Infant neuroscience: how to measure brain activity in the youngest minds

The functional properties of the infant brain are poorly understood. Recent advances in cognitive neuroscience are opening new avenues for measuring brain activity in human infants. These include novel uses of existing technologies such as electroencephalography (EEG) and magnetoencephalography (MEG), the availability of newer technologies including functional near-infrared spectroscopy (fNIRS) and optically pumped magnetometry (OPM), and innovative applications of functional magnetic resonance imaging (fMRI) in awake infants during cognitive tasks. In this review article we catalog these available non-invasive methods, discuss the challenges and opportunities encountered when applying them to human infants, and highlight the potential they may ultimately hold for advancing our understanding of the youngest minds.

Expectation adaptation for rare cadences in music: Item order matters in repetition priming

Humans make predictions about future events in many domains, including when they listen to music. Previous accounts of harmonic expectation in music have emphasised the role of implicit musical knowledge acquired in the long term through the mechanism of statistical learning. However, it is not known whether listeners can adapt their expectations for unusual harmonies in the short term through repetition priming, and whether the extent of any short-term adaptation depends on the unfolding statistical structure of the music. To explore these possibilities, we presented 150 participants with phrases from Bach chorales that ended with a cadence that was either a priori likely or unlikely based on the long-term statistical structure of the corpus of chorales. While holding the 50-50 incidence of likely vs. unlikely cadences constant, we manipulated the order in which these phrases were presented such that the local probability of hearing an unlikely cadence changed throughout the experiment. For each phrase, participants provided two judgements: (a) a prospective rating of how confident they were in their expectations for the cadence, and (b) a retrospective rating of how well the presented cadence matched their expectations. While confidence ratings increased over the course of the experiment, the rate of change decreased as the local probability of an unexpected cadence increased. Participants' expectations favoured likely cadences over unlikely cadences on average, but their expectation ratings for unlikely cadences increased at a faster rate over the course of the experiment than for likely cadences, particularly when the local probability of hearing an unlikely cadence was high. Thus, despite entrenched long-term statistics about cadences, listeners can indeed adapt to unusual musical harmonies and are sensitive to the local statistical structure of the musical environment. We suggest that this adaptation is an instance of Bayesian belief updating, a domain-general process that accounts for expectation adaptation in multiple domains.

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Functional near-infrared spectroscopy (fNIRS) is a portable neuroimaging methodology, more robust to motion and more cost-effective than functional magnetic resonance imaging (fMRI), which makes it highly suitable for conducting naturalistic studies of brain function and for use with developmental and clinical populations. Both fNIRS and fMRI methodologies detect changes in cerebral blood oxygenation during functional brain activation, and prior studies have shown high spatial and temporal correspondence between the two signals. There is, however, no quantitative comparison of the two signals collected simultaneously from the same subjects with whole-head fNIRS coverage. This comparison is necessary to comprehensively validate area-level activations and functional connectivity against the fMRI gold standard, which in turn has the potential to facilitate comparisons of the two signals across the lifespan. We address this gap by describing a protocol for simultaneous data collection of fMRI and fNIRS signals that: i) provides whole-head fNIRS coverage; ii) includes short-distance measurements for regression of the non-cortical, systemic physiological signal; and iii) implements two different methods for optode-to-scalp co-registration of fNIRS measurements. fMRI and fNIRS data from three subjects are presented, and recommendations for adapting the protocol to test developmental and clinical populations are discussed. The current setup with adults allows scanning sessions for an average of approximately 40 min, which includes both functional and structural scans. The protocol outlines the steps required to adapt the fNIRS equipment for use in the magnetic resonance (MR) environment, provides recommendations for both data recording and optode-to-scalp co-registration, and discusses potential modifications of the protocol to fit the specifics of the available MR-safe fNIRS system. Representative subject-specific responses from a flashing-checkerboard task illustrate the feasibility of the protocol to measure whole-head fNIRS signals in the MR environment. This protocol will be particularly relevant for researchers interested in validating fNIRS signals against fMRI across the lifespan.

fNIRS Studies of Individuals with Speech and Language Impairment Underreport Sociodemographics: A Systematic Review

Functional near-infrared spectroscopy (fNIRS) is a promising tool for scientific discovery and clinical application. However, its utility depends upon replicable reporting. We evaluate reporting of sociodemographics in fNIRS studies of speech and language impairment and asked the following: (1) Do refereed fNIRS publications report participant sociodemographics? (2) For what reasons are participants excluded from analysis? This systematic review was preregistered with PROSPERO (CRD42022342959) and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol. Searches in August 2022 included the terms: (a) fNIRS or functional near-infrared spectroscopy or NIRS or near-infrared spectroscopy, (b) speech or language, and (c) disorder or impairment or delay. Searches yielded 38 qualifying studies from 1997 to present. Eight studies (5%) reported at least partial information on race or ethnicity. Few studies reported SES (26%) or language background (47%). Most studies reported geographic location (100%) and gender/sex (89%). Underreporting of sociodemographics in fNIRS studies of speech and language impairment hinders the generalizability of findings. Replicable reporting is imperative for advancing the utility of fNIRS.

Novel idea generation in social networks is optimized by exposure to a "Goldilocks" level of idea-variability

Recent works suggest that striking a balance between maximizing idea stimulation and minimizing idea redundancy can elevate novel idea generation performances in self-organizing social networks. We explore whether dispersing the visibility of high-performing idea generators can help achieve such a trade-off. We employ popularity signals (follower counts) of participants as an external source of variation in network structures, which we control across four conditions in a randomized setting. We observe that popularity signals influence inspiration-seeking ties, partly by biasing people's perception of their peers' novel idea-generation performances. Networks that partially disperse the top ideators' visibility using this external signal show reduced idea redundancy and elevated idea-generation performances. However, extreme dispersal leads to inferior performances by narrowing the range of idea stimulation. Our work holds future-of-work implications for elevating idea generation performances of people.

