Unfamiliar Musical Style

It is still a matter of debate whether visual aids improve learning of music. In a multisession study, we investigated the neural signatures of novel music sequence learning with or without aids (auditory-only: AO, audiovisual: AV). During three training sessions on three separate days, participants (nonmusicians) reproduced (note by note on a keyboard) melodic sequences generated by an artificial musical grammar. The AV group (n = 20) had each note color-coded on screen, whereas the AO group (n = 20) had no color indication. We evaluated learning of the statistical regularities of the novel music grammar before and after training by presenting melodies ending on correct or incorrect notes and by asking participants to judge the correctness and surprisal of the final note, while EEG was recorded. We found that participants successfully learned the new grammar. Although the AV group, as compared to the AO group, reproduced longer sequences during training, there was no significant difference in learning between groups. At the neural level, after training, the AO group showed a larger N100 response to low-probability compared with high-probability notes, suggesting an increased neural sensitivity to statistical properties of the grammar; this effect was not observed in the AV group. Our findings indicate that visual aids might improve sequence reproduction while not necessarily promoting better learning, indicating a potential dissociation between sequence reproduction and learning. We suggest that the difficulty induced by auditory-only input during music training might enhance cognitive engagement, thereby improving neural sensitivity to the underlying statistical properties of the learned material.

Explaining individual differences in infant visual sensory seeking

Individual differences in infants’ engagement with their environment manifest early in development and are noticed by parents. Three views have been advanced to explain differences in seeking novel stimulation. The optimal stimulation hypothesis suggests that individuals seek further stimulation when they are under‐responsive to current sensory input. The processing speed hypothesis proposes that those capable of processing information faster are driven to seek stimulation more frequently. The information prioritization hypothesis suggests the differences in stimulation seeking index variation in the prioritization of incoming relative to ongoing information processing. Ten‐month‐old infants saw 10 repetitions of a video clip and changes in frontal theta oscillatory amplitude were measured as an index of information processing speed. Stimulus‐locked P1 peak amplitude in response to checkerboards briefly overlaid on the video at random points during its presentation indexed processing of incoming stimulation. Parental report of higher visual seeking did not relate to reduced P1 peak amplitude or to a stronger decrease in frontal theta amplitude with repetition, thus not supporting either the optimal stimulation or the processing speed hypotheses. Higher visual seeking occurred in those infants whose P1 peak amplitude was greater than expected based on their theta amplitude. These findings indicate that visual sensory seeking in infancy is explained by a bias toward novel stimulation, thus supporting the information prioritization hypothesis.

The multi-faceted nature of visual statistical learning: Individual differences in learning conditional and distributional regularities across time and space

Emerging research has demonstrated that statistical learning is a modality-specific ability governed by domain-general principles. Yet limited research has investigated different forms of statistical learning within modality. This paper explores whether there is one unified statistical learning mechanism within the visual modality, or separate task-specific abilities. To do so, we examined individual differences in spatial and nonspatial conditional and distributional statistical learning. Participants completed four visual statistical learning tasks: conditional spatial, conditional nonspatial, distributional spatial, and distributional nonspatial. Performance on all four tasks significantly correlated with each other, and performance on all tasks accounted for a large portion of the variance across tasks (57%). Interestingly, a portion of the variance of task performance (between 11% and 18%) was also accounted for by performance on each of the individual tasks. Our results suggest that visual statistical learning is the result of the interplay between a unified mechanism for extracting conditional and distributional statistical regularities across time and space, and an individual's ability to extract specific types of regularities.

