Cognitive model discovery framework for ill-structured domains.
problem Discover accurate cognitive models without student data or human knowledge.
method Cognitive Representation Learner (CogRL) framework for ill-structured domains.
result CogRL representations can accurately discover cognitive models in ill-structured domains.
This paper tackles the interpretability gap between deep learning models and human cognition.
problem The gap between deep learning models and human cognitive competence.
method A universal learning framework is proposed to evaluate and improve model interpretability.
result The uniqueness of solution and conditions for unique solution are proved.
Unified cognitive system from diverse fMRI studies using neural representations.
problem Aggregate heterogeneous brain function data into a universal cognitive system.
method Multi-task learning and multi-scale dimension reduction to learn low-dimensional cognitive representations.
result Achieves best prediction performance on large reference datasets and small datasets.
Pension benefits in rural China lead to cognitive decline among the elderly.
problem Cognitive decline in late adulthood among rural Chinese elderly.
method Examined the effects of a new pension scheme on cognitive performance.
result Pension benefits negatively impact cognitive functioning, particularly delayed recall.
Deep learning predicts cognitive decline in MCI patients using brain imaging.
problem Identifying subjects at risk of rapid cognitive decline in mild cognitive impairment.
method Developed a deep convolutional neural network framework trained on baseline PET studies of AD and normal subjects.
result CNN-based approach accurately predicts conversion to Alzheimer's disease in MCI patients with high accuracy.
Predicts cognitive subscores over time in Alzheimer's Disease.
problem Predicting cognitive changes over time in Alzheimer's Disease.
method Convolutional Neural Network architecture for subscore prediction from MRI scans.
result Mean performance metrics are comparable to existing techniques.
Study identifies key MRI features for predicting cognitive performance after mTBI.
problem Identify relevant diffusion MRI metrics for cognitive functions in mTBI patients.
method Proposes a novel feature selection method combining best-first search with genetic algorithm crossover.
result Achieves significantly more accurate predictions than other feature selection algorithms.
New method identifies latent variables in cognitive models using neural networks.
problem Inference of latent variables in complex cognitive models is limited.
method Recurrent neural networks and simulation-based inference for latent variable sequences.
result Extends neural Bayes estimation to broader classes of cognitive models.
End-to-end framework classifies cognitive workload in real-time driving scenarios.
problem Challenging task of classifying human cognitive states from behavioral and physiological signals.
method End-to-end framework using mixture Hyper Long Short Term Memory Networks (HyperNetworks).
result Framework outperforms previous methods with 83.9% precision and 87.8% recall.
Cognitive psychology insights reveal shape bias in DNNs.
problem Interpreting modern deep neural networks (DNNs).
method Applied developmental psychology's word learning theory to DNNs.
result DNNs exhibit a shape bias similar to human word learning.
The study examines how board diversity and CSR committee composition affect corporate governance and financial performance.
problem The relationship between corporate social responsibility (CSR) and corporate governance.
method Theoretical model development based on management and corporate governance theories, focusing on board diversity and CSR committee composition.
result Cognitive and demographic characteristics of board members provide more insights into the link between corporate governance and CSR.
Experiment shows cognitive biases impact human-AI collaboration, highlighting the need for diverse evaluator samples.
problem Cognitive biases affect human-AI collaboration, leading to suboptimal outcomes.
method Randomized experiment with 2,784 participants, manipulating AI suggestion quality, task burden, and financial incentives.
result Individual attitudes toward AI are the strongest predictor of performance, influencing accuracy and overcorrection.
Deep reinforcement learning boosts throughput in RF-powered cognitive radio networks.
problem Maximizing throughput in large-scale, decentralized RF-powered cognitive radio networks.
method Proposes deep reinforcement learning to find optimal policies for network throughput maximization.
result Deep reinforcement learning outperforms existing techniques in large-scale RF-CRN environments.
Cognitive radar selects optimal antenna subarrays using deep learning.
problem Optimize radar antenna selection for cost and performance.
method Convolutional Neural Network (CNN) for multi-class classification.
result CNN provides 22% better classification performance and 72% more accurate DoA estimates.
New framework for designing cognitive experiments to infer latent cognitive mechanisms.
problem Designing optimal cognitive experiments for Bayesian inference of latent cognitive mechanisms.
method Formulated as a Bayesian Experimental Design (BED) problem, treating environments as design variables. Introduced an amortized Bayesian experimental design framework for efficient posterior inference and design evaluation.
result No single environment is uniformly optimal for all cognitive inference objectives, revealing trade-offs between expected information gain, posterior recoverability, and information efficiency.
New method combines brain imaging data from multiple studies to improve cognitive decoding.
problem Low statistical power in individual neuroimaging studies.
method A new methodology to analyze brain responses across tasks without a unified theoretical framework.
result Improves decoding performance for 80% of 35 functional-imaging studies.
