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Gail Mckoon - One of the best experts on this subject based on the ideXlab platform.
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Dysphoria and memory for emotional material: A Diffusion-Model analysis
Cognition and Emotion, 2009Co-Authors: Corey White, Michael Vasey, Roger Ratcliff, Gail MckoonAbstract:Depression-related differences in memory for emotional material are well established, but recognition memory and lexical decision tasks often fail to produce consistent results. The null results from these tasks could be due to inadequacies in traditional analyses rather than the absence of effects. In particular, analyses of accuracy or mean reaction times rely on only a fraction of the behavioural data and are sensitive to individual differences in response biases. The Diffusion Model addresses these limitations by incorporating all of the behavioural data and separating out response biases. We applied the Diffusion Model to data from lexical decision and recognition memory tasks and showed consistent effects, specifically a positive emotional bias in non-dysphoric subjects and even-handedness in dysphoric subjects. This pattern was not apparent with comparisons of reaction times or accuracy, consistent with previous null findings. These results suggest a relationship between dysphoria and the internal representation of emotional information.
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a Diffusion Model account of criterion shifts in the lexical decision task
Journal of Memory and Language, 2008Co-Authors: Eric-jan Wagenmakers, Roger Ratcliff, Pablo Gomez, Gail MckoonAbstract: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 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 [Ratcliff, R., Gomez, P., & McKoon, G. (2004a). A Diffusion Model account of the lexical decision task. Psychological Review, 111, 159–182]. 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.
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a Diffusion Model account of the lexical decision task
Psychological Review, 2004Co-Authors: Roger Ratcliff, Pablo Gomez, Gail MckoonAbstract: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 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 lexical decision data, the Diffusion Model, and word recognition (lexical) Models, and shows how the data do not map directly to lexical processes but, instead, map to lexical processes only through the mediation of the Diffusion Model. Data enter the Diffusion Model, which produces the values of drift rates for the different classes of stimuli that give the best account of the data. In this framework, the role of a word recognition Model is to produce values for stimuli for how wordlike they are. We call the measure of how wordlike a stimulus is its wordness value (a term intended to be neutral for the purposes of this article). Wordness values map onto the drift rates that drive the Diffusion decision process to produce predictions about accuracy and response time. Figure 1 The relationship between data, the Diffusion Model fits, drift rates, and Models of word identification. The Diffusion Model fits the data and provides values of drift rate that represent how wordlike the stimulus is. The word identification Models need ... In our framework, wordness values place fewer constraints on word recognition Models for the lexical decision task than has been appreciated. All that is required is that a Model produce the appropriate ordering of wordness values: from high-frequency words to low- and very low-frequency words to pseudowords and random letter strings, with larger differences among them when the nonwords in an experiment are pseudowords than when they are random letter strings. In other words, the disturbing and simple conclusion from the Diffusion Model’s account of lexical decision is that, beyond what can be said from a bare ordering of wordness values, the lexical decision task may have nothing to say about lexical representations or about lexical processes such as lexical access. Lexical decision data do not provide the window into the lexicon that might have been supposed in earlier research. The framework shown in Figure 1 is counter to much previous work that has assumed lexical decision data do map directly onto lexical processes. Often, lexical decision response time (RT) has been interpreted as a direct measure of the speed with which a word can be accessed in the lexicon. For example, some researchers have argued that the well-known effect of word frequency—shorter RTs for higher frequency words—demonstrates the greater accessibility of high-frequency words (e.g., their order in a serial search, Forster, 1976; the resting levels of activation in units representing the words in a parallel processing system, Morton, 1969). However, other researchers have argued, as we do here, against a direct mapping from RT to accessibility. For example, Balota and Chumbley (1984) suggested that the effect of word frequency might be a by-product of the nature of the task itself and not a manifestation of accessibility. In the research presented here, the Diffusion Model makes explicit how such a by-product might come about. The sections below begin with a detailed description of the Diffusion Model; then nine experiments are presented, and the Model is fit to the data from each one. The main result is that the differences in performance for various classes of stimuli are all captured by drift rate, not by any of the other components of processing that make up the Diffusion Model.
Eric-jan Wagenmakers - One of the best experts on this subject based on the ideXlab platform.
