PhD Scientific Days 2024

Poster Session G - Mental Health Sciences 2.

The cognitive processing of social functioning dependent on mental problems in a linguistic approach

Text of the abstract

Introduction:
Our social functioning is affected by the cognitive processing of our social relationships. Self-reported questionnaires are accepted methods to measure consciously available social representations. Our implicit cognitive processing of social information could be detected by measuring the reaction time differences between socially relevant, potentially relevant, and irrelevant words.

Aims:
In our study, we tested the hypothesis of whether certain socially relevant words have longer reaction times and are made to be mistaken more often than the irrelevant words in the Emotional Stroop Task (EST) and the Lexical Decision Task (LDT). Our other aim is also to highlight actual words that can trigger people, which is to be seen through reaction times.

Method:
An online test package was advertised primarily on Facebook. This pilot study includes 30 anonymous participants. The test package was made by PsychoPy-2024.1.1, and contains questions about demographic data, mental health data, three tasks (EST, LDT, and Words Emotional Value and Intrusiveness Scale), and five types of questionnaires (Beck Depression Questionnaire, Borderline Personality Disorder Screening questionnaire, Early Trauma Questionnaire, certain parts of Young Schema Questionnaire, and Adult Attachment Questionnaire). The tasks were based on selected words, which were separated in threee groups as socially relevant, potentially relevant (certain pronoms), and irrelevant (neutral) words.

Results:
At this stage of the data processing our analysis showed not to have significantly relevant differences between socially relevant (t=0.66; p=0.52), potentially relevant (t=0.27; p=0.79), and irrelevant words (t=-0.39; p=0.7) when it is related to whether participants have mental problems or not. On the other hand, we found the reactiontime of "billentyűzettel" (F=4.12; p=0.05) and "megbocsátok" (F=4.19; p=0.05) showed significant relation with mental problems.

Conclusion:
In the further stage of the study more participants and Machine learning specifically Cluster analysis is needed to classify wordforms by other aspects.

Funding:
No funding.