Machine learning detects and classifies cognitive distortions in mental health texts.
problem Detect and classify cognitive distortions in mental health texts to improve treatment.
method Machine learning framework using crowdsourced and therapy dataset.
result Model achieved high F1 scores for detecting and classifying cognitive distortions.
The paper discusses challenges and proposed solutions for evaluating model explanations.
problem Challenges in evaluating model explanations for interpretability.
method Proposes a distinction between descriptive and persuasive explanations and discusses potential research directions.
result Evaluating model explanations using functional metrics may perpetuate cognitive bias.
New framework for designing cognitive experiments to infer latent cognitive mechanisms.
problem Designing cognitive experiments to infer latent cognitive mechanisms.
method Formulated as a Bayesian Experimental Design (BED) problem, treating the experimental environment as the design variable. Introduced an amortized Bayesian experimental design framework for efficient posterior inference and design evaluation.
result No single environment is uniformly optimal across cognitive inference objectives, revealing trade-offs between expected information gain, posterior recoverability, and information efficiency.
Personalized model improves ADAS-Cog13 cognitive score forecasting.
problem Improving accuracy in predicting cognitive changes in Alzheimer's Disease.
method Meta-Weighted Gaussian Process Experts (pGPE) model for personalized forecasting.
result Meta-weighting of expert models leads to significant improvements in forecasting accuracy.
Study improves robustness of Bayesian inference for cognitive models.
problem Outliers and contaminants affect parameter estimation in cognitive models.
method Robustness of parameter estimation using amortized Bayesian inference (ABI) with neural networks.
result Introducing contaminants from a Cauchy distribution increases robustness.
Model shows AI adoption amplifies financial market risk through prediction, herding, and cognitive dependency.
problem Systemic risk in financial markets due to AI adoption.
method Developed a unified model within an extended rational expectations framework, incorporating endogenous adoption, performative prediction, algorithmic herding, and cognitive dependency.
result Systemic risk multiplier grows superlinearly with AI penetration, implying tail-loss amplification of 18-54%.
Model predicts cognitive health risks based on smartphone usage patterns.
problem Identifying cognitive health risks through smartphone usage.
method Structured models of smartphone interactions analyzed over 12 weeks.
result AUROC of 0.79 in discriminating between healthy and symptomatic subjects.
The paper proposes a new framework to generate synthetic data with human-like imperfections to prevent model collapse.
problem Model collapse due to statistical optimization of synthetic data.
method Introduces Prompt-driven Cognitive Computing Framework (PMCSF) with Cognitive State Decoder (CSD) and Cognitive Text Encoder (CTE).
result The framework generates text with cognitive imperfections, reducing maximum drawdown and delivering defensive alpha.
Develops an inverse particle filter for cognitive systems.
problem Tracking cognitive adversaries in counter-adversarial applications.
method Global filtering approach using Monte Carlo methods and differentiable I-PF.
result Demonstrates convergence to optimal inverse filter and improved estimation performance.
Enactive learning shows agents can learn from their environment, but limited by action choices.
problem Learning and interaction of autonomous agents in complex environments.
method Simulation of artificial agents in maze environments, comparing enactive learning to classical reinforcement learning.
result Enactive agents can learn to avoid unfavorable interactions but performance is limited by action choices.
Deep learning ensemble improves Alzheimer vs. Mild Cognitive Impairment diagnosis.
problem Differentiating Alzheimer Disease from Mild Cognitive Impairment.
method Hybrid deep learning ensemble framework using MRI slices, pretrained models, and stacked ensemble learning.
result State-of-the-art accuracy (99.21%) for Alzheimer vs. Mild Cognitive Impairment classification.
The paper brings order to the diverse cognitive fallacies.
problem The proliferation of cognitive fallacies in human reasoning.
method Formally establishing implication relationships (IRs) between fallacies.
result A systematic approach to categorize and understand cognitive fallacies.
Study shows cognitive load impacts financial market efficiency, especially for less sophisticated investors.
problem Cognitive load's effect on financial market information processing.
method Developed a theoretical framework and tested it with exogenous disclosure complexity variation.
result Cognitive load significantly impairs price discovery, particularly for less sophisticated investors.
Paper proposes RL for efficient MCI diagnosis from dialogue data.
problem Efficiently diagnose MCI from conversational data with minimal interaction.
method Reinforcement learning framework trained on clinical trial transcripts.
result Significantly outperforms supervised learning approaches with minimal conversation turns.
This paper introduces MOCM for better fMRI analysis stability.
problem Stable performance of MVP models on new fMRI datasets.
method Integrated objective function and multi-objective optimization approach.
result Superior performance compared to other techniques.
Enhances prediction credibility for Alzheimer's conversion risk.
problem Lack of prediction credibility in machine learning for medical applications.
method Combines Ensemble learning with Conformal Predictors.
result Proposed approach outperforms standard ensemble methods.
