A Theory of Decision Making Under Dynamic Context
The dynamics of simple decisions are well understood and modeled as a class of random walk models [e.g. 1-4]. However, most real-life decisions include a dynamically-changing influence of additional information we call context. In this work, we describe a computational theory of decision making under dynamically shifting context. We show how the model generalizes the dominant existing model of fixed-context decision making [2] and can be built up from a weighted combination of fixed-context decisions evolving simultaneously. We also show how the model generalizes recent work on the control of attention in the Flanker task [5]. Finally, we show how the model recovers qualitative data patterns in another task of longstanding psychological interest, the AX Continuous Performance Test [6], using the same model parameters.
The authors extend the popular sequential probability ration test for decisions under perceptual uncertainty to situations in which a there are additional contextual stimuli. This is achieved by simply accumulating samples and computing a posterior distribution over both context and target from which the decision is being generated when a bound is being reached. As the onset and offset of the cues can be modeled separately, the authors decided to give examples for the cases in which target and context appear at the same time and where the target cue appears with a delay after the disappearance of the contextual cue, corresponding to the Eriksen flanker task and the continuous performance test.Finally, the authors set the parameters for the flanker task and use these same parameters for the second task. The behavioral performance is matched well and the corresponding reaction times are within 40% of the empirically measured values of the experiment.This is overall an interesting topic for the cognitive science and neuroscience community that follows NIPS and it is an extension of the sequential probability ration test, which has been extremely popular in the last years. On the other hand, there is other recent work emphasizing the importance of modeling contextual effects in explain human decision making behavior: the authors should relate their approach to previous work in decision-making with contexts e.g. Srivastava & Schrater's work, at least cite it. The current model assumes a perfect separation of the evidence provided for target and context given the sensory stimuli, which is potentially not very realistic. I would call the proposed model a 'model' and not a 'theory' because a model can certainly generate or accommodate multiple experimental settings, but this may be a matter of taste.