The diffusion model for 2-choice decisions (R. Ratcliff, 1978) was applied to data from lexical decision experiments in which word frequency, proportion of high-versus low-frequency words, and type of nonword were manipulated. The model gave a good account of all of the dependent variables -accuracy, correct and error response times, and their distributions-and provided a description of how the component processes involved in the lexical decision task were affected by experimental variables. All of the variables investigated affected the rate at which information was accumulated from the stimuli-called drift rate in the model. The different drift rates observed for the various classes of stimuli can all be explained by a 2-dimensional signal-detection representation of stimulus information. The authors discuss how this representation and the diffusion model's decision process might be integrated with current models of lexical access.The lexical decision task is one of the most widely used paradigms in psychology. The goal of the research described in this article was to account for lexical decision performance with the diffusion model (Ratcliff, 1978), a model that allows components of cognitive processing to be examined in two-choice decision tasks. Nine lexical decision experiments, manipulating a number of factors known to affect lexical decision performance, are presented. The diffusion model gives good fits to the data from all of the experiments, including mean response times for correct and error responses, the relative speeds of correct and error responses, the distributions of response times, and accuracy rates.In the diffusion model, the mechanism underlying two-choice decisions is the accumulation of noisy information from a stimulus over time. Information accumulates toward one or the other of two decision criteria until one of the criteria is reached; then the response associated with that criterion is initiated. In the lexical decision task, one of the criteria is associated with a word response, the other with a nonword response. The rate with which information is accumulated is called drift rate, and it depends on the quality of information from the stimulus. In lexical decision, some stimuli are more wordlike than others, and so their rate of accumulation of information toward the word criterion is faster; other stimuli, such as random letter strings, are so un-wordlike that information accumulates quickly toward the nonword criterion. For the nine experiments presented below, the drift rates can be summarized quite simply. First, the ordering of the drift rates from largest to smallest is as follows: high-frequency words, low-frequency words, very low-frequency words, pseudowords, and random letter NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript strings. Second, the differences among the drift rates are larger when the nonwords in an experiment are pseudowords than when they are random letter strings.For our framework, Figure 1 outlines the relationships among...

Recent research has shown that letter identity and letter position are not integral perceptual dimensions (e.g., jugde primes judge in word-recognition experiments). Most comprehensive computational models of visual word recognition (e.g., the interactive activation model, J. L. McClelland & D. E. Rumelhart, 1981, and its successors) assume that the position of each letter within a word is perfectly encoded. Thus, these models are unable to explain the presence of effects of letter transposition (trial-trail), letter migration (beard-bread), repeated letters (moose-mouse), or subset/superset effects (faulty-faculty). The authors extend R. Ratcliff's (1981) theory of order relations for encoding of letter positions and show that the model can successfully deal with these effects. The basic assumption is that letters in the visual stimulus have distributions over positions so that the representation of one letter will extend into adjacent letter positions. To test the model, the authors conducted a series of forced-choice perceptual identification experiments. The overlap model produced very good fits to the empirical data, and even a simplified 2-parameter model was capable of producing fits for 104 observed data points with a correlation coefficient of .91. Keywords lexical process; letter position coding; word recognition; modeling; perceptual matching A fundamental issue for any computational model of visual word recognition is how to represent the position in which letters are encoded. If letter position is not encoded, then anagrams like causal and casual or even desserts and stressed would not be able to be discriminated from each other. Some of the current computational models of visual word recognition make overly simplistic assumptions about how letter positions are coded, for example, that positions are perfectly encoded.The way in which letter positions are encoded needs to be a critical aspect of the front end of any computational model of visual word recognition. Letter position determines which words are considered orthographically similar and, therefore, which word representations are most likely to be selected for a particular string of letters. It also determines which words are likely to be confused with each other, especially when the stimulus is impoverished. Although the Correspondence to: Pablo Gomez.Correspondence concerning this article should be addressed to Pablo Gomez, DePaul University, Psychology Department, 2219 North Kenmore, Chicago, IL 60614. E-mail: pgomez1@condor.depaul.edu. Pablo Gomez, Psychology Department, DePaul University; Roger Ratcliff, Department of Psychology, The Ohio State University; Manuel Perea, Departmento de Metodología, Universitat de València, València, Spain. NIH Public Access Author ManuscriptPsychol Rev. Author manuscript; available in PMC 2008 December 9. Published in final edited form as:Psychol Rev. 2008 July ; 115(3): 577-600. doi:10.1037/a0012667. NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript encoding of letter posit...

