Mental state dynamics explain the origin of mental state concepts

Mapping Intimacies ◽

10.31234/osf.io/kbcsj ◽

2020 ◽

Author(s):

Mark Allen Thornton ◽

Milena Rmus ◽

Diana Tamir

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Mental State ◽

Mental States ◽

Social World ◽

The Social ◽

Artificial Neural ◽

Ebb And Flow

People’s thoughts and feelings ebb and flow in predictable ways: surprise arises quickly, anticipation ramps up slowly, regret follows anger, love begets happiness, and so forth. Predicting these transitions between mental states can help people successfully navigate the social world. We hypothesize that the goal of predicting state dynamics shapes people’ mental state concepts. Across seven studies, when people observed more frequent transitions between a pair of novel mental states, they judged those states to be more conceptually similar to each other. In an eighth study, an artificial neural network trained to predict real human mental state dynamics spontaneously learned the same conceptual dimensions that people use to understand these states: the 3d Mind Model. Together these results suggest that mental state dynamics explain the origins of mental state concepts.

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Exploratory Testing of an Artificial Neural Network Classification for Enhancement of the Social Vulnerability Index

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi4041774 ◽

2015 ◽

Vol 4 (4) ◽

pp. 1774-1790 ◽

Cited By ~ 6

Author(s):

Ryan Hile ◽

Thomas Cova

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Social Vulnerability ◽

Vulnerability Index ◽

Social Vulnerability Index ◽

Neural Network Classification ◽

Exploratory Testing ◽

The Social ◽

Artificial Neural

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An Ant-Lion Optimizer-Trained Artificial Neural Network System for Chaotic Electroencephalogram (EEG) Prediction

Applied Sciences ◽

10.3390/app8091613 ◽

2018 ◽

Vol 8 (9) ◽

pp. 1613 ◽

Cited By ~ 17

Author(s):

Utku Kose

Keyword(s):

Neural Network ◽

Time Series ◽

Artificial Neural Network ◽

Time Series Prediction ◽

Optimization Technique ◽

Mental States ◽

Target Time ◽

Ant Lion Optimizer ◽

Ant Lion ◽

Artificial Neural

The prediction of future events based on available time series measurements is a relevant research area specifically for healthcare, such as prognostics and assessments of intervention applications. A measure of brain dynamics, electroencephalogram time series, are routinely analyzed to obtain information about current, as well as future, mental states, and to detect and diagnose diseases or environmental factors. Due to their chaotic nature, electroencephalogram time series require specialized techniques for effective prediction. The objective of this study was to introduce a hybrid system developed by artificial intelligence techniques to deal with electroencephalogram time series. Both artificial neural networks and the ant-lion optimizer, which is a recent intelligent optimization technique, were employed to comprehend the related system and perform some prediction applications over electroencephalogram time series. According to the obtained findings, the system can successfully predict the future states of target time series and it even outperforms some other hybrid artificial neural network-based systems and alternative time series prediction approaches from the literature.

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Abstract Computation in Schizophrenia Detection through Artificial Neural Network Based Systems

The Scientific World JOURNAL ◽

10.1155/2015/467178 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 5

Author(s):

L. Cardoso ◽

F. Marins ◽

R. Magalhães ◽

N. Marins ◽

T. Oliveira ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Knowledge Representation ◽

Mental States ◽

Knowledge Representation And Reasoning ◽

Novel Approach ◽

Large Period ◽

Artificial Neural ◽

Diagnostic Support

Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that is, it may lead to unreliable perception, not suitable actions and feelings, and a sense of mental fragmentation. Indeed, its diagnosis is done over a large period of time; continuos signs of the disturbance persist for at least 6 (six) months. Once detected, the psychiatrist diagnosis is made through the clinical interview and a series of psychic tests, addressed mainly to avoid the diagnosis of other mental states or diseases. Undeniably, the main problem with identifying schizophrenia is the difficulty to distinguish its symptoms from those associated to different untidiness or roles. Therefore, this work will focus on the development of a diagnostic support system, in terms of its knowledge representation and reasoning procedures, based on a blended of Logic Programming and Artificial Neural Networks approaches to computing, taking advantage of a novel approach to knowledge representation and reasoning, which aims to solve the problems associated in the handling (i.e., to stand for and reason) of defective information.

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