Sleep Benefits Memory for Semantic Category Structure While Preserving Exemplar-Specific Information

It is well established that retrieval of names is harder than the retrieval of other identity specific information. This paper offers a review of the more influential accounts put forward as explanations of why names are so difficult to retrieve. A series of five experiments tests a number of these accounts. Experiments One to Three examine the claims that names are hard to recall because they are typically meaningless (Cohen 1990), or unique (Burton and Bruce 1992; Brédart, Valentine, Calder, and Gassi 1995). Participants are shown photographs of unfamiliar people (Experiments One and Two) or familiar people (Experiment Three) and given three pieces of information about each: a name, a unique piece of information, and a shared piece of information. Learning follows an incidental procedure, and participants are given a surprise recall test. In each experiment shared information is recalled most often, followed by unique information, followed by name. Experiment Four tests both the ‘uniqueness’ account and an account based on the specificity of the naming response (Brédart 1993). Participants are presented with famous faces and asked to categorise them by semantic group (occupation). Results indicate that less time is needed to perform this task when the group is a subset of a larger semantic category. A final experiment examines the claim that names might take longer to access because they are less often retrieved than other classes of information. Latencies show that participants remain more efficient when categorising faces by their occupation than by their name even when they have received extra practice of naming the faces. We conclude that the explanation best able to account for the data is that names are stored separately from other semantic information and can only be accessed after other identity specific information has been retrieved. However, we also argue that the demands we make of these explanations make it likely that no single theory will be able to account for all existing data.

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Language input and semantic categories: a relation between cognition and early word learning

Journal of Child Language ◽

10.1017/s0305000906007574 ◽

2006 ◽

Vol 33 (4) ◽

pp. 759-790 ◽

Cited By ~ 40

Author(s):

ARIELLE BOROVSKY ◽

JEFF ELMAN

Keyword(s):

Word Learning ◽

Cognitive Abilities ◽

Semantic Category ◽

Category Structure ◽

Learning Ability ◽

Computational Simulations ◽

Early Language ◽

Semantic Categories ◽

Language Input ◽

Over Time

Variations in the amount and nature of early language to which children are exposed have been linked to their subsequent ability (e.g. Huttenlocher, Haight, Bryk, Seltzer & Lyons, 1991; Hart & Risley, 1995). In three computational simulations, we explore how differences in linguistic experience can explain differences in word learning ability due to changes in the development of semantic category structure. More specifically, we manipulate the amount of language input, sentential complexity, and the frequency distribution of words within categories. In each of these simulations, improvements in category structure, are tightly correlated with subsequent improvements in word learning ability even when the nature of the input remains the same over time. These simulations suggest that variation in early language environments may result in differences in lexical proficiency by altering underlying cognitive abilities like categorization.

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Sleep Benefits Memory for Semantic Category Structure While Preserving Exemplar-Specific Information

10.1101/143172 ◽

2017 ◽

Author(s):

Anna C. Schapiro ◽

Elizabeth A. McDevitt ◽

Lang Chen ◽

Kenneth A. Norman ◽

Sara C. Mednick ◽

...

Keyword(s):

Semantic Category ◽

Sleep Stages ◽

Specific Information ◽

Semantic Learning ◽

Object Categories ◽

Novel Objects ◽

Category Exemplars ◽

The Impact ◽

Shared Structure ◽

Inference Task

AbstractSemantic memory encompasses knowledge about both the properties that typify concepts (e.g. robins, like all birds, have wings) as well as the properties that individuate conceptually related items (e.g. robins, in particular, have red breasts). We investigate the impact of sleep on new semantic learning using a property inference task in which both kinds of information are initially acquired equally well. Participants learned about three categories of novel objects possessing some properties that were shared among category exemplars and others that were unique to an exemplar, with exposure frequency varying across categories. In Experiment 1, memory for shared properties improved and memory for unique properties was preserved across a night of sleep, while memory for both feature types declined over a day awake. In Experiment 2, memory for shared properties improved across a nap, but only for the lower-frequency category, suggesting a prioritization of weakly learned information early in a sleep period. The increase was significantly correlated with amount of REM, but was also observed in participants who did not enter REM, suggesting involvement of both sleep stages. The results provide the first evidence that sleep improves memory for the shared structure of object categories, while simultaneously preserving object-unique information.

