In adults, cortical regions in the fusiform face area (FFA), superior temporal sulcus (STS), and medial prefrontal cortex (MPFC) respond selectively to faces, but underlie distinct perceptual and social processes. When do each of these regions, and their distinctive functions, develop? We review recent studies of awake human infants' cortical responses to faces using functional near infrared spectroscopy (fNIRS) and functional magnetic resonance imaging (fMRI). The results converge, and argue against slow sequential posterior-to-anterior development of face-selective responses. Instead, cortical face-selective responses arise very early and simultaneously in infancy, and may reflect distinctively social processes from the start.

Dancing to music is prevalent across human cultures. It is also developmentally precocious-most children display dance-like behaviors before their first birthday. This early emergence precedes a long maturational trajectory with broad individual differences. Here, we survey recent research on dance in infancy and childhood. We propose that investigating early musical movements is useful for understanding the development of dance itself and that dance can serve as a behavioral measure to better understand development in adjacent domains, such as auditory perception and musical memory. Finally, we discuss potential developmental mechanisms underlying early dance and highlight major remaining questions. Studying early dance provides an opportunity to highlight how fundamental processes in psychology, such as nature-nurture interactions, individual differences, and the interplay between abilities and environments, can impact the emergence and expression of this social and multimodal human behavior.

Across myriad, real-world contexts, we encounter the challenge of learning to take actions that bring about desirable outcomes. The theoretical framework of reinforcement learning proposes formal algorithms through which agents learn from experience to make rewarding choices. These formal models capture many aspects of reward-guided human behavior in controlled laboratory contexts. Here, we suggest that the constructs (i.e., states, actions, and rewards) and algorithms formalized within reinforcement learning theory can be operationally defined and extended to additionally account for learning in complex, natural environments. We discuss several recent examples of empirical studies that provide evidence of signatures of reinforcement learning across diverse human behaviors in everyday environments.

Adolescence is an "age of risk" for the emergence of depression. Despite its prevalence and public health significance, there are major unanswered questions regarding the mechanisms contributing to depression's etiology and surge in adolescence. Separate lines of research have investigated the roles of inflammation and low reward responsiveness in depression and recent evidence suggests bidirectional associations between inflammation and reward responsiveness. In this article, we review this evidence, present an integrated immuno-reward model of adolescent depression, specifically, motivational and somatic symptoms of depression, and discuss the role of stress and early adversity in amplifying immune-reward interactions. We end with implications of the immuno-reward model for future research, early identification of at-risk adolescents, intervention, and public health policy.

Speech conveys both linguistic messages and a wealth of social and identity information about a talker. This information arrives as complex variation across many acoustic dimensions. Ultimately, speech communication depends upon experience within a language community to develop shared long-term knowledge of the mapping from acoustic patterns to the category distinctions that support word recognition, emotion evaluation, and talker identification. A great deal of research has focused on the learning involved in acquiring long-term knowledge to support speech categorization. Inadvertently, this focus may give the impression of a mature learning endpoint. Instead, there seems to be no firm line between perception and learning in speech. The contributions of acoustic dimensions are malleably reweighted continuously as a function of regularities evolving in short term input. In this way, continuous learning across speech impacts the very nature of the mapping from sensory input to perceived category. Broadly, this presents a case study in understanding how incoming sensory input - and the learning that takes place across it -- interacts with existing knowledge to drive predictions that tune the system to support future behavior.

Exchanges of help in childhood produce many positive consequences: They increases academic success, promote happiness, and foster positive peer relations. For these reasons, caretakers encourage helping behavior early in life and schools implement intervention programs to nurture children's prosociality. An often-overlooked issue, however, is that providing and receiving help do not always produce positive outcomes. We review the latest research that converges to suggest that when children receive, witness, or provide help, there can be unintended negative consequences-for example, receiving help can produce feelings of incompetence. The discussion of the paper grapples with how to balance the negative and positive outcomes of helping behavior, with an eye toward promoting children's wellbeing and social cohesion in society.

People segment ongoing experience into meaningful chunks, which supports new learning and long-term memory. We synthesize evidence showing that scaffolding segmentation improves memory, possibly by reducing interference. These findings highlight the role of segmentation in memory formation and suggest mechanisms for improving memory in older adults and clinical populations.

Older adults report higher levels of emotional well-being in cross-sectional studies. Despite assertions that older adults are better at regulating emotions, studies investigating emotion regulation (ER) have not found consistent age differences. Instead, we propose a new frame on ER in aging, focusing instead on ER (how ER behavior is implemented in specific situations): the age-related shift hypothesis. Consistent with the hypothesis, older adults report relatively greater use of positive-approaching tactics. Positive-approaching tactics appear more effective in regulating emotions than negative-receding tactics and thus may be more adaptive. We consider how context influences tactic use and discuss open questions about the hypothesis. With recent longitudinal evidence showing mixed patterns of emotional well-being in aging, the APT shift hypothesis can guide future investigation of within-person changes in ER behavior.

