Neural tuning for ordinal processing

Processing ordinality, i.e., the rank of an item in a series such as 1st, 2nd, 3rd, etc., is a fundamental skill shared by humans and animals. While humans often use symbolic sequences like numbers or letters, ordinality does not depend on language or symbols. Across species, ordinality plays a critical role in behaviors such as decision-making, foraging, and social organization. We hypothesize that ordinality perception is supported by neuronal tuning, i.e., neurons selectively responsive to specific ranks. Using ultra-high field 7T fMRI and population receptive field (pRF) modeling in human participants (both female and male), we identified neural populations in parietal and premotor cortices that are tuned to non-symbolic ordinal positions. Comparable to other sensory domains, tuning width increased with preferred ordinal rank, suggesting reduced precision and potentially lower perceptual accuracy for higher ranks. Additionally, pRF measurements revealed that cortical territory devoted to higher ordinalities decreased with rank, reinforcing that neural precision is greatest for early positions (e.g., 1st and 2nd) and declines with rank. These responses did not generalize to symbolic ordinality. Similar tuning to non-symbolic ordinality emerged spontaneously in hierarchical convolutional neural networks trained on visual tasks. Together, these results suggest that the tuning properties of these neuronal populations support non-symbolic ordinality perception, and may reflect an inherent feature of neural processing.Significance Statement Processing ordinality, the rank of items in sequences, is a fundamental skill shared across humans and animals that plays a role in decision-making, foraging, and social organization. We hypothesized that ordinality processing relies on neuronal tuning where neurons selectively respond to particular ranks. Using ultra-high field 7T fMRI and population receptive field modeling, we identified neural populations in parietal and premotor cortices tuned to non-symbolic ordinal positions. Additionally, similar tuning responses were found to spontaneously emerge in hierarchical convolutional neural networks trained on a visual task. Our findings demonstrate that akin to other forms of quantity representation, neuronal tuning underlies non-symbolic ordinality perception. These results shed light on the neuronal processing of ordinality in the human brain.