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The Role of Sparsely Distributed Representations in Familiarity Recognition of Verbal and Olfactory Materials

Sikström, Sverker LU orcid ; Hellman, Johan LU ; Dahl, Mats LU ; Stenberg, Georg LU and Johansson, Marcus LU (2018) In Cognitive Processing 19(4). p.481-494
Abstract
We present the generalized signal detection theory (GSDT), where familiarity is described by a sparse binomial distribution of binary node activity rather than by normal distribution of familiarity. Items are presented in a distributed representation, where each node receives either noise only, or signal and noise. An old response (i.e., a ‘yes’ response) is made if at least one node receives signal plus noise that is larger than the activation threshold, and item variability is determined by the distribution of activated nodes as the threshold is varied. A distinct representation leads to better performance and a lower ratio of new to old item variability, than a more distributed and less distinct representations. Here we apply the GSDT... (More)
We present the generalized signal detection theory (GSDT), where familiarity is described by a sparse binomial distribution of binary node activity rather than by normal distribution of familiarity. Items are presented in a distributed representation, where each node receives either noise only, or signal and noise. An old response (i.e., a ‘yes’ response) is made if at least one node receives signal plus noise that is larger than the activation threshold, and item variability is determined by the distribution of activated nodes as the threshold is varied. A distinct representation leads to better performance and a lower ratio of new to old item variability, than a more distributed and less distinct representations. Here we apply the GSDT to empirical data on verbal and olfactory memory and suggest that verbal memory relies on a distinct neural item representation whereas olfactory memory has a fuzzy neural representation leading to poorer memory and inducing a larger ratio of new to old item variability.
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author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
memory, olfactory, verbal, recognition, Signal Detection Theory, Receiver-operating Characteristic (ROC), model
in
Cognitive Processing
volume
19
issue
4
pages
481 - 494
publisher
Springer
external identifiers
  • pmid:29679290
  • scopus:85045755160
ISSN
1612-4782
DOI
10.1007/s10339-018-0862-9
language
Swedish
LU publication?
yes
id
6791ad84-51fe-447a-acdf-1517055054c8
date added to LUP
2018-03-21 09:24:08
date last changed
2021-09-22 05:30:29
@article{6791ad84-51fe-447a-acdf-1517055054c8,
  abstract     = {We present the generalized signal detection theory (GSDT), where familiarity is described by a sparse binomial distribution of binary node activity rather than by normal distribution of familiarity. Items are presented in a distributed representation, where each node receives either noise only, or signal and noise. An old response (i.e., a ‘yes’ response) is made if at least one node receives signal plus noise that is larger than the activation threshold, and item variability is determined by the distribution of activated nodes as the threshold is varied. A distinct representation leads to better performance and a lower ratio of new to old item variability, than a more distributed and less distinct representations. Here we apply the GSDT to empirical data on verbal and olfactory memory and suggest that verbal memory relies on a distinct neural item representation whereas olfactory memory has a fuzzy neural representation leading to poorer memory and inducing a larger ratio of new to old item variability.<br/>},
  author       = {Sikström, Sverker and Hellman, Johan and Dahl, Mats and Stenberg, Georg and Johansson, Marcus},
  issn         = {1612-4782},
  language     = {swe},
  number       = {4},
  pages        = {481--494},
  publisher    = {Springer},
  series       = {Cognitive Processing},
  title        = {The Role of Sparsely Distributed Representations in Familiarity Recognition of Verbal and Olfactory Materials},
  url          = {http://dx.doi.org/10.1007/s10339-018-0862-9},
  doi          = {10.1007/s10339-018-0862-9},
  volume       = {19},
  year         = {2018},
}