scholarly journals Item-Position Binding Capacity Limits and Word Limits in Working Memory: A Reanalysis of Oberauer (2019)

2022 ◽  
Vol 5 (1) ◽  
Author(s):  
Nelson Cowan
Keyword(s):  
Working Memory ◽  
Binding Capacity ◽  
Capacity Limits ◽  
Item Position

Theory and Measurement of Working Memory Capacity Limits

2008 ◽  
pp. 49-104 ◽  
Author(s):  
Nelson Cowan ◽  
Candice C. Morey ◽  
Zhijian Chen ◽  
Amanda L. Gilchrist ◽  
J. Scott Saults
Keyword(s):  
Working Memory ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Capacity Limits

scholarly journals The effects of content-dependent competition on working memory capacity limits

2017 ◽  
Vol 17 (10) ◽  
pp. 109
Author(s):  
Jason Scimeca ◽  
Jacob Miller ◽  
Mark D'Esposito
Keyword(s):  
Working Memory ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Capacity Limits

scholarly journals Spatial Working Memory Capacity Predicts Bias in Estimates of Location

2016 ◽  
Author(s):  
David Landy ◽  
L. Elizabeth Crawford ◽  
Timothy A. Salthouse
Keyword(s):  
Working Memory ◽  
Spatial Memory ◽  
Cognitive Aging ◽  
Spatial Working Memory ◽  
Working Memory Capacity ◽  
Theoretical Work ◽  
Memory Capacity ◽  
Systematic Bias ◽  
Capacity Limits ◽  
Biased Estimates

Spatial memory research has attributed systematic bias in location estimates to a combination of a noisy memory trace with a prior structure that people impose on the space. Little is known about intra-individual stability and inter-individual variation in these patterns of bias. In the current work we align recent empirical and theoretical work on working memory capacity limits and spatial memory bias to generate the prediction that those with lower working memory capacity will show greater bias in memory of the location of a single item. Reanalyzing data from a large study of cognitive aging, we find support for this prediction. Fitting separate models to individuals’ data revealed a surprising variety of strategies. Some were consistent with Bayesian models of spatial category use, however roughly half of participants biased estimates outward in a way not predicted by current models and others seemed to combine these strategies. These analyses highlight the importance of studying individuals when developing general models of cognition.

Individual Differences in Value-Directed Remembering

2019 ◽  
Author(s):  
Blake L. Elliott ◽  
Samuel M. McClure ◽  
Gene Arnold Brewer
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Episodic Memory ◽  
Structural Equation ◽  
Working Memory Capacity ◽  
Human Memory ◽  
Memory Capacity ◽  
Equation Modeling ◽  
Memory Encoding ◽  
Capacity Limits

Prioritized encoding and retrieval of valuable information is an essential component of human memory due to capacity limits. Individual differences in value-directed encoding may derive from variability in stimulus valuation, memory encoding, or from strategic abilities related to maintenance in working memory. We collected multiple cognitive ability measures to test whether variation in episodic memory, working memory capacity, or both predict differences in value-directed remembering among a large sample of participants (n=205). Confirmatory factor analysis and structural equation modeling was used to assess the contributions of episodic and working memory to value sensitivity in value-directed remembering tasks. Episodic memory ability, but not working memory capacity, was predictive of value-directed remembering. These results suggest that cognitive processes may be differentially related to value-based memory encoding.

BEHIND THE MAGICAL NUMBERS: HIERARCHICAL CHUNKING AND THE HUMAN WORKING MEMORY CAPACITY

2013 ◽  
Vol 23 (04) ◽  
pp. 1350019 ◽  
Author(s):  
GUOQI LI ◽  
NING NING ◽  
KIRUTHIKA RAMANATHAN ◽  
WEI HE ◽  
LI PAN ◽  
...  
Keyword(s):  
Neural Networks ◽  
Working Memory ◽  
Upper Bound ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Optimal Number ◽  
Capacity Limits ◽  
Magical Number

To explore the influence of chunking on the capacity limits of working memory, a model for chunking in sequential working memory is proposed, using hierarchical bidirectional inhibition-connected neural networks with winnerless competition. With the assumption of the existence of an upper bound to the inhibitory weights in neurobiological networks, it is shown that chunking increases the number of memorized items in working memory from the "magical number 7" to 16 items. The optimal number of chunks and the number of the memorized items in each chunk are the "magical number 4".

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