Brain-inspired model for decision-making in the selection of beneficial information patterns among signals received by an unpredictable information-development environment

Asgari, Alireza; Beauregard, Yvan

doi:10.31224/osf.io/5rq6e

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…perception, memory, attention) [21,32,53,61,126]. Under these circumstances, the goal is often the creation of a model of behavior which fits the cognitive/neurological dynamics of the human brain [2,5,61,108,119,123,130,134].…”

Section: Principles and Definitionsmentioning

confidence: 99%

“…For instance, researchers may try to replicate the neurological pathways in an attempt to replicate the neuro-physical process underpinning reasoning [5], or they may instead attempt to generate a computational model which is meant to mimic heuristically biased behavior [71]. In any case, a common aspect is the desire to use humans as the template for desirable patterns of reasoning.…”

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Modeling Human Behavior Part II -- Cognitive approaches and Uncertainty

Fuchs¹,

Passarella²,

Conti³

2022

Preprint

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As we discussed in Part I of this topic [30], there is a clear desire to model and comprehend human behavior. Given the popular presupposition of human reasoning as the standard for learning and decision-making, there have been significant efforts and a growing trend in research to replicate these innate human abilities in artificial systems. In Part I, we discussed learning methods which generate a model of behavior from exploration of the system and feedback based on the exhibited behavior as well as topics relating to the use of or accounting for beliefs with respect to applicable skills or mental states of others. In this work, we will continue the discussion from the perspective of methods which focus on the assumed cognitive abilities, limitations, and biases demonstrated in human reasoning. We will arrange these topics as follows (i) methods such as cognitive architectures, cognitive heuristics, and related which demonstrate assumptions of limitations on cognitive resources and how that impacts decisions and (ii) methods which generate and utilize representations of bias or uncertainty to model human decision-making or the future outcomes of decisions.CCS Concepts: • Human-centered computing; • General and reference → Surveys and overviews;

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Section: Principles and Definitionsmentioning

confidence: 99%

mentioning

confidence: 99%

Modeling Human Behavior Part II -- Cognitive approaches and Uncertainty

Fuchs¹,

Passarella²,

Conti³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…A key distinction between the goals and approaches is often the fidelity of the replication and the expected deployment case. For instance, researchers may try to replicate the neurological pathways in an attempt to replicate the neurophysical process underpinning reasoning [8], or they may instead attempt to generate a computational model which is meant to mimic heuristically biased behavior [62]. In any case, a common aspect is the desire to use humans as the template for desirable patterns of reasoning.…”

Section: Introductionmentioning

confidence: 99%

Modeling Human Behavior Part I -- Learning and Belief Approaches

Fuchs¹,

Passarella²,

Conti³

2022

Preprint

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There is a clear desire to model and comprehend human behavior. Trends in research covering this topic show a clear assumption that many view human reasoning as the presupposed standard in artificial reasoning. As such, topics such as game theory, theory of mind, machine learning, etc. all integrate concepts which are assumed components of human reasoning. These serve as techniques to attempt to both replicate and understand the behaviors of humans. In addition, next generation autonomous and adaptive systems will largely include AI agents and humans working together as teams. To make this possible, autonomous agents will require the ability to embed practical models of human behavior, which allow them not only to replicate human models as a technique to "learn", but to to understand the actions of users and anticipate their behavior, so as to truly operate in symbiosis with them. The main objective of this paper it to provide a succinct yet systematic review of the most important approaches in two areas dealing with quantitative models of human behaviors. Specifically, we focus on (i) techniques which learn a model or policy of behavior through exploration and feedback, such as Reinforcement Learning, and (ii) directly model mechanisms of human reasoning, such as beliefs and bias, without going necessarily learning via trial-and-error.CCS Concepts: • Human-centered computing; • General and reference → Surveys and overviews;

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Modeling, Replicating, and Predicting Human Behavior: A Survey

Fuchs

Passarella

Conti

2023

ACM Trans. Auton. Adapt. Syst.

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Given the popular presupposition of human reasoning as the standard for learning and decision-making, there have been significant efforts and a growing trend in research to replicate these innate human abilities in artificial systems. As such, topics including Game Theory, Theory of Mind, Machine Learning, etc. integrate concepts which are assumed components of human reasoning. These serve as techniques to replicate and understand the behaviors of humans. In addition, next generation autonomous and adaptive systems will largely include AI agents and humans working together as teams. To make this possible, autonomous agents will require the ability to embed practical models of human behavior, allowing them not only to replicate human models as a technique to “learn”, but to understand the actions of users and anticipate their behavior, so as to truly operate in symbiosis with them. The main objective of this paper is to provide a succinct yet systematic review of important approaches in two areas dealing with quantitative models of human behaviors. Specifically, we focus on (i) techniques which learn a model or policy of behavior through exploration and feedback, such as Reinforcement Learning, and (ii) directly model mechanisms of human reasoning, such as beliefs and bias, without necessarily learning via trial-and-error.

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Brain-inspired model for decision-making in the selection of beneficial information patterns among signals received by an unpredictable information-development environment

Cited by 4 publications

References 20 publications

Modeling Human Behavior Part II -- Cognitive approaches and Uncertainty

Modeling Human Behavior Part II -- Cognitive approaches and Uncertainty

Modeling Human Behavior Part I -- Learning and Belief Approaches

Modeling, Replicating, and Predicting Human Behavior: A Survey

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