Models of recognition, repetition priming, and fluency: Exploring a new framework.

Berry, Christopher J.; Shanks, David R.; Speekenbrink, Maarten; Henson, Richard N.

doi:10.1037/a0025464

Cited by 100 publications

(141 citation statements)

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“…Thus the proportion of overall variance attributed to s and e are arbitrarily chosen, and other values could equally apply. The values estimated here attribute around 84% of the variance in RT (i.e., cuing effect) to s and around 84% of the variance in d (i.e., the awareness measure) to e. In contrast, the single-system model proposed by Berry, Shanks, Speekenbrink, & Henson (2012), for instance, is free to assume that any proportion of the observed variance could be attributable to s. When this proportion is small, the expected correlation is closer to zero. Later, we test whether alternate versions of the single-system model can better fit our observed data.…”

Section: Empirical Tests From Our Three Experimentsmentioning

confidence: 64%

Contextual cuing as a form of nonconscious learning: Theoretical and empirical analysis in large and very large samples

Colagiuri

Livesey

2016

Psychon Bull Rev

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Numerous studies have demonstrated that associative learning can affect visual cognition. In one such effect, search times for a target hidden among similar distractors are faster for repeated search configurations compared with novel configurations. This contextual cuing effect is particularly interesting, because researchers routinely have failed to find evidence of recognition of the repeated configurations, concluding that the effect is a form of nonconscious learning. Vadillo, Konstantinidis, and Shanks (2016) recently criticized this conclusion on a number of methodological and conceptual grounds that suggest the area suffers from a high probability of false-negative results on awareness tests and misinterpretation of weak or absent relationships between cuing and awareness measures. We developed further predictions from theoretical models assuming that single or independent memory sources drive learning and awareness and discuss how these predictions fare in three new contextual cuing experiments involving large (n > 60) and very large samples (n > 600). The data support the absence of a positive relationship between recognition and the cuing effect both at the participant and configuration level, the probability of which being a false negative is very low in a model assuming a single memory source drives learning and awareness. This was the case using both conventional and Bayesian analyses. The combination of this theoretical and empirical analysis suggests that contextual cuing is not dependent on cue recognition and provides evidence that it reflects a genuine form of nonconscious learning.

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Section: Empirical Tests From Our Three Experimentsmentioning

confidence: 64%

Contextual cuing as a form of nonconscious learning: Theoretical and empirical analysis in large and very large samples

Colagiuri

Livesey

2016

Psychon Bull Rev

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“…Within this framework, the various memory systems have distinct purposes and distinct anatomy, and different species can solve the same task using different systems. Interestingly, efforts have been made to account for some findings (e.g., priming or classification learning) with models based on a single system (Zaki et al 2003;Zaki 2004;Berry et al 2012). Yet, these accounts have difficulty explaining double dissociations (e.g., Packard et al 1989;Knowlton et al 1996), chance performance on tests of declarative memory when priming is intact (Hamann and Squire 1997b), and successful habit learning in the face of expressed ignorance about the task (Bayley et al 2005).…”

Section: Resultsmentioning

confidence: 99%

Conscious and Unconscious Memory Systems

Squire

Dede

2015

Cold Spring Harb Perspect Biol

254

234

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The idea that memory is not a single mental faculty has a long and interesting history but became a topic of experimental and biologic inquiry only in the mid-20th century. It is now clear that there are different kinds of memory, which are supported by different brain systems. One major distinction can be drawn between working memory and long-term memory. Long-term memory can be separated into declarative (explicit) memory and a collection of nondeclarative (implicit) forms of memory that include habits, skills, priming, and simple forms of conditioning. These memory systems depend variously on the hippocampus and related structures in the parahippocampal gyrus, as well as on the amygdala, the striatum, cerebellum, and the neocortex. This work recounts the discovery of declarative and nondeclarative memory and then describes the nature of declarative memory, working memory, nondeclarative memory, and the relationship between memory systems.T he idea that memory is not a single faculty has a long history. In his Principles of Psychology, William James (1890) wrote separate chapters on memory and habit. Bergson (1910) similarly distinguished between a kind of memory that represents our past and memory that is not representational but nevertheless allows the effect of the past to persist into the present. One finds other antecedents as well. McDougall (1923) wrote about explicit and implicit recognition memory, and Tolman (1948) proposed that there is more than one kind of learning. These early proposals were often expressed as a dichotomy involving two forms of memory. The terminologies differed, but the ideas were similar. Thus, Ryle (1949) distinguished between knowing how and knowing that, and Bruner (1969) identified memory without record and memory with record. Later, the artificial intelligence literature introduced a distinction between declarative and procedural knowledge (Winograd 1975). Yet constructs founded in philosophy and psychology are often abstract and have an uncertain connection to biology, that is, to how the brain actually stores information. History shows that as biological information becomes available about structure and function, understanding becomes more concrete and less dependent on terminology. THE DISCOVERY OF DECLARATIVE AND NONDECLARATIVE MEMORY SYSTEMSBiological and experimental inquiry into these matters began with studies of the noted patient H.M. (Scoville and Milner 1957;Squire 2009).