Deep RL for uncoordinated cognitive radios finds near-optimal policies.
problem Resource allocation in uncoordinated cognitive radio networks.
method Distributed deep reinforcement learning algorithm.
result Algorithm converges to near-optimal policies in finite time.
Proposes a new neural network system for improved reasoning and learning.
problem Limited prediction horizon and lack of accurate learning algorithms in FCMs.
method Introduces Short-term Cognitive Networks with non-constricted weights and a nonsynaptic learning algorithm.
result Improves network performance and prediction accuracy without modifying existing weights.
This paper explores how cognitive biases impact human understanding of rule-based machine learning models.
problem The impact of cognitive biases on human interpretation of machine learning models.
method A review of 20 cognitive biases and their effects on human understanding of machine learning models.
result Cognitive biases significantly affect human understanding of interpretable machine learning models, particularly logical rules.
Imaging neuroscience links brain activation maps to behavior and cognition via correlational studies. Due to the nature of the individual experiments, based on eliciting neural response from a small number of stimuli, this link is incomplete, and unidirectional from the causal point of view. To come to conclusions on t…
The paper investigates how ambiguous data and cognitive biases affect machine learning in humanitarian decision making.
problem Ambiguous data and cognitive biases impact the interpretability of machine learning models in humanitarian decision making.
method The study will explore the effects of data ambiguity and cognitive biases on machine learning algorithms in humanitarian contexts.
result The research aims to uncover the specific ways in which ambiguous data and cognitive biases influence the interpretability of machine learning models in humanitarian decision making.
The paper proposes a new definition of machine learning that includes internal learning objectives.
problem The traditional definition of machine learning focuses on performance metrics.
method Introduces a 'model entropy function' to quantify internal learning efficiency and concept formation.
result Minimizing model entropy leads to concept formation in machine learning.
Global feature model improves early AD diagnosis accuracy.
problem Early diagnosis of mild cognitive impairment (MCI) using structural MRI images.
method Gaussian discriminant analysis (GDA) with dual high-dimensional decision spaces for global feature extraction.
result Achieved F1 score of 91.06% for MCI vs. CN group.
Fan tokens surged before World Cup matches, but declined during them, revealing cognitive biases.
problem Analyzing the impact of FIFA World Cup matches on fan tokens.
method Event study and intraday analysis of blockchain-based fan tokens.
result Fan tokens experienced a surge in returns six months before the World Cup, followed by a decline during the matches, revealing asymmetries in performance.
Modeling alignment as resource-limited cognitive processes, researchers derive performance bounds.
problem Systematic deviations in feedback-based alignment of large language models.
method Modeling alignment as a two-stage cascade UoHoY given S, with cognitive and total capacities. result Capacity-coupled Alignment Performance Interval derived from Fano and PAC-Bayes bounds.
Cognitive KR model learns new facts from few examples.
problem Inferring new facts from small KG datasets.
method CogKR combines summary and reasoning modules with cognitive science principles.
result Significantly outperforms previous models on one-shot KG reasoning.
This paper improves radar performance against jammers using RL.
problem Improving radar performance against jammers.
method Reinforcement Learning (RL) with Deep Q-Network (DQN) and Long Short Term Memory (LSTM) networks.
result Softmax operator improves RL algorithm performance.
New methods assess neural connectivity to predict cognitive performance.
problem Predict cognitive performance using neural connectivity.
method Two novel approaches for characterizing functional network connectivity from EEG data.
result Fused AUC results of 0.63/0.58/0.61 for digit recall failure and 0.58/0.59/0.54 for sentence recall failure.
QCML improves bond similarity learning in illiquid markets.
problem Improving similarity learning for illiquid corporate bonds.
method Quantum Cognition Machine Learning (QCML) for supervised distance metric learning.
result QCML outperforms classical tree-based models in high-yield markets.
A new method learns FCMs from noisy data quickly and robustly.
problem Learning large-scale FCMs with noisy data is time-consuming and lacks robustness.
method Transformed learning problem to convex optimization with least-squares and maximum entropy terms.
result The method is rapid, robust, and performs well on synthetic and real-world datasets.
Study links cognitive effort to thermodynamic principles, optimizing decision-making.
problem Understanding the energy cost of decision-making in living systems.
method Applied Landauer's principle to analyze belief dynamics in a two-armed bandit game.
result Cognitive energy cost correlates with optimal decision-making outcomes.
DeepLight uses LSTM to decode fMRI data, improving cognitive state analysis.
problem Challenges in analyzing neuroimaging data due to high dimensionality, low sample size, and complex dependency structure.
method Introduces DeepLight framework using LSTM-based DL models to process fMRI data sequences, adapts LRP for interpretability.
result DeepLight outperforms conventional fMRI analysis methods in decoding cognitive states and identifying brain regions.