Figure 2

Individual data for the bodyline task. (A) Histogram showing the distribution of coefficients of determination (R²) for the linear fit. Most are above 0.95, suggesting that the categories were perceived as equidistant, and mapped accurately, save for a scaling constant. (B) Regression indexes of the individual subjects (1 minus the slope of best fitting linear regression to their bodyline data) as a function of precision thresholds, defined as average standard deviations for judgments at each category. There is no significant correlation between the two variables. (C) Magnitude of serial dependence of individual subjects (defined as the slope of the regression line for similar previous body sizes, illustrated in Fig. 3) as a function of regression index. Again there is no significant correlation, indicating that the two processes are independent. (D) Magnitude of serial dependence as a function of precision thresholds. There is a strong and significant correlation, with higher thresholds leading to greater dependency, as predicted by the Kalman filter model (eqn. 8). The top right data point in (D) is not an outlier but nevertheless we re-ran the analysis without this individual. The correlation remained highly significant: r₍₁₀₂₎ = 0.56, p < 0.0001.