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psychological interpretation of the ex gaussian and shifted wald parameters a Diffusion Model analysis
Psychonomic Bulletin & Review, 2009Co-Authors: Dora Matzke, Eric-jan WagenmakersAbstract:A growing number of researchers use descriptive distributions such as the ex-Gaussian and the shifted Wald to summarize response time data for speeded two-choice tasks. Some of these researchers also assume that the parameters of these distributions uniquely correspond to specific cognitive processes. We studied the validity of this cognitive interpretation by relating the parameters of the ex-Gaussian and shifted Wald distributions to those of the Ratcliff Diffusion Model, a successful Model whose parameters have well-established cognitive interpretations. In a simulation study, we fitted the ex-Gaussian and shifted Wald distributions to data generated from the Diffusion Model by systematically varying its parameters across a wide range of plausible values. In an empirical study, the two descriptive distributions were fitted to published data that featured manipulations of task difficulty, response caution, and a priori bias. The results clearly demonstrate that the ex-Gaussian and shifted Wald parameters do not correspond uniquely to parameters of the Diffusion Model. We conclude that researchers should resist the temptation to interpret changes in the ex-Gaussian and shifted Wald parameters in terms of cognitive processes. Supporting materials may be downloaded from http://pbr.psychonomic-journals .org/content/supplemental.
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EZ does it! Extensions of the EZ-Diffusion Model
Psychonomic Bulletin & Review, 2008Co-Authors: Eric-jan Wagenmakers, Han L. J. Van Der Maas, Conor V. Dolan, Raoul P. P. P. GrasmanAbstract:In this rejoinder, we address two of Ratcliff’s main concerns with respect to the EZ-Diffusion Model (Ratcliff, 2008). First, we introduce “robust-EZ,” a mixture Model approach to achieve robustness against the presence of response contaminants that might otherwise distort parameter estimates. Second, we discuss an extension of the EZ Model that allows the estimation of starting point as an additional parameter. Together with recently developed, user-friendly software programs for fitting the full Diffusion Model (Vandekerckhove & Tuerlinckx, 2007; Voss & Voss, 2007), the development of the EZ Model and its extensions is part of a larger effort to make Diffusion Model analyses accessible to a broader audience, an effort that is long overdue.
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a Diffusion Model account of criterion shifts in the lexical decision task
Journal of Memory and Language, 2008Co-Authors: Eric-jan Wagenmakers, Roger Ratcliff, Pablo Gomez, Gail MckoonAbstract: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 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 [Ratcliff, R., Gomez, P., & McKoon, G. (2004a). A Diffusion Model account of the lexical decision task. Psychological Review, 111, 159–182]. 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.
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an ez Diffusion Model for response time and accuracy
Psychonomic Bulletin & Review, 2007Co-Authors: Eric-jan Wagenmakers, Han L. J. Van Der Maas, Raoul P. P. P. GrasmanAbstract:The EZ-Diffusion Model for two-choice response time tasks takes mean response time, the variance of response time, and response accuracy as inputs. The Model transforms these data via three simple equations to produce unique values for the quality of information, response conservativeness, and nondecision time. This transformation of observed data in terms of unobserved variables addresses the speed—accuracy trade-off and allows an unambiguous quantification of performance differences in two-choice response time tasks. The EZ-Diffusion Model can be applied to data-sparse situations to facilitate individual subject analysis. We studied the performance of the EZ-Diffusion Model in terms of parameter recovery and robustness against misspecification by using Monte Carlo simulations. The EZ Model was also applied to a real-world data set.
Roger Ratcliff - One of the best experts on this subject based on the ideXlab platform.
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Dysphoria and memory for emotional material: A Diffusion-Model analysis
Cognition and Emotion, 2009Co-Authors: Corey White, Michael Vasey, Roger Ratcliff, Gail MckoonAbstract:Depression-related differences in memory for emotional material are well established, but recognition memory and lexical decision tasks often fail to produce consistent results. The null results from these tasks could be due to inadequacies in traditional analyses rather than the absence of effects. In particular, analyses of accuracy or mean reaction times rely on only a fraction of the behavioural data and are sensitive to individual differences in response biases. The Diffusion Model addresses these limitations by incorporating all of the behavioural data and separating out response biases. We applied the Diffusion Model to data from lexical decision and recognition memory tasks and showed consistent effects, specifically a positive emotional bias in non-dysphoric subjects and even-handedness in dysphoric subjects. This pattern was not apparent with comparisons of reaction times or accuracy, consistent with previous null findings. These results suggest a relationship between dysphoria and the internal representation of emotional information.
