kth.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Neurocomputational mechanisms of verbal omissions in free odor naming tasks
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).ORCID iD: 0000-0002-1290-0351
Department of Psychology, Stockholm University, Stockholm, Sweden.ORCID iD: 0000-0002-3985-1705
Department of Psychology, Stockholm University, Stockholm, Sweden.
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).ORCID iD: 0000-0002-9134-3601
Show others and affiliations
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Odor naming is considered a particularly challenging cognitive test, but the underlying cause of this difficulty is unknown. People often fail to report any source label to identify common odors, resulting in omissions (i.e., a lack of response). Here, with the support of a computational model, we offer a hypothesis about the neural network mechanisms underlying odor naming omissions. Based on an evaluation of behavioral data from almost 40,000 odor naming attempts, we suggest that high omission rates are driven by odors that are referred to by multiple linguistic labels. To explain this observation at the systems level, where olfactory perception and language (semantic) processing are produced by interacting cortical systems, we developed a computational model consisting of two associatively coupled attractor memory networks (odor and language networks), and investigated the effect of Hebbian-like learning on the simulated task performance. We used distributed network representations for the odor percepts and word label mental objects, and accounted for their statistical inter-relationships (correlations) extracted from collected data on odor perceptual similarity, and from a large Swedish odor language corpus, respectively. We evaluated a novel hypothesis, that Bayesian-Hebbian synaptic plasticity mechanisms can explain behavioral omissions in odor naming tasks, casting new light on the underlying mechanisms of this frequently observed memory phenomenon. Due to the nature of Bayesian-Hebbian associative learning connecting the two networks, there was a progressively weaker coupling for odors paired with multiple different labels in the encoding process (one-to-many mapping). Thus, when the model was cued with perceptual odor stimuli that established multiple word label associations (one-to-many mapping), the olfactory language network often produced subthreshold network responses, resulting in elevated omissions (opposite to one-to-few mapping scenario that led to improved performance scores). Our results are of theoretical interest, as they suggest a biologically plausible mechanism to explain a common, but poorly understood, behavioral phenomenon.

National Category
Neurosciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-362575DOI: 10.1101/2025.02.13.637907OAI: oai:DiVA.org:kth-362575DiVA, id: diva2:1953186
Note

QC 20250422

Available from: 2025-04-17 Created: 2025-04-17 Last updated: 2025-05-09Bibliographically approved
In thesis
1. Neurocomputational mechanisms of memory – Hebbian plasticity across short and long timescales
Open this publication in new window or tab >>Neurocomputational mechanisms of memory – Hebbian plasticity across short and long timescales
2025 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The mammalian brain is a complex structure, capable of processing sensory stimuli through the lens of prior knowledge and experiences to guide behavior and decision-making. This process relies on intricate neural dynamics, synaptic plasticity mechanisms, and interactions across brain networks. Despite the brain's remarkable ability to store information, even seemingly stable memories can be modified by new experiences or forgotten over time.

In this work, we use computational modelling to investigate the mechanisms underlying memory functionality, focusing on how the brain supports short- and long-term memory processes. Our research sheds light on episodic, semantic, and working memory phenomena by employing cortical memory models integrating neural plasticity, Bayesian-Hebbian synaptic plasticity across a range of short and long timescales, together with short-term non-Hebbian mechanisms. Inspired by behavioral memory tasks and experimental evidence, we explore processes such as memory semantization — where associated episodic memories are gradually decoupled, allowing for the extraction of abstract semantic meaning. We also investigate and propose hypothetical underlying neurocomputational mechanisms of verbal omissions (memory forgetting) in odor naming tasks. Additionally, we examine the interplay between episodic memory and recency effects in immediate recall. Expanding our framework to working memory, we investigate how different plasticity mechanisms interact to enable both stability and flexibility in memory maintenance.

By bridging computational models with cognitive neuroscience, this research provides new insights into the neural and synaptic basis of memory processes.

Abstract [sv]

Däggdjurshjärnan är en komplex struktur, kapabel att bearbeta sensoriska stimuli genom linsen av tidigare kunskap och erfarenheter och därigenom styra beteende och beslutsfattande. Denna process bygger på intrikat neural dynamik, synaptiska plasticitetsmekanismer och interaktioner mellan hjärnans olika nätverk. Trots hjärnans anmärkningsvärda förmåga att lagra information kan även till synes stabila minnen förändras av nya erfarenheter eller glömmas bort över tid.

I detta arbete använder vi oss av beräkningsmodeller för att undersöka de mekanismer som ligger till grund för olika minnesfunktioner, med fokus på hur hjärnan stödjer minnesprocesser pa lang och kort sikt. Vår forskning belyser fenomen kopplade till episodiskt, semantiskt och arbetsminne genom att använda kortikala minnesmodeller som integrerar neural dynamik, Bayesiansk-Hebbsk synaptisk plasticitet över både korta och långa tidsskalor tillsammans med kortsiktiga icke-Hebbska mekanismer. Inspirerade av olika minnesexperiment och och fynd från dessa undersöker vi processer såsom semantisering av minnet — där associerade episodiska minnen gradvis frikopplas, vilket möjliggör extrahering av abstrakt semantisk innebörd. Vi undersöker också och föreslår hypotetiska underliggande neuroberäkningsmekanismer för verbala utelämnanden (glömning) i luktidentifieringsuppgifter. Dessutom analyserar vi samspelet mellan episodiskt minne och s k recency-effekter i omedelbart minnesåterkallande. Genom att utvidga vår modell till arbetsminnet undersöker vi hur olika plasticitetsmekanismer samverkar för att möjliggöra både stabilitet och flexibilitet i minneslagring.

Genom att förena beräkningsmodeller med kognitiv neurovetenskap bidrar denna forskning med nya insikter om de neurala och synaptiska grunderna för våra minnesprocesser.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2025. p. 132
Series
TRITA-EECS-AVL ; 2025:52
Keywords
Hebbian-like plasticity, Cortical memory models, Attractor dynamics, Episodic memory, Working memory, Hebbsk-liknande plasticitet, Kortikala minnesmodeller, Attraktordynamik, Episodiskt minne, Arbetsminne
National Category
Neurosciences Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-363243 (URN)978-91-8106-282-3 (ISBN)
Public defence
2025-06-09, F2, Lindstedtsvägen 26 & 28, Stockholm, 10:00 (English)
Opponent
Supervisors
Funder
Swedish Research Council
Note

QC 20250509

Available from: 2025-05-09 Created: 2025-05-09 Last updated: 2025-05-09Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records

Chrysanthidis, NikolaosLindroos, RobertLansner, AndersHerman, Pawel

Search in DiVA

By author/editor
Chrysanthidis, NikolaosRaj, RohanLindroos, RobertLansner, AndersLaukka, ErikaOlofsson, JonasHerman, Pawel
By organisation
Computational Science and Technology (CST)
Neurosciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 78 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf