DOI: 10.57760/sciencedb.psych.00773
title: A dataset used to explain the cognitive neural mechanism by which situational stress enhances the efficiency of innovative discovery behavior
title: 用于解释"情境压力提高创新发现行为效率的认知神经机制"的数据集

description: This dataset originates from a study that employed event-related potential (ERP) technology to investigate the cognitive neural mechanisms underlying how situational pressure enhances the efficiency of creative discovery behavior. Data for Experiment 1 were collected from 32 healthy adult participants between December 2023 and January 2024, while data for Experiment 2 were collected from 33 healthy adult participants in September 2024. Both datasets were acquired in the laboratory of the School of Psychology at Jiangxi Normal University.The experiment utilized a creative discovery paradigm with stimuli presented via E-Prime software. EEG signals were recorded using a Neuroscan amplifier and a 64-channel electrode cap following the 10-20 system. The data were offline re-referenced to the average of the bilateral mastoids, with a sampling rate of 500 Hz and electrode impedance maintained below 10 kΩ throughout the recording. Behavioral responses were recorded synchronously.Data preprocessing and analysis were conducted in MATLAB 2023b, primarily using the EEGLAB and ERPLAB toolboxes. The processing pipeline included: importing raw data, applying a 0.1–40 Hz bandpass filter, identifying and interpolating bad channels, re-referencing to the whole-brain average, segmenting data into epochs from -200 to 1000 ms around the target stimulus, applying baseline correction (-200 to 0 ms), and handling artifacts. Ocular artifacts were removed using Independent Component Analysis. Finally, valid trials for each experimental condition were averaged to obtain individual and group-level ERP waveforms.The dataset includes the following files:Raw Behavioral Data: Contains participant IDs, trial numbers, responses and reaction times for novel tools, as well as five-level ratings (e.g., effectiveness, frequency, desirability) and their corresponding keypress reaction times.Analyzed Behavioral Data: Includes participant IDs and multi-level row labels categorizing core indicators (effectiveness, desirability, trust proportion, novelty) under "Novel-High Effectiveness" and "Novel-Low Effectiveness," further divided into "No Pressure" and "Pressure" conditions. A separate table summarizes Signal Detection Theory metrics (Hits, Misses, False Alarms, etc.) under different pressure conditions.Raw EEG Data: Stored in .cdt format.Analyzed EEG Data: Organized by participant ID and multi-level row labels indicating brain regions (Left, Midline, Right Prefrontal), task conditions ("Novel-High/Low Effectiveness"), and pressure states ("No Pressure" and "Pressure").Potential errors in EEG signals primarily stem from physiological artifacts (e.g., eye movements, muscle activity) and environmental noise, the impacts of which were minimized through standardized preprocessing. All data have been anonymized and contain no personally identifiable information.
description: This dataset originates from a study that employed event-related potential (ERP) technology to investigate the cognitive neural mechanisms underlying how situational pressure enhances the efficiency of creative discovery behavior. Data for Experiment 1 were collected from 32 healthy adult participants between December 2023 and January 2024, while data for Experiment 2 were collected from 33 healthy adult participants in September 2024. Both datasets were acquired in the laboratory of the School of Psychology at Jiangxi Normal University.The experiment utilized a creative discovery paradigm with stimuli presented via E-Prime software. EEG signals were recorded using a Neuroscan amplifier and a 64-channel electrode cap following the 10-20 system. The data were offline re-referenced to the average of the bilateral mastoids, with a sampling rate of 500 Hz and electrode impedance maintained below 10 kΩ throughout the recording. Behavioral responses were recorded synchronously.Data preprocessing and analysis were conducted in MATLAB 2023b, primarily using the EEGLAB and ERPLAB toolboxes. The processing pipeline included: importing raw data, applying a 0.1–40 Hz bandpass filter, identifying and interpolating bad channels, re-referencing to the whole-brain average, segmenting data into epochs from -200 to 1000 ms around the target stimulus, applying baseline correction (-200 to 0 ms), and handling artifacts. Ocular artifacts were removed using Independent Component Analysis. Finally, valid trials for each experimental condition were averaged to obtain individual and group-level ERP waveforms.The dataset includes the following files:Raw Behavioral Data: Contains participant IDs, trial numbers, responses and reaction times for novel tools, as well as five-level ratings (e.g., effectiveness, frequency, desirability) and their corresponding keypress reaction times.Analyzed Behavioral Data: Includes participant IDs and multi-level row labels categorizing core indicators (effectiveness, desirability, trust proportion, novelty) under "Novel-High Effectiveness" and "Novel-Low Effectiveness," further divided into "No Pressure" and "Pressure" conditions. A separate table summarizes Signal Detection Theory metrics (Hits, Misses, False Alarms, etc.) under different pressure conditions.Raw EEG Data: Stored in .cdt format.Analyzed EEG Data: Organized by participant ID and multi-level row labels indicating brain regions (Left, Midline, Right Prefrontal), task conditions ("Novel-High/Low Effectiveness"), and pressure states ("No Pressure" and "Pressure").Potential errors in EEG signals primarily stem from physiological artifacts (e.g., eye movements, muscle activity) and environmental noise, the impacts of which were minimized through standardized preprocessing. All data have been anonymized and contain no personally identifiable information.