Self-Emotion-Mediated Exploration in Artificial Intelligence Mirrors: Findings from Cognitive Psychology

TL;DR

This paper introduces a bio-inspired reinforcement learning framework where artificial agents develop autonomous exploration driven by simulated emotional states, mirroring human psychological responses to data observation.

Contribution

It presents a dual-module reinforcement framework that links emotional states to exploration, demonstrating causal relationships and aligning AI behavior with human psychological findings.

Findings

Agents show increased surprise and decreased pride during exploration.

Correlations of 0.461 for surprise and -0.237 for pride mirror human behavior.

Exploration driven by simulated emotions enhances AI autonomy.

Abstract

Background: Exploration of the physical environment is an indispensable precursor to information acquisition and knowledge consolidation for living organisms. Yet, current artificial intelligence models lack these autonomy capabilities during training, hindering their adaptability. This work proposes a learning framework for artificial agents to obtain an intrinsic exploratory drive, based on epistemic and achievement emotions triggered during data observation. Methods: This study proposes a dual-module reinforcement framework, where data analysis scores dictate pride or surprise, in accordance with psychological studies on humans. A correlation between these states and exploration is then optimized for agents to meet their learning goals. Results: Causal relationships between states and exploration are demonstrated by the majority of agents. A 15.4\% mean increase is noted for surprise, with a 2.8\% mean decrease for pride. Resulting correlations of $\rho_{surprise}=0.461$ and $\rho_{pride}=-0.237$ are obtained, mirroring previously reported human behavior. Conclusions: These findings lead to the conclusion that bio-inspiration for AI development can be of great use. This can incur benefits typically found in living beings, such as autonomy. Further, it empirically shows how AI methodologies can corroborate human behavioral findings, showcasing major interdisciplinary importance. Ramifications are discussed.