Testing Interactions in Multinomial Processing Tree Models [PDF]
Frontiers in Psychology, 2019 Multinomial processing tree (MPT) models allow testing hypotheses on latent psychological processes that underlie human behavior. However, past applications of this model class have mainly been restricted to the analysis of main effects.
Beatrice G. Kuhlmann +2 more
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Random Effects Multinomial Processing Tree Models: A Maximum Likelihood Approach. [PDF]
Psychometrika, 2023 Nestler S, Erdfelder E.
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On the Minimum Description Length Complexity of Multinomial Processing Tree Models. [PDF]
J Math Psychol, 2010 Wu H, Myung JI, Batchelder WH.
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Further evidence for the memory state heuristic: Recognition latency predictions for binary inferences [PDF]
Judgment and Decision Making, 2017 According to the recognition heuristic (RH), for decision domains where recognition is a valid predictor of a choice criterion, recognition alone is used to make inferences whenever one object is recognized and the other is not, irrespective of further ...
Marta Castela, Edgar Erdfelder
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On aggregation invariance of multinomial processing tree models. [PDF]
Behav Res MethodsErdfelder E +2 more
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Sociological Science, 2023 Dual-process perspectives have made substantial contributions to our understanding of behavior, but fundamental questions about how and when deliberate and automatic cognition shape action continue to be debated.
Andrew Miles +3 more
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On the role of recognition in consumer choice: A model comparison [PDF]
Judgment and Decision Making, 2014 One prominent model in the realm of memory-based judgments and decisions is the recognition heuristic. Under certain preconditions, it presumes that choices are based on recognition in a one-cue non-compensatory manner and that other information is ...
Benjamin E. Hilbig
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ABrox-A user-friendly Python module for approximate Bayesian computation with a focus on model comparison. [PDF]
PLoS ONE, 2018 We give an overview of the basic principles of approximate Bayesian computation (ABC), a class of stochastic methods that enable flexible and likelihood-free model comparison and parameter estimation.
Ulf Kai Mertens +2 more
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Classification of Bugs in Cloud Computing Applications Using Machine Learning Techniques
Applied Sciences, 2023 In software development, the main problem is recognizing the security-oriented issues within the reported bugs due to their unacceptable failure rate to provide satisfactory reliability on customer and software datasets.
Nadia Tabassum +5 more
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Detecting Spam Email With Machine Learning Optimized With Bio-Inspired Metaheuristic Algorithms
IEEE Access, 2020 Electronic mail has eased communication methods for many organisations as well as individuals. This method is exploited for fraudulent gain by spammers through sending unsolicited emails. This article aims to present a method for detection of spam emails
Simran Gibson +3 more
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