| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| 1.55 MB | Adobe PDF |
Authors
Abstract(s)
The Neuronal Recycling Hypothesis (Dehaene, 2005; Dehaene & Cohen, 2007) proposes that the efficient computation and representation of written words at the orthographic stage of processing is enabled through the adaptation of pre-existing visual functions, which in turn, lead to the emergence of a specialised reading system. The present
thesis aimed to investigate the emergence of neural detectors tuned to letters through
biologically plausible computational models. A Deep Belief Network (DBN) was implemented as a model of visual shape perception, inspired by Testolin et al. (2017), and used
to answer two questions: 1) does the DBN model generalise shape information that was
learned from images of geometrical shapes towards classification of letters and pseudoletters (i.e., nonletters sharing the same features as letters); for example, classifying A
as a triangle?; 2) is visual shape processing by a DBN sensitive to the same integration
processes as those reflected in crowding effects (i.e., integration of adjacent information)
by human observers; namely, by the congruency effect (better performance for targets
surrounding by congruent than incongruent shapes)? The results showed that classification of letters and pseudoletters by our DBN was nonuniform across the different tested
letter fonts, thus suggesting that decisions were not led by global shape. Interestingly,
our model exhibited a congruence effect, and hence, a perceptual strategy similar to that
previously found in illiterate adults (Fernandes et al., 2014). These results and further
analyses also showed that our model’s perceptual strategy was not driven by low-level
pixel similarities. The present work sets the stage to further emulate the transition from
the illiterate to the ex-illiterate state, as done in the work of Hannagan et al. (2021)
but with biologically more plausible learning algorithms (Bengio et al., 2015; Hinton &
Salakhutdinov, 2006).
Description
Tese de Mestrado, Ciência Cognitiva, 2023, Universidade de Lisboa, Faculdade de Ciências
Keywords
Reconhecimento visual de palavras Detector de letras Redes neurais Aprendizagem não supervisionada Redes Deep Belief Teses de mestrado - 2023