Decoding the temporal dynamics of spoken word and nonword processing from EEG

The efficiency of spoken word recognition is essential for real-time communication. There is consensus that this efficiency relies on an implicit process of activating multiple word candidates that compete for recognition as the acoustic signal unfolds in real-time. However, few methods capture the neural basis of this dynamic competition on a msec-by-msec basis. This is crucial for understanding the neuroscience of language, and for understanding hearing, language and cognitive disorders in people for whom current behavioral methods are not suitable. We applied machine-learning techniques to standard EEG signals to decode which word was heard on each trial and analyzed the patterns of confusion over time. Results mirrored psycholinguistic findings: Early on, the decoder was equally likely to report the target (e.g., baggage) or a similar sounding competitor (badger), but by around 500 msec, competitors were suppressed. Follow up analyses show that this is robust across EEG systems (gel and saline), with fewer channels, and with fewer trials. Results are robust within individuals and show high reliability. This suggests a powerful and simple paradigm that can assess the neural dynamics of speech decoding, with potential applications for understanding lexical development in a variety of clinical disorders.

Predictive brain signals mediate association between shared reading and expressive vocabulary in infants

The ability to predict upcoming information is crucial for efficient language processing and enables more rapid language learning. The present study explored how shared reading experience influenced predictive brain signals and expressive vocabulary of 12-month-old infants. The predictive brain signals were measured by fNIRS responses in the occipital lobe with an unexpected visual-omission task. The amount of shared reading experience was correlated with the strength of this predictive brain signal and with infants’ expressive vocabulary. Importantly, the predictive brain signal explained unique variance of expressive vocabulary beyond shared reading experience and maternal education. A further mediation analysis showed that the effect of shared reading experience on expressive vocabulary was explained by the infants’ predictive brain signal. This is the first evidence indicating that richer shared reading experience strengthens predictive signals in the infant brain and in turn facilitates expressive vocabulary acquisition.

Macaques preferentially attend to intermediately surprising information

Normative learning theories dictate that we should preferentially attend to informative sources, but only up to the point that our limited learning systems can process their content. Humans, including infants, show this predicted strategic deployment of attention. Here, we demonstrate that rhesus monkeys, much like humans, attend to events of moderate surprisingness over both more and less surprising events. They do this in the absence of any specific goal or contingent reward, indicating that the behavioural pattern is spontaneous. We suggest this U-shaped attentional preference represents an evolutionarily preserved strategy for guiding intelligent organisms toward material that is maximally useful for learning.

Attentional orienting abilities in bilinguals: Evidence from a large infant sample

A key question in studies of cognitive development is whether bilingual environments impact higher-cognitive functions. Inconclusive evidence in search of a "bilingual cognitive advantage" has sparked debates on the reliability of these findings. Few studies with infants have examined this question, but most of them include small samples. The current study presents evidence from a large sample of 6- and 10-month-old monolingual- and bilingual-exposed infants (N = 152), which includes a longitudinal subset (n = 31), who completed a cueing attentional orienting task. The results suggest bilingual infants showed significant developmental gains in latency performance during the condition that was most cognitively demanding (Incongruent). The results also revealed bilingual infants' performance was associated with their parents' dual-language switching behavior. Taken together, these results provide support that bilingual experiences (i.e., dual-language mixing) influence infants' shifting and orienting of attention.

Time-resolved multivariate pattern analysis of infant EEG data: A practical tutorial

Time-resolved multivariate pattern analysis (MVPA), a popular technique for analyzing magneto- and electro-encephalography (M/EEG) neuroimaging data, quantifies the extent and time-course by which neural representations support the discrimination of relevant stimuli dimensions. As EEG is widely used for infant neuroimaging, time-resolved MVPA of infant EEG data is a particularly promising tool for infant cognitive neuroscience. MVPA has recently been applied to common infant imaging methods such as EEG and fNIRS. In this tutorial, we provide and describe code to implement time-resolved, within-subject MVPA with infant EEG data. An example implementation of time-resolved MVPA based on linear SVM classification is described, with accompanying code in Matlab and Python. Results from a test dataset indicated that in both infants and adults this method reliably produced above-chance accuracy for classifying stimuli images. Extensions of the classification analysis are presented including both geometric- and accuracy-based representational similarity analysis, implemented in Python. Common choices of implementation are presented and discussed. As the amount of artifact-free EEG data contributed by each participant is lower in studies of infants than in studies of children and adults, we also explore and discuss the impact of varying participant-level inclusion thresholds on resulting MVPA findings in these datasets.

Towards a model of language neurobiology in early development

Understanding language neurobiology in early childhood is essential for characterizing the developmental structural and functional changes that lead to the mature adult language network. In the last two decades, the field of language neurodevelopment has received increasing attention, particularly given the rapid advances in the implementation of neuroimaging techniques and analytic approaches that allow detailed investigations into the developing brain across a variety of cognitive domains. These methodological and analytical advances hold the promise of developing early markers of language outcomes that allow diagnosis and clinical interventions at the earliest stages of development. Here, we argue that findings in language neurobiology need to be integrated within an approach that captures the dynamic nature and inherent variability that characterizes the developing brain and the interplay between behavior and (structural and functional) neural patterns. Accordingly, we describe a framework for understanding language neurobiology in early development, which minimally requires an explicit characterization of the following core domains: i) computations underlying language learning mechanisms, ii) developmental patterns of change across neural and behavioral measures, iii) environmental variables that reinforce language learning (e.g., the social context), and iv) brain maturational constraints for optimal neural plasticity, which determine the infant's sensitivity to learning from the environment. We discuss each of these domains in the context of recent behavioral and neuroimaging findings and consider the need for quantitatively modeling two main sources of variation: individual differences or trait-like patterns of variation and within-subject differences or state-like patterns of variation. The goal is to enable models that allow prediction of language outcomes from neural measures that take into account these two types of variation. Finally, we examine how future methodological approaches would benefit from the inclusion of more ecologically valid paradigms that complement and allow generalization of traditional controlled laboratory methods.

