A general-purpose mechanism of visual feature association in visual word identification and beyond

Abstract

Writing systems are a recent cultural invention, which makes it unlikely that specific cognitive mechanisms have developed through selective pressure for reading itself. Instead, reading might capitalize on evolutionary older mechanisms that originally supported other tasks. Accordingly, animals such as baboons can be trained to perform visual word recognition. This suggests that the visual mechanisms supporting reading might be phylogenetically old and domain-general. Here we propose that if the human reading system relies on domain-general visual mechanisms, effects that are typically found within the domain of reading should also be observable with non-orthographic visual stimuli. To test this hypothesis, we systematically tested different types of visual material with the same experimental design. Subjects were passively familiarized with a set of composite visual items, and then tested in an oddball paradigm for their ability to detect novel stimuli. Some of these novel stimuli shared their statistical structure with the familiar items, and were found to be hard to detect in two experiments using strings of letter-like symbols; this replicates the well-known, and supposedly reading-specific, bigram effect. Crucially, in two further experiments we show that the same effect emerges with made-up, 3D objects and sinusoidal gratings. The effect size was equivalent across experiments, despite the use of radically different stimuli. These data suggest that a fundamental mechanism behind visual word learning also supports the learning of other visual stimuli, implying that such mechanism is general-purpose. This mechanism would enable the statistical learning of regularities in the visual environment.

Out-of-vocabulary but not meaningless: Evidence for semantic-priming effects in pseudoword processing

“…These findings well complement non-arbitrary perspectives on language processing (Dingemanse et al, 2015). The semantic effect found for pseudowords can indeed be explained with reference to the humans' tendency to detect systematic and statistical regularities in the (language) environment (Romberg & Saffran, 2010;Vidal, Viviani, Zoccolan, & Crepaldi, 2021), here expressed in the form of nuanced distributional patterns of sub-word information. Accordingly, seminal studies have shown that children can extract syllabic patterns based on transitional probabilities between elements, when these patterns are presented in a continuous stream (Saffran, Aslin, & Newport, 1996).…”

Direct brain recordings reveal continuous encoding of structure in random stimuli

“…Since a-priori, the presence of patterns within stimuli are unknown, the brain might automatically encode their TP in order to detect potential structure and violations of such. Artificial grammar learning studies, where subjects learn patterns of TP to which they were never exposed before, confirm the relevance of this TP encoding in language learning (15,31,32,36,53).…”

Real-world size of objects serves as an axis of object space

“…Object space is then proposed as a framework to simplify and abstract visual object representation by projecting critical features into a representational space with a finite number of orthogonal axes (2,4,5,27,28). The finite number of axes prevents the confusion of processing infinite visual details within a limited time, and statistical orthogonality among axes effectively reduces the redundancy of visual details (29,30). Moreover, the existence of object space also permits perceptual invariance (2,28) and generalization (5) during object recognition.…”