On the role of word frequency in the detection of component letters

Abstract

The experiments reported in this study were conducted to explore the issue of race models versus holistic models of word processing. In both types of model, it is assumed that an available word-level encoding for a display will conceal letter information, and thereby inhibit component-letter detection. However, whereas in holistic models it is assumed that encoding always should occur at the word or pattern level first, in the race models it is assumed that encoding occurs at all levels (e.g., feature, letter, and word) simultaneously, with the final level of encoding being at whatever level has been completed first. If the rate of word-level encoding is facilitated by increasing word frequency, the holistic models predict a generally declining latency for letter detection, because the initial step in letter detection (i.e., word-level encoding) will be occurring more rapidly. The race models, on the other hand, predict that with increasing word frequency there will be an increasing chance that the word-level encoding will win the encoding race, resulting in an increase in the latency for letter detection (i.e., the word code will conceal the letter codes). Two experiments are reported, and the obtained pattern of latency data appears to be most consistent with the race models.