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

arXiv: https://arxiv.org/abs/2302.06615 · HTML: https://mlsanity.com/paper/2302.06615/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 52 references that are in the map.
- First posted on arXiv 13 Feb 2023.

## Builds on (most cited first, 10 of 10)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Guiding Deep Learning System Testing using Surprise Adequacy](https://mlsanity.com/paper/1808.08444/) (2018)
- [Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning](https://mlsanity.com/paper/1703.01732/) (2017)
- [Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning](https://mlsanity.com/paper/1708.02190/) (2017)
- [Curiosity-Driven Exploration via Latent Bayesian Surprise](https://mlsanity.com/paper/2104.07495/) (2021)
- [A Review and Refinement of Surprise Adequacy](https://mlsanity.com/paper/2103.05939/) (2021)
- [Deep Learning for Cognitive Neuroscience](https://mlsanity.com/paper/1903.01458/) (2019)
- [Deep Emotion: A Computational Model of Emotion Using Deep Neural Networks](https://mlsanity.com/paper/1808.08447/) (2018)
- [Intrinsic Motivation and Episodic Memories for Robot Exploration of High-Dimensional Sensory Spaces](https://mlsanity.com/paper/2001.01982/) (2020)

## Abstract (arXiv)

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 ρsurprise=0.461 and ρpride=-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.