Decision-making in dynamic, continuously evolving environments: Quantifying the flexibility of human choice

# Decision-making in dynamic, continuously evolving environments: Quantifying the flexibility of human choice

[https://doi.org/10.5061/dryad.02v6wwq6b](https://doi.org/10.5061/dryad.02v6wwq6b)

This repository contains EEG and behavioural data from a study of continuous decision making in a random-dot kinteogram paradigm, in which participants aim to detect consistent periods of motion ('response periods') in background noise. Participants complete 6 blocks, each consisting of 4 conditions (different 'environments'). Each condition lasts 5 minutes. Full details of the paradigm are given in the preprint at [https://www.biorxiv.org/content/10.1101/2022.08.18.504278.abstract](https://www.biorxiv.org/content/10.1101/2022.08.18.504278.abstract).

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## Description of the data and file structure

The location of data files in the repository are highlighted in **bold**, and the relevant MATLAB functions to load/explore the data are given in *italics*. Code to analyse this data is stored at ..., During perceptual decision-making tasks, centroparietal EEG potentials report an evidence accumulation-to-bound process that is time locked to trial onset. However, decisions in real-world environments are rarely confined to discrete trials; they instead unfold continuously, with accumulation of time-varying evidence being recency-weighted towards its immediate past. The neural mechanisms supporting recency-weighted continuous decision making remain unclear. Here, we use a novel continuous task design to study how the Centroparietal Positivity (CPP) adapts to different environments that place different constraints on evidence accumulation. We show that adaptations in evidence weighting to these different environments are reflected in changes in the CPP. The CPP becomes more sensitive to fluctuations in sensory evidence when large shifts in evidence are less frequent, and the potential is primarily sensitive to fluctuations in decision-relevant (not decision-irrelevant) sensory input. A ..., Full details of dataset collection and preprocessing steps are provided in the associated manuscript (Ruesseler/Weber et al., bioRxiv 2023)., The relevant code to analyse this data is stored at our online code repository, https://github.com/CCNHuntLab/ruesseler-eeg-analysis. 
Further information and usage notes for this repository are given in the associated README document with this dataset.

# 动态持续演化环境中的决策：量化人类选择的灵活性
[https://doi.org/10.5061/dryad.02v6wwq6b](https://doi.org/10.5061/dryad.02v6wwq6b)

本仓库包含一项基于随机点运动图（random-dot kinteogram）范式的连续决策研究的脑电图（electroencephalogram, EEG）与行为数据。在该范式中，参与者需在背景噪声中识别出一致的运动时段（即"响应时段"）。参与者需完成6个实验区块，每个区块包含4种不同的"环境"条件，每种条件持续5分钟。该范式的完整细节见下述预印本：[https://www.biorxiv.org/content/10.1101/2022.08.18.504278.abstract](https://www.biorxiv.org/content/10.1101/2022.08.18.504278.abstract)

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## 数据与文件结构说明
仓库中数据文件的位置以**粗体**标注，用于加载、探索数据的相关MATLAB函数以*斜体*呈现。用于分析该数据的代码存储于……。在知觉决策任务中，中央顶叶脑电图电位会反映与试次起始锁定的证据累积至阈值的过程。然而，现实环境中的决策极少局限于离散试次，而是持续展开的，且时变证据的累积会以近因效应加权于近期过往信息。支持近因加权式连续决策的神经机制仍不明确。本研究采用新颖的连续任务设计，探究中央顶叶正电位（Centroparietal Positivity, CPP）如何适应对证据累积施加不同约束的各类环境。研究结果表明，针对不同环境的证据加权调整可通过CPP的变化体现：当证据大幅变动的频率较低时，CPP对感官证据波动的敏感性会提升；且该电位主要对与决策相关（而非无关）的感官输入波动敏感。完整的数据集采集与预处理步骤细节见相关论文（Ruesseler/Weber 等，bioRxiv 2023）。用于分析该数据的相关代码存储于本团队的在线代码仓库：https://github.com/CCNHuntLab/ruesseler-eeg-analysis。本仓库的更多信息与使用说明见随数据集附带的README文档。