搜索结果：Current directions in psychological science

Digital technologies, such as mobile devices and wearable sensors, are ingrained in daily life, making them a promising vehicle for delivering health behavior interventions. However, a critical challenge that undermines the utility of digital interventions is the suboptimal engagement of participants, where participant engagement is defined as the investment of physical, cognitive, and affective energies in a focal stimulus or task. Recent years have seen substantial growth in research aiming to understand how to increase engagement with digital interventions. This paper highlights several limitations of the existing evidence that restrict its scientific and practical utility and discusses opportunities for advancing the science of engagement with digital interventions. Synthesizing the current body of evidence, we call for conceptualizing digital interventions as a collection of stimuli (e.g., notifications, reminders) and tasks (e.g., open the mobile app, practice a relaxation technique) and considering engagement with digital interventions as a process rather than a state (i.e., momentary conditions/experiences) or trait (i.e., a relatively stable disposition). This approach has the potential to enhance scientific rigor and transparency in measuring, reporting, and interpreting engagement with digital interventions that would ultimately serve to bolster progress towards developing strategies for optimizing engagement.

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.

Low-quality and misleading information online can hijack people's attention, often by evoking curiosity, outrage, or anger. Resisting certain types of information and actors online requires people to adopt new mental habits that help them avoid being tempted by attention-grabbing and potentially harmful content. We argue that digital information literacy must include the competence of critical ignoring-choosing what to ignore and where to invest one's limited attentional capacities. We review three types of cognitive strategies for implementing critical ignoring: self-nudging, in which one ignores temptations by removing them from one's digital environments; lateral reading, in which one vets information by leaving the source and verifying its credibility elsewhere online; and the do-not-feed-the-trolls heuristic, which advises one to not reward malicious actors with attention. We argue that these strategies implementing critical ignoring should be part of school curricula on digital information literacy. Teaching the competence of critical ignoring requires a paradigm shift in educators' thinking, from a sole focus on the power and promise of paying close attention to an additional emphasis on the power of ignoring. Encouraging students and other online users to embrace critical ignoring can empower them to shield themselves from the excesses, traps, and information disorders of today's attention economy.

Decades of research have uncovered the neural basis of place (or "scene") processing in adulthood, revealing a set of three regions that respond selectively to visual scene information, each hypothesized to support distinct functions within scene processing (e.g., recognizing a particular kind of place versus navigating through it). Despite this considerable progress, surprisingly little is known about how these cortical regions develop. Here we review the limited evidence to date, highlighting the first few studies exploring the origins of cortical scene processing in infancy, and the several studies addressing when the scene regions reach full maturity, unfortunately with inconsistent findings. This inconsistency likely stems from common pitfalls in pediatric functional magnetic resonance imaging, and accordingly, we discuss how these pitfalls may be avoided. Furthermore, we point out that almost all studies to date have focused only on general scene selectivity and argue that greater insight could be gleaned by instead exploring the more distinct functions of each region, as well as their connectivity. Finally, with this last point in mind, we offer a novel hypothesis that scene regions supporting navigation (including the occipital place area and retrosplenial complex) mature later than those supporting scene categorization (including the parahippocampal place area).

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.

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.

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.

Giving is a unique attribute of human sharing. In this review, we discuss evidence attesting to our species' preparedness to recognize interactions based on this behavior. We show that infants and adults require minimal cues of resource transfer to relate the participants of a giving event in an interactive unit (A gives X to B) and that such an interpretation does not systematically generalize to superficially similar taking events, which may be interpreted in nonsocial terms (A takes X). We argue that this asymmetry, echoed in language, reveals the operations of a mechanism of event construction where participant roles are encoded only when they are crucial to rendering an action teleologically well-formed. We show that such a representation of giving allows people to monitor the direction (who gave to whom) and kind (what was given) of resource transfer within a dyad, suggesting that giving may be interpreted as indicative of a relationship based on long-term balance. As this research suggests, advancing the study of the prelinguistic representation of giving has implications for cognitive linguistics, by clarifying the relation between event participants and syntactic arguments, as well as social cognition, by identifying which kinds of relational inferences people draw from attending to acts of sharing.

Psychiatric research is undergoing significant advances in an emerging subspeciality of computational psychiatry, building upon cognitive neuroscience research by expanding to neurocomputational modeling. Here, we illustrate some research trends in this domain using work on proactive cognitive control deficits in schizophrenia as an example. We provide a selective review of formal modeling approaches to understanding cognitive control deficits in psychopathology, focusing primarily on biologically plausible connectionist-level models as well as mathematical models that generate parameter estimates of putatively dissociable psychological or neural processes. We illustrate some of the advantages of these models in terms of understanding both cognitive control deficits in schizophrenia and the potential roles of effort and motivation. Further, we highlight critical future directions for this work, including a focus on establishing psychometric properties, additional work modeling psychotic symptoms and their interaction with cognitive control, and the need to expand both behavioral and neural modeling to samples that include individuals with different mental health conditions, allowing for the examination of dissociable neural or psychological substrates for seemingly similar cognitive impairments across disorders.

