Data：Representation of Task-Irrelevant Information in Social Statistical Learning

This study explores whether individuals in social statistical learning can process and integrate irrelevant information from their own or peer tasks. This study distinguished task-relevant stimuli from irrelevant stimuli by manipulating co-actors' attention patterns towards their own sequences. The database contains two data files. Experiment 1 verified whether individuals can learn the task regularity of the sequence under different attention patterns in the joint condition, further exploring the representation of irrelevant stimuli in their own or co-actor' tasks. Experiment 2 tested whether individuals can learn the task regularity of the sequence under two attentional modes during the solo condition, further exploring whether social contexts are a key factor in representing task-irrelevant stimuli. The process of generating the dataset: A psychological experiment was conducted using the Eprime3.0 software. In Experiment 1, 40 participants (20 pairs of female participants) were recruited, and in Experiment 2, 52 participants (26 pairs of female participants) were recruited to complete the experiment. The reaction times and error rates of each participant were collected as raw data. Data analysis methods and steps: Firstly, the data is preprocessed based on the R platform, excluding trials with reaction times exceeding 1500ms and excluding subjects with one or more conditions which reaction times or accuracy rates exceeding 3 standard deviations from the correct trials. Then, the data was subjected to a Linear Mixed Model (LMM) and maximum random effects were added (Barr et al., 2013). The optimal linear mixed model was determined using lme4 (Bates et al., 2015) and buildmer package (Voeten, 2020), and the effectsize package was used to estimate effect size f²，and according to the algorithm proposed by Aguasvivas et al. (2024), BF10 was obtained.