Imageability ratings for 3,000 monosyllabic words

By Michael J Cortese, April Fugett. 2004

328 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Developmental and Educational Psychology, Cognitive Science & Psychology

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In Cognitive Neuroscience

In Developmental and Educational Psychology

In Cognitive Science & Psychology

Citations in the corpus

Towards fairer datasets: filtering and balancing the distribution of the people subtree in the ImageNet hierarchy (2019, background): “Extensive research in psycholinguistics has studied the imageability (a.k.a. imagery) of words [5, 9, 17, 32, 57, 58], which is defined as “the ease with which the word arouses imagery” [58].”
Visual word recognition of multisyllabic words (2009, background): “Furthermore, from a practical standpoint, many of the measures available for monosyllabic words, such as phonological consistency (Balota, Cortese, Sergent-Marshall, Spieler, & Yap, 2004), familiarity (Balota, Pilotti, & Cortese, 2001), and imageability ( Cortese & Fugett, 2004 ), simply have not be”
Visual word recognition of single-syllable words. (2004, methodology): “Finally, as shown in Table 8, the semantic measures included in Step 3 indicate that there is again evidence of semantic variables influencing performance more in lexical decision than in naming, and again there is a consistent influence of the Cortese and Fugett (2003) imageability estimates.”
Morphological influences on the recognition of monosyllabic monomorphemic words (2006, background): “The second block included imageability based on Cortese and Fugett (2004) and the third block included semantic connectivity measures from Steyvers and Tanenbaum (2005).”
Modulation of the semantic system by word imageability (2005, methodology): “by Bird and colleagues (2001) and Cortese and Fugett (2004) .”
The spatial coding model of visual word identification. (2010, methodology): “The model that I develop in the present article draws on key aspects of the SOLAR model, notably the spatial coding scheme described in Davis (1999), the superposition matching algorithm subsequently developed in Davis (2001, 2004; see also Davis & Bowers, 2006), and the opponent processing model of”