Prefrontal and Hippocampal Structure Predict Statistical Learning Ability in Early Childhood

Integrating developmental neuroscience with community-engaged approaches to address mental health outcomes for housing-insecure youth: Implications for research, practice, and policy

One in three children in the United States is exposed to insecure housing conditions, including unaffordable, inconsistent, and unsafe housing. These exposures have detrimental impacts on youth mental health. Delineating the neurobehavioral pathways linking exposure to housing insecurity with children's mental health has the potential to inform interventions and policy. However, in approaching this work, carefully considering the lived experiences of youth and families is essential to translating scientific discovery to improve health outcomes in an equitable and representative way. In the current paper, we provide an introduction to the range of stressful experiences that children may face when exposed to insecure housing conditions. Next, we highlight findings from the early-life stress literature regarding the potential neurobehavioral consequences of insecure housing, focusing on how unpredictability is associated with the neural circuitry supporting cognitive and emotional development. We then delineate how community-engaged research (CEnR) approaches have been leveraged to understand the effects of housing insecurity on mental health, and we propose future research directions that integrate developmental neuroscience research and CEnR approaches to maximize the impact of this work. We conclude by outlining practice and policy recommendations that aim to improve the mental health of children exposed to insecure housing.

Sensitive periods in language development: Do children outperform adults on auditory word-form segmentation?

Children are more successful language learners than adults, yet the nature and cause of this phenomenon are still not well understood. Auditory statistical learning from speech has been a prominent focus of research in the field of language development because it is regarded as a fundamental learning mechanism underlying word segmentation in early language acquisition. However, a handful of studies that investigated developmental trajectories for auditory statistical learning found no clear child advantages. The degree to which the learning task measures explicit rather than implicit mechanisms might obscure a potential advantage for younger learners, as suggested by recent findings. In the present study, we compared children aged 7-12 years and young adults on an adapted version of the task that disentangles explicit and implicit contributions to learning. They were exposed to a continuous stream of speech sounds comprising four repeating trisyllabic pseudowords. Learning of the hidden words was tested (a) online through a target-detection task and (b) offline via a forced-choice word recognition test that included a memory judgement procedure. Both measures revealed comparable learning abilities. However, children's performance on the recognition task showed evidence for both explicit and implicit word knowledge while adults appeared primarily sensitive to explicit memory. Since implicit memory is more stable in time than explicit memory, we suggest that future work should focus more on developmental differences in the nature of the memory that is formed, rather than the strength of learning, when trying to understand child advantages in language acquisition.

Limited but specific engagement of the mature language network during linguistic statistical learning

Statistical learning (SL) is the ability to detect and learn regularities from input and is foundational to language acquisition. Despite the dominant role of SL as a theoretical construct for language development, there is a lack of direct evidence supporting the shared neural substrates underlying language processing and SL. It is also not clear whether the similarities, if any, are related to linguistic processing, or statistical regularities in general. The current study tests whether the brain regions involved in natural language processing are similarly recruited during auditory, linguistic SL. Twenty-two adults performed an auditory linguistic SL task, an auditory nonlinguistic SL task, and a passive story listening task as their neural activation was monitored. Within the language network, the left posterior temporal gyrus showed sensitivity to embedded speech regularities during auditory, linguistic SL, but not auditory, nonlinguistic SL. Using a multivoxel pattern similarity analysis, we uncovered similarities between the neural representation of auditory, linguistic SL, and language processing within the left posterior temporal gyrus. No other brain regions showed similarities between linguistic SL and language comprehension, suggesting that a shared neurocomputational process for auditory SL and natural language processing within the left posterior temporal gyrus is specific to linguistic stimuli.

Memories of structured input become increasingly distorted across development

Trajectories of cognitive and neural development suggest that, despite early emergence, the ability to extract environmental patterns changes across childhood. Here, 5- to 9-year-olds and adults (N = 211, 110 females, in a large Canadian city) completed a memory test assessing what they remembered after watching a stream of shape triplets: the particular sequence in which the shapes occurred and/or their group-level structure. After accounting for developmental improvements in overall memory, all ages remembered specific transitions, while memory for group membership was only observed in older children and adults (age by test-type interaction η2  = .05). Thus, while young children form memories for specifics of structured experience, memory for derived associations is refined later-underscoring that adults and young children form different memories despite identical experience.

