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Healthy individuals display systematic inaccuracies when allocating attention to perceptual space. Under many conditions, optimized spatial attention processing of the right hemisphere’s frontoparietal attention network directs more attention to the left side of perceptual space than the right. This is the pseudoneglect effect. We present evidence reshaping our fundamental understanding of this neural mechanism. buy PMSF We describe a previously unrecognized, but reliable, attention bias to the right side of perceptual space that is associated with semantic object processing. Using an object bisection task, we revealed a significant rightward bias distinct from the leftward bias elicited by the traditional line bisection task. In Experiment 2, object-like shapes that were not easily recognizable exhibited an attention bias between that of horizontal lines and objects. Our results support our proposal that the rightward attention bias is a product of semantic processing and its lateralization in the left hemisphere. In Experiment 3, our novel object-based adaptation of the landmark task further supported this proposition and revealed temporal dynamics of the effect. This research provides novel and crucial insight into the systems supporting intricate and complex attention allocation and provides impetus for a shift toward studying attention in ways that increasingly reflect our complex environments. (PsycInfo Database Record (c) 2021 APA, all rights reserved).The explosion of data generated during human interactions online presents an opportunity for psychologists to evaluate cognitive models outside the confines of the laboratory. Moreover, the size of these online data sets can allow researchers to construct far richer models than would be feasible with smaller in-lab behavioral data. In the current article, we illustrate this potential by evaluating 3 popular psychological models of generalization on 2 web-scale online data sets typically used to build automated recommendation systems. We show that each psychological model can be efficiently implemented at scale and in certain cases can capture trends in human judgments that standard recommendation systems from machine learning miss. We use these results to illustrate the opportunity Internet-scale data sets offer to psychologists and to underscore the importance of using insights from cognitive modeling to supplement the standard predictive-analytic approach taken by many existing machine learning approaches. (PsycInfo Database Record (c) 2020 APA, all rights reserved).The current study sought to examine the discriminant validity of 3 commonly used measures of mindfulness. The discriminative ability of the Mindful Attention Awareness Scale (MAAS), the Five Factor Mindfulness Questionnaire (FFMQ), and a breath counting task (BCT) was assessed in a randomized control trial involving an 8-week mindfulness training (MT) condition (n = 53) and an active control computerized attention training (CT) program (n = 33). No evidence to support the discriminant validity of MAAS or FFMQ scores was found, as these self-report measures responded to both the MT and CT conditions. Breath counting scores however demonstrated unique responsiveness to the MT program, suggesting this behavioral task may be useful in measuring changes in mindfulness as it closely resembles core cognitive processes trained during this practice. Implications of these findings for the construct validity of both self-report and behavioral measures of mindfulness are discussed, along with the suitability of current mindfulness-based interventions in studies aiming to assess mindfulness outcomes. (PsycInfo Database Record (c) 2020 APA, all rights reserved).The goal of the present study was to determine if the internalizing and externalizing model of psychopathology is applicable in a sample of adults with chronic illness. Confirmatory factor analyses were used to examine the factor structure of internalizing and externalizing symptoms in a sample of adults (N = 172) with a unique chronic physical health condition (postural orthostatic tachycardia syndrome; POTS) and in a sample of adults without any chronic illness diagnoses (N = 199). Measurement invariance was used to compare levels of internalizing and externalizing symptoms across samples. Confirmatory factor analyses suggested that psychological distress in individuals with chronic illness can be effectively characterized by an internalizing dimension composed of distress and fear subcomponents as well as an externalizing dimension. Measurement invariance testing reached adequate levels of fit, allowing for examination of latent means; individuals with chronic illness had higher scores on the internalizing dimension and lower scores on the externalizing dimension than healthy controls. Regression analyses suggested that among those with a chronic illness, internalizing symptoms were significantly, negatively related to acceptance of illness and higher health-related quality of life. Findings suggest that assessing internalizing symptoms broadly may allow for better identification of chronically ill individuals experiencing psychological distress than a focus on categorical diagnoses. However, professionals also need to be aware of the overlap between physical and psychological symptoms in adults with chronic illnesses in order to avoid inaccurate diagnoses. (PsycInfo Database Record (c) 2020 APA, all rights reserved).Implicit self-associations are theorized to be rigidly and excessively negative in affective disorders like depression. Such information processing patterns may be useful as an approach to parsing heterogeneous etiologies, substrates, and treatment outcomes within the broad syndrome of depression. However, there is a lack of sufficient data on the psychometric, neural, and computational substrates of Implicit Association Test (IAT) performance in patient populations. In a treatment-seeking, clinically depressed sample (n = 122), we administered five variants of the IAT-a dominant paradigm used in hundreds of studies of implicit cognition to date-at two repeated sessions (outside and inside a functional MRI scanner). We examined reliability, clinical correlates, and neural and computational substrates of IAT performance. IAT scores showed adequate (.67-.81) split-half reliability and convergent validity with one another and with relevant explicit symptom measures. Test-retest correlations (in vs. outside the functional MRI scanner) were present but modest (.