One could easily argue that the most commonly studied stimulus set in experimental psychology involves English words. The study of the memory and reading of words has been central to research since Cattell (1886). Words are well-described units that provide the link between perception and meaning, and so have been critical to developments in computational modeling (e.g., McClelland & Rumelhart, 1981), neuroimaging (e.g., Petersen, Fox, Posner, Mintun, & Raichle, 1989, and conceptions of attention and automaticity (e.g., Neely, 1977;Stroop, 1935), among many other research areas.Given the importance of words as a stimulus set, one might assume that there are relatively straightforward ways to study lexical processing, and that there is a wellconstrained set of findings to which one can appeal in building models of word processing. Although there has been considerable progress in understanding how people process words, there are some clear gaps in the available literature. This paper describes the English Lexicon Project (ELP), which provides a behavioral database for over 40,000 words and nonwords that will help fill some of these gaps. The present description will focus on visual word recognition, although, as described below, the current database has relevance for other aspects of word processing, such as memory and speech production. Before describing the ELP, we will briefly describe the behavioral measures in the database, the limitations in our current knowledge, and how this database will help address these limitations. LEXICAL DECISIONS AND NAMING AS THE BEHAVIORAL TARGETSAlthough there are multiple ways to measure lexical processing (e.g., eye-fixation data, probability of iden- The English Lexicon Project is a multiuniversity effort to provide a standardized behavioral and descriptive data set for 40,481 words and 40,481 nonwords. It is available via the Internet at elexicon.wustl.edu. Data from 816 participants across six universities were collected in a lexical decision task (approximately 3400 responses per participant), and data from 444 participants were collected in a speeded naming task (approximately 2500 responses per participant). The present paper describes the motivation for this project, the methods used to collect the data, and the search engine that affords access to the behavioral measures and descriptive lexical statistics for these stimuli.

Speeded visual word naming and lexical decision performance are reported for 2428 words for young adults and healthy older adults. Hierarchical regression techniques were used to investigate the unique predictive variance of phonological features in the onsets, lexical variables (e.g., measures of consistency, frequency, familiarity, neighborhood size, and length), and semantic variables (e.g. imageahility and semantic connectivity). The influence of most variables was highly task dependent, with the results shedding light on recent empirical controversies in the available word recognition literature. Semantic-level variables accounted for unique variance in both speeded naming and lexical decision performance, level with the latter task producing the largest semantic-level effects. Discussion focuses on the utility of large-scale regression studies in providing a complementary approach to the standard factorial designs to investigate visual word recognition.

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The stability of proteins in aqueous solution is routinely enhanced by cosolvents such as glycerol. Glycerol is known to shift the native protein ensemble to more compact states. Glycerol also inhibits protein aggregation during the refolding of many proteins. However, mechanistic insight into protein stabilization and prevention of protein aggregation by glycerol is still lacking. In this study, we derive mechanisms of glycerol-induced protein stabilization by combining the thermodynamic framework of preferential interactions with molecular-level insight into solvent-protein interactions gained from molecular simulations. Contrary to the common conception that preferential hydration of proteins in polyol/water mixtures is determined by the molecular size of the polyol and the surface area of the protein, we present evidence that preferential hydration of proteins in glycerol/water mixtures mainly originates from electrostatic interactions that induce orientations of glycerol molecules at the protein surface such that glycerol is further excluded. These interactions shift the native protein toward more compact conformations. Moreover, glycerol preferentially interacts with large patches of contiguous hydrophobicity where glycerol acts as an amphiphilic interface between the hydrophobic surface and the polar solvent. Accordingly, we propose that glycerol prevents protein aggregation by inhibiting protein unfolding and by stabilizing aggregation-prone intermediates through preferential interactions with hydrophobic surface regions that favor amphiphilic interface orientations of glycerol. These mechanisms agree well with experimental data available in the literature, and we discuss the extent to which these mechanisms apply to other cosolvents, including polyols, arginine, and urea.

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Empirical work and models of visual word recognition have traditionally focused on group-level performance. Despite the emphasis on the prototypical reader, there is clear evidence that variation in reading skill modulates word recognition performance. In the present study, we examined differences between individuals who contributed to the English Lexicon Project (http://elexicon.wustl.edu), an online behavioral database containing nearly four million word recognition (speeded pronunciation and lexical decision) trials from over 1,200 participants. We observed considerable within- and between-session reliability across distinct sets of items, in terms of overall mean response time (RT), RT distributional characteristics, diffusion model parameters (Ratcliff, Gomez, & McKoon, 2004), and sensitivity to underlying lexical dimensions. This indicates reliably detectable individual differences in word recognition performance. In addition, higher vocabulary knowledge was associated with faster, more accurate word recognition performance, attenuated sensitivity to stimuli characteristics, and more efficient accumulation of information. Finally, in contrast to suggestions in the literature, we did not find evidence that individuals were trading-off in their utilization of lexical and nonlexical information.

Across 3 different word recognition tasks, distributional analyses were used to examine the joint effects of stimulus quality and word frequency on underlying response time distributions. Consistent with the extant literature, stimulus quality and word frequency produced additive effects in lexical decision, not only in the means but also in the shape of the response time distributions, supporting an early normalization process that is separate from processes influenced by word frequency. In contrast, speeded pronunciation and semantic classification produced interactive influences of word frequency and stimulus quality, which is a fundamental prediction from interactive activation models of lexical processing. These findings suggest that stimulus normalization is specific to lexical decision and is driven by the task's emphasis on familiarity-based information.Keywords: distributional analysis, task-specific effects, stimulus quality, word frequency, visual word recognition Beginning with Donders (1868/1969), a central goal of understanding human cognition has been to isolate constituent subprocesses through the use of mental chronometry. As there were problems with the insertion procedure Donders advocated, Sternberg (1969a) developed additive-factors logic in which one can provide leverage on the manner in which stages of information processing are organized. Specifically, one can use response time (RT) data from factorial experiments to make inferences about the modules associated with a mental process. For example, Sternberg argued that in an experiment in which two variables are manipulated, additive effects of both variables (i.e., main effects for both variables and no interaction) suggest that the variables influence separately modifiable processing stages. In contrast, interactive effects are more consistent with the variables influencing at least one stage in common.In a classic application of additive-factors logic, stimulus quality (intact vs. degraded) and set size (number of items in memory) were manipulated in a memory search task, and these two factors were found to be additive (Sternberg, 1967(Sternberg, , 1969b. These additive effects were interpreted as being consistent with a stage model of memory search, in which stimulus quality influences an early encoding stage, and set size influences a subsequent serial comparison stage (see Figure 1). In contrast, factors that interact are assumed to influence a common processing locus. For example, Becker (1979) investigated the effects of word frequency (high vs. low frequency) and semantic context (related vs. unrelated context) on word recognition and reported that word frequency interacts with semantic context. This suggests that word frequency and semantic context influence a common stage.Following this early classic work, demonstrations of clear additivity have been observed across diverse studies (see Sternberg, 1998, for an extensive review; see also Roberts, 1987;Sanders, 1990), supporting the claim that additive effects reflect discrete sta...