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a Diffusion Model account of criterion shifts in the lexical decision task
Journal of Memory and Language, 2008Co-Authors: Eric-jan Wagenmakers, Roger Ratcliff, Pablo Gomez, Gail MckoonAbstract: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 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 [Ratcliff, R., Gomez, P., & McKoon, G. (2004a). A Diffusion Model account of the lexical decision task. Psychological Review, 111, 159–182]. 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.
-
a Diffusion Model account of the lexical decision task
Psychological Review, 2004Co-Authors: Roger Ratcliff, Pablo Gomez, Gail MckoonAbstract: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 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 lexical decision data, the Diffusion Model, and word recognition (lexical) Models, and shows how the data do not map directly to lexical processes but, instead, map to lexical processes only through the mediation of the Diffusion Model. Data enter the Diffusion Model, which produces the values of drift rates for the different classes of stimuli that give the best account of the data. In this framework, the role of a word recognition Model is to produce values for stimuli for how wordlike they are. We call the measure of how wordlike a stimulus is its wordness value (a term intended to be neutral for the purposes of this article). Wordness values map onto the drift rates that drive the Diffusion decision process to produce predictions about accuracy and response time. Figure 1 The relationship between data, the Diffusion Model fits, drift rates, and Models of word identification. The Diffusion Model fits the data and provides values of drift rate that represent how wordlike the stimulus is. The word identification Models need ... In our framework, wordness values place fewer constraints on word recognition Models for the lexical decision task than has been appreciated. All that is required is that a Model produce the appropriate ordering of wordness values: from high-frequency words to low- and very low-frequency words to pseudowords and random letter strings, with larger differences among them when the nonwords in an experiment are pseudowords than when they are random letter strings. In other words, the disturbing and simple conclusion from the Diffusion Model’s account of lexical decision is that, beyond what can be said from a bare ordering of wordness values, the lexical decision task may have nothing to say about lexical representations or about lexical processes such as lexical access. Lexical decision data do not provide the window into the lexicon that might have been supposed in earlier research. The framework shown in Figure 1 is counter to much previous work that has assumed lexical decision data do map directly onto lexical processes. Often, lexical decision response time (RT) has been interpreted as a direct measure of the speed with which a word can be accessed in the lexicon. For example, some researchers have argued that the well-known effect of word frequency—shorter RTs for higher frequency words—demonstrates the greater accessibility of high-frequency words (e.g., their order in a serial search, Forster, 1976; the resting levels of activation in units representing the words in a parallel processing system, Morton, 1969). However, other researchers have argued, as we do here, against a direct mapping from RT to accessibility. For example, Balota and Chumbley (1984) suggested that the effect of word frequency might be a by-product of the nature of the task itself and not a manifestation of accessibility. In the research presented here, the Diffusion Model makes explicit how such a by-product might come about. The sections below begin with a detailed description of the Diffusion Model; then nine experiments are presented, and the Model is fit to the data from each one. The main result is that the differences in performance for various classes of stimuli are all captured by drift rate, not by any of the other components of processing that make up the Diffusion Model.
Pablo Gomez - One of the best experts on this subject based on the ideXlab platform.
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a Diffusion Model account of criterion shifts in the lexical decision task
Journal of Memory and Language, 2008Co-Authors: Eric-jan Wagenmakers, Roger Ratcliff, Pablo Gomez, Gail MckoonAbstract: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 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 [Ratcliff, R., Gomez, P., & McKoon, G. (2004a). A Diffusion Model account of the lexical decision task. Psychological Review, 111, 159–182]. 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.
