Optimal feedback improves behavioral focus during self-regulated computer-based work

Distractions are omnipresent and can derail our attention, which is a precious and very limited resource. To achieve their goals in the face of distractions, people need to regulate their attention, thoughts, and behavior; this is known as self-regulation. How can self-regulation be supported or strengthened in ways that are relevant for everyday work and learning activities? To address this question, we introduce and evaluate a desktop application that helps people stay focused on their work and train self-regulation at the same time. Our application lets the user set a goal for what they want to do during a defined period of focused work at their computer, then gives negative feedback when they get distracted, and positive feedback when they reorient their attention towards their goal. After this so-called focus session, the user receives overall feedback on how well they focused on their goal relative to previous sessions. While existing approaches to attention training often use artificial tasks, our approach transforms real-life challenges into opportunities for building strong attention control skills. Our results indicate that optimal attentional feedback can generate large increases in behavioral focus, task motivation, and self-control-benefitting users to successfully achieve their long-term goals.

The Effect on Speech-in-Noise Perception of Real Faces and Synthetic Faces Generated with either Deep Neural Networks or the Facial Action Coding System

The prevalence of synthetic talking faces in both commercial and academic environments is increasing as the technology to generate them grows more powerful and available. While it has long been known that seeing the face of the talker improves human perception of speech-in-noise, recent studies have shown that synthetic talking faces generated by deep neural networks (DNNs) are also able to improve human perception of speech-in-noise. However, in previous studies the benefit provided by DNN synthetic faces was only about half that of real human talkers. We sought to determine whether synthetic talking faces generated by an alternative method would provide a greater perceptual benefit. The facial action coding system (FACS) is a comprehensive system for measuring visually discernible facial movements. Because the action units that comprise FACS are linked to specific muscle groups, synthetic talking faces generated by FACS might have greater verisimilitude than DNN synthetic faces which do not reference an explicit model of the facial musculature. We tested the ability of human observers to identity speech-in-noise accompanied by a blank screen; the real face of the talker; and synthetic talking face generated either by DNN or FACS. We replicated previous findings of a large benefit for seeing the face of a real talker for speech-in-noise perception and a smaller benefit for DNN synthetic faces. FACS faces also improved perception, but only to the same degree as DNN faces. Analysis at the phoneme level showed that the performance of DNN and FACS faces was particularly poor for phonemes that involve interactions between the teeth and lips, such as /f/, /v/, and /th/. Inspection of single video frames revealed that the characteristic visual features for these phonemes were weak or absent in synthetic faces. Modeling the real vs. synthetic difference showed that increasing the realism of a few phonemes could substantially increase the overall perceptual benefit of synthetic faces, providing a roadmap for improving communication in this rapidly developing domain.