Practice effects for visual resolution in the periphery

Resolution thresholds at 0° (fovea), 20°, 40°, and 60° along the horizontal meridian of the temporal visual field revealed a characteristic degradation in visual resolution with increasing stimulus eccentricity. However, substantial individual differences were found, particularly at 40° and 60° of eccentricity. Dramatic improvements in peripheral visual resolution occurred over a period of 11 practice sessions, with the time course of practice effects increasing for greater visual field eccentricities. Improvements with practice reduced, but did not eliminate, individual differences. The present visual resolution findings are compared to previous studies of peripheral motion detection and increment thresholds.     

A low-cost interactive coordinate plotter

An inexpensive system for on-line coordinate plotting is described. Users with access to a computer running ALGOL or FORTRAN, and equipped with analog-to-digital converters, can build the system for less than $50. The program is readily modified for the individual user’s requirements, and interactive features allow data correction and analysis to be carried out during coordinate plotting, with operators warned of plotting errors as they occur.     

Defining the Assessment and Treatment Competencies to Deliver Low-Intensity Cognitive Behavior Therapy: A Multi-Center Validation Study

Despite the vastly increased dissemination of the low-intensity (LI) version of cognitive behavior therapy (CBT) for the treatment of anxiety and depression, no valid and reliable indices of the LI-CBT clinical competencies currently exist. This research therefore sought to develop and evaluate two measures: the low-intensity assessment competency scale (LIAC) and the low-intensity treatment competency scale (LITC). Inductive and deductive methods were used to construct the competency scales and detailed rating manuals were prepared. Two studies were then completed. The first study used a quantitative, fully-crossed design and the second a multi-center, quantitative longitudinal design. In study one, novice, qualified, and expert LI-CBT practitioners rated an LI-CBT assessment session (using the LIAC) and an LI-CBT treatment session (using the LITC). Study two used the LIAC and LITC across four training sites to analyze the competencies of LI-CBT practitioners over time, across raters, and in relation to the actor/patients’ feedback concerning helpfulness, the alliance, and willingness to return. Both the LIAC and LITC were found to be single factor scales with good internal, test-retest reliability and reasonable inter-rater reliability. Both measures were sensitive to measuring change in clinical competence. The LIAC had good concurrent, criterion, discriminant, and predictive validity, while the LITC had good concurrent, criterion, and predictive validity, but limited discriminant validity. A score of 18 accurately delineated a minimum level of competence in LI-CBT assessment and treatment practice, with incompetent practice associated with patient disengagement. These observational ratings scales can contribute to the clinical governance of the burgeoning use of LI-CBT interventions for anxiety and depression in routine services and also in the methods of controlled studies.     

Maternal Emotion Socialization of Adolescent Girls Engaging in Non-Suicidal Self-Injury

Research on Child and Adolescent Psychopathology - Non-suicidal self injury (NSSI) is a transdiagnostic maladaptive behavior that is highly prevalent in adolescence. A greater understanding of the...     

A systematic review of the effectiveness of machine learning for predicting psychosocial outcomes in acquired brain injury: Which algorithms are used and why?

Clinicians working in the field of acquired brain injury (ABI, an injury to the brain sustained after birth) are challenged to develop suitable care pathways for an individual client’s needs. Being able to predict psychosocial outcomes after ABI would enable clinicians and service providers to make advance decisions and better tailor care plans. Machine learning (ML, a predictive method from the field of artificial intelligence) is increasingly used for predicting ABI outcomes. This review aimed to examine the efficacy of using ML to make psychosocial predictions in ABI, evaluate the methodological quality of studies, and understand researchers’ rationale for their choice of ML algorithms. Nine studies were reviewed from five databases, predicting a range of psychosocial outcomes from stroke, traumatic brain injury, and concussion. Eleven types of ML were employed with a total of 75 ML models. Every model was evaluated as having high risk of bias, unable to provide adequate evidence for predictive performance due to poor methodological quality. Overall, there was limited rationale for the choice of ML algorithms and poor evaluation of the methodological limitations by study authors. Considerations for overcoming methodological shortcomings are discussed, along with suggestions for assessing the suitability of data and suitability of ML algorithms for different ABI research questions.     

Flexible Utility Function Approximation via Cubic Bezier Splines

Psychometrika - In intertemporal and risky choice decisions, parametric utility models are widely used for predicting choice and measuring individuals’ impulsivity and risk aversion. However,...