Simple Aesthetic Sense and Addiction Emerge in Neural Relations of Cost-Benefit Decision in Foraging

Abstract

A rudimentary aesthetic sense is found in the stimulus valuations and cost-benefit decisions made by primitive generalist foragers. These are based on factors governing personal economic decisions: incentive, appetite, and learning. We find that the addictive process is an extreme expression of aesthetic dynamics. An interactive, agent-based model, ASIMOV, reproduces a simple aesthetic sense from known neural relations of cost-benefit decision in foraging. In the presence of very high reward, an addiction-like process emerges. A drug-like prey provides extreme reward with no nutritive value, initiating high selectivity and prolonged cravings for drug through reward learning. Varying reward experience, caused by homeostatic changes in the neural circuitry of reward, further establishes the course of addiction, consisting of desensitization, withdrawal, resensitization, and associated changes in nutritional choice and pain sensitivity. These observations are consistent with the early evolution of addiction mechanisms in simple generalist foragers as an aesthetic sense for evaluating prey. ASIMOV is accessible to inspection, modification, and experiment, is adaptable as an educational tool, and provides insight on the possible coevolutionary origins of aesthetics and the addiction process. The aesthetic sense is a subjective, evaluative faculty used to distinguish positive and negative qualities of situations, objects, and constructs, and to bias behavioral decision toward or away from those stimuli. It is based on built-in preferences and feature detection, as well as learned preferences established from experience through reward learning. In humans, the highly developed aesthetic sense extends from judgements of taste and beauty to disgust. In other animals, it notably functions in mate choice, nest building, and foraging. Darwin and others 1,2 attributed the origin of the aesthetic sense to mate choice and reproductive displays, as are notable in many vertebrates. However, here we explore the ramifications of a potentially earlier origin in the foraging decisions of generalist animal species, where valuations of potential prey are made in estimates of nutritional value that factor in need, learned attributes, and risk. A primitive basis of the aesthetic sense appeared in our studies of the neuronal circuitry of decision in the predatory, generalist sea slug, Pleurobranchaea californica, in the animal's ability to evaluate stimuli in contexts of motivation and reward learning. It was initially implemented in an agent-based foraging simulation, Cyberslug 3. That agent made foraging decisions for approach or avoidance like the real animal, based on stimulus quality, motivation, and reward learning, and satisfied requirements of optimized foraging. We introduced the original simulation as an example of simple neuronal relations that could be elaborated for more complex cognition and behavior, as may have happened to ancestral bilaterians in evolution. Accordingly, here we introduce a new version, ASIMOV, which is upg...

Cognitive Mapping and Episodic Memory Emerge From Simple Associative Learning Rules

“…The FAM implements an associative memory system in the agent-based model, ASIMOV 7 (Algorithm of Selectivity by Incentive, Motivation and Optimized Valuation).…”

“…We joined the FAM to a goal-directed artificial agent, the ASIMOV forager 7 , which is built with enhanced evaluative abilities onto the Cyberslug model of decision-making in foraging 8 . The FAM allows the ASIMOV agent to learn landmark features and rewarding items in its spatial environment, reproducing a potential early phase in the evolution of simpler episodic memory.…”

Cognitive Mapping and Episodic Memory Emerge From Simple Associative Learning Rules

“…Thus, the FAM enables memorization and retrieval of sequences of presented sensory cues, particularly when reinforced by reward input. We joined the FAM to a goal-directed artificial agent, the ASIMOV (Algorithm of Selectivity by Incentive, Motivation and Optimized Valuation) forager [7], which is built with enhanced evaluative abilities onto the Cyberslug model of decision-making in foraging [8].…”

“…The FAM implements an associative memory system in the agent-based model, ASIMOV [7], replacing an earlier Rescorla-Wagner learning algorithm (Fig. 3).…”

Does Amount of Information Support Aesthetic Values?

“…Most of us have noticed how we are addicted to information through the internet ( Widyanto and McMurran, 2004 ; Ferraro et al, 2006 ; Byun et al, 2009 ), social media ( Van den Eijnden et al, 2016 ; Blackwell et al, 2017 ), and our smartphones ( Kwon et al, 2013 ). Addictive and aesthetic gratifications are linked ( Mathis, 2015 ; Mathis and Han, 2017 ; Gribkova et al, 2020 ) through common neural pathways ( Adinoff, 2004 ; Esch and Stefano, 2004 ; Naqvi and Bechara, 2010 ), with the connection apparently extending to the realm of information ( Chou et al, 1998 ; Chou and Hsiao, 2000 ; Song et al, 2004 ). Addiction to information in the modern world may have a link to the exploration versus exploitation dilemma ( Gupta et al, 2006 ; Dayan and Daw, 2008 ; Laureiro-Martínez et al, 2015 ).…”