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A Color Coded STEM/SEM Image Storage System

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100097880 ◽

1981 ◽

Vol 39 ◽

pp. 272-273

Author(s):

Y. Kokubo ◽

W. H. Hardy ◽

J. Dance ◽

K. Jones

Keyword(s):

Image Processing ◽

Electron Beam ◽

Storage System ◽

Particle Analysis ◽

Specific Information ◽

X Rays ◽

Sem Image ◽

Backscattered Electrons ◽

Wide Range ◽

Scanning Electron

A color coded digital image processing is accomplished by using JEM100CX TEM SCAN and ORTEC’s LSI-11 computer based multi-channel analyzer (EEDS-II-System III) for image analysis and display. Color coding of the recorded image enables enhanced visualization of the image using mathematical techniques such as compression, gray scale expansion, gamma-processing, filtering, etc., without subjecting the sample to further electron beam irradiation once images have been stored in the memory.The powerful combination between a scanning electron microscope and computer is starting to be widely used 1) - 4) for the purpose of image processing and particle analysis. Especially, in scanning electron microscopy it is possible to get all information resulting from the interactions between the electron beam and specimen materials, by using different detectors for signals such as secondary electron, backscattered electrons, elastic scattered electrons, inelastic scattered electrons, un-scattered electrons, X-rays, etc., each of which contains specific information arising from their physical origin, study of a wide range of effects becomes possible.

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Electron Microscopy in the Study of Natural Phytoplankton Assemblages

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100119831 ◽

1985 ◽

Vol 43 ◽

pp. 620-623

Author(s):

Linda Sicko-Goad

Keyword(s):

Electron Microscopy ◽

Aquatic Environment ◽

Size Range ◽

Physical Parameters ◽

Specific Information ◽

Phytoplankton Assemblages ◽

Phytoplankton Productivity ◽

Ecosystem Effects ◽

Time Of Year ◽

Species Specific

Although the use of electron microscopy and its varied methodologies is not usually associated with ecological studies, the types of species specific information that can be generated by these techniques are often quite useful in predicting long-term ecosystem effects. The utility of these techniques is especially apparent when one considers both the size range of particles found in the aquatic environment and the complexity of the phytoplankton assemblages.The size range and character of organisms found in the aquatic environment are dependent upon a variety of physical parameters that include sampling depth, location, and time of year. In the winter months, all the Laurentian Great Lakes are uniformly mixed and homothermous in the range of 1.1 to 1.7°C. During this time phytoplankton productivity is quite low.

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Interindividual Differences in Cognitive Functioning Are Associated with Autobiographical Memory Retrieval Specificity in Older Adults

GeroPsych ◽

10.1024/1662-9647/a000219 ◽

2020 ◽

Vol 33 (1) ◽

pp. 15-29 ◽

Cited By ~ 1

Author(s):

Sarah Peters ◽

Signy Sheldon

Keyword(s):

Older Adults ◽

Cognitive Functioning ◽

Autobiographical Memory ◽

Memory Retrieval ◽

Cognitive Status ◽

Specific Information ◽

Generation Task ◽

Interindividual Differences ◽

Retrieval Cues ◽

Contextual Support

Abstract. We examined whether interindividual differences in cognitive functioning among older adults are related to episodic memory engagement during autobiographical memory retrieval. Older adults ( n = 49, 24 males; mean age = 69.93; mean education = 15.45) with different levels of cognitive functioning, estimated using the Montreal Cognitive Assessment (MoCA), retrieved multiple memories (generation task) and the details of a single memory (elaboration task) to cues representing thematic or event-specific autobiographical knowledge. We found that the MoCA score positively predicted the proportion of specific memories for generation and episodic details for elaboration, but only to cues that represented event-specific information. The results demonstrate that individuals with healthy, but not unhealthy, cognitive status can leverage contextual support from retrieval cues to improve autobiographical specificity.

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