Confidence Predicts Change of Mind

In the two snack experiments participants saw the same exact choice sets on more than one occasion. In Experiment 3 each pair was presented twice; in Experiment 4 each triad was presented three times (counterbalancing for different spatial locations). This design allowed me to determine factors affecting changes of mind when the same options are encountered again in a subsequent trial. Note that the way I define change of mind above is different from how it is often defined in perceptual decision making, namely as a choice reversal within the same trial

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after further processing of sensory information (Bronfman et al., 2015; Moran et al., 2015; Resulaj et al., 2009; Van Den Berg et al., 2016). The hypothesis I sought to test was that an explicit representation of uncertainty in a choice (reported as confidence) would influence behaviour when the same options were presented again during a different trial. In a hierarchical logistic regression, lower confidence at the first presentation was indeed associated with increase in change of mind at the following presentation, in both Experiments 3 and 4 (Experiment 3: z=-6.16, p<10-9_{; Experiment 4: z=-5.21, p<10}-6_{). The effect of confidence in predicting change} of mind remained robust after controlling for all the other factors that might correlate with the stability of a choice such as |DV| and RT. Notably none of the eye tracking measures played a significant role as predictor of change of mind when included in the regression analysis (Fig. 4

coefficients in blue). Note that this was still true when confidence was excluded from the regression

analysis (GSFExperiment 3: z=-0.68, p=.49; |DDT|Experiment 3: z=-0.32, p=.75; GSFExperiment 4: z=0.59, p=0.55; |DDT|Experiment 4: z=-0.13, p=0.90).

This is particularly interesting for GSF because of its significant negative relation with confidence (see Fig.3). This result suggests the hypothesis that the low level (and possibly implicit) measure of uncertainty gathered by GSF is insufficient to trigger a delayed change of mind. On the contrary, an explicit representation of uncertainty (expressed through confidence) allows individuals to capitalise on their ‘knowledge about their ignorance’ and make a difference choice when similar options are presented later.

It is important to note that just because confidence predicted changes of mind in subsequent trials, it does not necessarily follow that low confidence judgments are causing subsequent changes of mind. We know that low confidence judgments are associated with a noisier decision- process, leading to more decisions that violating the preferences of the participant. So perhaps confidence simply tags noisy decisions as part of some error monitoring process (Yeung & Summerfield, 2012, 2014). When the same choice repeats the decision process is less noisy because of recursion to the mean and the less noisy decision process causes the highest-value option to be chosen. This results in a change of mind in relation to the first low-confidence choice, but confidence is not having any causal influence. I attempted to account for this by adding a dummy-variable coding whether the highest-value option was chosen in the original trial, and confidence still predicted future changes of mind in Experiment 3 (z=-4.79, p<10-5₎ and Experiment 4 (z=-4.88, p<10-5_{). Therefore, it seems probable that confidence causally} influence future changes of mind.

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Next, I examined whether individual differences in metacognition related to changes of mind. I reasoned that the impact of confidence on changes of mind would be more prominent in participants who have enhanced metacognitive skills, i.e. those whose explicit confidence ratings more accurately track the level of uncertainty underlying their decision process. In order to test this hypothesis I calculated an individual index of metacognitive sensitivity by computing the difference in slope between psychometric functions on high and low confidence trials (De Gardelle & Mamassian, 2014; De Martino et al., 2013; Fleming & Lau, 2014). I then ran a logistic model to predict changes of mind at later presentations using confidence measured at earlier presentations with the same stimuli. In line with my initial hypothesis, the impact of confidence on changes of mind is stronger in those participants with greater metacognitive accuracy (r= - 0.35, p=0.01; Figure 4.5.). Note that the relationship is negative because the influence of confidence on changes of mind should be negative (so that changes of mind are more probable when confidence in the initial choice is low).

Figure 4.5. Confidence Predicts Change of Mind (a-b) Coefficient plots for the fixed effects coefficients from hierarchical logistic regression models predicting future changes of mind. Error bars show 95% CIs. *** = p < .001; ** = p < .01; * = p < .05. (|DV|= absolute difference in value; RT = reaction time; SV= summed value; GSF = gaze shift frequency; |DDT|= absolute difference in dwell time) (c) Correlation

between metacognitive accuracy and the coefficients for confidence ratings predicting future changes of mind (highlighted in pale green). Participants with greater metacognitive accuracy are

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more likely to change their mind following a low-confidence judgment; note that the correlation is negative because the relationship between confidence and changes of mind is itself negative (lower confidence increases the probability of subsequent changes of mind). Participants from Experiment 3 are represented by black dots, participants from Experiment 4 are represented by grey squares. Both axes (x and y) are z-scored for each experiment separately.