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Digital technology has facilitated additional means for human communication, allowing social connections across communities, cultures, and continents. However, little is known about the effect these communication technologies have on the ability to accurately recognize and utilize nonverbal behavior cues. We present two competing theories, which suggest (1) the potential for technology use to enhance nonverbal decoding skill or, (2) the potential for technology use to hinder nonverbal decoding skill. We present preliminary results from two studies to test these hypotheses. Study 1 (N = 410) found that global screen time was unrelated to nonverbal decoding skill. However, how participants spent their time using technology mattered. Participants who reported more active technology use (i.e., posting content) self-reported that their nonverbal decoding skill (as measured by the Emotional Sensitivity subscale of the Social Skills Inventory) was superior but performed worse on objective measures of decoding skill (using standardized tests including the Diagnostic Analysis of Nonverbal Accuracy-Adult Faces and the Workplace Interpersonal Perception Skill). By contrast, passive users performed significantly better on objective measures of nonverbal decoding skill; although they did not self-report any difference in their skill compared to less passive users. Study 2 (N = 190), and a mini-meta analysis of both studies, replicated this pattern. These effects suggest a roadmap for understanding the theoretical relationship between technology use and nonverbal communication skills. Chroman 1 inhibitor We also provide recommendations for future research, including the use of experimental designs to determine causal pathways and to advance our conceptual understanding of the relationship between technology use and nonverbal decoding skill.Cochlear implants electrically stimulate surviving auditory neurons in the cochlea to provide severely or profoundly deaf people with access to hearing. Signal processing strategies derive frequency-specific information from the acoustic signal and code amplitude changes in frequency bands onto amplitude changes of current pulses emitted by the tonotopically arranged intracochlear electrodes. This article first describes how parameters of the electrical stimulation influence the loudness evoked and then summarizes two different phenomenological models developed by McKay and colleagues that have been used to explain psychophysical effects of stimulus parameters on loudness, detection, and modulation detection. The Temporal Model is applied to single-electrode stimuli and integrates cochlear neural excitation using a central temporal integration window analogous to that used in models of normal hearing. Perceptual decisions are made using decision criteria applied to the output of the integrator. By fitting the model parameters to a variety of psychophysical data, inferences can be made about how electrical stimulus parameters influence neural excitation in the cochlea. The Detailed Model is applied to multi-electrode stimuli, and includes effects of electrode interaction at a cochlear level and a transform between integrated excitation and specific loudness. The Practical Method of loudness estimation is a simplification of the Detailed Model and can be used to estimate the relative loudness of any multi-electrode pulsatile stimuli without the need to model excitation at the cochlear level. Clinical applications of these models to novel sound processing strategies are described.Socio-economic status (SES) and mathematical performance seem to be risk factors of mathematics anxiety (MA) in both children and adults. However, there is little evidence about how exactly these three constructs are related, especially during early stages of mathematical learning. In the present study, we assessed longitudinal performance in symbolic and non-symbolic basic numerical skills in pre-school and second grade students, as well as MA in second grade students. Participants were 451 children (average pre-school age = 5 years, 6 months) from 12 schools in Chile, which differed in school vulnerability index (SVI), an indicator of SES. We tested an explanatory model of MA that included SES and longitudinal performance in basic numerical skills as predictors. The results showed a direct effect of SES on MA and a mediating effect of performance in symbolic and non-symbolic comparison tasks in pre-school. However, in second grade, only performance in symbolic comparison significantly mediated the SES-MA relationship. These findings suggest that performance in non-symbolic comparison plays an important role in explaining MA at initial stages, but that its influence is no longer significant by the time children reach formal instruction in second grade. By contrast, as children's formal educational experience in mathematics increases, MA becomes linked primarily to symbolic numerical tasks. In sum, SES affects MA and this is due in part to the effect of SES on the development of numerical learning in pre-school, which in turn has an impact on subsequent, more complex learning, ultimately leading to differences in MA. We discuss the implications of these findings for preventing and acting upon the emergence of MA.People dedicate significant attention to others' facial expressions and to deciphering their meaning. Hence, knowing whether such expressions are genuine or deliberate is important. Early research proposed that authenticity could be discerned based on reliable facial muscle activations unique to genuine emotional experiences that are impossible to produce voluntarily. With an increasing body of research, such claims may no longer hold up to empirical scrutiny. In this article, expression authenticity is considered within the context of senders' ability to produce convincing facial displays that resemble genuine affect and human decoders' judgments of expression authenticity. This includes a discussion of spontaneous vs. posed expressions, as well as appearance- vs. elicitation-based approaches for defining emotion recognition accuracy. We further expand on the functional role of facial displays as neurophysiological states and communicative signals, thereby drawing upon the encoding-decoding and affect-induction perspectives of emotion expressions.
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