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“…Formal single-system models have successfully reproduced several dissociations that have previously been taken as support for multiple memory systems (e.g., Berry, Shanks, & Henson, 2006;Berry, Shanks, & Henson, 2008a;Berry, Shanks, & Henson 2008b;Berry, Shanks, Li, Rains, & Henson, 2010;Berry, Shanks, Speekenbrink, & Henson, 2012;Kinder & Shanks 2001;Shanks & Perruchet, 2002;Shanks, Wilkinson, & Channon, 2003). The model by Berry and colleagues assumes that a single memory signal drives performance on explicit and implicit tasks, but that there are independent sources of random noise, the variance of which is greater in the implicit task (an assumption which is fortified by the generally lower reliability levels found in implicit relative to explicit tests; Buchner & Wippich, 2000).…”

mentioning

confidence: 95%

“…Berry et al (2012) developed two such models in which two independent signals either make unique contributions to explicit and implicit memory or are assumed to have some degree of correlation. Not only did the single-system model reproduce the qualitative dissociation observed in amnesia in the Conroy et al (2005) study, but model selection on the basis of the Akaike Information Criterion indicated that it fit the data better than the multiple-systems models.…”

mentioning

confidence: 99%

An effect of age on implicit memory that is not due to explicit contamination: Implications for single and multiple-systems theories.

Ward¹,

Berry²,

Shanks³

2013

Psychology and Aging

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An open access repository of Middlesex University research http://eprints.mdx.ac.uk Ward, Emma V. and Berry, Christopher J. and Shanks, David R. (2013) An effect of age on implicit memory that is not due to explicit contamination: implications for single and multiple-systems theories.Psychology and Aging, 28 (2 Copyright and moral rights to this thesis/research project are retained by the author and/or other copyright owners. The work is supplied on the understanding that any use for commercial gain is strictly forbidden. A copy may be downloaded for personal, non-commercial, research or study without prior permission and without charge. Any use of the thesis/research project for private study or research must be properly acknowledged with reference to the work's full bibliographic details.This thesis/research project may not be reproduced in any format or medium, or extensive quotations taken from it, or its content changed in any way, without first obtaining permission in writing from the copyright holder(s).If you believe that any material held in the repository infringes copyright law, please contact the Repository Team at Middlesex University via the following email address:The item will be removed from the repository while any claim is being investigated. AbstractRecognition memory is typically weaker in healthy older relative to young adults, while performance on implicit tests (e.g., repetition priming) is often comparable between groups. Such observations are commonly taken as evidence for independent explicit and implicit memory systems. On a picture version of the continuous identification with recognition (CID-R) task, we found a reliable age-related reduction in recognition memory, while the age effect on priming did not reach statistical significance (Experiment 1). This pattern was consistent with the predictions of a formal single-system model. Experiment 2 replicated these observations using separate priming (CID) and recognition phases, while a combined data analysis revealed a significant effect of age on priming. In Experiment 3 we provide evidence that priming in this task is unaffected by explicit processing, and we conclude that the age difference in priming is unlikely to have been driven by differences in explicit processing between groups of young and older adults ('explicit contamination'). The results support the view that explicit and implicit expressions of memory are driven by a single underlying memory system.Keywords: aging, priming, implicit memory, recognition, explicit contamination Memory can be measured directly or indirectly. Direct or explicit tests (e.g., recognition) require deliberate recollection of specific information from a prior study episode, whereas indirect or implicit tests measure memory of previously studied information in a seemingly unrelated task (e.g., perceptual identification). Priming is a commonly used index of implicit memory. It refers to a long-term change in behavioural response to an item as a result of prior exposure to it, and usually takes ...

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Models of recognition, repetition priming, and fluency: Exploring a new framework.

Cited by 100 publications

References 144 publications

Contextual cuing as a form of nonconscious learning: Theoretical and empirical analysis in large and very large samples

Contextual cuing as a form of nonconscious learning: Theoretical and empirical analysis in large and very large samples

Conscious and Unconscious Memory Systems

An effect of age on implicit memory that is not due to explicit contamination: Implications for single and multiple-systems theories.

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