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Top-down feedback normalizes distortion in early visual word recognition: Insights from masked priming

Fernández-López, Maria
•
Solaja, Olga
•
Crepaldi, Davide
•
Perea, Manuel
2024
  • journal article

Periodico
PSYCHONOMIC BULLETIN & REVIEW
Abstract
The identification of visually presented words tolerates distortions in the input format, as Hannagan et al. Plos One, 7, e32121, (2012) demonstrated in a masked priming lexical decision task, showing sizable identity-priming effects with CAPTCHA-like primes. This tolerance to distortion has two potential explanations: bottom-up normalization in the encoding stage (Dehaene et al., Trends in Cognitive Sciences, 9, 335–341, 2005) or top-down lexical feedback (McClelland & Rumelhart, Psychological Review, 88, 375–407, 1981). To disentangle the predictions of these accounts, we conducted two masked identity-priming experiments with printed and CAPTCHA-like distorted primes on high- and low-frequency words. The rationale was that, in the distorted format, high-frequency words would benefit more from top-down feedback than low-frequency words. Results in the lexical decision experiment showed that, for high-frequency words, identity-priming effects were only slightly greater for printed than for CAPTCHA-like primes, whereas this difference was larger for low-frequency words. In contrast, when employing the same-different matching task, which does not require lexical access, the identity-priming effect was greater for printed primes and was unaffected by word frequency. Thus, during lexical access, top-down feedback may help normalize the visual input in the early stages of word recognition, challenging bottom-up models of visual word recognition.
DOI
10.3758/s13423-024-02585-2
WOS
WOS:001335340600001
Archivio
https://hdl.handle.net/20.500.11767/143533
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85207266384
https://pubmed.ncbi.nlm.nih.gov/39424754/
https://ricerca.unityfvg.it/handle/20.500.11767/143533
Diritti
open access
Soggetti
  • Lexical acce

  • Masked priming

  • Models of word recogn...

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