Can the Lateralized Readiness Potential Detect Suppressed Manual Responses to Pure Tones?

BACKGROUND: Willfully not responding to auditory stimuli hampers accurate behavioral measurements. An objective measure of covert manual suppression recorded during response tasks may be useful to assess the veracity of responses to stimuli. PURPOSE: To investigate whether the lateralized readiness potential (LRP), an electrophysiological measure of corticomotor response and suppression, may be of use in determining when participants hear but do not respond to pure tones. RESEARCH DESIGN: Within-subject repeated measures with a Go-NoGo paradigm. STUDY SAMPLE: Five males and five females (mean age = 38.8 years, standard deviation = 8.8) underwent electrophysiology testing. All had normal hearing, except one. INTERVENTION: Participants were tested in a condition where they consistently responded to tonal stimuli, and in a condition where intensity cued whether they should respond or not. Scalp-recorded cortical potentials and behavioral responses were recorded, along with a question that probed the perceived effort required to suppress responses to the stimuli. DATA COLLECTION AND ANALYSIS: Electrophysiology data were processed with independent component analysis and epoch-based artifact rejection. Averaged group and individual LRPs were calculated. RESULTS: Group averaged waveforms show that suppressed responses, cued by NoGo stimuli, diverge positively at approximately 300 msec poststimulus, when compared with performed (Go) responses. LRPs were comparable when Go responses were recorded in a separate condition in which participants responded to all stimuli, and when Go and NoGo trials were included in the same condition. The LRP was not observed in one participant. CONCLUSIONS: Subsequent to further investigation, the LRP may prove suitable in assessing the suppression of responses to audiometric stimuli, and, thereby, useful in cases where functional hearing loss is suspected.

Word associations contribute to machine learning in automatic scoring of degree of emotional tones in dream reports.

Scientific study of dreams requires the most objective methods to reliably analyze dream content. In this context, artificial intelligence should prove useful for an automatic and non subjective scoring technique. Past research has utilized word search and emotional affiliation methods, to model and automatically match human judges' scoring of dream report's negative emotional tone. The current study added word associations to improve the model's accuracy. Word associations were established using words' frequency of co-occurrence with their defining words as found in a dictionary and an encyclopedia. It was hypothesized that this addition would facilitate the machine learning model and improve its predictability beyond those of previous models. With a sample of 458 dreams, this model demonstrated an improvement in accuracy from 59% to 63% (kappa=.485) on the negative emotional tone scale, and for the first time reached an accuracy of 77% (kappa=.520) on the positive scale.

A psychoacoustical model for specifying the level and spectrum of acoustic warning signals in the workplace.

A psychoacoustic model is presented to facilitate the installation of acoustic warning devices in noisy settings, reflecting a major upgrade of a former tool, Detectsound. The model can be used to estimate the optimal level and spectrum of acoustic warning signals based on the noise field in the workplace, the hearing status of workers, and the attenuation provided by hearing protectors. The new version can be applied to a wider range of situations. Analyses can now be conducted to meet the functional requirements for a specific worker or to suit the needs for a group of co-workers sharing a work area. Computation of optimal warning signals can also be made from estimated hearing parameters based on the worker age, gender, and level and duration of noise exposure. The results of a laboratory validation study showed that the mean error in estimating detection thresholds for normal hearing individuals is typically within +/-1 dB with a standard deviation of less than 2.5 dB in white noise or continuous noise fields. The model tends to yield slightly overestimated warning signal detection thresholds in fluctuating noises. Proper application of the tool also requires consideration of the variability in estimating noise levels, hearing status, and hearing protector attenuation under field conditions to ensure that acoustic warning signals are sufficiently loud and well adjusted in practice.