The effect of 'masking' on picture naming.
It is frequently assumed that because compared to nonliving things, living things are less familiar, have lower name frequency, and are more visually complex, this makes them more difficult to name by patients and normal subjects. This has also been implicitly accepted as an explanation for the greater incidence of living thing disorders. Patient studies do not, however, typically contain any premorbid data and so, we do not know that the same variables would have necessarily predicted their 'normal' performance. To examine this issue, we measured picture-naming latencies in normal subjects presented with unmasked and masked versions of the same line drawings. In accord with other recent studies, living things were named faster than nonliving things. Furthermore, contrary to some theories of category naming, the living thing advantage persisted regardless of whether stimuli were undegraded, degraded or the density of degradation. Finally, multiple simultaneous regression analyses showed that one visual variable (Euclidean Overlap) and one linguistic variable (Age of Acquisition) predicted naming latencies across all masked and unmasked conditions. Other variables either had no predictive value (Contour Overlap; Name Frequency; Category); predicted only high masking (Visual Complexity; Familiarity), or normal and low masking (Number of Phonemes). These findings imply that the more commonly documented deficits for living things do not reflect an exaggeration of the normal profile (be it with masked or unmasked stimuli) or the influence of the same variables that affect normal naming.
Laws
Keith R
KR
Department of Psychology, London Guildhall University, UK. klaws@lgu.ac.uk
Leeson
Verity C
VC
Gale
Tim M
TM
D000328:Adult; D000367:Age Factors; D003071:Cognition; D004193:Discrimination Learning; D005260:Female; D006801:Humans; D008297:Male; D009275:Names; D010364:Pattern Recognition, Visual / Q000502:physiology*; D010470:Perceptual Masking / Q000502:physiology*; D011930:Reaction Time; D012044:Regression Analysis; D012660:Semantics