Bilingualism alters infants' cortical organization for attentional orienting mechanisms

A bilingual environment is associated with changes in the brain's structure and function. Some suggest that bilingualism also improves higher-cognitive functions in infants as young as 6-months, yet whether this effect is associated with changes in the infant brain remains unknown. In the present study, we measured brain activity using functional near-infrared spectroscopy in monolingual- and bilingual-raised 6- and 10-month-old infants. Infants completed an orienting attention task, in which a cue was presented prior to an object appearing on the same (Valid) or opposite (Invalid) side of a display. Task performance did not differ between the groups but neural activity did. At 6-months, both groups showed greater activity for Valid (> Invalid) trials in frontal regions (left hemisphere for bilinguals, right hemisphere for monolinguals). At 10-months, bilinguals showed greater activity for Invalid (> Valid) trials in bilateral frontal regions, while monolinguals showed greater brain activity for Valid (> Invalid) trials in left frontal regions. Bilinguals' brain activity trended with their parents' reporting of dual-language mixing when speaking to their child. These findings are the first to indicate how early (dual) language experience can alter the cortical organization underlying broader, non-linguistic cognitive functions during the first year of life.

Cues to gender and racial identity reduce creativity in diverse social networks

The characteristics of social partners have long been hypothesized as influential in guiding group interactions. Understanding how demographic cues impact networks of creative collaborators is critical for elevating creative performances therein. We conducted a randomized experiment to investigate how the knowledge of peers’ gender and racial identities distorts people’s connection patterns and the resulting creative outcomes in a dynamic social network. Consistent with prior work, we found that creative inspiration links are primarily formed with top idea-generators. However, when gender and racial identities are known, not only is there (1) an increase of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$82.03\%$$\end{document}82.03% in the odds of same-gender connections to persist (but not for same-race connections), but (2) the semantic similarity of idea-sets stimulated by these connections also increase significantly compared to demography-agnostic networks, negatively impacting the outcomes of divergent creativity. We found that ideas tend to be significantly more homogeneous within demographic groups than between, taking away diversity-bonuses from similarity-based links and partly explaining the results. These insights can inform intelligent interventions to enhance network-wide creative performances.

A distributional perspective on the gavagai problem in early word learning

Word learning entails the mapping of an auditory word-form to its appropriate grammatical category (e.g., noun, verb, adjective), but before that mapping can occur, the naïve learner must infer which of the myriad of possible referents of that word was intended by the speaker. This creates a computational explosion of referential ambiguity referred to as the gavagai problem. In a set of corpus analyses of parent-directed speech to young infants, we describe the distributional information available to early word learners, with a focus on nouns and adjectives that refer to whole objects and object properties. And in two experiments on word-learning in adults spanning seven different distributional conditions, we document how variations in the ratio of novel labels for objects and properties affect the robustness of word learning. Our results suggest that the language input to 6- to 20-month-olds is robustly populated with high-frequency object words and high-frequency property words, but their co-occurrence is sparse. Although this distributional information slightly favors object words over property words, a more plausible account of the whole-object bias in early word learning is the inability to encode the details of an object/event during rapid naming. Our results from adults, presented with novel labels for multi-referent objects in a cross-situational statistical learning paradigm, also reveal this whole-object bias as well as the absence of property-label generalization to novel objects, even when the distribution of labels is shifted almost exclusively to property words. These results are discussed in terms of the relative ease of mapping auditory word-forms to whole objects vs. object properties, thereby limiting the combinatorics of the gavagai problem, especially in infants with immature encoding and memory representation abilities.

Comparison of short-channel separation and spatial domain filtering for removal of non-neural components in functional near-infrared spectroscopy signals

Significance: With the increasing popularity of functional near-infrared spectroscopy (fNIRS), the need to determine localization of the source and nature of the signals has grown. Aim: We compare strategies for removal of non-neural signals for a finger-thumb tapping task, which shows responses in contralateral motor cortex and a visual checkerboard viewing task that produces activity within the occipital lobe. Approach: We compare temporal regression strategies using short-channel separation to a spatial principal component (PC) filter that removes global signals present in all channels. For short-channel temporal regression, we compare non-neural signal removal using first and combined first and second PCs from a broad distribution of short channels to limited distribution on the forehead. Results: Temporal regression of non-neural information from broadly distributed short channels did not differ from forehead-only distribution. Spatial PC filtering provides results similar to short-channel separation using the temporal domain. Utilizing both first and second PCs from short channels removes additional non-neural information. Conclusions: We conclude that short-channel information in the temporal domain and spatial domain regression filtering methods remove similar non-neural components represented in scalp hemodynamics from fNIRS signals and that either technique is sufficient to remove non-neural components.

Categorization in infancy based on novelty and co-occurrence

categories (i.e., groups of objects that do not share perceptual features, such as food) abound in everyday situations. The present looking time study investigated whether infants are able to distinguish between two abstract categories (food and toys), and how this ability may extend beyond perceived information by manipulating object familiarity in several ways. Test trials displayed 1) the exact familiarized objects paired as they were during familiarization, 2) a cross-pairing of these same familiar objects, 3) novel objects in the same category as the familiarized items, or 4) novel objects in a different category. Compared to the most familiar test trial (i.e., Familiar Category, Familiar Objects, Familiar Pairings), infants looked longer to all other test trials. Although there was a linear increase in looking time with increased novelty of the test trials (i.e., Novel Category as the most novel test trial), the looking times did not differ significantly between the Novel Category and Familiar Category, Unfamiliar Objects trials. This study contributes to our understanding of how infants form object categories based on object familiarity, object co-occurrence, and information abstraction.

