Staying and Returning dynamics of young children's attention.

In this paper, we decompose selective sustained attending behavior into components of continuous attention maintenance and attentional transitions and study how each of these components develops in young children. Our results in two experiments suggest that changes in children's ability to return attention to a target locus after distraction ("Returning") play a crucial role in the development of selective sustained attention between the ages of 3.5-6 years, perhaps to a greater extent than changes in the ability to continuously maintain attention on the target ("Staying"). We further distinguish Returning from the behavior of transitioning attention away from task (i.e., becoming distracted) and investigate the relative contributions of bottom-up and top-down factors on these different types of attentional transitions. Overall, these results (a) suggest the importance of understanding the cognitive process of transitioning attention for understanding selective sustained attention and its development, (b) provide an empirical paradigm within which to study this process, and (c) begin to characterize basic features of this process, namely its development and its relative dependence on top-down and bottom-up influences on attention. RESEARCH HIGHLIGHTS: Young children exhibited an endogenously ability, Returning, to preferentially transition attention to task-relevant information over task-irrelevant information. Selective sustained attention and its development were decomposed into Returning and Staying, or task-selective attention maintenance, using novel eye-tracking-based measures. Returning improved between the ages of 3.5-6 years, to a greater extent than Staying. Improvements in Returning supported improvements in selective sustained attention between these ages.

A Literature Review on the Effects of Exergames on Executive Function in Youth.

Exergames (video games that promote cognitive and physical activity simultaneously) benefit executive function in elderly populations. It has been suggested that exergames may induce larger effects than cognitive or exercise training alone, but few reviews have synthesized the causal factors of exergames on executive function from experimental research with youth. This review investigates (1) the various types of exergames and associated comparison conditions (2) the executive function outcome assessments commonly utilized in exergame research with youth (3) the efficacy of exergames by evaluating experimental studies that compared exergaming to cognitive, exercise, and passive control conditions inclusive of effect sizes and (4) the potential mechanisms underlying the changes in executive function induced from exergames. Eligible outcome data were available from 607 participants across ten studies, with the age of participants ranging from 4-21 (Mage=10.46). The findings indicate that exergames improve aspects of executive function from both acute and chronic studies. Despite the high variability of exergame contexts, dosage, populations, and outcome assessments, improvements in executive function comparing exergaming to passive control conditions were exhibited across all studies. While there is evidence of exergaming demonstrating advantages over passive control conditions, evidence is mixed when comparing exergaming to sedentary cognitive and exercise comparison conditions. Potential sources of these mixed results and future directions to address current gaps in the field are identified. As video game and technology use grows exponentially and concerns of childhood sedentary behavior and play deprivation increase, evidence-based practices that promote both physical and cognitive activity are needed.

Longitudinal investigation of executive function development employing task-based, teacher reports, and fNIRS multimethodology in 4- to 5-year-old children.

Increased focus on resting-state functional connectivity (rsFC) and the use and accessibility of functional near-infrared spectroscopy (fNIRS) have advanced knowledge on the interconnected nature of neural substrates underlying executive function (EF) development in adults and clinical populations. Less is known about the relationship between rsFC and developmental changes in EF during preschool years in typically developing children, a gap the present study addresses employing task-based assessment, teacher reports, and fNIRS multimethodology. This preregistered study contributes to our understanding of the neural basis of EF development longitudinally with 41 children ages 4-5. Changes in prefrontal cortex (PFC) rsFC utilizing fNIRS, EF measured with a common task-based assessment (Day-Night task), and teacher reports of behavior (BRIEF-P) were monitored over multiple timepoints: Initial Assessment, 72 h follow-up, 1 Month Follow-up, and 4 Month Follow-up. Measures of rsFC were strongly correlated 72 h apart, providing evidence of high rsFC measurement reliability using fNIRS with preschool-aged children. PFC rsFC was positively correlated with performance on task-based and report-based EF assessments. Children's PFC functional connectivity at rest uniquely predicted later EF, controlling for verbal IQ, age, and sex. Functional connectivity at rest using fNIRS may potentially show the rapid changes in EF development in young children, not only neurophysiologically, but also as a correlate of task-based EF performance and ecologically-relevant teacher reports of EF in a classroom context.

