# Consumers can make decisions in as little as a third of a second

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4 Judgment and Decision Making, Vol. 6, No. 6, August 2011 Fast consumer decisions 523 weight relative to the latest observation. EWMA 0 was set to 0.5 (i.e. chance performance). Intuitively, the EWMA measure provides an estimate of how accuracy changes with increasing reaction times. This measure can then be compared against the null hypothesis of chance performance on all trials. This null hypothesis generates a confidence interval for the EWMA after i observations given by λ µ ± Nσ 2 λ (1 (1 λ)2i ). The first term in this expression is the mean EWMA statistic under the null hypothesis, which in our experiment is given by µ = E(X i ) = The second term provides an expression for the width of the confidence interval: N is the number of standard deviations included in the confidence interval, and σ = Std(X i ) = 0.5 is the standard deviation of each observation X i under the null hypothesis. Note that under the null hypothesis performance in every trial depends on the independent flip of a fair coin. The MRT can then be defined as the smallest ordered reaction time at which the EWMA measure permanently exceeds this confidence interval. For our analyses we chose conservative parameters to reduce the possibility of false positives: λ = 0.01 and N = 3. The EWMA analysis was run separately for each subject and condition. This test is meant to improve the previous measures of minimum reaction times introduced by Kirchner & Thorpe (2006) and extensively used in the literature (e.g., Crouzet, Kirchner, & Thorpe, 2010). Their typical analysis sorts reaction times in increasing order, divides them into discrete bins (e.g. 10 ms bins centered at 10 ms, 20 ms, etc.), and then tests the average accuracy of observations in each bin against chance. This procedure has raised some controversy because the resulting MRT measures are highly sensitive to the width and placement of the bins. 2.2 Results Trials with responses faster than 80 ms were excluded, since this is the approximate minimum time necessary to make a purposeful eye-movement (302 out of 9751 trials). Also, the trials that took much longer than what is required by the task were left out (4 out of 9751). These trials probably represent errors made by the subject or the eye-tracker, for example, due to a blink which may cause a subject to miss the 20 ms presentation of the food items. Figure 2 depicts the group reaction time curves for correct and error trials, as well as the mean estimate of the MRT. The mean response time was 407 ± 1 (SEM) for Figure 2: Reaction time distributions for Experiment 1, for correct trials = thick line, and error trials = thin line. Vertical line shows the mean MRT = 313 ms. correct trials, and 396 ± 3 ms for error trials. Furthermore, the reaction time distributions show that a small percentage of saccades took place between 80 and 200 ms (1.0% for correct trials, and.7% for error trials). These types of saccades are known as express saccades (Fischer & Ramsperger, 1984). Table 1 and Figure 3A show that, despite the speed of the choices (mean response time across subjects was 404 ± 21 ms), all subjects selected their preferred option well above chance: mean accuracy was 73.3 ± 1.6% (binomial test of above chance performance, p < ), ranging from 65.2 ± 1.8% for most difficult choices to 84.6 ± 3.0% for easiest choices. Figure 3B describes reaction times as a function of difficulty, where absolute distance in liking ratings of the two food items of d=1 represents difficult choices, and d=4 represents easy choices. For difficult choices (d=1) the mean RT was 411 ms, while for easy choices (d=4) the mean RT was 386 ms (paired t-test: t=3.82, df=11, p=.0028). Table 1 also reports the MRT for each individual. The mean MRT across subjects was 313 ± 17 ms. 3 Experiment 2: Pure perceptual detection control task One limitation of Experiment 1 is that the estimate of the minimum speed at which simple choices can be made includes the time necessary to perceive the items and initi-

5 Judgment and Decision Making, Vol. 6, No. 6, August 2011 Fast consumer decisions 524 Table 1: Individual statistics for Experiment 1. Subject Trials Accuracy (%) SEM Mean RT SEM Min RT All Figure 3: (A) Percentage of correct choices as a function of liking rating differences between the two food items (1=most difficult choices, 4=easiest choices). (B) Mean reaction time at each value distance. Error bars denote SEM. ate the eye-movement through which the choices are indicated. This is an important limitation because a key goal of the paper is to improve existing estimates of the minimum amount of time at which values can be computed and compared in the brain. To address this limitation we carried out a pure perceptual detection control task in which subjects were asked to detect the side of the screen on which a picture of a food item is displayed and make an eye-movement towards that location. Note that this task has similar perceptual and motor demands as those in Experiment 1, although not identical, since only one stimulus is shown on the screen. However, in the new task subjects do not need to compute and compare the value of stimuli. As a result, a subtraction of the reaction times in Experiment 2 from those in Experiment 1 provides an alternative and potentially better estimate of the amount of time that it takes to compute and compare values. 3.1 Methods Subjects. Four subjects from Experiment 1 participated in this experiment, which was conducted several weeks after the previous one. Table 2 shows which subjects participated in each experiment, as well as the order in which they did so.