Exploring Variation Between Artificial Grammar Learning Experiments: Outlining a Meta-Analysis Approach

Artificial grammar learning (AGL) has become an important tool used to understand aspects of human language learning and whether the abilities underlying learning may be unique to humans or found in other species. Successful learning is typically assumed when human or animal participants are able to distinguish stimuli generated by the grammar from those that are not at a level better than chance. However, the question remains as to what subjects actually learn in these experiments. Previous studies of AGL have frequently introduced multiple potential contributors to performance in the training and testing stimuli, but meta-analysis techniques now enable us to consider these multiple information sources for their contribution to learning-enabling intended and unintended structures to be assessed simultaneously. We present a blueprint for meta-analysis approaches to appraise the effect of learning in human and other animal studies for a series of artificial grammar learning experiments, focusing on studies that examine auditory and visual modalities. We identify a series of variables that differ across these studies, focusing on both structural and surface properties of the grammar, and characteristics of training and test regimes, and provide a first step in assessing the relative contribution of these design features of artificial grammars as well as species-specific effects for learning.

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.

Human factors in forensic science: The cognitive mechanisms that underlie forensic feature-comparison expertise

After a decade of critique from leading scientific bodies, forensic science research is at a crossroads. Whilst emerging research has shown that some forensic feature-comparison disciplines are not foundationally valid, others are moving towards establishing reliability and validity. Forensic examiners in fingerprint, face and handwriting comparison disciplines have skills and knowledge that distinguish them from novices. Yet our understanding of the basis of this expertise is only beginning to emerge. In this paper, we review evidence on the psychological mechanisms contributing to forensic feature-comparison expertise, with a focus on one mechanism: statistical learning, or the ability to learn how often things occur in the environment. Research is beginning to emphasise the importance of statistical learning in forensic feature-comparison expertise. Ultimately, this research and broader cognitive science research has an important role to play in informing the development of training programs and selection tools for forensic feature-comparison examiners.

Constructing and Forgetting Temporal Context in the Human Cerebral Cortex

How does information from seconds earlier affect neocortical responses to new input? We found that when two groups of participants heard the same sentence in a narrative, preceded by different contexts, the neural responses of each group were initially different but gradually fell into alignment. We observed a hierarchical gradient: sensory cortices aligned most quickly, followed by mid-level regions, while some higher-order cortical regions took more than 10 seconds to align. What computations explain this hierarchical temporal organization? Linear integration models predict that regions that are slower to integrate new information should also be slower to forget old information. However, we found that higher-order regions could rapidly forget prior context. The data from the cortical hierarchy were instead captured by a model in which each region maintains a temporal context representation that is nonlinearly integrated with input at each moment, and this integration is gated by local prediction error.

How does the brain learn environmental structure? Ten core principles for understanding the neurocognitive mechanisms of statistical learning

Despite a growing body of research devoted to the study of how humans encode environmental patterns, there is still no clear consensus about the nature of the neurocognitive mechanisms underpinning statistical learning nor what factors constrain or promote its emergence across individuals, species, and learning situations. Based on a review of research examining the roles of input modality and domain, input structure and complexity, attention, neuroanatomical bases, ontogeny, and phylogeny, ten core principles are proposed. Specifically, there exist two sets of neurocognitive mechanisms underlying statistical learning. First, a "suite" of associative-based, automatic, modality-specific learning mechanisms are mediated by the general principle of cortical plasticity, which results in improved processing and perceptual facilitation of encountered stimuli. Second, an attention-dependent system, mediated by the prefrontal cortex and related attentional and working memory networks, can modulate or gate learning and is necessary in order to learn nonadjacent dependencies and to integrate global patterns across time. This theoretical framework helps clarify conflicting research findings and provides the basis for future empirical and theoretical endeavors.

Implicit attentional biases in a changing environment

The current study investigates whether statistical regularities that change over time affect attentional selection. While searching for a target singleton, the distractor singleton was presented much more often in one location than in all other locations. Crucially, the location that had a distractor much more often, changed to new locations during the course of the experiment. Here we established exactly how the bias of attention followed these changes in the display. Unlike previous studies, we show that selection was remarkably flexible as the attentional bias followed the changes in the environment incorporating contributions of previous contingencies to the current attentional bias. Importantly, the initial learning experience had a lingering and enduring effect on subsequent attentional biases. We argue that the weights within the spatial priority map of selection are adjusted to changing environments, even though observers are unaware of these changes in the environment.