A pre-registered, multi-lab non-replication of the action-sentence compatibility effect (ACE).

The Action-sentence Compatibility Effect (ACE) is a well-known demonstration of the role of motor activity in the comprehension of language. Participants are asked to make sensibility judgments on sentences by producing movements toward the body or away from the body. The ACE is the finding that movements are faster when the direction of the movement (e.g., toward) matches the direction of the action in the to-be-judged sentence (e.g., Art gave you the pen describes action toward you). We report on a pre-registered, multi-lab replication of one version of the ACE. The results show that none of the 18 labs involved in the study observed a reliable ACE, and that the meta-analytic estimate of the size of the ACE was essentially zero.

Effects of language background on executive function: Transfer across task and modality.

The relation between linguistic experience and cognitive function has been of great interest, but recent investigations of this question have produced widely disparate results, ranging from proposals for a "bilingual advantage," to a "bilingual disadvantage," to claims of no difference at all as a function of language. There are many possible sources for this lack of consensus, including the heterogeneity of bilingual populations, and the choice of different tasks and implementations across labs. We propose that another reason for this inconsistency is the task demands of transferring from linguistic experience to laboratory tasks can differ greatly as the task is modified. In this study, we show that task modality (visual, audio, and orthographic) can yield different patterns of performance between monolingual and multilingual participants. The very same task can show similarities or differences in performance, as a function of modality. In turn, this may be explained by the distance of transfer - how close (or far) the laboratory task is to the day to day lived experience of language usage. We suggest that embodiment may provide a useful framework for thinking about task transfer by helping to define the processes of linguistic production and comprehension in ways that are easily connected to task manipulations.

Dual language statistical word segmentation in infancy: Simulating a language-mixing bilingual environment.

Infants are sensitive to syllable co-occurrence probabilities when segmenting words from fluent speech. However, segmenting two languages overlapping at the syllabic level is challenging because the statistical cues across the languages are incongruent. Successful segmentation, thus, relies on infants' ability to separate language inputs and track the statistics of each language. Here, we report three experiments investigating how infants statistically segment words from two overlapping languages in a simulated language-mixing bilingual environment. In the first two experiments, we investigated whether 9.5-month-olds can use French and English phonetic markers to segment words from two overlapping artificial languages produced by one individual. After showing that infants could segment the languages when the languages were presented in isolation (Experiment 1), we presented infants with two interleaved languages differing in phonetic cues (Experiment 2). Both monolingual and bilingual infants successfully segmented words from one of the two languages-the language heard last during familiarization. In Experiment 3, a conceptual replication, we replicated the findings of Experiment 2 with a different population and with different cues. As before, when 12-month-old monolingual infants heard two interleaved languages differing in English and Finnish phonetic cues, they learned only the last language heard during familiarization. Together, our findings suggest that segmenting words in a language-mixing environment is challenging, but infants possess a nascent ability to recruit phonetic cues to segment words from one of two overlapping languages in a bilingual-like environment. A video abstract of this article can be viewed at https://www.youtube.com/watch?v=92pNcpxZguw.

A hidden Markov model for analyzing eye-tracking of moving objects : Case study in a sustained attention paradigm.

Eye-tracking provides an opportunity to generate and analyze high-density data relevant to understanding cognition. However, while events in the real world are often dynamic, eye-tracking paradigms are typically limited to assessing gaze toward static objects. In this study, we propose a generative framework, based on a hidden Markov model (HMM), for using eye-tracking data to analyze behavior in the context of multiple moving objects of interest. We apply this framework to analyze data from a recent visual object tracking task paradigm, TrackIt, for studying selective sustained attention in children. Within this paradigm, we present two validation experiments to show that the HMM provides a viable approach to studying eye-tracking data with moving stimuli, and to illustrate the benefits of the HMM approach over some more naive possible approaches. The first experiment utilizes a novel 'supervised' variant of TrackIt, while the second compares directly with judgments made by human coders using data from the original TrackIt task. Our results suggest that the HMM-based method provides a robust analysis of eye-tracking data with moving stimuli, both for adults and for children as young as 3.5-6 years old.

