Analysis of goal, feedback and rewards on sustained attention via machine learning.

Introduction: Sustaining attention is a notoriously difficult task as shown in a recent experiment where reaction times (RTs) and pupillometry data were recorded from 350 subjects in a 30-min vigilance task. Subjects were also presented with different types of goal, feedback, and reward.

Methods: In this study, we revisit this experimental data and solve three families of machine learning problems: (i) RT-regression problems, to predict subjects' RTs using all available data, (ii) RT-classification problems, to classify responses more broadly as attentive, semi-attentive, and inattentive, and (iii) to predict the subjects' experimental conditions from physiological data.

Item Response Theory Analysis and Differential Item Functioning of the Social Appearance Anxiety Scale.

This study examined the item- and scale-level functioning of the Social Appearance Anxiety Scale (SAAS) as well as differential functioning by gender using an item response theory (IRT) analysis. SAAS data collected from 840 college students were analyzed. A graded response model was used to analyze the 16 items comprising the SAAS.

Mobilizing effort to reduce lapses of sustained attention: examining the effects of content-free cues, feedback, and points.

Three experiments with the psychomotor vigilance task examined whether presenting content-free cues, feedback, and points would reduce lapses of sustained attention. In all three experiments, behavioral lapses of attention (particularly slow reaction times) were reduced with the motivation manipulations compared with control conditions, but self-reports of off-task thinking (e.g.