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We recognize that we overemphasized the reduction to the Yu et al. flanker model relative to of our main contribution: generalizing the theory to a broader range of task conditions, while preserving the flanker model as a special case. In particular, we showed how the framework behaves when sources of information are not presented simultaneously, and involve qualitatively different patterns of stimulus-response dependencies. We will adjust this emphasis in our presentation of the work. We next address individual reviewers: Reviewer 1: we agree that Srivastava & Scharter's work bears mentioning. However, they update once for each decision, addressing only between-trial adjustments.

The dynamics of simple decisions are well understood and modeled as a class of random walk models [e.g.

The dynamics of simple decisions are well understood and modeled as a class of random walk models (e.g. Laming, 1968; Ratcliff, 1978; Busemeyer and Townsend, 1993; Usher and McClelland, 2001; Bogacz et al., 2006). However, most real-life decisions include a rich and 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 (Ratcliff, 1978) and can be built up from a weighted combination of fixed-context decisions evolving simultaneously. We also show how the model generalizes re- cent work on the control of attention in the Flanker task (Yu et al., 2009). Finally, we show how the model recovers qualitative data patterns in another task of longstanding psychological interest, the AX Continuous Performance Test (Servan-Schreiber et al., 1996), using the same model parameters.