The role of sparsely distributed representations in familiarity recognition of verbal and olfactory materials
| Autor: | Georg Stenberg, Johan Hellman, Sverker Sikström, Marcus Johansson, Mats Dahl |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Adult
Male Signal Detection Psychological Cognitive Neuroscience Speech recognition Experimental and Cognitive Psychology Models Psychological Signal-To-Noise Ratio behavioral disciplines and activities 050105 experimental psychology Normal distribution 03 medical and health sciences Young Adult 0302 clinical medicine Fuzzy Logic Artificial Intelligence Memory Humans 0501 psychology and cognitive sciences Detection theory Olfactory memory Verbal Behavior Node (networking) 05 social sciences Representation (systemics) Recognition Psychology General Medicine Middle Aged Binomial distribution Smell Noise ROC Curve Mental Recall Odorants Female Verbal memory 030217 neurology & neurosurgery |
| Zdroj: | Cognitive processing. 19(4) |
| ISSN: | 1612-4790 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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