The visual gamma response to faces reflects the presence of sensory evidence and not awareness of the stimulus
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The accompanying data is the data for the study: Perry, G. (2016) 'The visual gamma response to faces reflects the presence of sensory evidence and not awareness of the stimulus', Royal Society Open Science. For methods used to collect the data see that publication. Behavioural data: threshold_data.csv - Data from the session in which individuals' psychmetric functions for face detection were acquired. Column 1 contains partcipant number. Colums 2-10 contain response rates to each level of phase scrambling (w). Data in each cell corresponds to the proprtion of times the participant responded that they saw face for the corresponding value of w. From the resulting function fits the values of w required for each participant to identify the face 0.5% (subthreshold), 50% (threshold) and 99.5% (suprathreshold) were derived and these are given in colums 11-13. response_data.csv - Response data from the MEG session. Column 1 contains partcipant number. Colums 2-7 give total number of trials that partcipants reported seeing ('seen') or not seeing ('unseen') a face in each of the three conditions. Colums 8-9 give the number of trials in each of the analysed sub-conditions ('subthreshold unseen', 'threshold unseen', 'threshold seen', 'suprathreshold seen') after trial exclusions. MEG data: virtual_sensor_coordinates.csv - Coordinates in Talairach space for the locations used for virtual sensor analysis (as well as group mean and standard deviation). Dashed cells correspond to individuals for which no location was found. gamma_amplitude.csv - Gamma amplitudes used in the main t-test analyses. Column 1 contains partcipant number. Columns 2-9 contains mean gamma amplitude for each participant by sub-condition and hemisphere. virtual_sensor_participant_x_y_z.csv contains the virtual sensor timeseries for participant x from trials in sub-condition y from hemsiphere z. Column one gives time (in ms relative to stimulus onset). Subsequenct columns give the virtual sensor data for each trial (trials containing artefacts have been removed). tf_participant_x_y_z.csv contains the time-frequency data for participant x from sub-condition y from hemsiphere z. Column 1 gives time relative to stimulus onset (in ms), row 1 gives frequency (in Hz), other columns/rows give data at the corresponding time and frequency as % amplitude relative to baseline.Keywords:
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Purpose Distortion product otoacoustic emissions (DPOAEs) and audiometric thresholds have been used to account for the impacts of subclinical outer hair cell (OHC) dysfunction on auditory perception and measures of auditory physiology. However, the relationship between DPOAEs and the audiogram is unclear. This study investigated this relationship by determining how well DPOAE levels can predict the audiogram among individuals with clinically normal hearing. Additionally, the impacts of age, noise exposure, and the perception of tinnitus on the ability of DPOAE levels to predict the audiogram were evaluated. Method Suprathreshold DPOAE levels from 1 to 10 kHz and pure-tone thresholds from 0.25 to 16 kHz were measured in 366 ears from 194 young adults (19-35 years old) with clinically normal audiograms and middle ear function. The measured DPOAE levels at all frequencies were used to predict pure-tone thresholds at each frequency. Participants were grouped by age, self-reported noise exposure/Veteran status, and self-report of tinnitus. Results Including DPOAE levels in the pure-tone threshold prediction model improved threshold predictions at all frequencies from 0.25 to 16 kHz compared with a model based only on sample mean pure-tone thresholds, but these improvements were modest. DPOAE levels for
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Subclinical infection
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Presbycusis
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Sensory neuron
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This chapter provides an overview of sensory processes. It starts with the organisation of sensory systems in line with the classification of sensory receptor cells. Sensory systems provide animals with essentially all of the information they have about their external environment and their internal environment. Moreover, sensory systems of all kinds depend on specialised sensory receptor cells that respond to stimuli, either from the environment or arising inside the body. Sensory cells respond to different stimuli that vary greatly in sensitivity and specificity. The chapter then looks into the features and functions of vestibular organs, chemoreception, olfaction, photoreception, and visual sensory processing.
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It's a major challenge for marketers to combine various sensory information to create a synergistic effect of '1 + 1 > 2'. However, there is a lack of research on sensory synergy. This study attempts to quantitatively confirm the existence of sensory interaction and sensory conflict and to investigate their effects on consumer behavior. To measure the effect of sensory synergy, we use text classification and sentiment analysis methods to analyze online restaurant reviews. The results show both have significant effects on consumer rating behavior. Specifically, in sensory interaction, the more negative sensory information there is, the lower the consumer rating. However, it is not the case that the more positive sensory information, the higher the consumer rating, but rather it should be kept at an appropriate level. In addition, positive sensory sentiment can compensate for the negative in sensory conflict, both within and across senses. These findings can help marketers implement sensory marketing.
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Purpose: Although findings in human studies trying to identify signs of cochlear synaptopathy (CS) are still controversial, several methodological restrictions exist, including inconsistencies in participants profiles, definition of study groups and inclusion criteria, diverse electrophysiological measurements settings and noise exposure history metrics and participants’ age range, which question the reliability of conclusions. Method: Two groups of individuals aged 18–41 participated in this study. The first group consisted of 24 musicians with well documented occupational noise exposure and the second one of 24 healthy controls. Groups were matched for age and gender. Inclusion criteria were normal and symmetrical pure tone audiometry and extended high frequency audiometry thresholds, as well as present and normal TEOAEs and DPOAEs. Outcome measures included SNR scores of speech in bubble audiometry and auditory brainstem response waves I, II and V amplitudes and latencies at three different presentation rates: 11, 33 and 44 clicks/s. Results: Wave I amplitude in musicians was significantly lower compared to controls at all presentation rates (11/s: p .043, 33/s: p .007 and 44/s: p .014). There was also a significantly greater reduction in wave I amplitude as well as in wave Ι/V amplitude ratio in the 33/s stimulus presentation in musicians compared to the control group ( p .02 and .01, respectively). Finally, lower wave I amplitudes were found in musicians with auditory symptoms compared to musicians without symptoms at the 44/s presentation rate ( p = .002). Conclusion: The ABR findings in our study are suggestive of possible increased prevalence of CS in musicians. Wave I amplitude was lower in Musicians group compared to matched controls. Furthermore, there was a greater reduction of wave I amplitude and wave Ι/V amplitude ratio at higher stimulation rates, which would support their use in CS studies.
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