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A Lineage Explanation of Human Normative Guidance: The Coadaptive Model of Instrumental Rationality and Shared Intentionality

Gonzalez-Cabrera, Ivan (2022) A Lineage Explanation of Human Normative Guidance: The Coadaptive Model of Instrumental Rationality and Shared Intentionality. [Preprint]

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Abstract

This paper aims to contribute to the existing literature on normative cognition by providing a lineage explanation of human social norm psychology. This approach builds upon theories of goal-directed behavioral control in the reinforcement learning and control literature, arguing that this form of control defines an important class of intentional normative mental states that are instrumental in nature. I defend the view that great ape capacities for instrumental reasoning and our capacity (or family of capacities) for shared intentionality coadapted to each other and argue that the evolution of this capacity has allowed the representation of social norms and the emergence of our capacity for normative guidance.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Gonzalez-Cabrera, Ivanivan.gonzalezcabrera@philo.unibe.ch0000-0002-6523-5471
Keywords: Coadaptation; Conceptual space; Lineage explanation; Goal-directed behavioral control; Reinforcement learning; Social norm psychology
Subjects: Specific Sciences > Anthropology
Specific Sciences > Psychology > Evolutionary Psychology
Specific Sciences > Cognitive Science
Specific Sciences > Psychology > Comparative Psychology and Ethology
Specific Sciences > Cognitive Science > Concepts and Representations
Specific Sciences > Artificial Intelligence > Machine Learning
Specific Sciences > Psychology
Depositing User: Dr. Ivan Gonzalez-Cabrera
Date Deposited: 31 Oct 2022 17:54
Last Modified: 31 Oct 2022 17:54
Item ID: 21336
Subjects: Specific Sciences > Anthropology
Specific Sciences > Psychology > Evolutionary Psychology
Specific Sciences > Cognitive Science
Specific Sciences > Psychology > Comparative Psychology and Ethology
Specific Sciences > Cognitive Science > Concepts and Representations
Specific Sciences > Artificial Intelligence > Machine Learning
Specific Sciences > Psychology
Date: 28 September 2022
URI: https://philsci-archive.pitt.edu/id/eprint/21336

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