<p>Script is a fundamental cultural technology for preserving and communicating thought. Mastering literacy is thus essential for accessing these thoughts and communicating them effectively. Hence, understanding the neuro-cognitive mechanisms underpinning the processes of learning to read is highly relevant. Here, we use two orthographic learning datasets from baboons and humans to investigate how they implement visual orthographic representations in a learning task of known and novel letter strings. We use two connectionist neural-network models (i.e., CORnet-Z and ResNet-18, CNNs) and two variants of a mechanistic neuro-cognitive model (i.e., the Speechless Reader model, SLR) specific to the reading domain to investigate orthographic learning and infer the underlying neuro-cognitive processes. The connectionist models employ neuronally plausible architectures and are used across the domain of higher vision. The SLR versions are transparent neuro-cognitive models of orthographic decision behavior. Central to the domain-specific SLR implementations are three types of prediction-error representations that we use for computational phenotyping (i.e., pixel-, letter-, and letter-sequence-level prediction-errors). This approach allows us to infer the underlying representations in orthographic decisions. First, we fit the models and simulate the datasets to compare their performance (i.e., all models see the same stimuli as humans and baboons did). Second, after comparing model performance, we evaluate how orthographic decisions have been implemented based on the representations used in the SLR models. We find that the SLR, especially on the trial-wise metrics, outperforms the CNNs in both datasets, with both connectionist models generating behavioral responses without a considerable overlap with individual human or baboon responses. Inspecting the SLR representations, we found that both species implemented the most informative representations that developed from visual to more complex orthographic representations with increased learning. Thus, we show that domain-specific neuro-cognitive mechanistic models are highly valuable in understanding complex behavior and how it is learned across species.</p>