7 Judgment and Decision Making, Vol. 6, No. 6, August 2011 Fast consumer decisions 526 Table 3: Individual statistics for Experiment 2 and Experiment 1. Exp 2 Exp 1 Exp 2 Exp 1 Exp2 Exp 1 Subject Trials Accuracy SEM Accuracy SEM Mean RT SEM Mean RT SEM MinRT MinRT All Figure 5: Reaction time distribution for Experiment 1 (blue line) vs. Experiment 2 (green line). Thick line = correct trials. Thin line = error trials. Vertical lines = the estimate of the mean Minimum Reaction Time across subjects: Experiment 1 = 313 ms; Experiment 2 = 114 ms. Figure 6: Reaction time distribution for Experiment 3 (red line) vs. Experiment 4 (gray line). Thick lines = correct trials. Thin lines = error trials. Vertical lines = the mean Minimum Reaction Time across subjects: Experiment 3 = 365 ms; Experiment 4 = 418 ms. All omitted details are as in Experiment Method Subjects. Nine subjects participated in Experiment 3. Five subjects participated in Experiment 4. See Table 2 for details. Task. The procedure for Experiment 3 was identical to that of Experiment 1, except that subjects were asked to maintain the central fixation on the screen until they were confident of which food item they preferred, and to only then indicate their choice by making an eye-movement to the location of their preferred item. The procedure for Experiment 4 was identical to that of Experiment 1, except that now responses were indicated by a button press. 4.2 Results In Experiment 3, due to extremely fast or slow response times, 14 out of 6750 trials were omitted from the further analyses. Figure 6 and Table 4 show the reaction time distributions and accuracy for Experiment 3 (eye-tracking; N=9). The mean accuracy was 83.0% (binomial test of above chance performance, p < ) and the mean response time was 572 ± 44 ms. Accuracy increased and reaction time decreased as a function of choice difficulty (paired t-test for difference in accuracy between d=1=74.2% and d=4=95.0% is significant at p <.0001; paired t-test for reaction times between d=1=606 ms and d=4=510 ms is significant at p=.014).

8 Judgment and Decision Making, Vol. 6, No. 6, August 2011 Fast consumer decisions 527 Table 4: Individual statistics for Experiment 3. Subject Trials Accuracy (%) SEM Mean RT SEM MinRT All Table 5: Individual statistics for Experiment 4. Subject Trials Accuracy (%) SEM Mean RT SEM MinRT All In Experiment 4, due to extremely slow response times 2 out of 3742 trials were omitted from the further analyses. Figure 6 and Table 5 depict the reaction time distributions and accuracy for Experiment 4 (manual responses, N=5). The mean accuracy was 84.9% (binomial test of above chance performance, p < ) and the mean response time 632 ± 60 ms. Accuracy increased and reaction time decreased with decreasing choice difficulty (paired t-test for accuracy between d=1=74.2% and d=4=93.8% is significant at p=.004; paired t-test for reaction times between d=1=685 ms and d=4=559 ms is marginally significant at p=.07). Figure 7 shows the group reaction time curves for correct and error trials for Experiments 3 and 4. None of the differences (in RT or accuracy, over any value distance) between the two experiments were statistically different (for all t-tests, p>.3). In order to facilitate comparisons across experiments, Figure 8 shows the comparison of the Minimum Reaction Times (MRT), accuracy, and mean reaction times for all four experiments. 5 Discussion The experiments described here have generated three main findings. First, using a measure of Minimum Reaction Times (MRT) we found that subjects can compute and compare values significantly above chance in as little as 313 ms. Second, we found that, at average reaction times of 404 ms subjects were able to compute and compare values with accuracies as high as 73%, and that at most 235 ms of this time was devoted to the computation and comparison of values (the rest was spent on preparing and initiating the response). Third, we have found that slowing down subjects by either asking them explicitly to be confident about their choices, or by asking them to indicate choices using hand movements, increases mean reaction times by about 150 ms (comparison between the same 8 subjects who completed Experiments 1 and 3 shows a difference of 150 ms; the difference between the same 4 subjects who completed Experiments 1 and 4 was 123 ms), while generating only small increases in choice accuracies: 9.6% and 7.6%, respectively. A comparison of these estimates with the time that it takes to carry out other simple cognitive computations

11 Judgment and Decision Making, Vol. 6, No. 6, August 2011 Fast consumer decisions 530 Vandekerckhove, J., & Tuerlinckx, F. (2007). Fitting the Ratcliff diffusion model to experimental data. Psychonomic Bulletin & Review, 14, VanRullen, R., & Thorpe, S. J. (2001a). Is it a bird? Is it a plane? Ultra-rapid visual categorisation of natural and artifactual objects. Perception, 30, VanRullen, R., & Thorpe, S. J. (2001b). The time course of visual processing: From early perception to decision making. Journal of Cognitive Neuroscience, 13, Willis, J., & Todorov, A. (2006). First impressions: Making up your mind after a 100-ms exposure to a face. Psychological Science, 17, Wunderlich, K., Rangel, A., & O Doherty, J. P. (2010). Economic choices can be made using only stimulus values. Proceedings of the National Academy of Sciences, 107,

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