Contingent responsivity in E-books modeled from quality adult-child interactions: Effects on children's learning and attention.

Experiences of contingent responsivity during shared book reading predict better learning outcomes. However, it is unclear whether contingent responsivity from a digital book could provide similar support for children. The effects on story recall and engagement interacting with a digital book that responded contingently on children's vocalizations (contingent book) were investigated, with a focus on the role of individual differences in attention. The study used a within-subject design with 3 experiments from 90 3- to 5-year-old children. Children were presented with a contingent book and 3 noncontingent control conditions: a board book (Experiment 1), a static digital book (Experiment 2), and an animated book (Experiment 3). The use of the contingent book significantly increased children's story recall and was found to be especially useful for children with less developed attention regulation. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Early developing syntactic knowledge influences sequential statistical learning in infancy.

Adults' linguistic background influences their sequential statistical learning of an artificial language characterized by conflicting forward-going and backward-going transitional probabilities. English-speaking adults favor backward-going transitional probabilities, consistent with the head-initial structure of English. Korean-speaking adults favor forward-going transitional probabilities, consistent with the head-final structure of Korean. These experiments assess when infants develop this directional bias. In the experiments, 7-month-old infants showed no bias for forward-going or backward-going regularities. By 13 months, however, English-learning infants favored backward-going transitional probabilities over forward-going transitional probabilities, consistent with English-speaking adults. This indicates that statistical learning rapidly adapts to the predominant syntactic structure of the native language. Such adaptation may facilitate subsequent learning by highlighting statistical structures that are likely to be informative in the native linguistic environment.

What's statistical about learning? Insights from modelling statistical learning as a set of memory processes.

Statistical learning has been studied in a variety of different tasks, including word segmentation, object identification, category learning, artificial grammar learning and serial reaction time tasks (e.g. Saffran et al. 1996 Science 274: , 1926-1928; Orban et al. 2008 Proceedings of the National Academy of Sciences 105: , 2745-2750; Thiessen & Yee 2010 Child Development 81: , 1287-1303; Saffran 2002 Journal of Memory and Language 47: , 172-196; Misyak & Christiansen 2012 Language Learning 62: , 302-331). The difference among these tasks raises questions about whether they all depend on the same kinds of underlying processes and computations, or whether they are tapping into different underlying mechanisms. Prior theoretical approaches to statistical learning have often tried to explain or model learning in a single task. However, in many cases these approaches appear inadequate to explain performance in multiple tasks. For example, explaining word segmentation via the computation of sequential statistics (such as transitional probability) provides little insight into the nature of sensitivity to regularities among simultaneously presented features. In this article, we will present a formal computational approach that we believe is a good candidate to provide a unifying framework to explore and explain learning in a wide variety of statistical learning tasks. This framework suggests that statistical learning arises from a set of processes that are inherent in memory systems, including activation, interference, integration of information and forgetting (e.g. Perruchet & Vinter 1998 Journal of Memory and Language 39: , 246-263; Thiessen et al. 2013 Psychological Bulletin 139: , 792-814). From this perspective, statistical learning does not involve explicit computation of statistics, but rather the extraction of elements of the input into memory traces, and subsequent integration across those memory traces that emphasize consistent information (Thiessen and Pavlik 2013 Cognitive Science 37: , 310-343).This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'.

Statistical learning and the critical period: how a continuous learning mechanism can give rise to discontinuous learning.

Infants and children are generally more successful than adults in learning novel languages, a phenomenon referred to as a critical or sensitive period for language acquisition. One explanation for this critical period is the idea that children have access to a set of language learning processes or mechanisms unavailable to adults. From this perspective, developmental change is explained in terms of a discontinuity of learning processes. We suggest that this is not the only possible explanation for developmental change in language learning outcomes. Instead, we propose that the mechanisms underlying language acquisition (in particular, we highlight statistical learning) are largely continuous across the lifespan. From this perspective, developmental change is explained in terms of experience, differences in the input with age, and maturational changes in the cognitive architecture supporting learning, even while the learning process itself operates continuously across developmental time. WIREs Cogn Sci 2016, 7:276-288. doi: 10.1002/wcs.1394 For further resources related to this article, please visit the WIREs website.