Predictive modeling of neurobehavioral state and trait variation across development

A key goal of human neurodevelopmental research is to map neural and behavioral trajectories across both health and disease. A growing number of developmental consortia have begun to address this gap by providing open access to cross-sectional and longitudinal 'big data' repositories. However, it remains challenging to develop models that enable prediction of both within-subject and between-subject neurodevelopmental variation. Here, we present a conceptual and analytical perspective of two essential ingredients for mapping neurodevelopmental trajectories: state and trait components of variance. We focus on mapping variation across a range of neural and behavioral measurements and consider concurrent alterations of state and trait variation across development. We present a quantitative framework for combining both state- and trait-specific sources of neurobehavioral variation across development. Specifically, we argue that non-linear mixed growth models that leverage state and trait components of variance and consider environmental factors are necessary to comprehensively map brain-behavior relationships. We discuss this framework in the context of mapping language neurodevelopmental changes in early childhood, with an emphasis on measures of functional connectivity and their reliability for establishing robust neurobehavioral relationships. The ultimate goal is to statistically unravel developmental trajectories of neurobehavioral relationships that involve a combination of individual differences and age-related changes.

Temporal dynamics of visual representations in the infant brain

Tools from computational neuroscience have facilitated the investigation of the neural correlates of mental representations. However, access to the representational content of neural activations early in life has remained limited. We asked whether patterns of neural activity elicited by complex visual stimuli (animals, human body) could be decoded from EEG data gathered from 12-15-month-old infants and adult controls. We assessed pairwise classification accuracy at each time-point after stimulus onset, for individual infants and adults. Classification accuracies rose above chance in both groups, within 500 ms. In contrast to adults, neural representations in infants were not linearly separable across visual domains. Representations were similar within, but not across, age groups. These findings suggest a developmental reorganization of visual representations between the second year of life and adulthood and provide a promising proof-of-concept for the feasibility of decoding EEG data within-subject to assess how the infant brain dynamically represents visual objects.

Young children combine sensory cues with learned information in a statistically efficient manner: But task complexity matters

Human adults are adept at mitigating the influence of sensory uncertainty on task performance by integrating sensory cues with learned prior information, in a Bayes-optimal fashion. Previous research has shown that young children and infants are sensitive to environmental regularities, and that the ability to learn and use such regularities is involved in the development of several cognitive abilities. However, it has also been reported that children younger than 8 do not combine simultaneously available sensory cues in a Bayes-optimal fashion. Thus, it remains unclear whether, and by what age, children can combine sensory cues with learned regularities in an adult manner. Here, we examine the performance of 6- to 7-year-old children when tasked with localizing a 'hidden' target by combining uncertain sensory information with prior information learned over repeated exposure to the task. We demonstrate that 6- to 7-year-olds learn task-relevant statistics at a rate on par with adults, and like adults, are capable of integrating learned regularities with sensory information in a statistically efficient manner. We also show that variables such as task complexity can influence young children's behavior to a greater extent than that of adults, leading their behavior to look sub-optimal. Our findings have important implications for how we should interpret failures in young children's ability to carry out sophisticated computations. These 'failures' need not be attributed to deficits in the fundamental computational capacity available to children early in development, but rather to ancillary immaturities in general cognitive abilities that mask the operation of these computations in specific situations.

Cognitive Science Honors the Memory of Jeffrey Elman

Jeff Elman (1/22/1948–6/28/2018) was a major and much beloved figure in cognitive science, best known for his work on the TRACE model of speech perception, simple recurrent network models of the temporal dynamics of language processing, and his coauthored monograph, Rethinking Innateness. Beyond his individual and collaborative research, he is widely recognized for his lasting contributions to building our scientific community. Here we celebrate his contributions by briefly recounting his life’s work and sharing commentaries and reminiscences from a number of his closest colleagues over the years.

No Evidence That Abstract Structure Learning Disrupts Novel-Event Learning in 8- to 11-Month-Olds

Although infants acquire specific information (e.g., motion of a specific toy) and abstract information (e.g., likelihood of events repeating), it is unclear whether extraction of abstract information interferes with specific learning. In the present study, 8- to 11-month-old infants were shown four audio-visual movies, either with a mixed or uniform presentation structure. Learning of abstract information was operationally defined as the looking time to changes in presentation structure of the movies (mixed vs. uniform blocks), and learning of specific information was defined as the looking time to changes in content in the four movies (object properties and identities). We found evidence of both specific and abstract learning, but did not find evidence that extraction of the presentation structure (i.e., abstract learning) impacts specific learning of the events. We discuss the implications of the costs and benefits of the interaction between abstract and specific learning for infants.

Synesthetes perseverate in implicit learning: Evidence from a non-stationary statistical learning task

Synesthetes automatically and consistently experience additional sensory or cognitive perceptions in response to particular environmental stimuli. Recent evidence suggests that the propensity to develop synesthesia is genetic while the particular associations experienced by a given synesthete are influenced by learning. Despite the potential role of implicit learning in the formation of synesthetic associations, there has been minimal investigation of synesthetes' implicit learning abilities. In this study, we examine linguistic-colour synesthetes' ability to implicitly learn from and adjust to non-stationary statistics in a domain unrelated to their particular form of synesthesia. Engaging participants in a computer game Whack-the-mole, we utilise the online measure of reaction time to assess the time course of learning. Participants are exposed to "worlds" of probabilities that, unbeknownst to them, undergo unannounced changes, creating unpredictable statistical shifts devoid of accompanying cues. The same small set of probability worlds are repeated throughout the experiment to investigate participants' ability to retain and learn from this repetitive probabilistic information. The reaction time data provide evidence that synesthetes require more information than nonsynesthetes to benefit from the non-stationary probability distributions. These findings demonstrate that linguistic-colour synesthetes' implicit learning abilities-in a domain far from their synesthetic experiences-differ from those of nonsynesthetes.

