I See What You See: Inferring Sensor and Policy Models of Human Real-World Motor Behavior

Abstract

Human motor behavior is naturally guided by sensing the environment. To predict such sensori-motor behavior, it is necessary to model what is sensed and how actions are chosen based on the obtained sensory measurements. Although several models of human sensing haven been proposed, rarely data of the assumed sensory measurements is available. This makes statistical estimation of sensor models problematic. To overcome this issue, we propose an abstract structural estimation approach building on the ideas of Herman et al.'s Simultaneous Estimation of Rewards and Dynamics (SERD). Assuming optimal fusion of sensory information and rational choice of actions the proposed method allows to infer sensor models even in absence of data of the sensory measurements. To the best of our knowledge, this work presents the first general approach for joint inference of sensor and policy models. Furthermore, we consider its concrete implementation in the important class of sensor scheduling linear quadratic Gaussian problems. Finally, the effectiveness of the approach is demonstrated for prediction of the behavior of automobile drivers. Specifically, we model the glance and steering behavior of driving in the presence of visually demanding secondary tasks. The results show, that prediction benefits from the inference of sensor models. This is the case, especially, if also information is considered, that is contained in gaze switching behavior.

Putting perception into action with inverse optimal control for continuous psychophysics

“…Because subjects’ behavior is conceptualized as optimal control under uncertainty ( Åström, 1965 ; Kaelbling et al, 1998 ; Anderson and Moore, 2007 ; Hoppe and Rothkopf, 2019 ), the optimal actor model additionally contains action variability and a cost function encapsulating the behavioral goal of tracking the target. The present analysis method probabilistically inverts this model similarly to approaches in inverse reinforcement learning ( Ng and Russell, 2000 ; Ziebart et al, 2008 ; Rothkopf and Dimitrakakis, 2011 ) and inverse optimal control ( Chen and Ziebart, 2015 ; Herman et al, 2016 ; Schmitt et al, 2017 ). The inferred action variability can be attributed to motor variability ( Faisal et al, 2008 ) but also to other cognitive sources, including decision variability ( Gold and Shadlen, 2007 ).…”

A normative theory of social conflict

“…In biologically relevant multi-agent settings, beliefs are studied in the Theories of Mind (ToM) framework which proposes that human and animal agents may maintain beliefs about the beliefs of their adversaries (Baker et al, 2011) or aides (Khalvati et al, 2019). As previous studies were successful in inferring beliefs (Schmitt et al, 2017;Alefantis et al, 2021) and regressing them to neural activity in simulations (Wu et al, 2020) and low-resolution fMRI imaging (Koster-Hale & Saxe, 2013), here we propose a way to infer task-related beliefs in mice and compare them to high-resolution data of the whole-brain neural activity.…”