Decision SincNet: Neurocognitive models of decision making that predict cognitive processes from neural signals
Decision SincNet: Neurocognitive models of decision making that predict cognitive processes from neural signals
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DOI：
10.1109/ijcnn55064.2022.9892272
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Qi Sun;Khuong Vo;K. Lui;Michael D. Nunez;J. Vandekerckhove;R. Srinivasan
中科院分区：
文献类型：
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作者：
Qi Sun;Khuong Vo;K. Lui;Michael D. Nunez;J. Vandekerckhove;R. Srinivasan
在心理学实验中，人们用选择-反应时间数据来观察人类的决策行为。这些数据的漂移扩散模型由维纳首次通过时间（WFPT）分布组成，并由认知参数：漂移率，边界分离和起点描述。这些估计的参数是神经科学家感兴趣的，因为它们可以映射到决策的认知过程的特征（如速度，谨慎和偏见），并与大脑活动有关。观察到的RT模式也反映了由神经动力学介导的从审判到审判的认知过程的可变性。我们采用了基于SincNet的浅层神经网络架构，以适应漂移扩散模型，在每个实验试验中使用EEG信号。该模型由一个SincNet层、一个dependency空间卷积层和两个独立的完全连接层组成，这两个层并行预测每次试验的漂移率和边界。SincNet层对内核进行参数化，以便直接学习应用于EEG数据的带通滤波器的低截止频率和高截止频率，以预测漂移和边界参数。在训练过程中，模型参数进行了更新，最大限度地减少WFPT分布的负对数似然函数给定的审判RT。我们开发了单独的决策SincNet模型，为每个参与者执行两个替代的被迫选择任务。我们的研究结果表明，在训练和测试数据集中，漂移和边界的单次试验估计在预测RT方面优于中值估计，这表明我们的模型可以成功地使用EEG特征来估计有意义的单次试验扩散模型参数。此外，浅层SincNet架构确定了与证据积累和谨慎相关的信息处理的时间窗口以及反映每个参与者内部这些过程的EEG频带。
Human decision making behavior is observed with choice-response time data during psychological experiments. Drift-diffusion models of this data consist of a Wiener first-passage time (WFPT) distribution and are described by cognitive parameters: drift rate, boundary separation, and starting point. These estimated parameters are of interest to neuroscientists as they can be mapped to features of cognitive processes of decision making (such as speed, caution, and bias) and related to brain activity. The observed patterns of RT also reflect the variability of cognitive processes from trial to trial mediated by neural dynamics. We adapted a SincNet-based shallow neural network architecture to fit the Drift-Diffusion model using EEG signals on every experimental trial. The model consists of a SincNet layer, a depthwise spatial convolution layer, and two separate fully connected layers that predict drift rate and boundary for each trial in-parallel. The SincNet layer parametrized the kernels in order to directly learn the low and high cutoff frequencies of bandpass filters that are applied to the EEG data to predict drift and boundary parameters. During training, model parameters were updated by minimizing the negative log likelihood function of WFPT distribution given trial RT. We developed separate decision SincNet models for each participant performing a two-alternative forced-choice task. Our results showed that single-trial estimates of drift and boundary performed better at predicting RTs than the median estimates in both training and test data sets, suggesting that our model can successfully use EEG features to estimate meaningful single-trial Diffusion model parameters. Furthermore, the shallow SincNet architecture identified time windows of information processing related to evidence accumulation and caution and the EEG frequency bands that reflect these processes within each participant.