Spontaneous neural activity observed in resting-state fMRI is characterized by complex spatio-temporal dynamics. Different measures related to local and global brain connectivity and fluctuations in low-frequency amplitudes can quantify individual aspects of these neural dynamics. Even though such measures are derived from the same functional signals, they are often evaluated separately, neglecting their interrelations and potentially reducing the analysis sensitivity. In our study, we present a fusion searchlight (FuSL) framework to combine the complementary information contained in different resting-state fMRI metrics and demonstrate how this can improve the decoding of brain states. Moreover, we show how explainable AI allows us to reconstruct the differential impact of each metric on the decoding, which additionally increases spatial specificity of searchlight analysis. In general, this framework can be adapted to combine information derived from different imaging modalities or experimental conditions, offering a versatile and interpretable tool for data fusion in neuroimaging.

Large Language Models (LLMs) have recently been widely adopted on interactive systems requiring communications. As the false belief in a model can harm the usability of such systems, LLMs should not have cognitive biases that humans have. Especially psychologists focused on the False Consensus Effect (FCE), which can distract smooth communication by posing false beliefs. However, previous studies have less examined FCE in LLMs thoroughly, which needs more consideration of confounding biases, general situations, and prompt changes. Therefore, in this paper, we conduct two studies to deeply examine the FCE phenomenon in LLMs. In Study 1, we investigate whether LLMs have FCE. In Study 2, we explore how various prompting styles affect the demonstration of FCE. As a result of these studies, we identified that popular LLMs have FCE. Also, the result specifies the conditions when the strength of FCE becomes larger or smaller compared to normal usage.

Neural network-based decisions tend to be overconfident, where their raw outcome probabilities do not align with the true decision probabilities. Calibration of neural networks is an essential step towards more reliable deep learning frameworks. Prior metrics of calibration error primarily utilize crisp bin membership-based measures. This exacerbates skew in model probabilities and portrays an incomplete picture of calibration error. In this work, we propose a Fuzzy Calibration Error metric (FCE) that utilizes a fuzzy binning approach to calculate calibration error. This approach alleviates the impact of probability skew and provides a tighter estimate while measuring calibration error. We compare our metric with ECE across different data populations and class memberships. Our results show that FCE offers better calibration error estimation, especially in multi-class settings, alleviating the effects of skew in model confidence scores on calibration error estimation. We make our code and supplementary materials available at: https://github.com/bihani-g/fce

This paper studies a training method to jointly estimate an energy-based model and a flow-based model, in which the two models are iteratively updated based on a shared adversarial value function. This joint training method has the following traits. (1) The update of the energy-based model is based on noise contrastive estimation, with the flow model serving as a strong noise distribution. (2) The update of the flow model approximately minimizes the Jensen-Shannon divergence between the flow model and the data distribution. (3) Unlike generative adversarial networks (GAN) which estimates an implicit probability distribution defined by a generator model, our method estimates two explicit probabilistic distributions on the data. Using the proposed method we demonstrate a significant improvement on the synthesis quality of the flow model, and show the effectiveness of unsupervised feature learning by the learned energy-based model. Furthermore, the proposed training method can be easily adapted to semi-supervised learning. We achieve competitive results to the state-of-the-art semi-supervised learning methods.