Temperament traits are early appearing and relatively stable phenotypic profiles of behavior that are present across space and time. This definition invariably reflects the timescale imposed when gathering repeated measures of our variables of interest and our reliance on aggregate, mean-level values. However, if the time scale of observations is shortened, and the frequency of observations increased, underlying or latent fluctuations and variability may emerge. Embedding short-term fluctuations into slower developmental trajectories may improve our understanding of behavior in the moment, while also strengthening prediction. Researchers should embrace a more granular time scale in research, incorporating new technology and analytical approaches, enhancing our ability to capture developmental change. This review illustrates with the temperament literature how shifting time scales can provide new insight into social, behavioral, and cognitive processes across development.

Early caregiving experiences have strong, persistent links to emotion regulation. In this perspective, we offer a view that the content represented in emotion regulation neurobiology in part reflects consolidated interpersonal-affective memories abstracted from early caregiving experiences. We suggest that these interpersonal-affective memories, referred to here as "attachment schemas", are represented by cortico-subcortical (re)activations. Neural circuitry involving functional connections between subcortical and midline cortical regions is well-positioned to generate predictive inferences from attachment schemas that have implications for emotion regulation. Although speculative, this perspective is motivated by the convergence of empirical findings from cognitive and developmental neuroscience. Situating affective neural predictions within a neurodevelopmental framework has great potential to uncover mechanisms of attachment, and ultimately build toward a more complete understanding of the links between early caregiving experiences and emotional wellbeing.

Adolescence is a dynamic period of brain development, marked by profound changes in learning, decision-making, and higher-order cognition. This review explores how research on the adolescent brain can inform the development of biologically-based computational models of learning and behavior. We highlight how computational frameworks such as reinforcement learning (RL) and artificial neural networks (ANNs) capture key features of adolescent behavior, including shifts in exploration and decision-making strategies. By integrating principles of brain development, such as synaptic pruning and hierarchical development of neural circuits, computational models can offer insights into how the brain adapts to new experiences and challenges. We argue that studying adolescent brain development not only enhances our understanding of cognition but also provides a valuable framework for refining computational models of brain function. We propose future directions for how adolescent research can inform innovations in computational research to better capture dynamic brain states, individual variability, and risk for psychopathology.

The ability to learn from and remember experiences (episodic memory) depends on multiple neurocognitive systems. In this review, we highlight recent advances in methods and theory that are unveiling how mechanisms of attention impact episodic memory. We first provide a high-level overview of the construct and neural substrates underlying attention and related goal-state processes, along with their interactions with memory. We then highlight budding evidence supporting the rhythmic nature of memory and attention, raising key questions about the role that the oscillatory phase of attention rhythms plays on memory encoding and retrieval. Third, we consider how understanding age-related changes in memory and attention can be further advanced by assaying the precision of memory. Finally, we illustrate how real-time closed-loop experiments provide opportunities to test causal relationships between attention and memory. Along the way, we raise open questions and future research directions about how attention-memory interactions enable learning and remembering in the mind and brain.

Hearing a single word can initiate a sequence of activation that spreads from the representation of the word (e.g., "candy") to words that share auditory and visual form (e.g., "candle") and the concepts those words reference (e.g., the idea of a ). In bilinguals, this co-activation spreads both within and across languages to words that share form or meaning in either or both languages. This parallel activation across two languages has cascading effects on higher-order cognitive functions like attention (e.g., what people focus on in a visual scene), memory (e.g., what people remember seeing), and semantic organization (e.g., how concepts are represented and grouped based on their meanings). Here, we consider how the consequences of language co-activation extend beyond the linguistic domain to impact the broader cognitive system, and conclude that the interactivity of languages in the bilingual mind fundamentally transforms mental operations.

Advances in computer technology have produced a flood of new data sets for understanding human language. However, nearly all these new datasets are based on written, rather than spoken, language. This means that, despite their importance, open-access data on naturally occurring spoken language conversations are much more difficult to obtain. The major exception to this is the TalkBank system, which provides online multimedia data for 15 types of spoken language data: language in aphasia, child language, stuttering, child phonology, autism spectrum disorder (ASD), bilingualism, Conversation Analysis, classroom discourse, dementia, psychosis, right hemisphere damage (RHD), Danish conversation, second language learning, traumatic brain injury (TBI), and day-long recordings in the home. This article reviews these resources and describes the ways that they are being used to further our understanding of language learning and usage.