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.

The development of visual attention in infancy is typically indexed by where and how long infants look, focusing on changes in alerting, orienting, or attentional control. However, visual attention and looking are both complex systems that are multiply determined. Moreover, infants' visual attention, looking, and learning are intimately connected. Infants learn to look, reflecting cascading effects of changes in attention, the visual system and motor control, as well as the information infants learn about the world around them. Furthermore infants' looking behavior provides the input infants use to perceive and learn about the world. Thus, infants look to learn about the world around them. A deeper understanding of development will be gained by appreciating the cascading effects of changes across these intertwined domains.

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.

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.

This article provides an overview of a model of psychological well-being put forth over 30 years ago. The intent was to advance new dimensions of positive functioning based on integration of clinical, developmental, existential, and humanistic thinking, along with Aristotle's writings about eudaimonia. The operationalization and validation of the model are briefly described, followed by an overview of scientific findings organized around: (1) demographic and experiential predictors of well-being; (2) well-being as predictors of health and biomedical outcomes; (3) pathway studies that examine intervening processes (moderators, mediators); and (4) underlying mechanistic processes (neuroscience, genomics). Much prior work underscores the benefits of well-being, including for longevity. Widening socioeconomic inequality is, however, increasingly compromising the well-being of disadvantaged segments of the population. These problems have been exacerbated by recent historical stressors (Great Recession, COVID-19 pandemic). Cumulative hardships from these events and their implications for health are critical targets for future science and practice.

This brief review examines the potential to use decision science to objectively characterize depression. We provide a brief overview of the existing literature examining different domains of decision-making in depression. Because this overview highlights the specific role of reinforcement learning as an important decision process affected in the disorder, we then introduce reinforcement learning modeling and explain how this approach has identified specific reinforcement learning deficits in depression. We conclude with ideas for future research at the intersection of decision science and depression, emphasizing the potential for decision science to help uncover underlying mechanisms and targets for the treatment of depression.

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) strategies have not found consistent age differences. Instead, we propose a new frame on ER in aging, focusing instead on ER tactics (how ER behavior is implemented in specific situations): the age-related APT (Adaptive Positive Tactic) 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.

This article reviews recent research that examines how emotional processes change in response to exposure to new cultures and how successful changes in emotional processes play crucial roles in immigration outcomes. Social-psychology research has shown that emotional fit (i.e., having the "right" emotions in a given social context) is a pathway to social integration. Combining these findings with research on the crucial role of culture in shaping emotional experiences, this article aims to advance understanding of psychological adaptation processes among immigrants, cultural minorities, and cultural majorities, focusing on how they develop new emotional patterns to become calibrated to their cultural surroundings, a process termed "emotional acculturation." We also discuss the antecedents and consequences of adaptive emotional acculturation. We hope to generate interest in future research on acculturation that fully incorporates the cultural foundations of psychological processes.

Chronic pain and substance use disorders are both common, debilitating, and often persist over the longer term. On their own, each represents a significant health problem, with estimates indicating a substantial proportion of the adult population has chronic pain or a substance use disorder (SUD), and their co-occurrence is increasing. Chronic pain and SUD are also both often invisible, stigmatized disorders and persons with both regularly have difficulty accessing evidence-based treatments, particularly those that offer coordinated and integrated treatment for both conditions. But there is hope. Research is unraveling the mechanisms of chronic pain and substance use, as well as their co-occurrence, integrated behavioral treatment options based on acceptance- and mindfulness-based approaches are increasingly being developed and tested, government agencies are devoting more funds and resources to increase research on chronic pain and SUD, and there have been growing efforts in training, dissemination, and implementation of evidence-based treatments. At the very heart of the matter, though, is to recognize that everybody hurts sometimes, and treatments must empower people to life effectively with these experiences of being human.

Decades of research in human and non-human animals has examined responses to clear valence, including stimuli that either represent a relatively clear threat (e.g., electric shock) or a clear reward (e.g., money). But daily life is replete with events or situations that are ambiguous - they could be threatening and/or rewarding. This review describes work that examines the wide inter-individual variability with which humans respond to this dual-valence ambiguity. Although some individuals more readily categorize these events as negative, others are prone to arrive at more positive categorizations. This predilection to lean in a more positive versus negative direction represents a stable, trait-like difference, and is referred to as valence bias. Valence bias is generalizable across categories of dual-valence ambiguity and has important implications for health and well-being. Here we focus on extensive findings that lend support for the Initial Negativity Hypothesis, which posits that the initial or default response is negative across people, and that positivity relies on an additional regulatory mechanism that helps to overcome the initial negativity. Together, the present review describes the cognitive and brain mechanisms underlying the valence bias, including emphasizing the importance of a broad set of systems that support human responses to ambiguity.