Cognitive Assessment Estimation from Behavioral Responses in Emotional Faces Evaluation Task -- AI Regression Approach for Dementia Onset Prediction in Aging Societies

arXiv.org Machine Learning

We present a practical health-theme machine learning (ML) application concerning `AI for social good' domain for `Producing Good Outcomes' track. In particular, the solution is concerning the problem of a potential elderly adult dementia onset prediction in aging societies. The paper discusses our attempt and encouraging preliminary study results of behavioral responses analysis in a working memory-based emotional evaluation experiment. We focus on the development of digital biomarkers for dementia progress detection and monitoring. We present a behavioral data collection concept for a subsequent AI-based application together with a range of regression encouraging results of Montreal Cognitive Assessment (MoCA) scores in the leave-one-subject-out cross-validation setup. The regressor input variables include experimental subject's emotional valence and arousal recognition responses, as well as reaction times, together with self-reported education levels and ages, obtained from a group of twenty older adults taking part in the reported data collection project. The presented results showcase the potential social benefits of artificial intelligence application for elderly and establish a step forward to develop ML approaches, for the subsequent application of simple behavioral objective testing for dementia onset diagnostics replacing subjective MoCA.


A rational decision making framework for inhibitory control

Neural Information Processing Systems

Intelligent agents are often faced with the need to choose actions with uncertain consequences, and to modify those actions according to ongoing sensory processing and changing task demands. The requisite ability to dynamically modify or cancel planned actions is known as inhibitory control in psychology. We formalize inhibitory control as a rational decision-making problem, and apply to it to the classical stop-signal task. Using Bayesian inference and stochastic control tools, we show that the optimal policy systematically depends on various parameters of the problem, such as the relative costs of different action choices, the noise level of sensory inputs, and the dynamics of changing environmental demands. Our normative model accounts for a range of behavioral data in humans and animals in the stop-signal task, suggesting that the brain implements statistically optimal, dynamically adaptive, and reward-sensitive decision-making in the context of inhibitory control problems.


Solving Large Scale Phylogenetic Problems using DCM2

AAAI Conferences

Tandy J. Warnow Department of Computer Science University of Arizona Tucson AZ USA email: tandy cs, arizona, edu Abstract In an earlier paper, we described a new method for phylogenetic tree reconstruction called the Disk Covering Method, or DCM. This is a general method which can be used with an)' existing phylogenetic method in order to improve its performance, lCre showed analytically and experimentally that when DCM is used in conjunction with polynomial time distance-based methods, it improves the accuracy of the trees reconstructed. In this paper, we discuss a variant on DCM, that we call DCM2. DCM2 is designed to be used with phylogenetic methods whose objective is the solution of NPhard optimization problems. We also motivate the need for solutions to NPhard optimization problems by showing that on some very large and important datasets, the most popular (and presumably best performing) polynomial time distance methods have poor accuracy. Introduction 118 HUSON The accurate recovery of the phylogenetic branching order from molecular sequence data is fundamental to many problems in biology. Multiple sequence alignment, gene function prediction, protein structure, and drug design all depend on phylogenetic inference. Although many methods exist for the inference of phylogenetic trees, biologists who specialize in systematics typically compute Maximum Parsimony (MP) or Maximum Likelihood (ML) trees because they are thought to be the best predictors of accurate branching order. Unfortunately, MP and ML optimization problems are NPhard, and typical heuristics use hill-climbing techniques to search through an exponentially large space. When large numbers of taxa are involved, the computational cost of MP and ML methods is so great that it may take years of computation for a local minimum to be obtained on a single dataset (Chase et al. 1993; Rice, Donoghue, & Olmstead 1997). It is because of this computational cost that many biologists resort to distance-based calculations, such as Neighbor-Joining (NJ) (Saitou & Nei 1987), even though these may poor accuracy when the diameter of the tree is large (Huson et al. 1998). As DNA sequencing methods advance, large, divergent, biological datasets are becoming commonplace. For example, the February, 1999 issue of Molecular Biology and Evolution contained five distinct datascts of more than 50 taxa, and two others that had been pruned below that.


Reinforcement Learning Models of Human Behavior: Reward Processing in Mental Disorders

arXiv.org Artificial Intelligence

For AI community, the development of agents that react differently to different types of rewards can enable us to understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems. Empirically, the proposed model outperforms Q-Learning and Double Q-Learning in artificial scenarios with certain reward distributions and real-world human decision making gambling tasks. Moreover, from the behavioral modeling perspective, our parametric framework can be viewed as a first step towards a unifying computational model capturing reward processing abnormalities across multiple mental conditions and user preferences in long-term recommendation systems.


Stable specification search in structural equation model with latent variables

arXiv.org Machine Learning

In our previous study, we introduced stable specification search for cross-sectional data (S3C). It is an exploratory causal method that combines stability selection concept and multi-objective optimization to search for stable and parsimonious causal structures across the entire range of model complexities. In this study, we extended S3C to S3C-Latent, to model causal relations between latent variables. We evaluated S3C-Latent on simulated data and compared the results to those of PC-MIMBuild, an extension of the PC algorithm, the state-of-the-art causal discovery method. The comparison showed that S3C-Latent achieved better performance. We also applied S3C-Latent to real-world data of children with attention deficit/hyperactivity disorder and data about measuring mental abilities among pupils. The results are consistent with those of previous studies.