Context-sensitive active sensing in humans
Humans and animals readily utilize active sensing, or the use of self-motion, to focus sensory and cognitive resources on the behaviorally most relevant stimuli and events in the environment. Understanding the computational basis of natural active sensing is important both for advancing brain sciences and for developing more powerful artificial systems. Recently, we proposed a goal-directed, context-sensitive, Bayesian control strategy for active sensing, C-DAC (ContextDependent Active Controller) (Ahmad & Yu, 2013). In contrast to previously proposed algorithms for human active vision, which tend to optimize abstract statistical objectives and therefore cannot adapt to changing behavioral context or task goals, C-DAC directly minimizes behavioral costs and thus, automatically adapts itself to different task conditions. However, C-DAC is limited as a model of human active sensing, given its computational/representational requirements, especially for more complex, real-world situations. Here, we propose a myopic approximation to C-DAC, which also takes behavioral costs into account, but achieves a significant reduction in complexity by looking only one step ahead. We also present data from a human active visual search experiment, and compare the performance of the various models against human behavior. We find that C-DAC and its myopic variant both achieve better fit to human data than Infomax (Butko & Movellan, 2010), which maximizes expected cumulative future information gain. In summary, this work provides novel experimental results that differentiate theoretical models for human active sensing, as well as a novel active sensing algorithm that retains the context-sensitivity of the optimal controller while achieving significant computational savings.
The paper explores theoretical issues in active 
      sensing -- situations where people much actively gather information to 
      achieve some goal. A extension to a previous model (C-DAC) is proposed 
      which myopically gathers information in order to maximize a task-specific 
      cost function. The model is compared against an alternative that favors 
      information without respect to cost (Infomax). The overall goal of the 
      paper is interesting and important. The novelty of the paper is somewhat 
      reduced simply because it reports an extension to Ahmad & Yu (2013) to 
      deal myopically with action selection. This is fine but I wondered how 
      novel or important that aspect of the modeling actually was (e.g., it 
      didn't seem to figure centrally into explanations of the experiment 
      itself). There is some prior work on this issue. In particular, 
      the authors might check out Markant & Gureckis, 2012 "Does the utility 
      of information influence sampling behavior?" which explores a similar 
      issue: whether people optimize information or some task-specific cost 
      function. They found that information optimizing provided a better fit to 
      people's behavior. The paper was poorly written overall. The 
      abstract was much too long, the paper exceeded the 8 page limit at NIPS, 
      and the paper cites Ahmad & Yu, 2013 in almost every sentence 
      including twice in the abstract alone! I had a really hard time 
      understanding the experiment design. Terms like "patch" are introduced 
      without definition (is a patch a set of trials with a particular 
      distribution of target probabilities, or possible target locations? I 
      couldn't really tell). Also, while the overall theoretical issue seemed 
      well motivated, the paper itself focused on this "confirmatory search" 
      phenomena which the authors seemed to ad-hoc identify in their data. Why 
      was this phenomena selected or critical for differentiating the models? 
      Also, the model result for section 3 were not that compelling. The 
      full C-DAC hits all the qualitative patterns as do the myopic one. The 
      authors don't discuss what this means. Is it the case that the measures 
      the authors selected here just don't tell any of the models apart very 
      well? No fit statistics or quantification of the advantage for the more 
      limited model is offered other than to describe is as "more 
      psychologically plausible." Section 4 was also very opaque. I didn't 
      understand the policy-space graphs in Figure 3 and I suspect that most 
      readers will have significant difficulty with this as well. It is unclear 
      what "Greedy MAP" is as well as this model is never mentioned in the 
      paper. The analysis presented in Figure 4 is odd because if this 
      is meant to be a model of humans, no mention is made of human performance. 
      It is fine to say that the myopic C-DAC model performs well on a number of 
      metrics, but as a model it should match human observer as well (unless 
      this is meant as a engineering analysis of a computer vision based visual 
      search algorithm?). *Note* My initial evaluation was a little more 
      negative, but after discussion with the other reviewers I have given a 7. 
      I think some of the critical comments above still apply, but there are 
      also strengths including the emphasis on multiple model comparison, the 
      empirical data, and the novelty of deriving theorems about the optimal 
      stopping rule. I thought the author's response was helpful and it would be 
      nice if the paper was edited somewhat in accordance with the reviewer 
      questions to be more clear if it is accepted.