Non-Human Recognition of Orthography: How is it implemented and how does it differ from Human orthographic processing

The ability to robustly recognize strings of letters, a cornerstone of reading, was observed in baboons and pigeons despite their lack of phonological and semantic knowledge. Here, we apply a comparative modeling approach to investigate the neuro-cognitive basis of orthographic decision behavior in humans, baboons, and pigeons, addressing whether phylogenetic relatedness entails similar underlying neuro-cognitive phenotypes. We use the highly transparent SpeechLess Reader (SLR) model, which assumes letter-string recognition based on a computational implementation of predictive coding, whereby orthographic decisions rely on prediction-error signals emerging from multiple representational levels: visual-pixel, letter, and letter-sequence representations. We investigate which representations species use during successful orthographic decision-making. We introduce multiple SLR variants, each including one or more prediction-error representations, and compare the simulations of each variant with orthographic decisions from individuals of three species after learning letter strings without meaning. Humans predominantly relied on letter-sequence-level representations, resulting in the highest task performance in behavior and model simulations. Baboons also relied on sequence-based representations, but in combination with pixel- and letter-level representations. In contrast, pigeons relied more on pixel and letter-level representations. These findings suggest that the orthographic representations used in orthographic decisions reflect phylogenetic distance: Humans and baboons use more similar representations than pigeons. Overall, the description of orthographic decisions based on a small set of representations and computations was highly successful in describing behavior, even for humans who mastered reading in its entirety.