Speeded old-new recognition of multidimensional perceptual stimuli: Modeling performance at the individual-participant and individual-item levels.

Nosofsky, Robert M.; Stanton, Roger D.

doi:10.1037/0096-1523.32.2.314

Cited by 20 publications

(17 citation statements)

References 55 publications

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“…Why it would occur in such a way as to produce roughly equivalent RT distributions between conditions is not clear, and the chief virtue of the ARC model (Diller et al, 2001) is that it provides an a priori reason for such a finding. Our model does not entail an equivalence in RT distributions between conditions, but like the diffusion model, is capable of fitting a much broader range of findings than the ARC model (Nosofsky & Stanton, 2006). We can only say that the good fit of our model to the Nobel and Shiffrin (2001) data means that it is possible to explain their results without recourse to a model built especially to fit their data.…”

Section: Discussionmentioning

confidence: 95%

A Dynamic Approach to Recognition Memory

Cox¹,

Shiffrin²

2017

Preprint

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ii Acknowledgements First, I extend my thanks to my committee members for their time, consideration, and guidance. I would especially like to thank my advisors, Rich Shiffrin and Mike Jones, for first convincing me to come to Indiana, making me feel welcome, and guiding me into the world I now inhabit. I must also thank Isaiah Harbison, Michael Dougherty, and Sharona Atkins for introducing me to the very idea of mathematical psychology and for teaching me and trusting me. show how the dynamic model can be extended to account for paradigms like associative recognition and list discrimination, leading to another novel test of the presence of recall-like processes. Associative recognition, list discrimination, recognition of similar foils, and source exclusion are all better explained by the formation of a compound cue rather than recall, although source memory is found to be better modeled by a recall process.

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Section: Discussionmentioning

confidence: 95%

A Dynamic Approach to Recognition Memory

Cox¹,

Shiffrin²

2017

Preprint

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“…Thus, the evidence generated by memory models must pass through a decision process before the models’ predictions can be compared to data, and the details of this decision process can dramatically change how a model is assessed. Memory models have traditionally assumed a signal detection model for decision making (Clark & Gronlund, 1996), but a handful of models are beginning to adopt a sequential sampling approach (Malmberg, 2008; Nosofsky & Stanton, 2006). Our results suggest that switching to a sequential sampling decision process will be an important advance in the process model literature.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the unequal-variance and dual-process explanations of zROC slopes with response time data and the diffusion model

Starns

Ratcliff

McKoon

2012

Cognitive Psychology

118

147

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We tested two explanations for why the slope of the z-transformed receiver operating characteristic (zROC) is less than 1 in recognition memory: the unequal-variance account (target evidence is more variable than lure evidence) or the dual-process account (responding reflects both a continuous familiarity process and a threshold recollection process). These accounts are typically implemented in signal detection models that do not make predictions for response time (RT) data. We tested them using RT data and the diffusion model. Participants completed multiple study/test blocks of an “old”/”new” recognition task with the proportion of targets on the test varying from block to block (.21, .32, .50, .68, or .79 targets). The same participants completed sessions with both speed-emphasis and accuracy-emphasis instructions. zROC slopes were below one for both speed and accuracy sessions, and they were slightly lower for speed. The extremely fast pace of the speed sessions (mean RT = 526) should have severely limited the role of the slower recollection process relative to the fast familiarity process. Thus, the slope results are not consistent with the idea that recollection is responsible for slopes below 1. The diffusion model was able to match the empirical zROC slopes and RT distributions when between-trial variability in memory evidence was greater for targets than for lures, but missed the zROC slopes when target and lure variability were constrained to be equal. Therefore, unequal variability in continuous evidence is supported by RT modeling in addition to signal detection modeling. Finally, we found that a two-choice version of the RTCON model could not accommodate the RT distributions as successfully as the diffusion model.

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“…In the next section, we evaluate current practice in model comparison within the field of categorization research, and make a series of best-practice recommendations designed to maximize the chances for further progress. The issue of model comparison is clearly pertinent for many areas of psychology, including areas with a close relation to categorization such as recognition memory (Nosofsky & Stanton, 2006) and magnitude estimation (Bergert & Nosofsky, 2007); however, the specific examples upon which we draw in this article are from studies of categorization.…”

Section: Discussionmentioning

confidence: 99%

On the adequacy of current empirical evaluations of formal models of categorization.

Wills

Pothos

2012

Psychological Bulletin

124

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This is the unspecified version of the paper.This version of the publication may differ from the final published version. Permanent repository link: AbstractCategorization is one of the fundamental building blocks of cognition, and the study of categorization is notable for the extent to which formal modeling has been a central and influential component of research. However, the field has seen a proliferation of divergent, non-complementary models with little consensus on the relative adequacy of these accounts.Progress on assessing relative adequacy of formal categorization models against these criteria has, to date, been limited because (a) formal model comparisons are narrow in the number of models and phenomena considered, and (b) models do not often clearly define their explanatory scope. Progress is further hampered by the practice of fitting models with arbitrarily variable parameters to each data set independently. Reviewing examples of good practice in the literature, we conclude that model comparisons are most fruitful when relative adequacy is assessed by comparing well-defined models on the basis of the number and proportion of irreversible, ordinal, penetrable successes (principles of minimal flexibility, breadth, good-enough precision, maximal simplicity, and psychological focus).Keywords: categorization; cluster; exemplar; model selection; modeling; prototype. EMPIRICAL EVALUATION OF CATEGORIZATION MODELS 2The study of categorization is a fascinating endeavor. The process of constructing and using categories underpins our capacity to encode and apply information in the world in an efficient and competent manner but also, ultimately, our ability to think in terms of abstract kind of categorization models should we aim to develop? The lack of consensus regarding such key issues has resulted in categorization research being carried out in increasingly independent strands and this has been inhibiting overall progress in the field. Nosofsky, Gluck, Palmeri, McKinley and Glauthier (1994) wrote, "Recent years have seen an avalanche of newly proposed models of category learning and representation. As such models grow increasingly more sophisticated, there is a need to develop increasingly more rigorous testing grounds so that one may choose among them" (p. 352). Almost 20 years later, progress towards this goal remains limited.In the current article, we first provide a definition of the term formal model, consider the principal advantages of formal modeling over other forms of theorizing, briefly summarize some of the leading formal models of categorization, and assess progress to date on the empirical evaluation and comparison of these models. We then set out the approaches EMPIRICAL EVALUATION OF CATEGORIZATION MODELS 3 we believe are most likely to lead to progress in the future. We organize our conclusions in terms of a set of criteria for assessing the relative adequacy of models, and a list of dependent and independent variables that any adequate formal model of categorization should be expected to...

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Speeded old-new recognition of multidimensional perceptual stimuli: Modeling performance at the individual-participant and individual-item levels.

Cited by 20 publications

References 55 publications

A Dynamic Approach to Recognition Memory

A Dynamic Approach to Recognition Memory

Evaluating the unequal-variance and dual-process explanations of zROC slopes with response time data and the diffusion model

On the adequacy of current empirical evaluations of formal models of categorization.

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