A model-based characterization of individual differences in prospective memory monitoring

2010 ◽  
Author(s):  
Adam C. Savine ◽  
Jill T. Shelton ◽  
Michael K. Scullin ◽  
Mark A. McDaniel
Keyword(s):  
Individual Differences ◽  
Prospective Memory ◽  
Model Based ◽  
Memory Monitoring

A characterization of individual differences in prospective memory monitoring using the Complex Ongoing Serial Task.

2012 ◽  
Vol 141 (2) ◽  
pp. 337-362 ◽  
Author(s):  
Adam C. Savine ◽  
Mark A. McDaniel ◽  
Jill Talley Shelton ◽  
Michael K. Scullin
Keyword(s):  
Individual Differences ◽  
Prospective Memory ◽  
Serial Task ◽  
Memory Monitoring

Efficient characterization of individual differences in compression ratio preference

2018 ◽  
Vol 144 (5) ◽  
pp. 2662-2673
Author(s):  
Lucas S Baltzell ◽  
Jing Xia ◽  
Sridhar Kalluri
Keyword(s):  
Individual Differences ◽  
Compression Ratio

scholarly journals p-box: a new graph model

2015 ◽  
Vol Vol. 17 no. 1 (Graph Theory) ◽  
Author(s):  
Mauricio Soto ◽  
Christopher Thraves-Caro
Keyword(s):  
Graph Theory ◽  
Graph Model ◽  
Directed Path ◽  
Outerplanar Graphs ◽  
New Model ◽  
Model Based ◽  
Representative Element ◽  
International Audience ◽  
Graph Families

Graph Theory International audience In this document, we study the scope of the following graph model: each vertex is assigned to a box in ℝd and to a representative element that belongs to that box. Two vertices are connected by an edge if and only if its respective boxes contain the opposite representative element. We focus our study on the case where boxes (and therefore representative elements) associated to vertices are spread in ℝ. We give both, a combinatorial and an intersection characterization of the model. Based on these characterizations, we determine graph families that contain the model (e. g., boxicity 2 graphs) and others that the new model contains (e. g., rooted directed path). We also study the particular case where each representative element is the center of its respective box. In this particular case, we provide constructive representations for interval, block and outerplanar graphs. Finally, we show that the general and the particular model are not equivalent by constructing a graph family that separates the two cases.

scholarly journals Individual Differences in Prospective Memory Offloading in Laboratory and Naturalistic Settings

2021 ◽  
Author(s):  
Hunter Ball ◽  
Philip Peper ◽  
Durna Alakbarova ◽  
Sam Gilbert ◽  
Gene Arnold Brewer
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Cognitive Ability ◽  
Prospective Memory ◽  
High Ability ◽  
Memory Ability ◽  
Multiple Measures ◽  
Good Working

The current study examined whether offloading prospective memory (PM) demands onto the environment through the use of reminders eliminates PM differences typically seen between individuals that have poor or good working memory ability. Over two laboratory sessions scheduled one week apart, participants completed three versions of a PM offloading task with and without the use of reminders, along with multiple measures of working memory. Participants also generated a list of naturalistic intentions to fulfill between sessions and were given an intention to email the experimenter every day. They later indicated which intentions were completed with and without the use of reminders. Consistent with prior research, high working memory participants did better in both laboratory and naturalistic settings when having to rely on their own memory. Critically, however, working memory ability was no longer predictive of performance with the use of reminders. Participants with lower working memory also offloaded more often that high ability participants, but this was not optimally calibrated to actual PM performance. These findings suggest that offloading may be particularly beneficial for those with poor cognitive ability. The theoretical and applied ramifications of these findings are discussed.

Characterization of a commercial hybrid iterative and model-based reconstruction algorithm in radiation oncology

2014 ◽  
Vol 41 (8Part1) ◽  
pp. 081907 ◽  
Author(s):  
Ryan G. Price ◽  
Sean Vance ◽  
Richard Cattaneo ◽  
Lonni Schultz ◽  
Mohamed A. Elshaikh ◽  
...  
Keyword(s):  
Radiation Oncology ◽  
Reconstruction Algorithm ◽  
Model Based

Model-based learning and individual differences in depression: The moderating role of stress

2018 ◽  
Vol 111 ◽  
pp. 19-26 ◽  
Author(s):  
Aaron S. Heller ◽  
C.E. Chiemeka Ezie ◽  
A. Ross Otto ◽  
Kiara R. Timpano
Keyword(s):  
Individual Differences ◽  
Model Based ◽  
Moderating Role

Characterization of Energy Demand and Energy Services Using Model-Based and Data-Driven Approaches

2021 ◽  
pp. 229-248
Author(s):  
Carlos A. Santos Silva ◽  
Manar Amayri ◽  
Kaustav Basu
Keyword(s):  
Energy Demand ◽  
Data Driven ◽  
Energy Services ◽  
Model Based

Classification Trees as Proxies

2015 ◽  
Vol 2 (2) ◽  
pp. 31-44 ◽  
Author(s):  
Anthony Scime ◽  
Nilay Saiya ◽  
Gregg R. Murray ◽  
Steven J. Jurek
Keyword(s):  
Data Analysis ◽  
Classification Trees ◽  
Original Model ◽  
Classification Model ◽  
Model Based ◽  
Alternative Characterization

In data analysis, when data are unattainable, it is common to select a closely related attribute as a proxy. But sometimes substitution of one attribute for another is not sufficient to satisfy the needs of the analysis. In these cases, a classification model based on one dataset can be investigated as a possible proxy for another closely related domain's dataset. If the model's structure is sufficient to classify data from the related domain, the model can be used as a proxy tree. Such a proxy tree also provides an alternative characterization of the related domain. Just as important, if the original model does not successfully classify the related domain data the domains are not as closely related as believed. This paper presents a methodology for evaluating datasets as proxies along with three cases that demonstrate the methodology and the three types of results.

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