AIMC Topic: Models, Psychological

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Predicting one-year outcome in first episode psychosis using machine learning.

PloS one
BACKGROUND: Early illness course correlates with long-term outcome in psychosis. Accurate prediction could allow more focused intervention. Earlier intervention corresponds to significantly better symptomatic and functional outcomes. Our study object...

Estimating Scale-Invariant Future in Continuous Time.

Neural computation
Natural learners must compute an estimate of future outcomes that follow from a stimulus in continuous time. Widely used reinforcement learning algorithms discretize continuous time and estimate either transition functions from one step to the next (...

A model of event knowledge.

Psychological review
Our knowledge of events and situations in the world plays a critical role in our ability to understand what is happening around us, to predict what might happen next, and to comprehend language. What has not been so clear is the form and structure of...

Learning from data to predict future symptoms of oncology patients.

PloS one
Effective symptom management is a critical component of cancer treatment. Computational tools that predict the course and severity of these symptoms have the potential to assist oncology clinicians to personalize the patient's treatment regimen more ...

What is a cognitive map? Unravelling its mystery using robots.

Cognitive processing
Despite years of research into cognitive mapping, the process remains controversial and little understood. A computational theory of cognitive mapping is needed, but developing it is difficult due to the lack of a clear interpretation of the empirica...

The stability of memristive multidirectional associative memory neural networks with time-varying delays in the leakage terms via sampled-data control.

PloS one
In this paper, we propose a new model of memristive multidirectional associative memory neural networks, which concludes the time-varying delays in leakage terms via sampled-data control. We use the input delay method to turn the sampling system into...

Bio-inspired visual self-localization in real world scenarios using Slow Feature Analysis.

PloS one
We present a biologically motivated model for visual self-localization which extracts a spatial representation of the environment directly from high dimensional image data by employing a single unsupervised learning rule. The resulting representation...

Dynamics of brain activity reveal a unitary recognition signal.

Journal of experimental psychology. Learning, memory, and cognition
Dual-process models of recognition memory typically assume that independent familiarity and recollection signals with distinct temporal profiles can each lead to recognition (enabling 2 routes to recognition), whereas single-process models posit a un...

Emergent Solutions to High-Dimensional Multitask Reinforcement Learning.

Evolutionary computation
Algorithms that learn through environmental interaction and delayed rewards, or reinforcement learning (RL), increasingly face the challenge of scaling to dynamic, high-dimensional, and partially observable environments. Significant attention is bein...

Determining Anxiety in Obsessive Compulsive Disorder through Behavioural Clustering and Variations in Repetition Intensity.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Over the last decade, the application of computer vision techniques to the analysis of behavioural patterns has seen a considerable increase in research interest. One such interesting and recent application is the visual be...