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  1. 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 by discriminating whether a Gabor patch presented with noise is high or low spatial frequency. 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. 
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  2. Abstract

    Global ocean mean salinityis a key indicator of the Earth's hydrological cycle and the exchanges of freshwater between land and ocean, but its determination remains a challenge. Aside from traditional methods based on gridded salinity fields derived from in situ measurements, we explore estimates ofbased on liquid freshwater changes derived from space gravimetry data corrected for sea ice effects. For the 2005–2019 period analyzed, the differentseries show little consistency in seasonal, interannual, and long‐term variability. In situ estimates show sensitivity to choice of product and unrealistic variations. A suspiciously large rise insince ∼2015 is enough to measurably affect halosteric sea level estimates and can explain recent discrepancies in the global mean sea level budget. Gravimetry‐basedestimates are more realistic, inherently consistent with estimated freshwater contributions to global mean sea level, and provide a way to calibrate the in situ estimates.

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