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It was predicted that there should be a lack of bias in the LTU unforced choice condition, placing the hypothesis on the side of the null. To negotiate this issue, a Bayes factor was calculated. The Bayes factor pits two competing models against each other (e.g., a null and alternative hypothesis) . As a result, it can either find (a) positive evidence in favor of the null, (b) positive evidence in favor of the alternative, or (c) the data are inconclusive, e.g. due to a lack of power. In this way we can ask whether our data show evidence of a lack of bias, rather than merely an inconclusive result that may be due to a lack of power.6 The BayesFactor package version 0.9.4 (Morey & Rouder, 2013) designed for R (R Development Core, 2011) was used to calculate the Bayes factor. A prior Cauchy distribution with a scaling factor of r = √2/2 was used over the standardized effect sizes, as recommended by Rouder, Speckman, Sun, Morey and Iverson (2009; see also Jeffreys, 1961; Zellner & Siow, 1980).7 Results A Bayes factor was calculated to further examine the lack of bias difference in the LTU condition when judging items learnt under cognitive load versus items learnt without load. The odds of no difference in bias to there being a difference in bias are 10.88 to 1. Put another way, in order to prefer the Spinozan interpretation of there being a bias difference, we would need prior odds favoring it of greater than 10.88. Thus the data show strong support for there being no difference in bias between interrupted and uninterrupted items in the LTU condition. As a (very) rough guide, Bayes factors between 1 and 3 are generally considered to be indicative of a lack of power. However, we caution against using these arbitrary cut-off values as a categorical boundary in the way that NHST has adopted p=0.05 as its categorical boundary of significance. This prior also has the advantages of being readily computable and gives a stable integration of the likelihood. Appendix B: Three-Way ANOVA on PTJ A 2 (response condition: LT or LTU, between subjects) x 2 (speaker veracity: lie or truth, within subjects) x 2 (cognitive load: load or no load during learning, within subjects) mixed ANOVA was conducted on the PTJ. The critical analyses related to the Spinozan and Cartesian accounts are presented in the main body of the text. Here, unplanned comparisons for the remaining main effects and interactions are reported. We recommend caution in interpreting these unplanned analyses. The main effect of interruption was negligible, F (1, 79) = 1.43, p = .236, ηp2 = 0.02. The interaction between response condition (LT or LTU) and speaker veracity was also of a very small effect size, F (1, 79) = 1.42, p = .236, ηp2 = 0.02, as was the interaction between cognitive load and speaker veracity, F (1, 79) = 0.73, p = .396, ηp2 = 0.01. Finally, the three-way interaction also had a negligible effect size, F (1, 79) = 0.07, p = .787, ηp2 < 0.01.