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  1. Gilles Dutilh, Eric-Jan Wagenmakers, Ingmar Visser & Han L. J. van der Maas (2011). A Phase Transition Model for the Speed-Accuracy Trade-Off in Response Time Experiments. Cognitive Science 35 (2):211-250.
    Most models of response time (RT) in elementary cognitive tasks implicitly assume that the speed-accuracy trade-off is continuous: When payoffs or instructions gradually increase the level of speed stress, people are assumed to gradually sacrifice response accuracy in exchange for gradual increases in response speed. This trade-off presumably operates over the entire range from accurate but slow responding to fast but chance-level responding (i.e., guessing). In this article, we challenge the assumption of continuity and propose a phase transition model for (...)
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  2. Thomas Pronk & Ingmar Visser (2010). The Role of Reversal Frequency in Learning Noisy Second Order Conditional Sequences. Consciousness and Cognition 19 (2):627-635.
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  3. Denny Borsboom & Ingmar Visser (2008). Semantic Cognition or Data Mining? Behavioral and Brain Sciences 31 (6):714-715.
    We argue that neural networks for semantic cognition, as proposed by Rogers & McClelland (R&M), do not acquire semantics and therefore cannot be the basis for a theory of semantic cognition. The reason is that the neural networks simply perform statistical categorization procedures, and these do not require any semantics for their successful operation. We conclude that this has severe consequences for the semantic cognition views of R&M.
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  4. Brenda R. J. Jansen, Maartje E. J. Raijmakers & Ingmar Visser (2007). Rule Transition on the Balance Scale Task: A Case Study in Belief Change. Synthese 155 (2):211 - 236.
    For various domains in proportional reasoning cognitive development is characterized as a progression through a series of increasingly complex rules. A multiplicative relationship between two task features, such as weight and distance information of blocks placed at both sides of the fulcrum of a balance scale, appears difficult to discover. During development, children change their beliefs about the balance scale several times: from a focus on the weight dimension (Rule I) to occasionally considering the distance dimension (Rule II), guessing (Rule (...)
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  5. Ingmar Visser (2000). Hidden Markov Model Interpretations of Neural Networks. Behavioral and Brain Sciences 23 (4):494-495.
    Page's manifesto makes a case for localist representations in neural networks, one of the advantages being ease of interpretation. However, even localist networks can be hard to interpret, especially when at some hidden layer of the network distributed representations are employed, as is often the case. Hidden Markov models can be used to provide useful interpretable representations.
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