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  1. Interference and memory capacity limitations.Ansgar D. Endress & Szilárd Szabó - 2017 - Psychological Review 124 (5):551-571.
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  • Representing word meaning and order information in a composite holographic lexicon.Michael N. Jones & Douglas J. K. Mewhort - 2007 - Psychological Review 114 (1):1-37.
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  • Context Noise and Item Noise Jointly Determine Recognition Memory: A Comment on Dennis and Humphreys (2001).Amy H. Criss & Richard M. Shiffrin - 2004 - Psychological Review 111 (3):800-807.
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  • Signal detection with criterion noise: Applications to recognition memory.Aaron S. Benjamin, Michael Diaz & Serena Wee - 2009 - Psychological Review 116 (1):84-115.
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  • The Temporal Context Model in Spatial Navigation and Relational Learning: Toward a Common Explanation of Medial Temporal Lobe Function Across Domains.Marc W. Howard, Mrigankka S. Fotedar, Aditya V. Datey & Michael E. Hasselmo - 2005 - Psychological Review 112 (1):75-116.
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  • Modeling Recognition Memory Using the Similarity Structure of Natural Input.Joyca P. W. Lacroix, Jaap M. J. Murre, Eric O. Postma & H. Jaap Herik - 2006 - Cognitive Science 30 (1):121-145.
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  • A context maintenance and retrieval model of organizational processes in free recall.Sean M. Polyn, Kenneth A. Norman & Michael J. Kahana - 2009 - Psychological Review 116 (1):129-156.
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  • The Demise of Short-Term Memory Revisited: Empirical and Computational Investigations of Recency Effects.Eddy J. Davelaar, Yonatan Goshen-Gottstein, Amir Ashkenazi, Henk J. Haarmann & Marius Usher - 2005 - Psychological Review 112 (1):3-42.
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  • Criterion Setting and the Dynamics of Recognition Memory.Gregory E. Cox & Richard M. Shiffrin - 2012 - Topics in Cognitive Science 4 (1):135-150.
    Models of recognition memory have traditionally struggled with the puzzle of criterion setting, a problem that is particularly acute in cases in which items for study and test are of widely varying types, with differing degrees of baseline familiarity and experience (e.g., words vs. random dot patterns). We present a dynamic model of the recognition process that addresses the criterion setting problem and produces joint predictions for choice and reaction time. In this model, recognition decisions are based not on the (...)
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  • It's about time: Delay-dependent forgetting of item- and contextual-information.Avi Gamoran, Matar Greenwald-Levin, Stav Siton, Dan Halunga & Talya Sadeh - 2020 - Cognition 205 (C):104437.
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  • Likelihood-free Bayesian analysis of memory models.Brandon M. Turner, Simon Dennis & Trisha Van Zandt - 2013 - Psychological Review 120 (3):667-678.
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  • Modeling Recognition Memory Using the Similarity Structure of Natural Input.Joyca P. W. Lacroix, Jaap M. J. Murre, Eric O. Postma & H. Jaap van den Herik - 2006 - Cognitive Science 30 (1):121-145.
    The natural input memory (NIM) model is a new model for recognition memory that operates on natural visual input. A biologically informed perceptual preprocessing method takes local samples (eye fixations) from a natural image and translates these into a feature‐vector representation. During recognition, the model compares incoming preprocessed natural input to stored representations. By complementing the recognition memory process with a perceptual front end, the NIM model is able to make predictions about memorability based directly on individual natural stimuli. We (...)
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  • Finding feature representations of stimuli: Combining feature generation and similarity judgment tasks.Matthew D. Zeigenfuse & Michael D. Lee - 2008 - In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 1825--1830.
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  • Perceptual Inference Through Global Lexical Similarity.Brendan T. Johns & Michael N. Jones - 2012 - Topics in Cognitive Science 4 (1):103-120.