Expectation affects neural repetition suppression in infancy

Recent work provides evidence that the infant brain is able to make top-down predictions, but this has been explored only in limited contexts and domains. We build upon this evidence of predictive processing in infants using a new paradigm to examine auditory repetition suppression (RS). RS is a well-documented neural phenomenon in which repeated presentations of the same stimulus result in reduced neural activation compared to non-repeating stimuli. Many theories explain RS using bottom-up mechanisms, but recent work has posited that top-down expectation and predictive coding may bias, or even explain, RS. Here, we investigate whether RS in the infant brain is similarly sensitive to top-down mechanisms. We use fNIRS to measure infants’ neural response in two experimental conditions, one in which variability in stimulus presentation is expected (occurs 75% of the time) and a control condition where variability and repetition are equally likely (50% of the time). We show that 6-month-old infants exhibit attenuated frontal lobe response to blocks of variable auditory stimuli during contexts when variability is expected as compared to the control condition. These findings suggest that young infants’ neural responses are modulated by predictions gained from experience and not simply by bottom-up mechanisms.

Dynamics of neural representations when searching for exemplars and categories of human and non-human faces

Face perception abilities in humans exhibit a marked expertise in distinguishing individual human faces at the expense of individual faces from other species (the other-species effect). In particular, one behavioural effect of such specialization is that human adults search for and find categories of non-human faces faster and more accurately than a specific non-human face, and vice versa for human faces. However, a recent visual search study showed that neural responses (event-related potentials, ERPs) were identical when finding either a non-human or human face. We used time-resolved multivariate pattern analysis of the EEG data from that study to investigate the dynamics of neural representations during a visual search for own-species (human) or other-species (non-human ape) faces, with greater sensitivity than traditional ERP analyses. The location of each target (i.e., right or left) could be decoded from the EEG, with similar accuracy for human and non-human faces. However, the neural patterns associated with searching for an exemplar versus a category target differed for human faces compared to non-human faces: Exemplar representations could be more reliably distinguished from category representations for human than non-human faces. These findings suggest that the other-species effect modulates the nature of representations, but preserves the attentional selection of target items based on these representations.

Classifying the mental representation of word meaning in children with Multivariate Pattern Analysis of fNIRS

This study presents the implementation of a within-subject neural decoder, based on Support Vector Machines, and its application for the classification of distributed patterns of hemodynamic activation, measured with Functional Near Infrared Spectroscopy (fNIRS) on children, in response to meaningful and meaningless auditory stimuli. Classification accuracy nominally exceeds chance level for the majority of the participants, but fails to reach statistical significance. Future work should investigate whether individual differences in classification accuracy may relate to other characteristics of the children, such as their cognitive, speech or reading abilities.

Emergence of the benefits and costs of grouping for visual search

The present study investigated how grouping related items leads to the emergence of benefits (facilitation when related items are search targets) and costs (interference when related items are distractors) in visual search. Participants integrated different views (related items) of a novel Lego object via (a) assembling the object, (b) disassembling the object, or (c) sitting quietly without explicit instructions. An omnibus ANOVA revealed that neural responses (N2pc ERP) for attentional selection increased between pretest to posttest regardless of the training condition when a specific target view appeared (benefit) and when a nontarget view from the same object as the target view appeared (cost). Bonferroni-corrected planned comparisons revealed that assembling the object (but not disassembling the object or no training) had a significant impact from pretest to posttest, although the ANOVA did not reveal any interaction effects, suggesting that the effects might not differ across training conditions. This study is one of the first to demonstrate the emergence of the costs and benefits of grouping novel targets on visual search efficiency.

Decoding semantic representations from functional near-infrared spectroscopy signals

This study uses representational similarity-based neural decoding to test whether semantic information elicited by words and pictures is encoded in functional near-infrared spectroscopy (fNIRS) data. In experiment 1, subjects passively viewed eight audiovisual word and picture stimuli for 15 min. Blood oxygen levels were measured using the Hitachi ETG-4000 fNIRS system with a posterior array over the occipital lobe and a left lateral array over the temporal lobe. Each participant's response patterns were abstracted to representational similarity space and compared to the group average (excluding that subject, i.e., leave-one-out cross-validation) and to a distributional model of semantic representation. Mean accuracy for both decoding tasks significantly exceeded chance. In experiment 2, we compared three group-level models by averaging the similarity structures from sets of eight participants in each group. In these models, the posterior array was accurately decoded by the semantic model, while the lateral array was accurately decoded in the between-groups comparison. Our findings indicate that semantic representations are encoded in the fNIRS data, preserved across subjects, and decodable by an extrinsic representational model. These results are the first attempt to link the functional response pattern measured by fNIRS to higher-level representations of how words are related to each other.

Nature and origins of the lexicon in 6-mo-olds

Recent research reported the surprising finding that even 6-mo-olds understand common nouns [Bergelson E, Swingley D (2012) Proc Natl Acad Sci USA 109:3253-3258]. However, is their early lexicon structured and acquired like older learners? We test 6-mo-olds for a hallmark of the mature lexicon: cross-word relations. We also examine whether properties of the home environment that have been linked with lexical knowledge in older children are detectable in the initial stage of comprehension. We use a new dataset, which includes in-lab comprehension and home measures from the same infants. We find evidence for cross-word structure: On seeing two images of common nouns, infants looked significantly more at named target images when the competitor images were semantically unrelated (e.g., milk and foot) than when they were related (e.g., milk and juice), just as older learners do. We further find initial evidence for home-lab links: common noun "copresence" (i.e., whether words' referents were present and attended to in home recordings) correlated with in-lab comprehension. These findings suggest that, even in neophyte word learners, cross-word relations are formed early and the home learning environment measurably helps shape the lexicon from the outset.