Statistical learning in epilepsy: Behavioral, anatomical, and causal mechanisms in the human brain

Statistical learning, the fundamental cognitive ability of humans to extract regularities across experiences over time, engages the medial temporal lobe in the healthy brain. This leads to the hypothesis that statistical learning may be impaired in epilepsy patients, and that this impairment could contribute to their varied memory deficits. In turn, epilepsy patients provide a platform to advance basic understanding of statistical learning by helping to evaluate the necessity of medial temporal lobe circuitry through disease and causal perturbations. We implemented behavioral testing, volumetric analysis of the medial temporal lobe substructures, and direct electrical brain stimulation to examine statistical learning across a cohort of 61 epilepsy patients and 28 healthy controls. Behavioral performance in a statistical learning task was negatively associated with seizure frequency, irrespective of where seizures originated in the brain. The volume of hippocampal subfields CA1 and CA2/3 correlated with statistical learning performance, suggesting a more specific role of the hippocampus. Indeed, transient direct electrical stimulation of the hippocampus disrupted statistical learning. Furthermore, the relationship between statistical learning and seizure frequency was selective: behavioral performance in an episodic memory task was impacted by structural lesions in the medial temporal lobe and by antiseizure medications, but not by seizure frequency. Overall, these results suggest that statistical learning may be hippocampally dependent and that this task could serve as a clinically useful behavioral assay of seizure frequency distinct from existing neuropsychological tests. Simple and short statistical learning tasks may thus provide patient-centered endpoints for evaluating the efficacy of novel treatments in epilepsy.

What sticks after statistical learning: The persistence of implicit versus explicit memory traces

Statistical learning is a powerful mechanism that extracts even subtle regularities from our information-dense worlds. Recent theories argue that statistical learning can occur through multiple mechanisms-both the conventionally assumed automatic process that precipitates unconscious learning, and an attention-dependent process that brings regularities into conscious awareness. While this view has gained popularity, there are few empirical dissociations of the hypothesized implicit and explicit forms of statistical learning. Here we provide strong evidence for this dissociation in two ways. First, we show in healthy adults (N = 60) that implicit and explicit traces have divergent consolidation trajectories, with implicit knowledge of structure strengthened over a 24-h period, while precise explicit representations tend to decay. Second, we demonstrate that repeated testing strengthens the retention of explicit representations but that implicit statistical learning is uninfluenced by testing. Together these dissociations provide much needed support for the reconceptualization of statistical learning as a multi-component construct.

Silent word-reading fluency is strongly associated with orthotactic sensitivity among elementary school children

Some written languages (the so-called "deep orthographies" such as English) have often unpredictable links to word sounds, making some written words difficult to associate with their spoken forms (i.e., to decode), thereby impeding comprehension. To read these languages efficiently for comprehension, readers require visual cues such as predictable spelling patterns (orthotactic conventions). Sensitivity to English orthotactic conventions (e.g., which letters are sometimes doubled, where configurations such as wh can typically be found in a word) was assessed in a cross-sectional sample of children (N = 271, ages 5-11 years) in kindergarten through Grade 5 using a word-likeness task. Orthotactic sensitivity was strongly correlated with silent word-reading fluency, an important reading skill used frequently in daily life to obtain information, and was modestly correlated with lexical spelling recognition. Among fluent decoders of predictable letter-sound relations, orthotactic sensitivity began to emerge prior to formal reading instruction and developed rapidly from kindergarten to Grade 2. About two thirds of dysfluent decoders (a proxy for dyslexia) demonstrated above-chance orthotactic sensitivity; however, their performance lagged behind that of fluent decoders through Grade 5. Orthotactic acquisition, possible reasons for impairment, and classroom implications are discussed.

Children automatically abstract categorical regularities during statistical learning

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