Ancient visual channels have a causal role in arithmetic calculations

Abstract

Humans exhibit complex arithmetic skills, often attributed to our exceptionally large neocortex. However, the past decade has provided ample evidence that the functional domain of the subcortex extends well beyond basic functions. Using a sensitive behavioral method, for the first time, we explored the contributions of lower-order visual monocular channels to symbolic arithmetic operations, addition and subtraction. The pattern of results from 4 different experiments provides converging evidence for a causal relation between mental arithmetic and primitive subcortical regions. The results have major implications for our understanding of the neuroevolutionary development of general numerical abilities-subcortical regions, which are shared across different species, are essential to complex numerical operations. In a bigger conceptual framework, these findings and others call for a shift from the modal view of the exclusive role of the neocortex in high-level cognition to a view that emphasizes the interplay between subcortical and cortical brain networks.

Figure 1

Schematic illustration of the experimental apparatus and visual pathways from the eyes to the brain. Each side of the computer monitor provided visual information to a different eye. From the eye, the visual information passes first through monocularly segregated subcortical regions (grey lines: left eye; black lines: right eye). This information is then projected to the lateral geniculate nucleus (LGN) and subsequently reaches striate and binocular extrastriate regions.

Figure 2

The verification task in which participants were asked to solve equations (by subtraction and addition) composed of an arithmetic problem of three numbers and a solution (e.g., 5 + 2 + 1 = 8). The three numbers and the equation’s solution were either presented: (A) All in one-eye or (B) Solution to a different-eye: arithmetic problem to one eye and the solution to the other eye or (C) Computational term split: one of the numbers to one eye and the other two numbers and the solution to the other eye.

Figure 3

RT as a function of eye-of-origin in Experiment 1. Error bars = SEM. *** = p < .001.

Figure 4

RT as a function of eye-of-origin when equations were vertically aligned. Error bars = SEM. ** = p < .005.

Figure 5

The verification task in which participants were asked to solve equations (by subtraction and addition). The four eye-of-origin conditions were: (A) all in one-eye; (B) each equation in a different-eye; (C) left-hand computational term split; (D) right-hand computational term split.

Figure 6

RT as a function of eye-of-origin in Experiment 3. Error bars = SEM. * = p < .01.

Figure 7

RT as a function of eye-of-origin in Experiment 4. Error bars = SEM. No significant effect of eye-of-origin was found.