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

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Mean Accuracy Data.xlsx

data 14994 2 47331 document 14994 Mean Reaction Times.xlsx application/vnd.openxmlformats-officedocument.spreadsheetml.sheet ab3a159505ce0901f2a9d36ce5d5f081 MD5 19187 2021-06-14 13:05:24 https://researchdata.bath.ac.uk/1023/2/Mean%20Reaction%20Times.xlsx 1023 2 2 application/vnd.openxmlformats-officedocument.spreadsheetml.sheet other en public cc_by

Mean Reaction Times.xlsx

data 14995 2 47333 document 14995 MLE Model Analysis.xlsx application/vnd.openxmlformats-officedocument.spreadsheetml.sheet 99cea2e1a29e8ca9ff1012083413d715 MD5 15562 2021-06-14 13:05:27 https://researchdata.bath.ac.uk/1023/3/MLE%20Model%20Analysis.xlsx 1023 3 3 application/vnd.openxmlformats-officedocument.spreadsheetml.sheet other en public cc_by

MLE Model Analysis.xlsx

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Race Model Analysis.xlsx

data 14997 2 47343 document 14997 Participant Information Sheet RPS.pdf application/pdf c29689a53a580c4e48774e879bab1c37 MD5 447550 2021-06-14 14:45:36 https://researchdata.bath.ac.uk/1023/5/Participant%20Information%20Sheet%20RPS.pdf 1023 5 5 application/pdf other Participant Information Sheet en staffonly

Participant Information Sheet RPS.pdf

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Multiple Session Study Consent Form.docx

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indexcodes.txt

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http://eprints.org/relation/isVersionOf https://researchdata.bath.ac.uk/id/document/14997 http://eprints.org/relation/isVolatileVersionOf https://researchdata.bath.ac.uk/id/document/14997 http://eprints.org/relation/issmallThumbnailVersionOf https://researchdata.bath.ac.uk/id/document/14997 archive 10229 disk0/00/00/10/23 2021-09-20 10:43:18 2024-07-15 10:59:44 2021-09-20 10:43:18 data_collection show Heffer Naomi N.R.Heffer@bath.ac.uk 0000-0002-6597-0134 University of Bath TRUE DataCollector Gradidge Molly mjrg20@bath.ac.uk University of Bath Supervisor Karl Anke A.Karl@exeter.ac.uk 0000-0002-6472-2876 University of Exeter Supervisor Ashwin Chris C.Ashwin@bath.ac.uk 0000-0003-4606-7318 University of Bath Supervisor Petrini Karin K.Petrini@bath.ac.uk 0000-0001-5354-5600 University of Bath Dataset supporting the paper: High trait anxiety enhances optimal integration of auditory and visual threat cues JN0010 dept_psy Anxiety, Emotion, Multisensory processing, Negative bias, Maximum Likelihood Estimation, Race Model Inequality Information on the layout of the data has been provided in the README sheet in each Excel spreadsheet file. This dataset includes data on behavioural outcomes for the audiovisual emotion recognition tasks used in the publication, "High Trait Anxiety Enhances Optimal Integration of Auditory and Visual Threat Cues". In this study the authors investigated perception of happy, sad and angry emotions within unimodal (audio- and visual-only) and audiovisual displays in adults with low vs. high levels of trait anxiety. The data is organised to facilitate replication of the analyses carried out in the aforementioned study, which includes two model-based analyses to elucidate how multisensory integration of emotional information operates in high trait anxiety. This was done by comparing performance in the audiovisual condition for both high and low trait anxiety groups to performance predicted by the Maximum Likelihood Estimation (MLE) model (Ernst & Banks, 2002; Rohde et al., 2016) and Miller’s Race Model (Miller, 1982; Ulrich et al., 2007). Data included in this dataset has already been pre-processed (i.e., univariate outliers have already been identified and dealt with). 2021-09-15 University of Bath public RightsHolder University of Bath Medical Research Council https://doi.org/10.13039/501100000265 2110628 Studentship - Examining the link between multisensory and socio-emotional processing in anxiety and post-traumatic stress disorder 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. 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). 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). en 1 10.15125/BATH-01023 https://doi.org/10.1016/j.jbtep.2021.101693 pub open Dataset