, 1998, Sagiv and Bentin, 2001 and Taylor et al., 2001c). Object-based attentional effects (larger P1 for attended as compared to unattended faces) are also reported for faces (e.g.,
Gazzaley et al., 2008). Lexical decision tasks (requiring a word vs. non-word decision) allow the investigation of sensory-, syntactic- and semantic categorization processes. With respect to the P1 component, several studies have reported increased amplitudes with increasing orthographic neighborhood size (N), increasing word length, but decreasing word frequency, and decreasing orthographic typicality (e.g., Hauk and Pulvermüller, 2004, Hauk et al., 2006a, Hauk et al., 2006b and Segalowitz and Zheng, 2009; for a review, cf. Dien, 2009). According to Coltheart et al. (1977), N is a variable reflecting the orthographic relatedness of a letter string http://www.selleckchem.com/products/BIBF1120.html with words stored in memory. A large N indicates that many related words are stored in lexical
memory. This most likely elicits competition/inhibition which increases processing complexity during early categorization of a letter string. This seems to be indeed the case as e.g., the results from Hauk et al. (2009) show. A very similar interpretation applies for the effects of word length, because it is plausible to assume that long words increase processing complexity. In a study where the effects of word length were studied by controlling for the negative correlation with word frequency,
Hauk and Pulvermüller (2004) observed that long words produced a larger Z-VAD-FMK datasheet P1 than short words. An interesting aspect of the findings of Adenosine Hauk and Pulvermüller (2004) is that the latency of the P1-word length effect was shorter than that for word frequency. This finding suggests that word length affects early graphemic search/categorization processes that precede those related to accessing the lexicon. Thus, it appears that processing complexity affects the amplitude of the P1. If early categorization is difficult because processing complexity is high (for a large N and long words a large number of similar memory entries or features must be processed), the P1 tends to be large. A similar interpretation holds true for infrequent words and low orthographic typicality. Another interesting finding is that the P1 for words and pseudowords usually is of similar magnitude (e.g. Hauk et al., 2006a and Khateb et al., 2002). This is not surprising, if we consider the fact that pseudowords are constructed to exhibit a similar orthographic ‘surface characteristic’ as real words and that the P1 reflects early categorization (related to graphemic–phonetic features) that precedes access to lexical memory. Target-search paradigms clearly show that the P1 to the target stimulus is larger than the P1 to non-target stimuli (cf. the data reviewed by Taylor, 2002).