Performance in the lexical decision task is highly dependent on decision criteria. These criteria can be influenced by speed versus accuracy instructions and word/nonword proportions. Experiment 1 showed that error responses speed up relative to correct responses under instructions to respond quickly. Experiment 2 showed that that responses to less probable stimuli are slower and less accurate than responses to more probable stimuli. The data from both experiments support the diffusion model for lexical decision . At the same time, the data provide evidence against the popular deadline model for lexical decision. The deadline model assumes that "nonword" responses are given only after the "word" response has timed out -consequently, the deadline model cannot account for the data from experimental conditions in which "nonword" responses are systematically faster than "word" responses.

The effects of aging on response time (RT) are examined in 2 lexical-decision experiments with young and older subjects (age 60-75). The results show that the older subjects were slower than the young subjects, but more accurate. R. diffusion model provided a good account of RTs, their distributions, and response accuracy. The fits show an 80-100-ms slowing of the nondecision components of RT for older subjects relative to young subjects and more conservative decision criterion settings for older subjects than for young subjects. The rates of accumulation of evidence were not significantly different for older compared with young subjects (less than 2% and 5% higher for older subjects relative to young subjects in the 2 experiments).Across a wide variety of cognitive tasks, research has shown that processing slows with age. For some tasks, especially those like letter discrimination that depend heavily on peripheral processes, this is not surprising (e.g., . However, for other tasks it might be expected that performance would improve with age. One such task is lexical decision, the task of interest in this article. Over a lifetime of 60 to 70 years, the number of encounters with many words must greatly exceed the number of encounters in the first 20 years. Yet despite so many years of practice, lexical-decision response times (RTs) increase with age. For example, Allen, Madden, and Crozier (1991) found average RTs of 800 ms for older adults compared with 500 ms for young adults. Word frequency effects, longer RTs with lower frequency words, are also larger for older adults (see Allen et al., 1991;Allen, Madden, Weber, & Groth, 1993;Allen, Sliwinski, & Bowie, 2002;Lima, Hale, & Myerson, 1991).Recently, Ratcliff, Gomez, and McKoon (2004) have applied the diffusion model for twochoice decisions (Ratcliff, 1978(Ratcliff, , 1981(Ratcliff, , 1985(Ratcliff, , 1988(Ratcliff, , 2002Ratcliff & Rouder, 1998Ratcliff & Smith, 2004;Ratcliff, Van Zandt, & McKoon, 1999) to lexical-decision data. The model allows processing to be separated into several components: the rate at which information about the stimulus string of letters accumulates in the decision system (which reflects the goodness of match between the test string and lexical memory), the criteria that determine the amounts of information that must be accumulated before a decision can be made, nondecision

In this article, the first explicit, theory-based comparison of 2-choice and go/no-go variants of 3 experimental tasks is presented. Prior research has questioned whether the underlying core-information processing is different for the 2 variants of a task or whether they differ mostly in response demands. The authors examined 4 different diffusion models for the go/no-go variant of each task along with a standard diffusion model for the 2-choice variant (R. Ratcliff, 1978). The 2-choice and the go/no-go models were fit to data from 4 lexical decision experiments, 1 numerosity discrimination experiment, and 1 recognition memory experiment, each with 2-choice and go/no-go variants. The models that assumed an implicit decision criterion for no-go responses produced better fits than models that did not. The best model was one in which only response criteria and the nondecisional components of processing changed between the 2 variants, supporting the view that the core information on which decisions are based is not different between them.

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In the past decades, hundreds of articles have explored the mechanisms underlying priming. Most researchers assume that masked and unmasked priming are qualitatively different. For masked priming, the effects are often assumed to reflect savings in the encoding of the target stimulus, whereas for unmasked priming, it has been suggested that the effects reflect the familiarity of the prime-target compound cue. In contrast, other researchers have claimed that masked and unmasked priming reflect essentially the same core processes. In this article, we use the diffusion model (Ratcliff, 1978) to account for the effects of masked and unmasked priming for identity and associatively related primes. The fits of the model lead us to the following conclusion: masked related primes give a head start to the processing of the target compared to unrelated primes, while unmasked priming affects primarily the quality of the lexical information.