Transcranial Direct Current Stimulation over the Parietal Cortex Improves Approximate Numerical Averaging

The parietal cortex has been implicated in a variety of numerosity and numerical cognition tasks and was proposed to encompass dedicated neural populations that are tuned for analogue magnitudes as well as for symbolic numerals. Nonetheless, it remains unknown whether the parietal cortex plays a role in approximate numerical averaging (rapid, yet coarse computation of numbers' mean)—a process that is fundamental to preference formation and decision-making. To causally investigate the role of the parietal cortex in numerical averaging, we have conducted a transcranial direct current stimulation (tDCS) study, in which participants were presented with rapid sequences of numbers and asked to convey their intuitive estimation of each sequence's average. During the task, the participants underwent anodal (excitatory) tDCS (or sham), applied either on a parietal or a frontal region. We found that, although participants exhibit above-chance accuracy in estimating the average of numerical sequences, they did so with higher precision under parietal stimulation. In a second experiment, we have replicated this finding and confirmed that the effect is number-specific rather than domain-general or attentional. We present a neurocomputational model postulating population-coding underlying rapid numerical averaging to account for our findings. According to this model, stimulation of the parietal cortex elevates neural activity in number-tuned dedicated detectors, leading to increase in the system's signal-to-noise level and thus resulting in more precise estimations.

Distribution Characteristics as Predictors of Error in Intuitive Estimation of Means

33 university students made intuitive estimates of the means of 27 lists of two-digit whole numbers. The lists varied independently with respect to length (3, 5 and 7 numbers), standard deviation (approximately 6, 12 and 24) and distribution shape (symmetrical, positively skewed and negatively skewed). Multiple regression analysis showed these three variables taken together to be a fairly efficient set of predictors of absolute estimation error. List standard deviation was the best predictor, with list-length and distribution-shape accounting for much smaller proportions of criterion variance.