From DDMs to DNNs: Using process data and models of decision-making to improve human-AI interactions

TL;DR

This paper advocates for integrating process data and evidence-accumulation models from cognitive neuroscience into AI systems to enhance decision-making predictions and human-AI interactions.

Contribution

It introduces the evidence-accumulation framework and discusses how incorporating process data can improve AI decision models and human-AI collaboration.

Findings

Evidence-accumulation models are well-established in psychology and neuroscience.

Current AI approaches often lack integration of process data.

Incorporating process models can enhance AI predictions and interactions.

Abstract

Over the past decades, cognitive neuroscientists and behavioral economists have recognized the value of describing the process of decision making in detail and modeling the emergence of decisions over time. For example, the time it takes to decide can reveal more about an agent's true hidden preferences than only the decision itself. Similarly, data that track the ongoing decision process such as eye movements or neural recordings contain critical information that can be exploited, even if no decision is made. Here, we argue that artificial intelligence (AI) research would benefit from a stronger focus on insights about how decisions emerge over time and incorporate related process data to improve AI predictions in general and human-AI interactions in particular. First, we introduce a highly established computational framework that assumes decisions to emerge from the noisy accumulation of evidence, and we present related empirical work in psychology, neuroscience, and economics. Next, we discuss to what extent current approaches in multi-agent AI do or do not incorporate process data and models of decision making. Finally, we outline how a more principled inclusion of the evidence-accumulation framework into the training and use of AI can help to improve human-AI interactions in the future.