Comparison of these simulated RT distribution functions to the actual measured data (Figure 3) clearly demonstrates that the integrator model provides a better account of behavior than the nonintegrative model, and AZD2281 ic50 implies that the human olfactory system integrates sensory information over time in order to improve identification accuracy. An important follow-up question to the above analysis is how choice accuracy on this task relates to predictions from the DDM, and whether it can be used to demonstrate that the system benefits from increased sampling.

Of note, if the decision-bound criterion is fixed over time (though see next paragraph), then in an open-response-time task, the accumulated information at the time of decision will be perceived to be of the same quality—upon reaching the decision bound—regardless of the time taken to reach that decision. It therefore follows that in an open-sniff task, accuracy for a given odor mixture will be the same for all observed RTs. selleck inhibitor That being said, for more difficult mixtures, overall accuracy may actually be lower, because the general quality of stimulus information is weaker, and subjects will have a greater probability of making the wrong choice. Plots of response accuracy conditional on number of sniffs

(Figure 4A) demonstrate this mean reduction in decision accuracy for the hardest mixtures. Interestingly, with regard to whether or not decision bounds are fixed, the fact that choice accuracy slightly declined for longer

trials (compare three-sniff to five-sniff trials in all Figure 4A) implies that subjects might be willing to accept a lower quality of evidence with the passage of time. This observation would be consistent with decision bounds that collapse over time, and such mechanisms have been hypothesized to occur in the visual system (Resulaj et al., 2009). Indeed a DDM simulation model with collapsing bounds closely reproduced behavioral accuracy on the open-sniff task from Experiment 2 (Figure 4B). Given these findings, we performed a new analysis to test whether the fixed-bounds (standard) or collapsing-bounds DDM (cbDDM) provided a better fit to the behavioral data. A mean cumulative distribution function (CDF) of the RTs from the standard DDM was significantly different from the mean CDF of behavioral RTs (p < 0.001; Kolmogorov-Smirnov test), indicating that this model was a poor fit to the data (Figure 4C). However, the mean CDF of the cbDDM did not differ significantly from the mean CDF of behavioral RTs (p = 0.1) (Figure 4D), demonstrating that a DDM with collapsing bounds more accurately reflects the behavioral data than one with fixed bounds. Importantly, in terms of model selection, the cbDDM provided a statistically stronger fit than the standard DDM, even after adjusting for the number of free parameters using the Bayesian Information Criterion (BIC) (BIC: 7.61 ± 1.06; p = 0.005, t test; p = 0.