Distributional learning of subcategories in an artificial grammar: Category generalization and subcategory restrictions

There has been significant recent interest in clarifying how learners use distributional information during language acquisition. Many researchers have suggested that distributional learning mechanisms play a major role during grammatical category acquisition, since linguistic form-classes (like noun and verb) and subclasses (like masculine and feminine grammatical gender) are primarily defined by the ways lexical items are distributed in syntactic contexts. Though recent experimental work has affirmed the importance of distributional information for category acquisition, there has been little evidence that learners can acquire linguistic subclasses based only on distributional cues. Across two artificial grammar-learning experiments, we demonstrate that subclasses can be acquired from distributional cues alone. These results add to a body of work demonstrating rational use of distributional information to acquire complex linguistic structures.

Sensory cue-combination in the context of newly learned categories

A large body of prior research has evaluated how humans combine multiple sources of information pertaining to stimuli drawn from continuous dimensions, such as distance or size. These prior studies have repeatedly demonstrated that in these circumstances humans integrate cues in a near-optimal fashion, weighting cues according to their reliability. However, most of our interactions with sensory information are in the context of categories such as objects and phonemes, thereby requiring a solution to the cue combination problem by mapping sensory estimates from continuous dimensions onto task-relevant categories. Previous studies have examined cue combination with natural categories (e.g., phonemes), providing qualitative evidence that human observers utilize information about the distributional properties of task-relevant categories, in addition to sensory information, in such categorical cue combination tasks. In the present study, we created and taught human participants novel audiovisual categories, thus allowing us to quantitatively evaluate participants’ integration of sensory and categorical information. Comparing participant behavior to the predictions of a statistically optimal observer that ideally combines all available sources of information, we provide the first evidence, to our knowledge, that human observers combine sensory and category information in a statistically optimal manner.

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 effect of Zipfian frequency variations on category formation in adult artificial language learning

Successful language acquisition hinges on organizing individual words into grammatical categories and learning the relationships between them, but the method by which children accomplish this task has been debated in the literature. One proposal is that learners use the shared distributional contexts in which words appear as a cue to their underlying category structure. Indeed, recent research using artificial languages has demonstrated that learners can acquire grammatical categories from this type of distributional information. However, artificial languages are typically composed of a small number of equally frequent words, while words in natural languages vary widely in frequency, complicating the distributional information needed to determine categorization. In a series of three experiments we demonstrate that distributional learning is preserved in an artificial language composed of words that vary in frequency as they do in natural language, along a Zipfian distribution. Rather than depending on the absolute frequency of words and their contexts, the conditional probabilities that words will occur in certain contexts (given their base frequency) is a better basis for assigning words to categories; and this appears to be the type of statistic that human learners utilize.

Implicit associative learning in synesthetes and nonsynesthetes

Although cross-modal neural connections and genetic underpinnings are prominent in most current theories regarding the development of synesthesia, the potential role of associative learning in the formation of synesthetic associations has recently been revitalized. In this study, we investigated implicit associative learning in synesthetes and nonsynesthetes by recording reaction times to a target whose color was probabilistically correlated with its shape. A continuous measure of target detection at multiple time points during learning revealed that synesthetes and nonsynesthetes learn associations differently. Specifically, our results demonstrated a "fast-facilitation" learning effect for nonsynesthetes and a "fast-interference, slow-facilitation" learning effect for synesthetes. Additionally, synesthetes exhibited superior long-term memory for the learned associations in a surprise delayed retest. After this retest, participants implicitly learned new (shuffled) shape-color associations. We found that synesthetes experienced greater interference while learning these new shape-color associations. These results detail ways in which implicit associative learning and memory differ between synesthetes and nonsynesthetes.

Indexical and linguistic processing by 12-month-olds: Discrimination of speaker, accent and vowel differences

Infants preferentially discriminate between speech tokens that cross native category boundaries prior to acquiring a large receptive vocabulary, implying a major role for unsupervised distributional learning strategies in phoneme acquisition in the first year of life. Multiple sources of between-speaker variability contribute to children's language input and thus complicate the problem of distributional learning. Adults resolve this type of indexical variability by adjusting their speech processing for individual speakers. For infants to handle indexical variation in the same way, they must be sensitive to both linguistic and indexical cues. To assess infants' sensitivity to and relative weighting of indexical and linguistic cues, we familiarized 12-month-old infants to tokens of a vowel produced by one speaker, and tested their listening preference to trials containing a vowel category change produced by the same speaker (linguistic information), and the same vowel category produced by another speaker of the same or a different accent (indexical information). Infants noticed linguistic and indexical differences, suggesting that both are salient in infant speech processing. Future research should explore how infants weight these cues in a distributional learning context that contains both phonetic and indexical variation.

Decoding the infant mind: Multivariate pattern analysis (MVPA) using fNIRS

The MRI environment restricts the types of populations and tasks that can be studied by cognitive neuroscientists (e.g., young infants, face-to-face communication). FNIRS is a neuroimaging modality that records the same physiological signal as fMRI but without the constraints of MRI, and with better spatial localization than EEG. However, research in the fNIRS community largely lacks the analytic sophistication of analogous fMRI work, restricting the application of this imaging technology. The current paper presents a method of multivariate pattern analysis for fNIRS that allows the authors to decode the infant mind (a key fNIRS population). Specifically, multivariate pattern analysis (MVPA) employs a correlation-based decoding method where a group model is constructed for all infants except one; both average patterns (i.e., infant-level) and single trial patterns (i.e., trial-level) of activation are decoded. Between subjects decoding is a particularly difficult task, because each infant has their own somewhat idiosyncratic patterns of neural activation. The fact that our method succeeds at across-subject decoding demonstrates the presence of group-level multi-channel regularities across infants. The code for implementing these analyses has been made readily available online to facilitate the quick adoption of this method to advance the methodological tools available to the fNIRS researcher.

