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13
Modeling hippocampal and neocortical contributions to recognition memory: A complementary-learning-systems approach
- PSYCHOLOGICAL REVIEW
, 2003
"... We present a computational neural network model of recognition memory based on the biological structures of the hippocampus and medial temporal lobe cortex (MTLC), which perform complementary learning functions. The hippocampal component of the model contributes to recognition by recalling specific ..."
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Cited by 186 (22 self)
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We present a computational neural network model of recognition memory based on the biological structures of the hippocampus and medial temporal lobe cortex (MTLC), which perform complementary learning functions. The hippocampal component of the model contributes to recognition by recalling specific studied details. MTLC can not support recall, but it is possible to extract a scalar familiarity signal from MTLC that tracks how well the test item matches studied items. We present simulations that establish key qualitative differences in the operating characteristics of the hippocampal recall and MTLC familiarity signals, and we identify several manipulations (e.g., target-lure similarity, interference) that differentially affect the two signals. We also use the model to address the stochastic relationship between recall and familiarity (i.e., are they independent), and the effects of partial vs. complete hippocampal
A neural network model of retrieval-induced forgetting (Tech
- Princeton University, Center for the Study of Brain, Mind, and Behavior
, 2006
"... Retrieval-induced forgetting (RIF) refers to the finding that retrieving a memory can impair subsequent recall of related memories. Here, the authors present a new model of how the brain gives rise to RIF in both semantic and episodic memory. The core of the model is a recently developed neural netw ..."
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Cited by 29 (9 self)
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Retrieval-induced forgetting (RIF) refers to the finding that retrieving a memory can impair subsequent recall of related memories. Here, the authors present a new model of how the brain gives rise to RIF in both semantic and episodic memory. The core of the model is a recently developed neural network learning algorithm that leverages regular oscillations in feedback inhibition to strengthen weak parts of target memories and to weaken competing memories. The authors use the model to address several puzzling findings relating to RIF, including why retrieval practice leads to more forgetting than simply presenting the target item, how RIF is affected by the strength of competing memories and the strength of the target (to-be-retrieved) memory, and why RIF sometimes generalizes to independent cues and sometimes does not. For all of these questions, the authors show that the model can account for existing results, and they generate novel predictions regarding boundary conditions on these results.
Associative retrieval processes in episodic memory
, 2008
"... This Article is brought to you for free and open access by the College of Arts and Sciences at SURFACE. It has been accepted for inclusion in ..."
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Cited by 18 (11 self)
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This Article is brought to you for free and open access by the College of Arts and Sciences at SURFACE. It has been accepted for inclusion in
ADHD and working memory: the impact of central executive deficits and exceeding storage/rehearsal capacity on observed inattentive behavior.
- Journal of Abnormal Child Psychology,
, 2010
"... Abstract Inattentive behavior is considered a core and pervasive feature of ADHD; however, an alternative model challenges this premise and hypothesizes a functional relationship between working memory deficits and inattentive behavior. The current study investigated whether inattentive behavior in ..."
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Cited by 13 (7 self)
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Abstract Inattentive behavior is considered a core and pervasive feature of ADHD; however, an alternative model challenges this premise and hypothesizes a functional relationship between working memory deficits and inattentive behavior. The current study investigated whether inattentive behavior in children with ADHD is functionally related to the domain-general central executive and/or subsidiary storage/rehearsal components of working memory. Objective observations of children's attentive behavior by independent observers were conducted while children with ADHD (n=15) and typically developing children (n=14) completed counterbalanced tasks that differentially manipulated central executive, phonological storage/rehearsal, and visuospatial storage/rehearsal demands. Results of latent variable and effect size confidence interval analyses revealed two conditions that completely accounted for the attentive behavior deficits in children with ADHD: (a) placing demands on central executive processing, the effect of which is evident under even low cognitive loads, and (b) exceeding storage/rehearsal capacity, which has similar effects on children with ADHD and typically developing children but occurs at lower cognitive loads for children with ADHD.
Bridging the gap: Transitive associations between items presented in similar temporal contexts
, 2007
"... Associations in episodic memory are formed between items presented close together in time. The temporal context model (TCM) hypothesizes that this contiguity effect is a consequence of shared temporal contexts rather than temporal proximity per se. Using double function lists of paired associates, w ..."
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Cited by 12 (2 self)
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Associations in episodic memory are formed between items presented close together in time. The temporal context model (TCM) hypothesizes that this contiguity effect is a consequence of shared temporal contexts rather than temporal proximity per se. Using double function lists of paired associates, which include chains of pairs (e.g. A-B, B-C), we examined associations between items that were not presented close together in time but were presented in similar temporal contexts. For instance A and C do not appear together, but both occur in the context of B. Although within-pair associations (e.g. A-B) were asymmetric, across-pair associations (e.g. A-C) showed no evidence for asymmetry. We attempted to describe these transitive associations using two models. One was a heteroassociative model in which the A-C associations resulted from mediated chaining as a result of “stepping through ” the links in the chain. Although this heteroassociative model and TCM make identical predictions regarding simple contiguity effects, the heteroassociative model had great difficulty accounting for the form of transitive associations between items. TCM provided an excellent fit to the data. These data raise the surprising possiblity that episodic contiguity effects do not reflect direct associations between items but rather a process of binding, encoding and retrieval of a gradually-changing
Decision-Making Models of Remember–Know Judgments: Comment on . . .
