No abstract

Attempts to identify physiological correlates of listening effort have mainly focused on peripheral measures (e.g. pupillometry) and auditory-evoked/event-related potentials. Although nonauditory studies have suggested that sustained time-frequency electroencephalographic (EEG) features in the θ-band (4-7 Hz) are correlated with domain-general mental effort, little work has characterized such features during effortful listening. Here, high-density EEG data was collected while listeners performed a sentence-recognition task in noise, the signal-to-noise ratio (SNR) of which varied across blocks. Frontal midline θ (Fmθ), largely driven by sources localized in or near the medial frontal cortex, showed greater power with decreasing SNR and was positively correlated with self-reports of effort. Increased Fmθ was present before speech onset and during speech presentation. Fmθ power also differed across SNRs when including only trials in which all words were recognized, suggesting that the effects were unrelated to performance differences. Results suggest that frontal cortical networks play a larger role in listening as acoustic signals are increasingly masked. Further, sustained time-frequency EEG features may usefully supplement previously used peripheral and event-related potential measures in psychophysiological investigations of effortful listening.

In recent years researchers have presented a number of new methods for recovering physiological parameters using just low-cost digital cameras and image processing. The ubiquity of digital cameras presents the possibility for many new, low-cost applications of vital sign monitoring. In this paper we present a review of the work on remote photoplethysmographic (PPG) imaging using digital cameras. This review specifically focuses on the state-of-the-art in PPG imaging where: 1) measures beyond pulse rate are evaluated, 2) non-ideal conditions (e.g., the presence of motion artifacts) are explored, and 3) use cases in relevant environments are demonstrated. We discuss gaps within the literature and future challenges for the research community. To aid in the continuing advancement of PPG imaging research, we are making available a website with the references collected for this review as well as information on available code and datasets of interest. It is our hope that this website will become a valuable resource for the PPG imaging community. The site can be found at: http://web.mit.edu/~djmcduff/www/ remote-physiology.html.

Expanding interest in oxytocin, particularly the role of endogenous oxytocin in human social behavior, has created a pressing need for replication of results and verification of assay methods. In this study, we sought to replicate and extend previous results correlating plasma oxytocin with trust and trustworthy behavior. As a necessary first step, the two most commonly used commercial assays were compared in human plasma via the addition of a known quantity of exogenous oxytocin, with and without sample extraction. Plasma sample extraction was found to be critical in obtaining repeatable concentrations of oxytocin. In the subsequent trust experiment, twelve samples in duplicate, from each of 82 participants, were collected over approximately six hours during the performance of a Prisoner’s Dilemma task paradigm that stressed human interpersonal trust. We found no significant relationship between plasma oxytocin concentrations and trusting or trustworthy behavior. In light of these findings, previous published work that used oxytocin immunoassays without sample extraction should be reexamined and future research exploring links between endogenous human oxytocin and trust or social behavior should proceed with careful consideration of methods and appropriate biofluids for analysis.

With the rise of increasingly complex artificial intelligence (AI), there is a need to design new methods to monitor AI in a transparent, human-aware manner. Decades of research have demonstrated that people, who are not aware of the exact performance levels of automated algorithms, often experience a mismatch in expectations. Consequently, they will often provide either too little or too much trust in an algorithm. Detecting such a mismatch in expectations, or trust calibration, remains a fundamental challenge in research investigating the use of automation. Due to the context-dependent nature of trust, universal measures of trust have not been established. Trust is a difficult construct to investigate because even the act of reflecting on how much a person trusts a certain agent can change the perception of that agent. We hypothesized that electroencephalograms (EEGs) would be able to provide such a universal index of trust without the need of self-report. In this work, EEGs were recorded for 21 participants (mean age = 22.1; 13 females) while they observed a series of algorithms perform a modified version of a flanker task. Each algorithm’s degree of credibility and reliability were manipulated. We hypothesized that neural markers of action monitoring, such as the observational error-related negativity (oERN) and observational error positivity (oPe), are potential candidates for monitoring computer algorithm performance. Our findings demonstrate that (1) it is possible to reliably elicit both the oERN and oPe while participants monitored these computer algorithms, (2) the oPe, as opposed to the oERN, significantly distinguished between high and low reliability algorithms, and (3) the oPe significantly correlated with subjective measures of trust. This work provides the first evidence for the utility of neural correlates of error monitoring for examining trust in computer algorithms.