Sampling over Nonuniform Distributions: A Neural Efficiency Account of the Primacy Effect in Statistical Learning

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

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

The impact of spatial variance on precision estimates in an auditory oddball paradigm

Predictive processing theories suggest that a principal function of the brain is to reduce the surprise of incoming sensory information by creating accurate and precise models of the environment. These models are commonly explored by looking at the prediction errors elicited when experience departs from predictions. One such prediction error is the mismatch negativity (MMN). Using this component, it is possible to examine the effect of external noise on the precision of the developed model. Recent studies have shown that the brain may not update its model every time there is a change in the environment, rather it will only update it when doing so will increase precision and or accuracy of the model. The current study examined this process using oddball sound sequences with high and low spatial variability and examining how this affected the elicited MMN to a duration deviant sound. The results showed a strong null effect of spatial variance both at a local and sequence levels. These results indicate that variability in the sound sequence will not invariably affect model precision estimates and thus the amplitude of the MMN component.

Differences in Prediction May Underlie Language Disorder in Autism

Language delay is often one of the first concerns of parents of toddlers with autism spectrum disorder (ASD), and early language abilities predict broader outcomes for children on the autism spectrum. Yet, mechanisms underlying language deficits in autistic children remain underspecified. One prominent component of linguistic behavior is the use of predictions or expectations during learning and processing. Several researcher teams have posited prediction deficit accounts of ASD. The basic assumption of the prediction accounts is that information is processed by making predictions and testing violations against expectations (prediction errors). Flexible (neurotypical) brains attribute differential weights to prediction errors to determine when new learning is appropriate, while autistic individuals are thought to assign disproportionate weight to prediction errors. According to some views, these prediction deficits are hypothesized to lead to higher levels of perceived novelty, resulting in “hyperplasticity” of learning based on the most recent input. In this article, we adopt the perspective that it would be useful to investigate whether language deficits in children with ASD can be attributed to atypical domain-general prediction processes.

Shorter Contextual Timescale Rather Than Memory Deficit in Aging

Many aspects of cognitive ability and brain function that change as we age look like deficits on account of measurable differences in comparison to younger adult groups. One such difference occurs in auditory sensory responses that index perceptual learning. Meta-analytic findings show reliable age-related differences in auditory responses to repetitive patterns of sound and to rare violations of those patterns, variously attributed to deficits in auditory sensory memory and inhibition. Here, we determine whether proposed deficits would render older adults less prone to primacy effects, robustly observed in young adults, which present as a tendency for first learning to have a disproportionate influence over later perceptual inference. The results confirm this reduced sensitivity to primacy effects but do not support impairment in auditory sensory memory as the origin of this difference. Instead, the aging brain produces data consistent with shorter timescales of contextual reference. In conclusion, age-related differences observed previously for perceptual inference appear highly context-specific necessitating reconsideration of whether and to what function the notion of deficit should be attributed, and even whether the notion of deficit is appropriate at all.

The influence of variability on mismatch negativity amplitude

Mismatch Negativity (MMN) to pattern deviations reveals exquisite pattern detection ability in the brain. MMN amplitude is proposed to be precision-weighted, being inversely proportional to variability within a patterned sound sequence. Two experiments were conducted to determine whether pattern variability, shown to influence MMN to simple pattern deviance, also extends to MMN elicited to abstract pattern deviants. Participants were presented with 3-tone triplet sequences that were defined by regular frequency ascendance with adjacent (A<B<C) or non-adjacent (A<C) dependency. The triplets were defined by an abstract pattern in that the starting frequency of A roamed randomly between 500-3700 Hz. Using variants of these sequences over two studies the results show that MMN was elicited to rare A > C deviants for adjacent and non-adjacent dependencies, was smaller for the latter, was impervious to variance in tone loudness, but showed prolonged sensitivity to the level of variability at sequence onset.

One language or two? Navigating cross-language conflict in statistical word segmentation

Bilingual infants must navigate the similarities and differences between their languages to achieve native proficiency in childhood. Bilinguals learning to find individual words in fluent speech face the possibility of conflicting cues to word boundaries across their languages. Despite this challenge, bilingual infants typically begin to segment and learn words in both languages around the same time as monolinguals. It is possible that early bilingual experience may support infants' abilities to track regularities relevant for word segmentation separately across their languages. In a dual speech stream statistical word segmentation task, we assessed whether 16-month-old infants could track syllable co-occurrence regularities in two artificial languages despite conflicting information across the languages. We found that bilingual, but not monolingual, infants were able to segment the dual speech streams using statistical regularities. Although the two language groups did not differ on secondary measures of cognitive and linguistic development, bilingual infants' real-world experience with bilingual speakers was predictive of their performance in the dual language statistical segmentation task.

