Dataset supporting the paper: High trait anxiety enhances optimal integration of auditory and visual threat cues

Data collection method:

This dataset contains data for behavioural outcomes from the audiovisual emotion recognition task described in the paper: "High Trait Anxiety Enhances Optimal Integration of Auditory and Visual Threat Cues". Participants made emotional judgements about dynamic face and voice stimuli, having to classify them as being either happy, angry or sad by a making a speeded key press. The task included three different stimulus modality conditions: visual-only (faces), audio-only (voices) and audiovisual (faces and voices together). The stimuli were approximately 500ms long and visual noise was added to the central face region of the videos by the addition of a Gaussian blur. Participants were screened before they were recruited in the study such that only individuals with a trait anxiety score of 37 or below (low anxiety group) and 48 or above (high anxiety group) were included. The trait subscale of the Spielberger State-Trait Anxiety scale was used to measure trait anxiety.

Data processing and preparation activities:

All the data in this dataset has been anonymised prior to sharing. Univariate outliers were identified as datapoints lying more than +/- 3 times the value of the interquartile range from the median value. Outliers have been winsorized prior to sharing (i.e. replaced with the next most extreme non-outlier value in the dataset).

Technical details and requirements:

Analyses of violation of the race model inequality were carried out using the RMITest software which implements the algorithm described in Ulrich et al. (2007).

Additional information:

Information on the layout of the data has been provided in the README sheet in each Excel spreadsheet file.