We present a new model for lexical decision, REM-LD, that is based on REM theory (e.g., Shiffrin & Steyvers, 1997). REM-LD uses a principled (i.e., Bayes' rule) decision process that simultaneously considers the diagnosticity of the evidence for the #WORD# response and the #NONWORD# response. The model calculates the odds ratio that the presented stimulus is a word or a nonword by averaging likelihood ratios for lexical entries from a small neighborhood of similar words. We report two experiments that used a signal-to-respond paradigm to obtain information about the time course of lexical processing. Experiment 1 verified the prediction of the model that the frequency of the word stimuli affects performance for nonword

& An important method for studying how the brain processes familiar stimuli is to present the same item on more than one occasion and measure how responses change with repetition. Here we use repetition priming in a sparse functional magnetic resonance imaging (fMRI) study to probe the neuroanatomical basis of spoken word recognition and the representations of spoken words that mediate repetition priming effects. Participants made lexical decisions to words and pseudowords spoken by a male or female voice that were presented twice, with half of the repetitions in a different voice. Behavioral and neural priming was observed for both words and pseudowords and was not affected by voice changes. The fMRI data revealed an elevated response to words compared to pseudowords in both posterior and anterior temporal regions, suggesting that both contribute to word recognition. Both reduced and elevated activation for second presentations (repetition suppression and enhancement) were observed in frontal and posterior regions. Correlations between behavioral priming and neural repetition suppression were observed in frontal regions, suggesting that repetition priming effects for spoken words reflect changes within systems involved in generating behavioral responses. Based on the current results, these processes are sufficiently abstract to display priming despite changes in the physical form of the stimulus and operate equivalently for words and pseudowords. &

A key claim of current theoretical analyses of the memory impairments associated with amnesia is that certain distinct forms of learning and memory are spared. A compelling example is that amnesic patients and controls are indistinguishable in repetition priming but amnesic patients are impaired at recognizing the study items. The authors show that this pattern of results is predicted by a single-system connectionist model of learning in which amnesia is simulated by a reduced learning rate. They also demonstrate that the model can reproduce the converse pattern in which priming but not recognition is impaired if the input is assumed to be additionally degraded in a priming test. The authors conclude that dissociations between priming and recognition do not require functionally or neurally distinct memory systems. According to an influential view, memory is not a unitary faculty but is composed of multiple systems that work independently of each other (Gabrieli, 1998; Squire, 1994). The most prominent distinction that has been proposed is between declarative and nondeclarative memory. Declarative (or explicit) memory is usually characterized by the conscious and intentional recollection of knowledge. Typical tests of declarative memory involve

this article. Mean proportions of correctly identified target words in the masked word identification test are shown in Figure 1. These data were submitted to an analysis of variance (ANOVA) with modality of the generation cues (visual and auditory) as a between-subjects factor and encoding task (read, generate, and nonstudied) as a within-subjects factor. The effect of encoding task was significant, F(2, 92) = 27.29, MSE = 0.010. The effect of modality of generation cues approached significance, F(1, 46) = 3.43, MSE = 0.112, p = .07. The interaction of these two factors was not significant, F < 1. Figure 1 indicates that the read and the generate encoding tasks produced similar priming effects, relative to the nonstudied condition. This pattern held for both modality groups. Pairwise comparisons collapsing across modality groups and using the interaction error term from the ANOVA showed that although read and generate encoding conditions did not differ reliably (d = .03), F(1, 92) = 2.28, both produced priming relative to the nonstudied condition, F(1, 92) = 49.25, and F(1, 92) = 30.35, for read (d = .14) and generate (d = .11), respectively

We tested and confirmed the hypothesis that the prior presentation of nonwords in lexical decision is the net result of two opposing processes:(1) a relatively fast inhibitory process based on global familiarity, and (2) a relatively slow facilitatory process based on the retrieval of specific episodic information. In three studies, we manipulated speed-stress to influence the balance between the two processes. Experiment 1 showed item-specific improvement for repeated nonwords in a standard `respond-when-ready' lexical decision task. Experiment 2 used a 400 ms deadline procedure and showed performance for nonwords to be unaffected by up to four prior presentations. In Experiment 3 we used a signal-torespond procedure with variable time intervals and found negative repetition priming for repeated nonwords. These results can be accounted for by dual process models of lexical decision (e.g., Balota & Chumbley, 1984; Balota & Spieler, 1999). One of the most often used tasks in the field of visual word recognition is the lexical decision task. In lexical decision, participants have to decide as quickly and accurately as possible whether a presented letter string is a word (e.g., CHAIR) or a nonword (e.g., GREACH). The general assumption that underlies the use of the lexical decision task is that the speed and accuracy of responding to word stimuli indicate the efficiency with which word representations are activated or retrieved from lexical memory. Several variables are thought to reflect the speed of retrieval from lexical memory. For instance, Scarborough, Cortese, and Scarborough (1977) found that performance for high frequency words was better than performance for low frequency words. This phenomenon is known as the word frequency effect. Another extensively studied ph...

this memory to guide target processing (e.g., Forster & Davis, 1984) and (2) modulating their use of the primes in a context-sensitive manner (e.g., Forster, 1998). If these two assumptions are correct, masking the primes might actually provide an especially clear view of the specialized mechanisms responsible for visual word recognition

We present a new model for lexical decision, REM-LD, that is based on REM theory (e.g., Shiffrin & Steyvers, 1997). REM-LD uses a principled (i.e., Bayes ’ rule) decision process that simultaneously considers the diagnosticity of the evidence for the ‘WORD ’ response and the ‘NONWORD ’ response. The model calculates the odds ratio that the presented stimulus is a word or a nonword by accumulating likelihood ratios for each lexical entry in a small neighborhood of similar words. We report two experiments that used the signal-to-respond paradigm to obtain information about the time course of lexical processing. Experiment 1 verified the prediction of the model that the frequency of the word stimuli affects performance for nonword stimuli. Experiment 2 was done to study the effects of nonword lexicality, word frequency, and repetition priming and to demonstrate how REM-LD can account for the observed results. We discuss how REM-LD can be extended to account for effects of phonology such as the pseudohomophone effect, and how REM-LD can predict response times in the popular ‘respond-when-ready ’ paradigm. Several other quantitative models of lexical decision are evaluated with respect to the findings reported here.

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This paper present a new readability formula for French as a foreign language (FFL), which relies on 46 textual features representative of the lexical, syntactic, and semantic levels as well as some of the specificities of the FFL context. We report comparisons between several techniques for feature selection and various learning algorithms. Our best model, based on support vector machines (SVM), significantly outperforms previous FFL formulas. We also found that semantic features behave poorly in our case, in contrast with some previous readability studies on English as a first language. 1