Exploring pattern recognition: what is the relationship between the recognition of words, faces and other objects?

Abstract

Debate surrounds processes of visual recognition, with no consensus as to whether recognition of distinct object categories (faces, bodies, cars, and words) is domain specific or subserved by domain-general visual recognition mechanisms. Here, we investigated correlations between the performance of 74 participants on recognition tasks for words, faces and other object categories. Participants completed a counter-balanced test battery of the Cambridge Face, Car and Body Parts Memory tests, as well as a standard four category lexical decision task, with response time and recognition accuracy as dependent variables. Results revealed significant correlations across domains for both recognition accuracy and response time, providing some support for domain-general pattern recognition. Further exploration of the data using principal component analysis (PCA) revealed a two-component model for both the response time and accuracy data. However, how the various word and object recognition tasks fitted these components varied considerably but did hint at familiarity/expertise as a common factor. In sum, we argue a complex relationship exists between domain-specific processing and domain-general processing, but that this is shaped by expertise. To further our understanding of pattern recognition, research investigating the recognition of words, faces and other objects in dyslexic individuals is recommended, as is research exploiting neuroimaging methodologies, with excellent temporal resolution, to chart the temporal specifics of different forms of visual pattern recognition.

Domain Specificity vs. Domain Generality: The Case of Faces and Words

“…Indeed, the majority of variance in recognition of novel object categories can be reduced to such a factor, hinting at the presence of a common system dedicated to processing of novel visual stimuli, regardless of their category [58]. Maratos and collaborators [59] extended these conclusions to known objects, by finding an underlying component that reflects performance for bodies, cars, faces, and words. This general recognition factor is also found in super recognizers (i.e., with superior face recognition abilities), who are better than controls in recognizing other visual objects, such as words [60].…”