Using fNIRS to examine occipital and temporal responses to stimulus repetition in young infants: Evidence of selective frontal cortex involvement

How does the developing brain respond to recent experience? Repetition suppression (RS) is a robust and well-characterized response of to recent experience found, predominantly, in the perceptual cortices of the adult brain. We use functional near-infrared spectroscopy (fNIRS) to investigate how perceptual (temporal and occipital) and frontal cortices in the infant brain respond to auditory and visual stimulus repetitions (spoken words and faces). In Experiment 1, we find strong evidence of repetition suppression in the frontal cortex but only for auditory stimuli. In perceptual cortices, we find only suggestive evidence of auditory RS in the temporal cortex and no evidence of visual RS in any ROI. In Experiments 2 and 3, we replicate and extend these findings. Overall, we provide the first evidence that infant and adult brains respond differently to stimulus repetition. We suggest that the frontal lobe may support the development of RS in perceptual cortices.

Deficits in Top-Down Sensory Prediction in Infants At Risk due to Premature Birth

A prominent theoretical view is that the brain is inherently predictive [1, 2] and that prediction helps drive the engine of development [3, 4]. Although infants exhibit neural signatures of top-down sensory prediction [5, 6], in order to establish that prediction supports development, it must be established that deficits in early prediction abilities alter trajectories. We investigated prediction in infants born prematurely, a leading cause of neuro-cognitive impairment worldwide [7]. Prematurity, independent of medical complications, leads to developmental disturbances [8-12] and a broad range of developmental delays [13-17]. Is an alteration in early prediction abilities the common cause? Using functional near-infrared spectroscopy (fNIRS), we measured top-down sensory prediction in preterm infants (born <33 weeks gestation) before infants exhibited clinically identifiable developmental delays (6 months corrected age). Whereas preterm infants had typical neural responses to presented visual stimuli, they exhibited altered neural responses to predicted visual stimuli. Importantly, a separate behavioral control confirmed that preterm infants detect pattern violations at the same rate as full-terms, establishing selectivity of this response to top-down predictions (e.g., not in learning an audiovisual association). These findings suggest that top-down sensory prediction plays a crucial role in development and that deficits in this ability may be the reason why preterm infants experience altered developmental trajectories and are at risk for poor developmental outcomes. Moreover, this work presents an opportunity for establishing a neuro-biomarker for early identification of infants at risk and could guide early intervention regimens.

Statistical learning: a powerful mechanism that operates by mere exposure

How do infants learn so rapidly and with little apparent effort? In 1996, Saffran, Aslin, and Newport reported that 8-month-old human infants could learn the underlying temporal structure of a stream of speech syllables after only 2 min of passive listening. This demonstration of what was called statistical learning, involving no instruction, reinforcement, or feedback, led to dozens of confirmations of this powerful mechanism of implicit learning in a variety of modalities, domains, and species. These findings reveal that infants are not nearly as dependent on explicit forms of instruction as we might have assumed from studies of learning in which children or adults are taught facts such as math or problem solving skills. Instead, at least in some domains, infants soak up the information around them by mere exposure. Learning and development in these domains thus appear to occur automatically and with little active involvement by an instructor (parent or teacher). The details of this statistical learning mechanism are discussed, including how exposure to specific types of information can, under some circumstances, generalize to never-before-observed information, thereby enabling transfer of learning. WIREs Cogn Sci 2017, 8:e1373. doi: 10.1002/wcs.1373 For further resources related to this article, please visit the WIREs website.

Cue Integration for Continuous and Categorical Dimensions by Synesthetes

For synesthetes, sensory or cognitive stimuli induce the perception of an additional sensory or cognitive stimulus. Grapheme-color synesthetes, for instance, consciously and consistently experience particular colors (e.g., fluorescent pink) when perceiving letters (e.g., u). As a phenomenon involving multiple stimuli within or across modalities, researchers have posited that synesthetes may integrate sensory cues differently than non-synesthetes. However, findings to date present mixed results concerning this hypothesis, with researchers reporting enhanced, depressed, or normal sensory integration for synesthetes. In this study we quantitatively evaluated the multisensory integration process of synesthetes and non-synesthetes using Bayesian principles, rather than employing multisensory illusions, to make inferences about the sensory integration process. In two studies we investigated synesthetes' sensory integration by comparing human behavior to that of an ideal observer. We found that synesthetes integrated cues for both continuous and categorical dimensions in a statistically optimal manner, matching the sensory integration behavior of controls. These findings suggest that synesthetes and controls utilize similar cue integration mechanisms, despite differences in how they perceive unimodal stimuli.

Incremental implicit learning of bundles of statistical patterns

Forming an accurate representation of a task environment often takes place incrementally as the information relevant to learning the representation only unfolds over time. This incremental nature of learning poses an important problem: it is usually unclear whether a sequence of stimuli consists of only a single pattern, or multiple patterns that are spliced together. In the former case, the learner can directly use each observed stimulus to continuously revise its representation of the task environment. In the latter case, however, the learner must first parse the sequence of stimuli into different bundles, so as to not conflate the multiple patterns. We created a video-game statistical learning paradigm and investigated (1) whether learners without prior knowledge of the existence of multiple "stimulus bundles" - subsequences of stimuli that define locally coherent statistical patterns - could detect their presence in the input and (2) whether learners are capable of constructing a rich representation that encodes the various statistical patterns associated with bundles. By comparing human learning behavior to the predictions of three computational models, we find evidence that learners can handle both tasks successfully. In addition, we discuss the underlying reasons for why the learning of stimulus bundles occurs even when such behavior may seem irrational.