Infants' selective use of reliable cues in multidimensional language input

Learning always happens from input that contains multiple structures and multiple sources of variability. Though infants possess learning mechanisms to locate structure in the world, lab-based experiments have rarely probed how infants contend with input that contains many different structures and cues. Two experiments explored infants' use of two naturally occurring sources of variability-different sounds and different people-to detect regularities in language. Monolingual infants (9-10 months) heard a male and female talker produce two different speech streams, one of which followed a deterministic pattern (e.g., AAB, le-le-di) and one of which did not. For half of the infants, each speaker produced only one of the streams; for the other half of the infants, each speaker produced 50% of each stream. In Experiment 1, each stream consisted of distinct sounds, and infants successfully demonstrated learning regardless of the correspondence between speaker and stream. In Experiment 2, each stream consisted of the same sounds, and infants failed to show learning, even when speakers provided a perfect cue for separating each stream. Thus, monolingual infants can learn in the presence of multiple speech streams, but these experiments suggest that infants may rely more on sound-based rather than speaker-based distinctions when breaking into the structure of incoming information. This selective use of some cues over others highlights infants' ability to adaptively focus on distinctions that are most likely to be useful as they sort through their inherently multidimensional surroundings. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Differences in relative frequency facilitate learning abstract rules

Humans learn the rules that govern how the elements of their language are organized over an input that is often not homogeneous (it might contain noise, or even include rules from different linguistic systems, as it might be the case for bilinguals). In the present study we explore the conditions under which participants can learn an abstract rule when it is presented in a heterogeneous context. Results from six experiments show that listeners can learn a token-independent rule even if it is presented together with some exemplars that implement a different regularity (Experiment 1a and 1b). In fact, learning rules from an input containing several patterns does not seem to differ from learning them from an input containing only one (Experiment 1c). More surprisingly, we observed that listeners can even learn an abstract rule that is only implemented over 10% of the exemplars that compose a familiarization stream (Experiments 2a and 2b). When the proportion of tokens implementing the target and the non-target rules is balanced, we did not observe any learning (Experiment 3). Our results suggest that listeners use differences in relative frequency to keep separate linguistic rules apart. This allows them to learn different abstract regularities from a non-homogeneous linguistic signal.

Neurophysiological Markers of Statistical Learning in Music and Language: Hierarchy, Entropy, and Uncertainty

Statistical learning (SL) is a method of learning based on the transitional probabilities embedded in sequential phenomena such as music and language. It has been considered an implicit and domain-general mechanism that is innate in the human brain and that functions independently of intention to learn and awareness of what has been learned. SL is an interdisciplinary notion that incorporates information technology, artificial intelligence, musicology, and linguistics, as well as psychology and neuroscience. A body of recent study has suggested that SL can be reflected in neurophysiological responses based on the framework of information theory. This paper reviews a range of work on SL in adults and children that suggests overlapping and independent neural correlations in music and language, and that indicates disability of SL. Furthermore, this article discusses the relationships between the order of transitional probabilities (TPs) (i.e., hierarchy of local statistics) and entropy (i.e., global statistics) regarding SL strategies in human's brains; claims importance of information-theoretical approaches to understand domain-general, higher-order, and global SL covering both real-world music and language; and proposes promising approaches for the application of therapy and pedagogy from various perspectives of psychology, neuroscience, computational studies, musicology, and linguistics.

