DOI: 10.57760/sciencedb.psych.00701
title: Visual Awareness Sharpens and Accelerates Attentional Sampling Through Enhancing Inhibitory Neural Modulation in the Attention Network
title: Visual Awareness Sharpens and Accelerates Attentional Sampling Through Enhancing Inhibitory Neural Modulation in the Attention Network

description: This dataset supports the research paper entitled “Visual Awareness Sharpens and Accelerates Attentional Sampling Through Enhancing Inhibitory Neural Modulation in the Attention Network”. It is composed of two core components: **publicly accessible Source Data files** and **a compressed package (containing raw data and analysis code)**. For the compressed package, access permissions can be requested from the corresponding author. The dataset integrates experimental data, preprocessing results, and matching analysis code for both Experiment 1 and Experiment 2, providing comprehensive and direct support for the experimental findings reported in the paper. Its specific structure is organized into two main folders, as detailed below: ### 1. Behavioral Experiment 1 (Folder Name: Beh_Exp1) This folder stores the primary experimental data of Behavioral Experiment 1, with subfolders categorized by experimental purpose: - **Subfolder 1: Exp1_main** Contains the raw experimental data of the main experiment in Experiment 1, as well as the complete analysis code used to process and analyze this main experimental data. - **Subfolder 2: Exp1_supple** Includes the supplementary experimental data of Experiment 1 (supporting supplementary analyses in the paper) and additional analysis code corresponding to these supplementary experiments. ### 2. EEG Experiment 2 (Folder Name: EEG_Exp2) This folder houses the preprocessing results of EEG data from Experiment 2, along with step-by-step processing scripts and derivative analysis data. It is divided into five functional subfolders: - **Subfolder 1: EEG_data** Stores the preprocessed EEG data (after quality control and standard preprocessing steps) and the preprocessing scripts used to generate this processed data (ensuring reproducibility of the preprocessing process). - **Subfolder 2: Beh_data** Contains the statistical analysis results of behavioral performance in EEG Experiment 2, as well as supplementary experimental data related to this experiment. - **Subfolder 3: TRF_data** Includes the temporal response function (TRF) data derived from the preprocessed EEG data (a key derivative index for analyzing neural response timing). - **Subfolder 4: timefreq_domain_results** Stores the results of time-frequency analysis (e.g., event-related spectral perturbations) and functional connectivity analysis of the EEG data, which are core neural data results reported in the paper. - **Subfolder 5: function** Contains all the code used for data analysis in EEG Experiment 2, including built-in function packages (or custom function modules) that must be called to execute the analysis code (ensuring the analysis process can be directly reproduced by other researchers). In summary, this dataset covers the full chain of research data—from raw data to preprocessed results, and from core experimental data to supplementary data—paired with complete and traceable analysis code. The above description clarifies its content scope and structural logic, facilitating other researchers to locate, use, and reproduce the study’s results efficiently.
description: This dataset supports the research paper entitled “Visual Awareness Sharpens and Accelerates Attentional Sampling Through Enhancing Inhibitory Neural Modulation in the Attention Network”. It is composed of two core components: **publicly accessible Source Data files** and **a compressed package (containing raw data and analysis code)**. For the compressed package, access permissions can be requested from the corresponding author. The dataset integrates experimental data, preprocessing results, and matching analysis code for both Experiment 1 and Experiment 2, providing comprehensive and direct support for the experimental findings reported in the paper. Its specific structure is organized into two main folders, as detailed below: ### 1. Behavioral Experiment 1 (Folder Name: Beh_Exp1) This folder stores the primary experimental data of Behavioral Experiment 1, with subfolders categorized by experimental purpose: - **Subfolder 1: Exp1_main** Contains the raw experimental data of the main experiment in Experiment 1, as well as the complete analysis code used to process and analyze this main experimental data. - **Subfolder 2: Exp1_supple** Includes the supplementary experimental data of Experiment 1 (supporting supplementary analyses in the paper) and additional analysis code corresponding to these supplementary experiments. ### 2. EEG Experiment 2 (Folder Name: EEG_Exp2) This folder houses the preprocessing results of EEG data from Experiment 2, along with step-by-step processing scripts and derivative analysis data. It is divided into five functional subfolders: - **Subfolder 1: EEG_data** Stores the preprocessed EEG data (after quality control and standard preprocessing steps) and the preprocessing scripts used to generate this processed data (ensuring reproducibility of the preprocessing process). - **Subfolder 2: Beh_data** Contains the statistical analysis results of behavioral performance in EEG Experiment 2, as well as supplementary experimental data related to this experiment. - **Subfolder 3: TRF_data** Includes the temporal response function (TRF) data derived from the preprocessed EEG data (a key derivative index for analyzing neural response timing). - **Subfolder 4: timefreq_domain_results** Stores the results of time-frequency analysis (e.g., event-related spectral perturbations) and functional connectivity analysis of the EEG data, which are core neural data results reported in the paper. - **Subfolder 5: function** Contains all the code used for data analysis in EEG Experiment 2, including built-in function packages (or custom function modules) that must be called to execute the analysis code (ensuring the analysis process can be directly reproduced by other researchers). In summary, this dataset covers the full chain of research data—from raw data to preprocessed results, and from core experimental data to supplementary data—paired with complete and traceable analysis code. The above description clarifies its content scope and structural logic, facilitating other researchers to locate, use, and reproduce the study’s results efficiently.