Age of acquisition (AoA) ratings were obtained and were used in hierarchical regression analyses to predict naming and lexical-decision performance for 2,342 words (from Balota, Cortese, Sergent-Marshall, Spieler, & Yap, 2004). In the analyses, AoA was included in addition to the set of predictors used by Balota et al. (2004). AoA significantly predicted latency performance on both tasks above and beyond the standard predictor set. However, AoA was more strongly related to lexical-decision performance than to naming performance. Finally, the previously reported effect of imageability on naming latencies by Balota et al. was not significant with AoA included as a factor. These results are consistent with the idea either that AoA has a semantic/lexical locus or that AoA effects emerge primarily in situations in which the input-output mapping is arbitrary.

Attention control training may address aberrant fluctuations in attention allocation in PTSD, thereby reducing PTSD symptoms. Further study of treatment efficacy and its underlying neurocognitive mechanisms is warranted.

In two studies, participants studied 30 lists of 50 words and were tested on 30 lists of 100 words. Item-level multiple regression analyses were conducted on hits, false alarms, hits minus false alarms, d', and C. The predictor variables were objective frequency, subjective frequency, imageability, orthographic similarity, phonological similarity, phonological-to-orthographic N (PON), age of acquisition (AoA), and word length. The regression equations accounted for 45.9% of the variance in hit rates, 14.9% of the variance in false alarm rates, and 29.2% of the variance in hits minus false alarms. Other noteworthy results were that: (a) hit rates positively correlated with false alarms, (b) objective frequency negatively correlated with both hit rates and false alarm rates, (c) AoA positively correlated with hit rates and negatively correlated with false alarm rates, (d) length negatively correlated with hit rates and positively correlated with false alarm rates, (e) orthographic uniqueness was positively correlated with hit rates and negatively correlated with false alarms, (f) PON positively correlated with false alarm rates, (g) imageability produced the typical mirror pattern, and (h) imageability and length were the strongest predictors of performance. Results were largely compatible with predictions made by single- and dual-process theories of recognition memory.

To the extent that individual differences in working memory capacity (WMC) reflect differences in attention (Baddeley, 1993; Engle, Kane, & Tuholski, 1999), differences in WMC should predict performance on visual attention tasks. Individuals who scored in the upper and lower quartiles on the OSPAN working memory test performed a modification of Egly and Homa's (1984) selective attention task. In this task, the participants identified a central letter and localized a displaced letter flashed somewhere on one of three concentric rings. When the displaced letter occurred closer to fixation than the cue implied, high-WMC, but not low-WMC, individuals showed a cost in the letter localization task. This suggests that low-WMC participants allocated attention as a spotlight, whereas those with high WMC showed flexible allocation.

Recently, much attention has been focused on the relationship between age of acquisition (AoA) and word recognition (e.g., Bonin, Barry, Méot, & Chalard, 2004;Ellis & Lambon Ralph, 2000;Monaghan & Ellis, 2002;Morrison & Ellis, 1995;Zevin & Seidenberg, 2002; for a review, see Juhasz, 2005). The present study provides age of acquisition ratings for 3,000 monosyllabic words. The primary purpose of these ratings is to provide researchers with a source of information that can be used to control, manipulate, or analyze AoA in word processing and memory studies.Some important debates in the literature on word recognition have focused on AoA and its relationship to word frequency (e.g., Zevin & Seidenberg, 2002 and imageability (e.g., Monaghan & Ellis, 2002;Strain, Patterson, & Seidenberg, 2002). In each case, there has been uncertainty regarding the extent to which each variable relates to word recognition performance because the variables in question are correlated. In the case of word frequency, high-frequency words (e.g., book) are typically acquired early, and low frequency words (e.g., boon) are typically acquired later. Similarly, in the case of imageability, high-imageable words (e.g., doll ) are typically acquired early, and low-imageable words (e.g., trust) are acquired later.Because of the high correlations among these factors, it is difficult to design experiments that cross these variables in a factorial design. Furthermore, because the AoA ratings currently available are limited to a relatively small number of words, and the number of variables that must be controlled in standard word recognition studies is high, it is difficult to generate enough items for there to be sufficient power to find an effect of AoA while controlling for the other factors.One possible remedy to this problem involves collecting reaction times for large sets of words and performing regression analyses on the data (e.g., Besner & Bourassa, 1995;Spieler & Balota, 1997). However, in the multiple regression analyses of 2,428 monosyllabic words conducted by Balota and colleagues (Balota, Cortese, Sergent-Marshall, Spieler, & Yap, 2004) on naming and lexical decision latencies and error rates, AoA was not assessed because of the low number of items in the corpus for which AoA ratings were available. This limitation of the Balota et al. (2004) study was one of the motivating factors for collecting AoA ratings found in the archive. In fact, we (Cortese & Khanna, 2007) reanalyzed the data originally analyzed by Balota et al. (2004). In our analyses, AoA predicted naming and lexical decision performance above and beyond the 22 predictor variables analyzed by Balota et al. (2004). This result has important implications for theories of word processing (e.g., Lambon Ralph & Ehsan, 2006), as well as theoretical conceptualizations of AoA (e.g., Brysbaert, Van Wijnendaele, & De Deyne, 2000). For example, when AoA was entered into the regression analysis of naming latencies, the formerly significant effect of imageability that was reported by Balo...