Linguistic entrenchment: Prior knowledge impacts statistical learning performance

Statistical Learning (SL) is typically considered to be a domain-general mechanism by which cognitive systems discover the underlying statistical regularities in the input. Recent findings, however, show clear differences in processing regularities across modalities and stimuli as well as low correlations between performance on visual and auditory tasks. Why does a presumably domain-general mechanism show distinct patterns of modality and stimulus specificity? Here we claim that the key to this puzzle lies in the prior knowledge brought upon by learners to the learning task. Specifically, we argue that learners' already entrenched expectations about speech co-occurrences from their native language impacts what they learn from novel auditory verbal input. In contrast, learners are free of such entrenchment when processing sequences of visual material such as abstract shapes. We present evidence from three experiments supporting this hypothesis by showing that auditory-verbal tasks display distinct item-specific effects resulting in low correlations between test items. In contrast, non-verbal tasks - visual and auditory - show high correlations between items. Importantly, we also show that individual performance in visual and auditory SL tasks that do not implicate prior knowledge regarding co-occurrence of elements, is highly correlated. In a fourth experiment, we present further support for the entrenchment hypothesis by showing that the variance in performance between different stimuli in auditory-verbal statistical learning tasks can be traced back to their resemblance to participants' native language. We discuss the methodological and theoretical implications of these findings, focusing on models of domain generality/specificity of SL.

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.

Neural Signatures of Spatial Statistical Learning: Characterizing the Extraction of Structure from Complex Visual Scenes

Behavioral evidence has shown that humans automatically develop internal representations adapted to the temporal and spatial statistics of the environment. Building on prior fMRI studies that have focused on statistical learning of temporal sequences, we investigated the neural substrates and mechanisms underlying statistical learning from scenes with a structured spatial layout. Our goals were twofold: (1) to determine discrete brain regions in which degree of learning (i.e., behavioral performance) was a significant predictor of neural activity during acquisition of spatial regularities and (2) to examine how connectivity between this set of areas and the rest of the brain changed over the course of learning. Univariate activity analyses indicated a diffuse set of dorsal striatal and occipitoparietal activations correlated with individual differences in participants' ability to acquire the underlying spatial structure of the scenes. In addition, bilateral medial-temporal activation was linked to participants' behavioral performance, suggesting that spatial statistical learning recruits additional resources from the limbic system. Connectivity analyses examined, across the time course of learning, psychophysiological interactions with peak regions defined by the initial univariate analysis. Generally, we find that task-based connectivity with these regions was significantly greater in early relative to later periods of learning. Moreover, in certain cases, decreased task-based connectivity between time points was predicted by overall posttest performance. Results suggest a narrowing mechanism whereby the brain, confronted with a novel structured environment, initially boosts overall functional integration and then reduces interregional coupling over time.

The neurobiology of uncertainty: implications for statistical learning

The capacity for assessing the degree of uncertainty in the environment relies on estimating statistics of temporally unfolding inputs. This, in turn, allows calibration of predictive and bottom-up processing, and signalling changes in temporally unfolding environmental features. In the last decade, several studies have examined how the brain codes for and responds to input uncertainty. Initial neurobiological experiments implicated frontoparietal and hippocampal systems, based largely on paradigms that manipulated distributional features of visual stimuli. However, later work in the auditory domain pointed to different systems, whose activation profiles have interesting implications for computational and neurobiological models of statistical learning (SL). This review begins by briefly recapping the historical development of ideas pertaining to the sensitivity to uncertainty in temporally unfolding inputs. It then discusses several issues at the interface of studies of uncertainty and SL. Following, it presents several current treatments of the neurobiology of uncertainty and reviews recent findings that point to principles that serve as important constraints on future neurobiological theories of uncertainty, and relatedly, SL. This review suggests it may be useful to establish closer links between neurobiological research on uncertainty and SL, considering particularly mechanisms sensitive to local and global structure in inputs, the degree of input uncertainty, the complexity of the system generating the input, learning mechanisms that operate on different temporal scales and the use of learnt information for online prediction.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'.

Anchors aweigh: The impact of overlearning on entrenchment effects in statistical learning

Previous research has revealed that when learners encounter multiple artificial languages in succession only the first is learned, unless there are contextual cues correlating with the change in structure or if exposure to the second language is protracted. These experiments provided a fixed amount of exposure irrespective of when learning occurred. Here, the authors presented learners with 2 consecutive artificial languages testing learning after each minute of familiarization. In Experiment 1, learners received fixed input, and the authors replicated the primacy effect. In Experiment 2, learners advanced to the second language immediately following robust learning of the first language (thereby limiting additional exposure past the point of learning). Remarkably, learners tended to acquire and retain both languages, although contextual cues did not boost performance further. Notably, there was no correlation between performance on this task and a flanker task that measured inhibitory control. Overall, the findings suggest that anchoring effects in statistical learning may be because of overlearning. We speculate that learners may reduce their attention to the input once they achieve a low level of estimation uncertainty. (PsycINFO Database Record