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

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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.

Detaljer

Författare
Enheter & grupper
Externa organisationer
  • Kristianstad University
Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Psykologi

Nyckelord

  • memory, olfactory, verbal, recognition, Signal Detection Theory, Receiver-operating Characteristic (ROC), model
Originalspråksvenska
Sidor (från-till)481-494
TidskriftCognitive Processing
Volym19
Utgivningsnummer4
StatusPublished - 2018 nov
PublikationskategoriForskning
Peer review utfördJa