Sensing the Mood-Application of Machine Learning in Human Psychology Analysis and Cognitive Science

2021 ◽  
pp. 101-114
Author(s):  
Ahona Ghosh ◽  
Sriparna Saha
Keyword(s):  
Machine Learning ◽  
Cognitive Science ◽  
Human Psychology

scholarly journals The Wisdom of the Market: Using Human Factors to Design Prediction Markets for Collective Intelligence

2019 ◽  
Vol 63 (1) ◽  
pp. 1471-1475
Author(s):  
Lorenzo Barberis Canonico ◽  
Christopher Flathmann ◽  
Nathan McNeese
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Game Theory ◽  
Human Factors ◽  
Cognitive Science ◽  
Collective Intelligence ◽  
Human Intelligence ◽  
Prediction Markets ◽  
New Model ◽  
Large Groups

There is an ever-growing literature on the power of prediction markets to harness “the wisdom of the crowd” from large groups of people. However, traditional prediction markets are not designed in a human-centered way, often restricting their own potential. This creates the opportunity to implement a cognitive science perspective on how to enhance the collective intelligence of the participants. Thus, we propose a new model for prediction markets that integrates human factors, cognitive science, game theory and machine learning to maximize collective intelligence. We do this by first identifying the connections between prediction markets and collective intelligence, to then use human factors techniques to analyze our design, culminating in the practical ways with which our design enables artificial intelligence to complement human intelligence.

scholarly journals Natural Morphological Computation as Foundation of Learning to Learn in Humans, Other Living Organisms, and Intelligent Machines

Philosophies ◽  
2020 ◽  
Vol 5 (3) ◽  
pp. 17 ◽  
Author(s):  
Gordana Dodig-Crnkovic
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Cognitive Science ◽  
Philosophy Of Mind ◽  
Computational Models ◽  
Common Ground ◽  
Natural Philosophy ◽  
Learning To Learn ◽  
Natural Systems ◽  
Integrative View

The emerging contemporary natural philosophy provides a common ground for the integrative view of the natural, the artificial, and the human-social knowledge and practices. Learning process is central for acquiring, maintaining, and managing knowledge, both theoretical and practical. This paper explores the relationships between the present advances in understanding of learning in the sciences of the artificial (deep learning, robotics), natural sciences (neuroscience, cognitive science, biology), and philosophy (philosophy of computing, philosophy of mind, natural philosophy). The question is, what at this stage of the development the inspiration from nature, specifically its computational models such as info-computation through morphological computing, can contribute to machine learning and artificial intelligence, and how much on the other hand models and experiments in machine learning and robotics can motivate, justify, and inform research in computational cognitive science, neurosciences, and computing nature. We propose that one contribution can be understanding of the mechanisms of ‘learning to learn’, as a step towards deep learning with symbolic layer of computation/information processing in a framework linking connectionism with symbolism. As all natural systems possessing intelligence are cognitive systems, we describe the evolutionary arguments for the necessity of learning to learn for a system to reach human-level intelligence through evolution and development. The paper thus presents a contribution to the epistemology of the contemporary philosophy of nature.

scholarly journals What do RL Models Measure? Interpreting Model Parameters in Cognition and Neuroscience

2021 ◽  
Author(s):  
Maria Eckstein ◽  
Linda Wilbrecht ◽  
Anne Collins
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Cognitive Science ◽  
Empirical Evidence ◽  
Cognitive Processes ◽  
Future Research ◽  
Model Parameters ◽  
Research Needs ◽  
Research Fields ◽  
Computational Research

Reinforcement learning (RL) is a concept that has been invaluable to research fields including machine learning, neuroscience, and cognitive science. However, what RL entails partly differs between fields, leading to difficulties when interpreting and translating findings.This paper lays out these differences and zooms in on cognitive (neuro)science, revealing that we often overinterpret RL modeling results, with severe consequences for future research. Specifically, researchers often assume---implicitly---that model parameters \textit{generalize} between tasks, models, and participant populations, despite overwhelming negative empirical evidence for this assumption. We also often assume that parameters measure specific, unique, and meaningful (neuro)cognitive processes, a concept we call \textit{interpretability}, for which empirical evidence is also lacking. We conclude that future computational research needs to pay increased attention to these implicit assumptions when using RL models, and suggest an alternative framework that resolves these issues and allows us to unleash the potential of RL in cognitive (neuro)science.

Case-based reasoning foundations

2005 ◽  
Vol 20 (3) ◽  
pp. 203-207 ◽  
Author(s):  
MICHAEL M. RICHTER ◽  
AGNAR AAMODT
Keyword(s):  
Machine Learning ◽  
Knowledge Representation ◽  
Cognitive Science ◽  
Case Based Reasoning ◽  
Science Knowledge ◽  
Processing Machine ◽  
And Mathematics ◽  
Case Based

A basic observation is that case-based reasoning has roots in different disciplines: cognitive science, knowledge representation and processing, machine learning and mathematics. As a consequence, there are foundational aspects from each of these areas. We briefly discuss them and comment on the relations between these types of foundations.

Aesthetics and Cognitive Science

2009 ◽  
Author(s):  
Gregory Currie
Keyword(s):  
Social Psychology ◽  
Natural History ◽  
Cognitive Science ◽  
Philosophy Of Mind ◽  
Developmental Psychology ◽  
Moral Character ◽  
Human Psychology ◽  
Human Motivation ◽  
History Of ◽  
The Subject

The subject of this article is the connection between art and all those aspects of mind that have, to some degree, an empirical side. It covers results in neuropsychology and neuroscience, in cognitive and developmental psychology, as well as in various parts of the philosophy of mind. This article, however, ignores questions about the natural history of our mental capacities. To the extent that art has human psychology as its subject, there must be potential for conflict with the sciences of mind. As philosophers have recently noted, results in social psychology challenge our ordinary conception of human motivation, suggesting that moral character either does not exist at all or plays an insignificant role in shaping behaviour.

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