. 2013 Apr 24;78(2):339-51.
Epub 2013 Mar 28.
The Limits of Deliberation in a Perceptual Decision Task
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The Limits of Deliberation in a Perceptual Decision Task
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While it is commonly assumed that decisions taken slowly result in superior outcomes, is it possible that optimal decision making does not always require sacrificing speed? For odor categorization decisions, it was previously shown that rats use <300 ms regardless of difficulty, but these findings could be interpreted as a tradeoff of accuracy for speed. Here, by systematically manipulating the task contingencies, we demonstrate that this is the maximum time over which sampling time can improve accuracy. Furthermore, we show that decision accuracy increases at no temporal cost when rats can better anticipate either the identity of stimuli or the required timing of responses. These experiments suggest that uncertainty in odor category decisions arises from noise sources that fluctuate slowly from trial-to-trial rather than rapidly within trials and that category decisions in other species and modalities might likewise be optimally served by rapid choices.
Copyright © 2013 Elsevier Inc. All rights reserved.
Figure 1. Two Alternative Odor Mixture Categorization Task
(A) Stimulus design. Two odorants (the stereoisomers S-(+)-2-octanol and R-(−)-2-octanol) were mixed in different ratios and rewarded according to the majority component. Odor mixture contrasts determine the difficulty of the stimulus, with lower contrasts being harder. (B) Sequence of events in a behavioral trial, illustrated using a schematic of the ports and the position of the snout of the rat. (C) Illustration of the timing of events in a typical trial in both the original task and low urgency conditions task. Nose port photodiode and valve command signals are shown (thick lines). Measurements of odor sampling duration (OSD) and movement time (MT), as well as imposed delays (
d, odor d, and water d) are indicated by arrows for two conditions. In the original conditions, intertrial d and odor d were drawn from uniform distributions with ranges of [0.3, 0.6] and [0.1, 0.3], respectively, and water d had a minimum of 4 s. In the low urgency conditions, intertrial d was drawn from an exponential distribution with mean of 0.5 s clipped between 0.1 and 2.0 s, odor d had a minimum of 2 s, and water d had a minimum of 10 s. See Experimental Procedures for details. Note that the intervals between events varied from trial to trial because of the variability in the timing of port entries and exits, and the random delays between port entries and odor or water valve openings. Dashed lines indicated omitted time. (D–F) Comparison of performance in low urgency conditions designed to minimize incentives for rapid responding compared to the “original” task conditions of Uchida and Mainen (2003). Population data for the comparison of task performance under low-urgency conditions (filled symbols) and original task (open symbols). Mean accuracy (D), mean of the median OSD (E), and mean of the median MT (F) are plotted as a function of stimulus difficulty (mixture contrast). Error bars are mean ± SEM (n = 4 rats). Accuracy data was fitted to a Weibull function. See also Figure S1. intertrial
Figure 2. Impact of Manipulations of Motivation (Reward, Punishment) on Speed and Accuracy of Discrimination
(A) Effect of air puff punishment. Comparison of the original task conditions (filled circles) to the low-urgency task with air puff punishment for error choices (open circles). (Ai) Schematic showing delivery of air puff to the snout of an animal from the water delivery port. (Aii) Accuracy as a function of stimulus difficulty (mixture contrast). (Aiii) Median OSD as a function of stimulus difficulty (mixture contrast). Error bars are mean ± SEM (n = 4 rats). (B) Water restriction experiments. Control group (open symbols): 50 min session duration, no time out for errors, and a fixed amount of water (
W) was given outside the task (see Experimental Procedures). Test group (open symbols): 30 min session duration, 10 s time out for errors, and no water given outside task. (Bi) Mean body weight as fraction of ad libitum weight. See also Figure S2. (Bii) and (Biii) are as for (Aii) and (Aiii). (C) One-direction reward (1DR) experiment. (Ci) Choice performance was biased in blocks where correct choices were rewarded only on one side (open triangles and solid lines) compared to when correct choices were rewarded on both side (filled square dashed line). The direction of bias was always toward the rewarded port. Error bars are mean ± SEM (n = 3 rats). (Cii) and (Ciii) are as for (Aii) and (Aiii). free
Figure 3. Prolonged OSDs Instructed using a Go Signal Fail to Improve Performance Accuracy
(A) Schematic of the go-signal task. An auditory go signal was played after a delay