-
a Diffusion Model account of the lexical decision task
Psychological Review, 2004Co-Authors: Roger Ratcliff, Pablo Gomez, Gail MckoonAbstract: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 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 lexical decision data, the Diffusion Model, and word recognition (lexical) Models, and shows how the data do not map directly to lexical processes but, instead, map to lexical processes only through the mediation of the Diffusion Model. Data enter the Diffusion Model, which produces the values of drift rates for the different classes of stimuli that give the best account of the data. In this framework, the role of a word recognition Model is to produce values for stimuli for how wordlike they are. We call the measure of how wordlike a stimulus is its wordness value (a term intended to be neutral for the purposes of this article). Wordness values map onto the drift rates that drive the Diffusion decision process to produce predictions about accuracy and response time. Figure 1 The relationship between data, the Diffusion Model fits, drift rates, and Models of word identification. The Diffusion Model fits the data and provides values of drift rate that represent how wordlike the stimulus is. The word identification Models need ... In our framework, wordness values place fewer constraints on word recognition Models for the lexical decision task than has been appreciated. All that is required is that a Model produce the appropriate ordering of wordness values: from high-frequency words to low- and very low-frequency words to pseudowords and random letter strings, with larger differences among them when the nonwords in an experiment are pseudowords than when they are random letter strings. In other words, the disturbing and simple conclusion from the Diffusion Model’s account of lexical decision is that, beyond what can be said from a bare ordering of wordness values, the lexical decision task may have nothing to say about lexical representations or about lexical processes such as lexical access. Lexical decision data do not provide the window into the lexicon that might have been supposed in earlier research. The framework shown in Figure 1 is counter to much previous work that has assumed lexical decision data do map directly onto lexical processes. Often, lexical decision response time (RT) has been interpreted as a direct measure of the speed with which a word can be accessed in the lexicon. For example, some researchers have argued that the well-known effect of word frequency—shorter RTs for higher frequency words—demonstrates the greater accessibility of high-frequency words (e.g., their order in a serial search, Forster, 1976; the resting levels of activation in units representing the words in a parallel processing system, Morton, 1969). However, other researchers have argued, as we do here, against a direct mapping from RT to accessibility. For example, Balota and Chumbley (1984) suggested that the effect of word frequency might be a by-product of the nature of the task itself and not a manifestation of accessibility. In the research presented here, the Diffusion Model makes explicit how such a by-product might come about. The sections below begin with a detailed description of the Diffusion Model; then nine experiments are presented, and the Model is fit to the data from each one. The main result is that the differences in performance for various classes of stimuli are all captured by drift rate, not by any of the other components of processing that make up the Diffusion Model.
Raoul P. P. P. Grasman - One of the best experts on this subject based on the ideXlab platform.
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EZ does it! Extensions of the EZ-Diffusion Model
Psychonomic Bulletin & Review, 2008Co-Authors: Eric-jan Wagenmakers, Han L. J. Van Der Maas, Conor V. Dolan, Raoul P. P. P. GrasmanAbstract:In this rejoinder, we address two of Ratcliff’s main concerns with respect to the EZ-Diffusion Model (Ratcliff, 2008). First, we introduce “robust-EZ,” a mixture Model approach to achieve robustness against the presence of response contaminants that might otherwise distort parameter estimates. Second, we discuss an extension of the EZ Model that allows the estimation of starting point as an additional parameter. Together with recently developed, user-friendly software programs for fitting the full Diffusion Model (Vandekerckhove & Tuerlinckx, 2007; Voss & Voss, 2007), the development of the EZ Model and its extensions is part of a larger effort to make Diffusion Model analyses accessible to a broader audience, an effort that is long overdue.
-
an ez Diffusion Model for response time and accuracy
Psychonomic Bulletin & Review, 2007Co-Authors: Eric-jan Wagenmakers, Han L. J. Van Der Maas, Raoul P. P. P. GrasmanAbstract:The EZ-Diffusion Model for two-choice response time tasks takes mean response time, the variance of response time, and response accuracy as inputs. The Model transforms these data via three simple equations to produce unique values for the quality of information, response conservativeness, and nondecision time. This transformation of observed data in terms of unobserved variables addresses the speed—accuracy trade-off and allows an unambiguous quantification of performance differences in two-choice response time tasks. The EZ-Diffusion Model can be applied to data-sparse situations to facilitate individual subject analysis. We studied the performance of the EZ-Diffusion Model in terms of parameter recovery and robustness against misspecification by using Monte Carlo simulations. The EZ Model was also applied to a real-world data set.