Endogenously and exogenously driven selective sustained attention: Contributions to learning in kindergarten children.

Selective sustained attention is vital for higher order cognition. Although endogenous and exogenous factors influence selective sustained attention, assessment of the degree to which these factors influence performance and learning is often challenging. We report findings from the Track-It task, a paradigm that aims to assess the contribution of endogenous and exogenous factors to selective sustained attention within the same task. Behavioral accuracy and eye-tracking data on the Track-It task were correlated with performance on an explicit learning task. Behavioral accuracy and fixations to distractors during the Track-It task did not predict learning when exogenous factors supported selective sustained attention. In contrast, when endogenous factors supported selective sustained attention, fixations to distractors were negatively correlated with learning. Similarly, when endogenous factors supported selective sustained attention, higher behavioral accuracy was correlated with greater learning. These findings suggest that endogenously and exogenously driven selective sustained attention, as measured through different conditions of the Track-It task, may support different kinds of learning.

Impaired Statistical Learning in Developmental Dyslexia.

PURPOSE: Developmental dyslexia (DD) is commonly thought to arise from phonological impairments. However, an emerging perspective is that a more general procedural learning deficit, not specific to phonological processing, may underlie DD. The current study examined if individuals with DD are capable of extracting statistical regularities across sequences of passively experienced speech and nonspeech sounds. Such statistical learning is believed to be domain-general, to draw upon procedural learning systems, and to relate to language outcomes. METHOD: DD and control groups were familiarized with a continuous stream of syllables or sine-wave tones, the ordering of which was defined by high or low transitional probabilities across adjacent stimulus pairs. Participants subsequently judged two 3-stimulus test items with either high or low statistical coherence as being the most similar to the sounds heard during familiarization. RESULTS: As with control participants, the DD group was sensitive to the transitional probability structure of the familiarization materials as evidenced by above-chance performance. However, the performance of participants with DD was significantly poorer than controls across linguistic and nonlinguistic stimuli. In addition, reading-related measures were significantly correlated with statistical learning performance of both speech and nonspeech material. CONCLUSION: Results are discussed in light of procedural learning impairments among participants with DD.

Spectral information in nonspeech contexts influences children's categorization of ambiguous speech sounds.

For both adults and children, acoustic context plays an important role in speech perception. For adults, both speech and nonspeech acoustic contexts influence perception of subsequent speech items, consistent with the argument that effects of context are due to domain-general auditory processes. However, prior research examining the effects of context on children's speech perception have focused on speech contexts; nonspeech contexts have not been explored previously. To better understand the developmental progression of children's use of contexts in speech perception and the mechanisms underlying that development, we created a novel experimental paradigm testing 5-year-old children's speech perception in several acoustic contexts. The results demonstrated that nonspeech context influences children's speech perception, consistent with claims that context effects arise from general auditory system properties rather than speech-specific mechanisms. This supports theoretical accounts of language development suggesting that domain-general processes play a role across the lifespan.

iMinerva: a mathematical model of distributional statistical learning.

Statistical learning refers to the ability to identify structure in the input based on its statistical properties. For many linguistic structures, the relevant statistical features are distributional: They are related to the frequency and variability of exemplars in the input. These distributional regularities have been suggested to play a role in many different aspects of language learning, including phonetic categories, using phonemic distinctions in word learning, and discovering non-adjacent relations. On the surface, these different aspects share few commonalities. Despite this, we demonstrate that the same computational framework can account for learning in all of these tasks. These results support two conclusions. The first is that much, and perhaps all, of distributional statistical learning can be explained by the same underlying set of processes. The second is that some aspects of language can be learned due to domain-general characteristics of memory.

The extraction and integration framework: a two-process account of statistical learning.

