Timing evaluation for subconscious stimulus presentation

AUTHORS

Julia Schräder 1,2, Ute Habel 1,2, Han-Gue Jo 3, Franziska Walter 1, Lisa Wagels 1,2

• _{^{1 Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, Uniklinik RWTH Aa-chen University, Pauwelstraße 30, 52074 Aachen, Germany}}
• _{^{2 Institute of Neuroscience and Medicine: JARA-Institute Brain Structure Function Relationship (INM 10), Research Center Jülich, Jülich, Germany}}
• _{^{3 School of Computer Information and Communication Engineering, Kunsan National University, Gun-san, Korea}}

Table of contents

• General info
  • Authors
  • Publication
  • License
• Experimental design
• Folder descripion
• Technologies
• Setup

General info

This study is about the perception of healthy individuals of emotional stimuli presented at an unconscious level. On an 120Hz Screen, three drifferent timing options for unconscious presentation were elaborated (8.3ms, 16.7ms, 25ms) and compared to a conscious presentation time of 141.7ms. As stimuli, happy, neutral and sad faces taken from the FACE Database were used [1].

Preprint DOI

osf.io/mscz5

License

CC-By Attribution 4.0 International

Experimental design

The participants completed 360 trials in 3 block where one short timing condition counterbalanced with the long timing condition appeared per block. This paradigm leads to a 4x3 factorial design (4 timing conditions and 3 stimulus emotions) with 7 main effects (happy, neutral, sad, 8.3ms, 16.7ms, 25ms, 141.7ms) and 12 further conditions. The paradigms were programmed with PsychoPy [2] and can be found on this respiratory in the tasksfolder.

For both tasks, we used a backward mask paradigm which provides conscious and unconscious masked stimulus presentation. A total of 36 images (12 happy, 12 neutral, 12 sad) served as emotional stimuli. Pictures were gender balanced and taken from the FACES database (Ebner, Riediger, & Lindenberger, 2010). Each image was presented 10 times against a grey background at the center of a LCD monitor (120Hz). At the beginning of each trial, a fixation cross appeared for 300ms (36 frames) followed by the stimulus. The mask stimulus appeared for 41.6ms (5 frames) followed by a response phase of maximal 1.5s. A blank screen served as an inter-stimulus-interval (ISI) ranging be-tween 1 and 2s. Participants complete 3 blocks of 120 trials in randomized order.

Task one

For the strongly masked stimuli, the presentation time varied between blocks. The stimuli images were presented for 25ms (3 frames) in the first block, for 16.7ms (2 frames) in the second block and for 8.3ms (1 frame) in the third block. In each block, the number of strong-ly (8.3ms, 16.7ms or 25ms) and weakly masked trails (141.7ms) were counterbalanced.

Task two

Strongly (8.3ms, 16.7ms or 25ms) and weakly masked stimuli (150ms) were presented in randomized order regardless of the block number. First, participants rated the stimulus emo-tion. Second, they were asked to rate how well the stimulus was visible (“seen”, “not seen”, “don’t know”).

Folder description

• data includes mean performance accuracy, mean reaction time during emotion classification and mean rating of subjective awareness. Mean accuracy and mean reaction time was additionally uploaded for each task
• glmm includes script and dataset for glmm analysis
• statistics includes scripts and results of descriptive statistics and comparison of mean accuracy and mean reaction time of all 40 participants within main effects and all conditions.
• hddm includes script and dataset for hddm analysis as well as statistical results
• tasks includes the paradigm, stimuli and datasheets to run the backward mask task
• supplements includes supplement information, e.g. glmm for Bias and Drift-Rates with BDI and BVAQ Score effects

Technologies

Project is created with:

• PsychoPy3: Version v2020.2.4
• RStudio: Version 1.4.1106
• Python 3
• GraphPad Prism 9.1.1 (225)

Setup

• To run the paradigm, install PsychoPy and download the tasks folder.

• To run hddm and statistical analysis install it locally using Python:

$ pip install pandas
$ pip install pymc
$ pip install kabuki
$ pip install hddm

For detailled instruction for hddm see here

• To run the glmm analysis install R

References

[1] Ebner, N. C., Riediger, M., & Lindenberger, U. (2010). FACES—A database of facial expressions in young, middle-aged, and older women and men: Development and validation. Behavior research methods, 42(1), 351-362

[2] Peirce J, Gray JR, Simpson S, MacAskill M, Höchenberger R, Sogo H, Kastman E, Lindeløv JK. (2019) PsychoPy2: Experiments in behavior made easy Behav Res 51: 195.