The Role of Sparsely Distributed Representations in Familiarity Recognition of Verbal and Olfactory Materials
(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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Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/6791ad84-51fe-447a-acdf-1517055054c8
- author
- Sikström, Sverker LU ; Hellman, Johan LU ; Dahl, Mats LU ; Stenberg, Georg LU and Johansson, Marcus LU
- organization
- publishing date
- 2018-11
- 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
- 2022-03-25 00:49:48
@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}}, keywords = {{memory; olfactory; verbal; recognition; Signal Detection Theory; Receiver-operating Characteristic (ROC); model}}, 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}}, }