Recording
Speaker
Huadong Xiong
Bio
I am a first-year Ph.D student in the CNS program, the department of psychology at the University of Arizona, directed by Dr. Robert Wilson. I build models to understand behaviors and I study how computation could be implemented in neural networks. Following the release of GPT-4, my research interest has partially shifted towards understanding the emergence of intelligence within large language models.
Abstract
The exploration-exploitation trade-off, balancing the acquisition of new information with the utilization of known resources, is a fundamental dilemma faced by all adaptive intelligence. Despite our understanding of models based on normative principles, the diverse explore-exploit behaviors of natural intelligence remain elusive. Here, using neural network behavioral modeling and state space analysis, we examined the diverse human exploration behaviors under a novel two-armed bandit task, designed to simulate real-world environmental volatility where exploration becomes essential. Examining behavior in the belief state space of this task, we characterized the disparities across artificial agents with decision boundaries. To extend this analysis to human data, a circumstance where choices are too sparse in the belief state space, we trained a recurrent neural network (RNN) model to predict humans’ choices given past observations. This RNN model outperforms all existing cognitive models. Probing the RNN’s decision boundaries, we found substantial individual differences that evade classical cognitive models. Additionally, our RNN revealed a model-based pattern employed by humans in response to higher environmental volatilities. Our work offers a promising approach for investigating diverse decision-making strategies in humans and animals.