(Figure 3D, open circles versus filled circles, and Figure S4). Onalespib The failure to observe an increase in performance

accuracy with longer go signals was surprising, given that Rinberg et al. (2006) did find such an increase using apparently similar conditions. Therefore, we next turned to examine whether overlooked differences in task structure might account for this discrepancy. We first noted that while we had tested subjects on a given go-signal delay for hundreds of trials in a row, Rinberg et al. randomly interleaved go signals of different delays in a single session. Previous studies have shown that the ability to anticipate the time at which a brief stimulus will be presented can affect reaction time and accuracy of performance (Griffin et al., 2001; Nobre, 2001; Correa et al., 2006; Katzner et al., 2012). We therefore hypothesized that expectation of (or

readiness to respond to) the timing of the go signal would also affect performance in this task. Specifically, we reasoned that when go-signal delays vary randomly from trial-to-trial, the subject may not respond as accurately as when responses are self-paced or instructed by a go signal delivered at a constant delay. The predictability of random go-signal times has been Selleckchem Sunitinib formalized by the notion of “hazard rate,” defined as the probability that a signal will occur given that it has not already occurred (Luce, 1986). The “subjective hazard rate” (Janssen and Shadlen, 2005) is an extension of this concept that takes into account the finding that the variance of subjective time estimation increases proportionally to the interval duration (Gibbon, 1977; Gallistel and Gibbon, 2000). By calculating the subjective hazard rate for the experimental distribution of go-signal times, a quantitative prediction of performance as a function of not go-signal delay can be obtained. To test the idea that hazard rate impacts go signal performance, we compared performance of subjects on two different distributions of go signals, formed using uniform and exponential probability

densities, which have very different hazard rates. These distributions, their hazard rates and subjective hazard rates are depicted in Figure S4. The subjective hazard rate for go signals in the uniform condition rises with time toward the end of the distribution interval; therefore performance in this condition is expected to increase relatively slowly over the distribution interval. In contrast, the exponential distribution has a much flatter subjective hazard rate; therefore, performance in this condition is expected to rise relatively more quickly resulting in relatively better performance at short delays. Rats were tested first on the uniform distribution for several consecutive sessions (phase I), then on the exponential distribution (phase II) and then again on the uniform distribution (phase III) (Figure 4A).