d from odor onset and subjects were required to remain in the odor port until after go d (see Experimental Procedures). (B) Time line of go-signal delays across sessions. (C) Mean of the median OSD as a function of mixture contrast and the length of go-signal delay (n = 4 rats). Shades of gray represent different go-signal delays. Open circles and dashed line indicate the results from a different set of subjects in the original RT task (Figure 2). (D) Mean performance accuracy with different go-signal delays. See also Figure S3. go
Figure 4. Performance Accuracy Depends on the Distribution of Go-Signal Delays
Go-signal delays are chosen from a uniform distribution (filled symbols) or exponential distribution (open symbols). Note that an exponential distribution results in a flat hazard rate function while a uniform distribution result in a rising hazard rate function. A difficult single mixture pair (12% mixture contrast) was used throughout. (A) Mean difference in performance accuracy between trials with late (0.7–1.0 s) and early (0.1–0.3 s) go-signal delays (n = 4 rats). Time 0 denotes the last point before changing from uniform distribution (rising hazard rate) to exponential distribution (flat hazard rate). Filled symbols: uniform distribution (phases I and III); open symbols: exponential distribution (phase II). Note that the switch in performance accuracy from exponential to uniform distribution is expressed only on the second day after the switch (arrow) but that there is no such delay switching back to the uniform distribution. (B) Mean performance accuracy as a function of OSD for two different go-signal distributions pooled across all rats (n = 4 rats). Filled circles: uniform distribution (phase III); open circles: exponential distribution (phase II). Star: accuracy significantly different (for the largest OSD) between the two conditions (p < 0.01; one tailed z test for proportion). (C) Mean T95 (shortest OSD that gave 95% of maximum accuracy) for two different go-signal distributions (n = 4 rats; p < 0.05, Friedman paired test). Individual rats are shown with different symbols. Filled symbols: uniform distribution (phase III); open symbols: exponential distribution (phase II). (D) Mean performance accuracy as a function of go-signal delays for the population data. The solid line is the fitted subjective anticipation function in the two conditions (black solid line: go signal with a uniform distribution, phase III; dashed line: go signal with an exponential distribution; phase II). (E) Weights associated with rising and flat theoretical subjective anticipation functions after fitting to the discrimination performance curve of individual rats (different symbol shapes) in two different conditions (open symbols: go signals with a uniform distribution; closed symbols: go signals with an exponential distribution). Note that in the uniform condition weights are higher for the rising anticipation and in the exponential condition weights are higher for the flat anticipation. See also Figure S4. (F) Comparison of reaction times to short (0.1–0.3 s) go signals in the two conditions. Individual rats shown in different symbols as shown in (C) (n = 5 sessions).
Figure 5. Reducing the Range of Mixture Contrasts Results in Increase in Performance Accuracy at No Cost of Speed
(A) Mean performance accuracy of 6 rats over the course of 30 days of training. Filled circles: sessions with interleaved mixture contrasts; open circles: sessions with the blocked (noninterleaved) condition. Color indicates mixture contrast. The mean accuracy during the last 100 trials in a session is shown. (B) Mean performance accuracy (n = 6 rats). Psychometric curve was fit using a Weibull function. Filled and open symbols represent interleaved and blocked conditions. (C) Mean of the median OSD (n = 6 rats).
Figure 6. Accuracy in RT Paradigm Is as High as that Obtained with Prolonged Odor Sampling
(A) Accuracy of rats in a RT paradigm and in fixed 1.0 s go-signal paradigm (n = 4 rats). (B) Mean median OSD. Day 9 and 39 are control sessions using the same odor in all odor channels keeping all other task and reward parameters constant. Day 19 is a control session with a 50/50 air mixing of two odorized air streams each of a 55/45 and a 45/55 premixed odors. (C) Comparison of performance accuracy for rats trained on 1.0 s go signal (closed circles) and on RT paradigm (open circles) (n = 4 rats). (D) Mean median OSDs in the two paradigms (n = 4 rats). Note that error bars are smaller than the symbols. (E) Comparison of the mean accuracies between the go-signal and RT paradigms for two mixture contrasts (2% and 4%) for four individual rats (different symbols) (n = 5 sessions). See Figure S5.
Figure 7. Dissociation of Accuracy and Speed
(A) Summary of experiments in which OSD was affected. (B) Summary of experiments in which accuracy was affected. Each pair of connected dots compares population performance accuracy and OSD for the difficult mixture contrast (12%) for a given manipulation (different manipulations shown with different symbols and control groups are shown with filled symbols and experimental groups with open symbols; see legend). Error bars indicate SEM. For all experiments n = 4 rats except blocked versus interleaved (n = 6 rats).
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