    The literature contains a disconnect between accounts of how humans learn lexical semantic representations for words. Theories generally propose that lexical semantics are learned either through perceptual experience or through exposure to regularities in language. We propose here a model to integrate these two information sources. Specifically, the model uses the global structure of memory to exploit the redundancy between language and perception in order to generate inferred perceptual representations for words with which the model has no perceptual experience. We (...)
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  • A dynamic approach to recognition memory.Gregory E. Cox & Richard M. Shiffrin - 2017 - Psychological Review 124 (6):795-860.
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  • Modeling hippocampal and neocortical contributions to recognition memory: A complementary-learning-systems approach.Kenneth A. Norman & Randall C. O'Reilly - 2003 - Psychological Review 110 (4):611-646.
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  • A Memory‐Based Theory of Verbal Cognition.Simon Dennis - 2005 - Cognitive Science 29 (2):145-193.
    The syntagmatic paradigmatic model is a distributed, memory‐based account of verbal processing. Built on a Bayesian interpretation of string edit theory, it characterizes the control of verbal cognition as the retrieval of sets of syntagmatic and paradigmatic constraints from sequential and relational long‐term memory and the resolution of these constraints in working memory. Lexical information is extracted directly from text using a version of the expectation maximization algorithm. In this article, the model is described and then illustrated on a number (...)
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  • The Episodic Nature of Experience: A Dynamical Systems Analysis.Sreekumar Vishnu, Dennis Simon & Doxas Isidoros - 2017 - Cognitive Science 41 (5):1377-1393.
    Context is an important construct in many domains of cognition, including learning, memory, and emotion. We used dynamical systems methods to demonstrate the episodic nature of experience by showing a natural separation between the scales over which within-context and between-context relationships operate. To do this, we represented an individual's emails extending over about 5 years in a high-dimensional semantic space and computed the dimensionalities of the subspaces occupied by these emails. Personal discourse has a two-scaled geometry with smaller within-context dimensionalities (...)
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  • Modeling memory and perception.Richard M. Shiffrin - 2003 - Cognitive Science 27 (3):341-378.
    I present a framework for modeling memory, retrieval, perception, and their interactions. Recent versions of the models were inspired by Bayesian induction: We chose models that make optimal decisions conditioned on a memory/perceptual system with inherently noisy storage and retrieval. The resultant models are, fortunately, largely consistent with my models dating back to the 1960s, and are therefore natural successors. My recent articles have presented simplified models in order to focus on particular applications. This article takes a larger perspective and (...)
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  • Evaluating the evidence for nonconscious processes in producing false memories.Jeroen G. W. Raaijmakers & René Zeelenberg - 2004 - Consciousness and Cognition 13 (1):169-172.
    In response to the failure of Zeelenberg, Plomp, and Raaijmakers to replicate the results of Seamon, Luo, and Gallo regarding their purported finding of a reliable false memory effect in the absence of memory for the list items, Gallo and Seamon report a new experiment that they claim shows that conscious activation of a related lure during study is not necessary for its subsequent recognition. We critically evaluate their conclusion and argue that the evidence clearly shows that false recognition is (...)
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  • Postscript: Reply to Macmillan and Rotello (2006).Bennet Murdock - 2006 - Psychological Review 113 (3):655-656.
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  • Decision-making models of remember-know judgments: Comment on Rotello, Macmillan, and Reeder (2004).Bennet Murdock - 2006 - Psychological Review 113 (3):648-655.
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  • Binding of intrinsic and extrinsic features in working memory.Ullrich Kh Ecker, Murray Maybery & Hubert D. Zimmer - 2013 - Journal of Experimental Psychology: General 142 (1):218.
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  • Manifestations and Consequences of Negative Information’s Great Diversity.Hans Alves - unknown
    In the present dissertation, I propose a general, robust, and objective characteristic of the information environment, according to which negative information is more diverse than positive information. I present an explanatory framework for this phenomenon based on the non-extremity of positive qualities. Specifically, most attribute dimensions host one “positive” range which is surrounded by two distinct “negative” ranges, resulting in a greater diversity of negative compared to positive attributes, stimuli, and information in general. Chapter 1 of my dissertation reviews evidence (...)
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