Scientists have long focused on intrapersonal factors and solitary drinking settings in researching addiction etiology. Yet evidence has accumulated to indicate a key role for social contexts in alcohol use disorder development. Here we review four core characteristics of social drinking contexts relevant for the understanding of disordered drinking, including prevalence, developmental timing, negative consequences, and reward value. We present a social-cognitive model aimed at elucidating reinforcement from alcohol in social context, proposing a role for alcohol in inhibiting higher-order cognitive processes that otherwise dampen the experience of social reward. Finally, we review a series of empirical studies providing evidence for the role of social context in alcohol use disorder development, highlighting methodological challenges and indicating directions for future research.

Social interactions are a ubiquitous part of human life. They are also complex and dynamic, posing a challenge for traditional psychology methods. This paper provides an overview of a dynamical systems approach to the study of social interactions that manages this complexity and enables the quantification of interdependence between people. We also discuss key empirical findings that demonstrate how different forms of interdependence and interaction dynamics shape social outcomes. Finally, we highlight the utility of this approach for advancing theories of social behavior and practical application. By adopting this dynamical systems approach, researchers can gain a deeper understanding of the patterns underlying social interactions and test hypotheses about the mechanisms driving human connection and coordination.

A growing body of research documents the positive impact that action video game play has on a range of cognitive skills. Such a result, in which training on one task promotes a broad variety of benefits, is a rarity in the cognitive training domain. Instead, the more typical result is that training on one task promotes benefits on that task alone with only limited transfer to untrained tasks. We have proposed that action video game play promotes broad generalization by first enhancing attentional control abilities. This in turn allows for more information to be accrued as one experiences a new task and thus faster learning of that new task. Possible theoretical and practical considerations of such a view are discussed.

Recent years have seen a surge in research on why people fall for misinformation and what can be done about it. Drawing on a framework that conceptualizes truth judgments of true and false information as a signal-detection problem, the current article identifies three inaccurate assumptions in the public and scientific discourse about misinformation: (1) People are bad at discerning true from false information, (2) partisan bias is not a driving force in judgments of misinformation, and (3) gullibility to false information is the main factor underlying inaccurate beliefs. Counter to these assumptions, we argue that (1) people are quite good at discerning true from false information, (2) partisan bias in responses to true and false information is pervasive and strong, and (3) skepticism against belief-incongruent true information is much more pronounced than gullibility to belief-congruent false information. These conclusions have significant implications for person-centered misinformation interventions to tackle inaccurate beliefs.

People with schizophrenia suffer from hallucinations and delusions as well as from significant cognitive impairments. Working memory is a critical resource for many complex cognitive operations and is a critical area of impairment in schizophrenia. Here we present our hyperfocusing hypothesis, which suggests that an overly narrow and intense focusing of attention may underlie the working memory deficits seen in schizophrenia. The hyperfocusing hypothesis also provides an account for some areas of surprisingly intact cognitive performance also seen in people with schizophrenia.

Population-level administrative data-data on individuals' interactions with administrative systems, such as healthcare, social-welfare, criminal-justice, and education systems-are a fruitful resource for research into behavior, development, and wellbeing. However, administrative data are underutilized in psychological science. Here, we review advantages of population-level administrative data for psychological research, with examples of advances in psychological theory arising from administrative-data studies. We focus on advantages in three areas: (1) How population-level administrative data are collected and recorded, (2) the data's large scale, and (3) unique data-linkages. We also describe ethical issues as well as methodological considerations and limitations in population administrative-data research, and future directions to enable psychological scientists to more fully capitalize on administrative-data resources.

Over the last decade, deep neural networks (DNNs) have transformed the state of the art in artificial intelligence. In domains like language production and reasoning, long considered uniquely human abilities, contemporary models have proven capable of strikingly human-like performance. However, in contrast to classical symbolic models, neural networks can be inscrutable even to their designers, making it unclear what significance, if any, they have for theories of human cognition. Two extreme reactions are common. Neural network enthusiasts argue that, because the inner workings of DNNs do not seem to resemble any of the traditional constructs of psychological or linguistic theory, their success renders these theories obsolete and motivates a radical paradigm shift. Neural network skeptics instead take this inability to interpret DNNs in psychological terms to mean that their success is irrelevant to psychological science. In this paper, we review recent work that suggests that the internal mechanisms of DNNs can, in fact, be interpreted in the functional terms characteristic of psychological explanations. We argue that this undermines the shared assumption of both extremes and opens the door for DNNs to inform theories of cognition and its development.