Abstract
If our choices make us who we are, then what does that mean when these choices are made in the human-machine interface? Developing a clear understanding of how human decision making is influenced by automated systems in the environment is critical because, as human-machine interfaces and assistive robotics become even more ubiquitous in everyday life, many daily decisions will be an emergent result of the interactions between the human and the machine – not stemming solely from the human. For example, choices can be influenced by the relative locations and motor costs of the response options, as well as by the timing of the response prompts. In drift diffusion model simulations of response-prompt timing manipulations, we find that it is only relatively equibiased choices that will be successfully influenced by this kind of perturbation. However, with drift diffusion model simulations of motor cost manipulations, we find that even relatively biased choices can still show some influence of the perturbation. We report the results of a two-alternative forced-choice experiment with a computer mouse modified to have a subtle velocity bias in a pre-determined direction for each trial, inducing an increased motor cost to move the cursor away from the pre-designated target direction. With queries that have each been normed in advance to be equibiased in people’s preferences, the participant will often begin their mouse movement before their cognitive choice has been finalized, and the directional bias in the mouse velocity exerts a small but significant influence on their final choice. With queries that are not equibiased, a similar influence is observed. By exploring the synergies that are developed between humans and machines and tracking their temporal dynamics, this work aims to provide insight into our evolving decisions.
Highlights
Human-machine interfaces of various kinds are ubiquitous in everyday life
To demonstrate how a human-machine interface may influence decisions by exploiting the dynamics of visual attention, we present a simplified simulation of the experimental task in Pärnamets et al (2015b), using an adapted version of the attention-drift-diffusion model (aDDM)
This simple model was not intended to precisely characterize psychometric variables in our population, but rather to show that drift diffusion models straightforwardly predict the pattern of results obtained when using biased or equibiased stimuli
Summary
Human-machine interfaces of various kinds are ubiquitous in everyday life. The technology for allowing one’s eye movements to be tracked from a smart phone’s camera has recently been developed (Valliappan et al, 2020). A variety of mundane human choices and decisions are no longer being made purely inside a human anymore but instead at the interface between a human and some form of technology (Clark, 2004). A variety of laboratory human choices and behaviors are being studied with human-machine interfaces to uncover the mechanics of embodied cognition (Pezzulo et al, 2013; Beckerle et al, 2019). We examine exactly how those choices and decisions can be influenced by that interface
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