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

Successful knowledge acquisition requires a cognitive system that is both sensitive to statistical information and able to distinguish among multiple structures (i.e., to detect pattern shifts and form distinct representations). Extensive behavioral evidence has highlighted the importance of cues to structural change, demonstrating how, without them, learners fail to detect pattern shifts and are biased in favor of early experience. Here, we seek a neural account of the mechanism underpinning this primacy effect in learning. During fMRI scanning, adult participants were presented with two artificial languages: a familiar language (L1) on which they had been pretrained followed by a novel language (L2). The languages were composed of the same syllable inventory organized according to unique statistical structures. In the absence of cues to the transition between languages, posttest familiarity judgments revealed that learners on average more accurately segmented words from the familiar language compared with the novel one. Univariate activation and functional connectivity analyses showed that participants with the strongest learning of L1 had decreased recruitment of fronto-subcortical and posterior parietal regions, in addition to a dissociation between downstream regions and early auditory cortex. Participants with a strong new language learning capacity (i.e., higher L2 scores) showed the opposite trend. Thus, we suggest that a bias toward neural efficiency, particularly as manifested by decreased sampling from the environment, accounts for the primacy effect in learning. Potential implications of this hypothesis are discussed, including the possibility that "inefficient" learning systems may be more sensitive to structural changes in a dynamic environment.

Explicit Associative Learning and Memory in Synesthetes and Nonsynesthetes

Most current theories regarding the development of synesthesia focus on cross-modal neural connections and genetic underpinnings, but recent evidence has revitalized the potential role of associative learning. In the present study, we compared synesthetes’ and controls’ ability to explicitly learn shape-color pairings. Using a continuous measure of accuracy and multiple testing blocks, we found that synesthetes learned these pairings faster than controls. In a delayed retest, synesthetes outperformed controls, demonstrating enhanced long-term memory for shape–color associations. Following this retest, participants learned shuffled associations, and we found little evidence for group differences in subsequent learning ability. Overall, our findings support the hypothesis that synesthetes have exceptional associative learning abilities and further specify that this advantage pertains to the initial learning rate and long-term retention of associations.

Learning and inference using complex generative models in a spatial localization task

A large body of research has established that, under relatively simple task conditions, human observers integrate uncertain sensory information with learned prior knowledge in an approximately Bayes-optimal manner. However, in many natural tasks, observers must perform this sensory-plus-prior integration when the underlying generative model of the environment consists of multiple causes. Here we ask if the Bayes-optimal integration seen with simple tasks also applies to such natural tasks when the generative model is more complex, or whether observers rely instead on a less efficient set of heuristics that approximate ideal performance. Participants localized a "hidden" target whose position on a touch screen was sampled from a location-contingent bimodal generative model with different variances around each mode. Over repeated exposure to this task, participants learned the a priori locations of the target (i.e., the bimodal generative model), and integrated this learned knowledge with uncertain sensory information on a trial-by-trial basis in a manner consistent with the predictions of Bayes-optimal behavior. In particular, participants rapidly learned the locations of the two modes of the generative model, but the relative variances of the modes were learned much more slowly. Taken together, our results suggest that human performance in a more complex localization task, which requires the integration of sensory information with learned knowledge of a bimodal generative model, is consistent with the predictions of Bayes-optimal behavior, but involves a much longer time-course than in simpler tasks.

Isolating the effects of surface vasculature in infant neuroimaging using short-distance optical channels: a combination of local and global effects

Functional near-infrared spectroscopy (fNIRS) records hemodynamic changes in the cortex arising from neurovascular coupling. However, (noninvasive) fNIRS recordings also record surface vascular signals arising from noncortical sources (e.g., in the skull, skin, dura, and other tissues located between the sensors and the brain). A current and important focus in the fNIRS community is determining how to remove these noncortical vascular signals to reduce noise and to prevent researchers from erroneously attributing responses to cortical sources. The current study is the first to test a popular method for removing signals from the surface vasculature (removing short, 1 cm, channel recordings from long, 3 cm, channel recordings) in human infants, a population frequently studied using fNIRS. We find evidence that this method does remove surface vasculature signals and indicates the presence of both local and global surface vasculature signals. However, we do not find that the removal of this information changes the statistical inferences drawn from the data. This latter result not only questions the importance of removing surface vasculature responses for empiricists employing this method, but also calls for future research using other tasks (e.g., ones with a weaker initial result) with this population and possibly additional methods for removing signals arising from the surface vasculature in infants.

Methodology for high-yield acquisition of functional near-infrared spectroscopy data from alert, upright infants

Functional near-infrared spectroscopy (fNIRS) research to date has tended to publish group-averaged rather than individual infant data due to normative basic research goals. Acquisition of individual infant time courses holds interest, however, both for cognitive science and particularly for clinical applications. Infants are more difficult to study than adults as they cannot be instructed to remain still. In addressing this, upright infants pose several associated complications for the researcher. We identified and optimized the factors that affect the quality of fNIRS data from individual 6- to 9-month-old infants exposed to a visual stimulation paradigm. The fNIRS headpiece was reconfigured to reduce inertia, increase comfort, and improve conformity to the head, while preserving fiber density to avoid missing the visual cortex activation. The visual-stimulation protocol was modified to keep the attention of infants throughout the measurement, thus helping to reduce motion artifacts. Adequate optical contact was verified by checking power levels before each measurement. By revising our experimental process and our data rejection criteria to prioritize good optical contact, we report for the first time usable hemodynamic data from 83% of infants and that two-thirds of infants produced a statistically significant fNIRS response.

Computation of Conditional Probability Statistics by 8-Month-Old Infants

A recent report demonstrated that 8-month-olds can segment a continuous stream of speech syllables, containing no acoustic or prosodic cues to word boundaries, into wordlike units after only 2 min of listening experience (Saffran, Aslin, & Newport, 1996). Thus, a powerful learning mechanism capable of extracting statistical information from fluent speech is available early in development. The present study extends these results by documenting the particular type of statistical computation—transitional (conditional) probability—used by infants to solve this word-segmentation task. An artificial language corpus, consisting of a continuous stream of trisyllabic nonsense words, was presented to 8-month-olds for 3 min. A postfamiliarization test compared the infants' responses to words versus part-words (trisyllabic sequences spanning word boundaries). The corpus was constructed so that test words and part-words were matched in frequency, but differed in their transitional probabilities. Infants showed reliable discrimination of words from part-words, thereby demonstrating rapid segmentation of continuous speech into words on the basis of transitional probabilities of syllable pairs.