Texture perception is a rich subdomain of vision science that focuses on how the visual system encodes and interprets images that can be defined in terms of self-similarity over space. The field’s understanding of the computational and neural bases of texture perception has advanced, drawing upon key results from psychophysics, cognitive neuroscience, and visual development. The relevance of texture representations to a broader set of visual mechanisms supporting “statistical vision” is also discussed, with an emphasis on the challenges and potential rewards of studying texture perception in the context of natural stimuli and ecologically relevant tasks.
Trevor A. Harley
Research in the psychology of language has been dogged by some enduring controversies, many of which continue to divide researchers. Furthermore, language research has been riven by too many dichotomies and too many people taking too extreme a position, and progress is only likely to be made when researchers recognize that language is a complex system where simple dichotomies may not be relevant. The enduring controversies cover the width of psycholinguistics, including the work of Chomsky and the nature of language, to what extent language is innately determined and the origin of language and how it evolved. Chomsky’s work has also influenced our conceptions of the modularity of the structure of the mind and the nature of psychological processing. Advances in the sophistication of brain imaging techniques have led to debate about exactly what these techniques can tell us about the psychological processing of language. There has also been much debate about whether psychological processing occurs through explicit rules or statistical mapping, a debate driven by connectionist modeling, deep learning, and techniques for the analysis of “big data.” Another debate concerns the role of prediction in language and cognition and the related issues of the relationship between language comprehension and language production. To what extent is language processing embodied, and how does it relate to controversies about “embedded cognition”? Finally, there has been debate about the purpose and use of language.
Grace W. Lindsay and Thomas Serre
Deep learning is an approach to artificial intelligence (AI) centered on the training of deep artificial neural networks to perform complex tasks. Since the early 21st century, this approach has led to record-breaking advances in AI, allowing computers to solve complex board games, video games, natural language-processing tasks, and vision problems. Neuroscientists and psychologists have also utilized these networks as models of biological information processing to understand language, motor control, cognition, audition, and—most commonly—vision. Specifically, early feedforward network architectures were inspired by visual neuroscience and are used to model neural activity and human behavior. They also provide useful representations of the perceptual space of images. The extent to which these models match data, however, depends on the methods used to characterize and compare them. The limitations of these feedforward neural networks to account for, for example, simple visual reasoning tasks, suggests that feedback mechanisms may be necessary to solve visual recognition tasks beyond image categorization.
Patricia M. Rodriguez Mosquera
Honor is complex, deeply relational, and important in many cultures and social groups. A definition of honor as multifaceted and consisting of a set of interrelated honor codes, i.e., the honor-as-multifaceted approach to honor, is presented and discussed by Rodriguez Mosquera. This definition provides researchers the conceptual boundaries of honor as a construct as well as methodological guidelines on how to operationalize honor in empirical research. Furthermore, the honor-as-multifaceted approach provides researchers with a definition of cultures of honor as those in which honor codes become culturally shared psychological concerns that individuals evaluate as important to their self-esteem and self-concept, thereby influencing their cognitions, motivations, emotions, and behaviors. The Honor Scale measures honor codes in line with this definition. A review of existing empirical research on honor in a wide variety of cultures and social groups is also presented and discussed. Some of the work reviewed is cross-cultural in nature, whereas other work focuses on how honor operates in particular cultures or social groups (e.g., British Muslims; Moroccan Dutch and Turkish Dutch youth; Southern Italian criminal organizations; the Canadian Army). The reviewed research provides empirical support for the honor-as-multifaceted approach and demonstrates the centrality of honor codes in a variety of psychological and social processes, including personality, the negotiation of gendered roles within the family, attitudes toward in-group members, emotions in response to threats to collective honor, intergroup conflict, the negotiation of power in intergroup relations, in-group identification processes, and prosocial motivations. Thus, the reviewed research shows that honor codes play an important role in processes at the different levels of analysis typically studied in the social sciences—individual, interpersonal, group, cultural—thereby making honor an important topic of inquiry for psychologists and other social scientists. Avenues for future research are also discussed.
The most dynamic postnatal brain development takes place during human infancy. Decades of histological studies have identified strong spatial and functional maturation gradients in human brain gray and white matter. The improvements in noninvasive imaging techniques, especially magnetic resonance imaging, magnetic resonance spectroscopy, electroencephalography, magnetoencephalography, positron emission tomography, and near-infrared spectroscopy, have provided unprecedented opportunities to quantify and map the early developmental changes at whole brain and regional levels. Unique to infant brain imaging, tailored infant image acquisition and analysis methods—such as motion correction, high-resolution imaging, optimization of imaging parameters for smaller and immature brain, age-specific brain atlas and parcellation scheme, age-specific white matter tractography, functional connectivity analysis given incomplete brain networks, and advanced gray and white matter segmentation for infant brains should be taken into consideration. Delineating functional, physiological, and structural changes of the infant brain through imaging provides insights into the complicated processes of both typical development and the neuropathological mechanisms underlying various brain disorders with early onset in infancy, such as autistic spectrum disorder. Identification of imaging biomarkers of neurodevelopmental disorders during infancy by leveraging techniques such as machine learning may offer a valuable time window for early intervention.
Sara B. Festini, Laura Zahodne, and Patricia A. Reuter-Lorenz
Cognitive neuroimaging studies often report that older adults display more activation of neural networks relative to younger adults, referred to as overactivation. Greater or more widespread activity frequently involves bilateral recruitment of both cerebral hemispheres, especially the frontal cortex. In many reports, overactivation has been associated with superior cognitive performance, suggesting that this activity may reflect compensatory processes that offset age-related decline and maintain behavior. Several theories have been proposed to account for age differences in brain activation, including the Hemispheric Asymmetry Reduction in Older Adults (HAROLD) model, the Posterior-Anterior Shift in Aging (PASA) theory, the Compensation-Related Utilization of Neural Circuits Hypothesis (CRUNCH), and the Scaffolding Theory of Aging and Cognition (STAC and STAC-r). Each model has a different explanatory scope with regard to compensatory processes, and each has been highly influential in the field. HAROLD contrasts the general pattern of bilateral prefrontal activation in older adults with that of more unilateral activation in younger adults. PASA describes both anterior (e.g., frontal) overactivation and posterior (e.g., occipital) underactivation in older adults relative to younger adults. CRUNCH emphasizes that the level or extent of brain activity can change in response to the level of task demand at any age. Finally, STAC and STAC-r take the broadest perspective to incorporate individual differences in brain structure, the capacity to implement functional scaffolding, and life-course neural enrichment and depletion factors to predict cognition and cognitive change across the lifespan. Extant empirical work has documented that compensatory overactivation can be observed in regions beyond the prefrontal cortex, that variations in task difficulty influence the degree of brain activation, and that younger adults can show compensatory overactivation under high mental demands. Additional research utilizing experimental designs (e.g., transcranial magnetic stimulation), longitudinal assessments, greater regional precision, both verbal and nonverbal material, and measures of individual difference factors will continue to refine our understanding of age-related activation differences and adjudicate among these various accounts of neurocognitive aging.