Different operations may be advantageous in different contexts (e.g., differentiation for a detection task, or belief propagation for inference about whether two parts of an object are connected). Nonetheless, a simple but powerful Selleckchem BMS 754807 idea is that in many situations evidence is accumulated to some threshold level, whence the decision terminates in a choice, even if provisional (Resulaj et al., 2009). If the two directions are equally likely (i.e., neutral

prior probability), then we represent the process as an accumulation of signal plus noise to symmetric decision bounds (Figure 2A). The upper and lower bounds support termination in favor of a right or left choice, respectively. In the brain, this process looks more like a race between two mechanisms, one that accumulates evidence for right (against left), and the other that does the opposite (Figure 2B). This detail matters for correspondence with the physiology (Figure 3). The beauty of this idea is that a single mechanism can thus account for both which decision is made and how much time (or how many samples) it takes to commit to an answer—in other words, the balance between accuracy and speed. As shown in Figure 3B the framework is so powerful that one can fit the reaction time data to establish the Selleck OSI906 model parameters—an estimate of the bound height and a coefficient that converts motion strength to units

of SNR—and then predict the accuracy at each of the motion strengths (solid curve, upper graph). This is a rare feat in psychophysics: to make

a set of measurements and to use it to predict another. It convinced us that there is merit to the idea (Box 2). Here is a cautionary tale that ought to interest Terminal deoxynucleotidyl transferase theorists, experimentalists, philosophers, and historians of science. The concept of bounded evidence integration originated in the field of quality control, which draws on statistical inference from sequential samples of data. Abraham Wald began this secretly as a way to decide whether batches of munitions were of sufficient quality to ship. He developed the sequential probability ratio test as the optimal procedure to test a hypothesis against its alternative, using the minimal number of samples (effectively a speed versus accuracy tradeoff) (Wald, 1947 and Wald and Wolfowitz, 1947). The test involves accumulation of evidence in the form of a log-likelihood ratio (logLR; or a proportional quantity) to a pair of terminating bounds, which trigger acceptance of the respective hypotheses. Alan Turing developed the same algorithm as a part of his code-breaking work in WWII (Gold and Shadlen, 2002 and Good, 1979). A decade later, several psychologists recognized the implications for choice and reaction time (RT) (e.g., Laming, 1968 and Stone, 1960). However, the field realized that this model predicts that for a fixed stimulus strength (e.g.