The term statistical learning in infancy research originally referred to sensitivity to transitional probabilities. Subsequent research has demonstrated that statistical learning contributes to infant development in a wide array of domains. The range of statistical learning phenomena necessitates a broader view of the processes underlying statistical learning. Learners are sensitive to a much wider range of statistical information than the conditional relations indexed by transitional probabilities, including distributional and cue-based statistics. We propose a novel framework that unifies learning about all of these kinds of statistical structure. From our perspective, learning about conditional relations outputs discrete representations (such as words). Integration across these discrete representations yields sensitivity to cues and distributional information. To achieve sensitivity to all of these kinds of statistical structure, our framework combines processes that extract segments of the input with processes that compare across these extracted items. In this framework, the items extracted from the input serve as exemplars in long-term memory. The similarity structure of those exemplars in long-term memory leads to the discovery of cues and categorical structure, which guides subsequent extraction. The extraction and integration framework provides a way to explain sensitivity to both conditional statistical structure (such as transitional probabilities) and distributional statistical structure (such as item frequency and variability), and also a framework for thinking about how these different aspects of statistical learning influence each other.

Discovering words in fluent speech: the contribution of two kinds of statistical information.

To efficiently segment fluent speech, infants must discover the predominant phonological form of words in the native language. In English, for example, content words typically begin with a stressed syllable. To discover this regularity, infants need to identify a set of words. We propose that statistical learning plays two roles in this process. First, it provides a cue that allows infants to segment words from fluent speech, even without language-specific phonological knowledge. Second, once infants have identified a set of lexical forms, they can learn from the distribution of acoustic features across those word forms. The current experiments demonstrate both processes are available to 5-month-old infants. This demonstration of sensitivity to statistical structure in speech, weighted more heavily than phonological cues to segmentation at an early age, is consistent with theoretical accounts that claim statistical learning plays a role in helping infants to adapt to the structure of their native language from very early in life.

When variability matters more than meaning: the effect of lexical forms on use of phonemic contrasts.

During the first half of the 2nd year of life, infants struggle to use phonemic distinctions in label-object association tasks. Prior experiments have demonstrated that exposure to the phonemes in distinct lexical forms (e.g., /d/ and /t/ in daddy and tiger, respectively) facilitates infants' use of phonemic contrasts but also that they struggle to generalize the use of phonemic contrasts to novel syllabic contexts (Thiessen, 2007; Thiessen & Yee, 2010). Further, in prior research, infants have been provided only with experience in lexical forms that refer to novel objects, while many lexical forms in the natural environment do not have easily identified visual referents. The experiments in this article show that even lexical forms without referents can facilitate use of phonemic contrasts. Additionally, the results indicate that when lexical forms provide infants with enough variability (for example, a consonant followed by multiple different vowels), infants are able to generalize to novel contexts.

Domain general constraints on statistical learning.

All theories of language development suggest that learning is constrained. However, theories differ on whether these constraints arise from language-specific processes or have domain-general origins such as the characteristics of human perception and information processing. The current experiments explored constraints on statistical learning of patterns, such as the phonotactic patterns of an infants' native language. Infants in these experiments were presented with a visual analog of a phonotactic learning task used by J. R. Saffran and E. D. Thiessen (2003). Saffran and Thiessen found that infants' phonotactic learning was constrained such that some patterns were learned more easily than other patterns. The current results indicate that infants' learning of visual patterns shows the same constraints as infants' learning of phonotactic patterns. This is consistent with theories suggesting that constraints arise from domain-general sources and, as such, should operate over many kinds of stimuli in addition to linguistic stimuli.

Dogs, bogs, labs, and lads: what phonemic generalizations indicate about the nature of children's early word-form representations.

Whereas young children accept words that differ by only a single phoneme as equivalent labels for novel objects, older children do not (J. F. Werker, C. J. Fennell, K. M. Corcoran, & C. L. Stager, 2002). In these experiments, 106 children were exposed to a training regime that has previously been found to facilitate children's use of phonemic contrasts (E. D. Thiessen, 2007). The results indicate that the effect of this training is limited to contexts that are highly similar to children's initial experience with the phonemic contrast, suggesting that early word-form representations are not composed of entirely abstract units such as phonemes or features. Instead, these results are consistent with theories suggesting that children's early word-form representations retain contextual and perceptual features associated with children's prior experience with words.