, 2006
"... The sum-difference theory of remembering and knowing (STREAK) provides a sophisticated account of many interactions in the remember–know (R–K) area (C. M. Rotello, N. A. Macmillan, & J. A. Reeder, 2004). It assumes 2 orthogonal strength dimensions and oblique criterion planes. Another dual-proc ..."
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Cited by 5 (0 self)
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The sum-difference theory of remembering and knowing (STREAK) provides a sophisticated account of many interactions in the remember–know (R–K) area (C. M. Rotello, N. A. Macmillan, & J. A. Reeder, 2004). It assumes 2 orthogonal strength dimensions and oblique criterion planes. Another dual-process model (J. T. Wixted & V. Stretch, 2004) with one decision axis has also been applied to R–K judgments with considerable success and provides new insights into the processes involved. An analysis of the 4 major R–K interactions can also be explained by a simpler one-dimensional signal detection theory (J. C. Dunn, 2004a). However these models do not make contact with standard work on recognition memory, so their scope is limited. To bridge this gap, a global-matching model (a theory of distributed associative memory [TODAM]) for R–K judgments is proposed. This model can produce good fits to the data, and there are established experimental manipulations with which to test it. It provides further support for the idea that R judgments are based on associative information, whereas K judgments are based on item information.
Brandeis University
"... Why are some people’s names easier to learn than others? The effects of face similarity on memory for face-name associations ..."
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Why are some people’s names easier to learn than others? The effects of face similarity on memory for face-name associations
ANALYSIS OF CONTRIBUTING FACTORS TO HIGH ATTRITION RATES IN ONLINE EDUCATIONAL PROGRAMS
"... With the increase of technology and the prominence of internet usage, many universities and colleges are offering an alternative way of obtaining a degree through online learning. Unfortunately, many of these schools are experiencing increasingly high attrition rates in their online learning program ..."
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With the increase of technology and the prominence of internet usage, many universities and colleges are offering an alternative way of obtaining a degree through online learning. Unfortunately, many of these schools are experiencing increasingly high attrition rates in their online learning programs. High dropout rates are not only costly to the school, but can also cause a financial burden for the student. Based on early research, several factors have been identified as potential causes of low completion rates in online degrees. These factors include, but are not limited to, student persistence, perception, technology limitations, and poor student typing skills. The purpose of this research was to identify potential contributing factors leading to high attrition rates by surveying current and past online students. The survey was distributed to undergraduate and graduate students pursuing an online degree at Embry-Riddle Aeronautical University (ERAU). The survey asked questions regarding persistence and distracters, technology limitations and distracters, and student perceptions. The researcher concluded that student employment responsibilities were the biggest distracters for online students. An
Memory & Cognition in press Recognition and position information in working memory for visual textures
"... Three experiments examined connections between old/new item recognition and memory for item position information. With series of compound gratings as study and probe items, subjects made item position judgments (Experiments 1 and 2) by identifying the serial position of the study item that matched t ..."
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Three experiments examined connections between old/new item recognition and memory for item position information. With series of compound gratings as study and probe items, subjects made item position judgments (Experiments 1 and 2) by identifying the serial position of the study item that matched the probe, or recognition judgments (Experiment 3) by judging whether the probe had or had not been presented in the study series. Integrating a summed similarity account of recognition into a signal detection framework shows that the variance of summed similarities on lure trials (probe not present in the study series) exceeds the variance on target trials (probe present in the study series). This prediction is borne out by the empirical zROC functions, all of which had slopes>1.0. Additionally, about 25 % of correct recognitions were accompanied by incorrect item position identification. Misidentifications of item position arose from two sources, structural similarity and positional similarity, which combined in an approximately additive fashion.
Submitted to Psychological Review
, 2007
"... Retrieval-induced forgetting (RIF) refers to the finding that retrieving a memory can impair subsequent recall of related memories. Here, we present a new model of how the brain gives rise to RIF in both semantic and episodic memory. The core of the model is a recently developed neural network learn ..."
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Retrieval-induced forgetting (RIF) refers to the finding that retrieving a memory can impair subsequent recall of related memories. Here, we present a new model of how the brain gives rise to RIF in both semantic and episodic memory. The core of the model is a recently developed neural network learning algorithm that leverages regular oscillations in feedback inhibition to strengthen weak parts of target memories and to weaken competing memories. We use the model to address several puzzling findings relating to RIF including: why retrieval practice leads to more forgetting than simply presenting the target item; how RIF is affected by the strength of competing memories and the strength of the target (to-be-retrieved) memory; and why RIF sometimes generalizes to “independent cues”, and sometimes does not. For all of these questions, we show that the model can account for existing results, and we generate novel predictions regarding boundary conditions on these results. Contents Introduction: The puzzle of retrievalinduced forgetting 4 The scope of the paper.......... 4
