The quantity and nature of the processes underlying recognition memory remains an open question. A majority of behavioral, neuropsychological, and brain studies have suggested that recognition memory is supported by two dissociable processes: recollection and familiarity. It has been conversely argued, however, that recollection and familiarity map onto a single continuum of mnemonic strength and hence that recognition memory is mediated by a single process. Previous electrophysiological studies found marked dissociations between recollection and familiarity, which have been widely held as corroborating the dual-process account. However, it remains unknown whether a strength interpretation can likewise apply for these findings. Here we describe an ERP study, using a modified remember–know (RK) procedure, which allowed us to control for mnemonic strength. We find that ERPs of high and low mnemonic strength mimicked the electrophysiological distinction between R and K responses, in a lateral positive component (LPC), 500–1000 msec poststimulus onset. Critically, when contrasting strength with RK experience, by comparing weak R to strong K responses, the electrophysiological signal mapped onto strength, not onto subjective RK experience. Invoking the LPC as support for dual-process accounts may, therefore, be amiss.

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.