A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
We propose a neural superstatistics method to estimate dynamic cognitive models from time series data.
Study links cognitive effort to thermodynamic principles, optimizing decision-making.
Study reveals AI's spontaneous topic changes in text prediction.
Model compresses event-like contexts using gated surprise signals.
Develops an inverse particle filter for cognitive systems.
Understanding the functional architecture of the brain in terms of networks is becoming increasingly common. In most fMRI applications functional networks are assumed to be stationary, resulting in a single network estimated for the entire time course. However recent results suggest that the connectivity between brain …
Model predicts cognitive health risks based on smartphone usage patterns.
Model shows AI adoption amplifies financial market risk through prediction, herding, and cognitive dependency.
The paper proposes a new framework to generate synthetic data with human-like imperfections to prevent model collapse.
A cognitive model of human learning provides information about skills a learner must acquire to perform accurately in a task domain. Cognitive models of learning are not only of scientific interest, but are also valuable in adaptive online tutoring systems. A more accurate model yields more effective tutoring through b…
Extracts causal brain dynamics across multiple scales.
Two key challenges in underlay dynamic spectrum access (DSA) are how to establish an interference limit from the primary network (PN) and how cognitive radios (CRs) in the secondary network (SN) become aware of the interference they create on the PN, especially when there is no exchange of information between the two n…
Adaptive learning, also known as adaptive teaching, relies on learning path recommendation, which sequentially recommends personalized learning items (e.g., lectures, exercises) to satisfy the unique needs of each learner. Although it is well known that modeling the cognitive structure including knowledge level of lear…
Method identifies dual cognitive system interactions using eye-tracking data.
New method identifies latent variables in cognitive models using neural networks.
Cognitive neuroscience is enjoying rapid increase in extensive public brain-imaging datasets. It opens the door to large-scale statistical models. Finding a unified perspective for all available data calls for scalable and automated solutions to an old challenge: how to aggregate heterogeneous information on brain func…
Conventional modeling approaches have found limitations in matching the increasingly detailed neural network structures and dynamics recorded in experiments to the diverse brain functionalities. On another approach, studies have demonstrated to train spiking neural networks for simple functions using supervised learnin…
A novel approach of training data augmentation and domain adaptation is presented to support machine learning applications for cognitive radio. Machine learning provides effective tools to automate cognitive radio functionalities by reliably extracting and learning intrinsic spectrum dynamics. However, there are two im…
Study shows cognitive load impacts financial market efficiency, especially for less sophisticated investors.
Functional Magnetic Resonance Imaging (fMRI) provides dynamical access into the complex functioning of the human brain, detailing the hemodynamic activity of thousands of voxels during hundreds of sequential time points. One approach towards illuminating the connection between fMRI and cognitive function is through dec…
While the interpretability of machine learning models is often equated with their mere syntactic comprehensibility, we think that interpretability goes beyond that, and that human interpretability should also be investigated from the point of view of cognitive science. The goal of this paper is to discuss to what exten…
The paper investigates how ambiguous data and cognitive biases affect machine learning in humanitarian decision making.
Develops methods for finding counterfactual explanations in sequential decision making.
New methods make Bayesian inference feasible for complex cognitive models.
This paper presents a novel deep reinforcement learning-based resource allocation technique for the multi-agent environment presented by a cognitive radio network that coexists through underlay dynamic spectrum access (DSA) with a primary network. The resource allocation technique presented in this work is distributed,…
Study improves robustness of Bayesian inference for cognitive models.
CogFormer trains a transformer to estimate parameters across various cognitive models.
New filters improve radar target inference in complex scenarios.
Pension benefits in rural China lead to cognitive decline among the elderly.
This paper improves radar performance against jammers using RL.
Fan tokens surged before World Cup matches, but declined during them, revealing cognitive biases.
The mathematical model proposed by George Soros for his theory of reflexivity is analyzed under the framework of discrete dynamical systems. We show the importance of the notion of fixed points for explaining the behavior of a reflexive system governed by its cognitive and manipulative functions. The interrelationship …
Diffusion models explained via cognitive science.
Framework enhances AI explainability by aligning with human cognitive models.
In the eyes of a rationalist like Descartes or Spinoza, human reasoning is flawless, marching toward uncovering ultimate truth. A few centuries later, however, culminating in the work of Kahneman and Tversky, human reasoning was portrayed as anything but flawless, filled with numerous misjudgments, biases, and cognitiv…
Model place cells as spatial embeddings for efficient path planning and cognitive map construction.
New method learns dynamic brain communication patterns across regions.
Deep learning ensemble improves Alzheimer vs. Mild Cognitive Impairment diagnosis.
In cognitive psychology, automatic and self-reinforcing irrational thought patterns are known as cognitive distortions. Left unchecked, patients exhibiting these types of thoughts can become stuck in negative feedback loops of unhealthy thinking, leading to inaccurate perceptions of reality commonly associated with anx…
Human decision-making underlies all economic behavior. For the past four decades, human decision-making under uncertainty has continued to be explained by theoretical models based on prospect theory, a framework that was awarded the Nobel Prize in Economic Sciences. However, theoretical models of this kind have develop…
New technologies for recording the activity of large neural populations during complex behavior provide exciting opportunities for investigating the neural computations that underlie perception, cognition, and decision-making. Nonlinear state space models provide an interpretable signal processing framework by combinin…
Neuromorphic column performs online unsupervised clustering.
Product Kanerva Machines dynamically combine smaller models for better memory organization.
Transparency, user trust, and human comprehension are popular ethical motivations for interpretable machine learning. In support of these goals, researchers evaluate model explanation performance using humans and real world applications. This alone presents a challenge in many areas of artificial intelligence. In this …
UniPhyNet improves cognitive load classification accuracy using EEG, ECG, and EDA signals.
A relatively recent advance in cognitive neuroscience has been multi-voxel pattern analysis (MVPA), which enables researchers to decode brain states and/or the type of information represented in the brain during a cognitive operation. MVPA methods utilize machine learning algorithms to distinguish among types of inform…
An important problem for HCI researchers is to estimate the parameter values of a cognitive model from behavioral data. This is a difficult problem, because of the substantial complexity and variety in human behavioral strategies. We report an investigation into a new approach using approximate Bayesian computation (AB…
Develops a new method to analyze brain networks for cognitive traits.