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The ASE is calculated on the ratio by taking |1-ratio|. Results Table ?Table11 shows the Ratio and ASE for the two time judgments (prediction and estimation), by MAO expertise (experts vs. intermediates). To compare these judgments and assess if the expertise produced an effect of these judgments, a 2 �� 2 factorial design ANOVA was first conducted on the Ratio, with time judgment being a repeated-measure factor and expertise a between-subject factor. The ANOVA revealed a significant expertise effect, F(1,69) = 7.67, p = 0.007, ��2 = 0.100 and a significant interaction between time judgment and expertise, F(1,66) = 4.55, p = 0.036, ��2 = 0.062. A breakdown of the interaction revealed that expert runners were closer to 1 than intermediate for both temporal judgments. Furthermore, for the expert runners, the estimated time was more precise than the predicted time, while there was no difference between these two temporal judgments for the intermediate runners. The same ANOVA design was used and conducted on the ASE. This time, only the effect of expertise is significant F(1,69) = 13.371, p �� 0.001, ��2 = 0.109, showing that experts are more accurate for both tasks. TABLE 1 Mean (M) Ratio and ASE as a function of the task and expertise level. Since the previous analyses are based on self reported group attribution, the relation between expertise and temporal performance was further analyzed with correlational analyses. Indeed, correlations between the number of training per week and perceived time were calculated. They show that the more weekly training sessions a runner complete, the more precise the temporal judgments are, and this finding applies to both prediction (R = �C0.575, p �� 0.001 for the ASE and R = �C0.403, p �� 0.001 for the Ratio) and estimation (R = �C0.498, p �� 0.001 for the ASE and R = �C0.248, p = 0.036 for the Ratio). Furthermore, runners were asked to report the frequency to which they use measures of distance, time, and speed. Correlational analyses were conducted to assess if the use of a specific feedback was associated with temporal accuracy (again using the percentage of error). The analyses revealed that the use of speed was the only feedback type that correlated significantly with time prediction (R = �C0.285, p = 0.019 for the ASE and R = �C0.239, p = 0.033). Thus, the more runners reported using measures of running speed while training (regardless of their expertise levels), the more precise was their predicted time. A mediation analysis revealed that the use of speed-related feedbacks did not mediate the effect of expertise. Although correlated to predicted time, the usage of feedback was not correlated to estimated time. Discussion This section will first discuss about the effect of extensive training on temporal performance and secondly, will contrast the prediction and the subsequent estimation.