Cost-sensitive Bayesian control policy in human active sensing

Abstract

An important but poorly understood aspect of sensory processing is the role of active sensing, the use of self-motion such as eye or head movements to focus sensing resources on the most rewarding or informative aspects of the sensory environment. Here, we present behavioral data from a visual search experiment, as well as a Bayesian model of within-trial dynamics of sensory processing and eye movements. Within this Bayes-optimal inference and control framework, which we call C-DAC (Context-Dependent Active Controller), various types of behavioral costs, such as temporal delay, response error, and sensor repositioning cost, are explicitly minimized. This contrasts with previously proposed algorithms that optimize abstract statistical objectives such as anticipated information gain (Infomax) (Butko and Movellan, 2010) and expected posterior maximum (greedy MAP) (Najemnik and Geisler, 2005). We find that C-DAC captures human visual search dynamics better than previous models, in particular a certain form of “confirmation bias” apparent in the way human subjects utilize prior knowledge about the spatial distribution of the search target to improve search speed and accuracy. We also examine several computationally efficient approximations to C-DAC that may present biologically more plausible accounts of the neural computations underlying active sensing, as well as practical tools for solving active sensing problems in engineering applications. To summarize, this paper makes the following key contributions: human visual search behavioral data, a context-sensitive Bayesian active sensing model, a comparative study between different models of human active sensing, and a family of efficient approximations to the optimal model.

Proactive Information Sampling in Value-Based Decision-Making: Deciding When and Where to Saccade

“…Thus, to make efficient decisions, participants need to balance between getting a more accurate estimation on the currently fixated item by continuously sampling and potentially more information gain by switching fixation to the other item. Similar ideas of active sampling have also been proposed in the field of visual search and in perceptual decision tasks (Najemnik and Geisler, 2005; Cassey et al, 2013; Ahmad et al, 2014).…”

Dynamic modulation of visual and electrosensory gains for locomotor control

“…Second, active sensing behaviour violates the separation of sensing and action, an implicit assumption in most engineering approaches to state estimation. An exciting frontier lies in integrating Bayesian inference and active sensing [39,40].…”

Efficient Active Sensing with Categorized Further Explorations for a Home Behavior-Monitoring Robot

“…Active sensing is the ability to infer information under an uncertain environment by spontaneous sensing activities [ 12 – 17 ]. There are two types of active sensing, one uses self-generated signals to probe the environment, such as echolocation chirps in bats [ 16 ], and the other uses a self-motion, such as a move to find out an object hidden in the shadows [ 12 ].…”

“…In the literature, the active sensing has been studied as a theoretic framework for understanding biological sensing mechanism [ 15 – 17 ]. One control framework for active sensing by the human sight is Context-Dependent Active Controller (C-DAC).…”

“…One control framework for active sensing by the human sight is Context-Dependent Active Controller (C-DAC). C-DAC assumes that the observer aims to optimize a context-sensitive objective function that takes into account behavioral costs such as temporal delay, response error, and the cost of switching from one sensing location to another [ 17 ]. This framework allows us to derive behaviorally optimal procedures for making decisions about where to acquire sensory inputs, when to move from one observation location to another, and how to negotiate the exploration-exploitation tradeoff between collecting additional data versus terminating the observation process [ 17 ].…”