Wagers for work: Decomposing the costs of cognitive effort
Fig 2
Model-agnostic behavioral results.
A. Distributions of mean accuracies across all subjects for the default task (1-detect), and the three rated tasks (1-back, 3-detect, and 2-back). The black bars depict the means and standard errors of the mean (SEMs) of each distribution. The distribution of all subjects’ mean accuracies was plotted using a Gaussian kernel via violin.m. All mean accuracies for each task were significantly different from each other (all p’s < 0.001). B. Distributions of mean fair wages across all subjects for the three rated tasks. The lowest possible rating was 1, and the highest possible rating was 5. The black bars depict the means and SEMs of each distribution. The distribution of ratings was plotted using violin.m. **** indicates significance at the p < 0.0001 level. C. Mean accuracy across all subjects on each iteration of each task. Due to the stochasticity inherent to the BDM auction procedure, individual subjects completed the 1-back, 3-detect, and 2-back tasks a variable number of times, but a maximum of 11 times each. The relative number of subjects who completed each iteration is depicted by the size of the dot plotted at the mean. Error bars are SEMs. A two-way ANOVA of task and task iteration revealed a main effect of task identity (F = 15, p < 0.0001) but no effect of task iteration (F = 1.3, p > 0.05). Thus mean accuracy was different across tasks but did not change with task experience. D. Mean fair wage rating by rating number, where the maximum is 11 ratings of one task. A 2-way ANOVA on BDM ratings showed a main effect of task identity (Table 1; F = 33; p < 0.0001) and a main effect of task iteration (Fig 1; F = 21; p < 0.0001). Error bars are SEMs.