Repeatedly experiencing the McGurk effect induces long-lasting changes in auditory speech perception.

In the McGurk effect, presentation of incongruent auditory and visual speech evokes a fusion percept different than either component modality. We show that repeatedly experiencing the McGurk effect for 14 days induces a change in auditory-only speech perception: the auditory component of the McGurk stimulus begins to evoke the fusion percept, even when presented on its own without accompanying visual speech. This perceptual change, termed fusion-induced recalibration (FIR), was talker-specific and syllable-specific and persisted for a year or more in some participants without any additional McGurk exposure.

The noisy encoding of disparity model predicts perception of the McGurk effect in native Japanese speakers.

In the McGurk effect, visual speech from the face of the talker alters the perception of auditory speech. The diversity of human languages has prompted many intercultural studies of the effect in both Western and non-Western cultures, including native Japanese speakers. Studies of large samples of native English speakers have shown that the McGurk effect is characterized by high variability in the susceptibility of different individuals to the illusion and in the strength of different experimental stimuli to induce the illusion.

Synthetic faces generated with the facial action coding system or deep neural networks improve speech-in-noise perception, but not as much as real faces.

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.

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.

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.