A feature-based neurocomputational model of semantic memory

An increasingly large body of converging evidence supports the idea that the semantic system is distributed across brain areas and that the information encoded therein is multimodal. Within this framework, feature norms are typically used to operationalize the various parts of meaning that contribute to define the distributed nature of conceptual representations. However, such features are typically collected as verbal strings, elicited from participants in experimental settings. If the semantic system is not only distributed (across features) but also multimodal, a cognitively sound theory of semantic representations should take into account different modalities in which feature-based representations are generated, because not all the relevant semantic information may be easily verbalized into classic feature norms, and different types of concepts (e.g., abstract vs. concrete concepts) may consist of different configurations of non-verbal features.In this paper we acknowledge the multimodal nature of conceptual representations and we propose a novel way of collecting non-verbal semantic features. In a crowdsourcing task we asked participants to use emoji to provide semantic representations for a sample of 300 English nouns referring to abstract and concrete concepts, which account for (machine readable) visual features. In a formal content analysis with multiple annotators we then classified the cognitive strategies used by the participants to represent conceptual content through emoji.The main results of our analyses show that abstract (vs. concrete) concepts are characterized by representations that: 1. consist of a larger number of emoji; 2. include more face emoji (expressing emotions); 3. are less stable and less shared among users; 4. use representation strategies based on figurative operations (e.g., metaphors) and strategies that exploit linguistic information (e.g. rebus); 5. correlate less well with the semantic representations emerging from classic features listed through verbal strings.

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“…Interestingly, the degradation of salient features, but not of marginal ones, prevented object identification. (Ursino et al 2018).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Emoji-based semantic representations for abstract and concrete concepts

Wicke

Bolognesi

2020

Cogn Process

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“…Distinctive features, on the other hand, have very low correlatedness with any other features. The pattern of correlatedness is also thought to differ across categories, such that natural kinds, which have a hierarchical taxonomy, tend to have more highly correlated shared features than do man-made artifacts (McRae & Cree, 2002;McRae, De Sa, & Seidenberg, 1997;Moss & Tyler, 2000), and patterns of correlatedness have been included in recent computational models that predict behavioral deficits with high accuracy (Ursino, Cuppini, Cappa, & Catricalá, 2018). Thus, the stimuli in the current study are divided into natural kinds and man-made artifacts in order to investigate whether distinctive feature effects emerge across both domains.…”

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confidence: 99%

Distinctive semantic features in the healthy adult brain

Reilly

Machado

Blumstein

2018

Cogn Affect Behav Neurosci

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The role of semantic features, which are distinctive (e.g., a zebra's stripes) or shared (e.g. has four legs) for accessing a concept, has been studied in detail in early neurodegenerative disease such as semantic dementia (SD). However, potential neural underpinnings of such processing have not been studied in healthy adults. The current study examines neural activation patterns using fMRI while participants completed a feature verification task, in which they identified shared or distinctive semantic features for a set of natural kinds and manmade artifacts. The results showed that the anterior temporal lobe bilaterally is an important area for processing distinctive features, and that this effect is stronger within natural kinds than man-made artifacts. These findings provide converging evidence from healthy adults that is consistent with SD research, and support a model of semantic memory in which patterns of specificity of semantic information can partially explain differences in neural activation between categories.

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“…The common idea is that episodes are only temporarily encoded in the hippocampus and then transmitted to a long-term store in the cortex. Here, episodes can also be integrated with other kinds of memory, like the semantic one ( Ursino et al, 2015 , 2018 ; Ursino and Pirazzini, 2023 ). It is worth-noting that this theta-acetylcholine modulation could be easily implemented within the present model in future work, whereas it is of difficult implementation with the previous model.…”

Section: Discussionmentioning

confidence: 99%

Modeling the contribution of theta-gamma coupling to sequential memory, imagination, and dreaming

Pirazzini,

Ursino

2024

Front. Neural Circuits

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Gamma oscillations nested in a theta rhythm are observed in the hippocampus, where are assumed to play a role in sequential episodic memory, i.e., memorization and retrieval of events that unfold in time. In this work, we present an original neurocomputational model based on neural masses, which simulates the encoding of sequences of events in the hippocampus and subsequent retrieval by exploiting the theta-gamma code. The model is based on a three-layer structure in which individual Units oscillate with a gamma rhythm and code for individual features of an episode. The first layer (working memory in the prefrontal cortex) maintains a cue in memory until a new signal is presented. The second layer (CA3 cells) implements an auto-associative memory, exploiting excitatory and inhibitory plastic synapses to recover an entire episode from a single feature. Units in this layer are disinhibited by a theta rhythm from an external source (septum or Papez circuit). The third layer (CA1 cells) implements a hetero-associative net with the previous layer, able to recover a sequence of episodes from the first one. During an encoding phase, simulating high-acetylcholine levels, the network is trained with Hebbian (synchronizing) and anti-Hebbian (desynchronizing) rules. During retrieval (low-acetylcholine), the network can correctly recover sequences from an initial cue using gamma oscillations nested inside the theta rhythm. Moreover, in high noise, the network isolated from the environment simulates a mind-wandering condition, randomly replicating previous sequences. Interestingly, in a state simulating sleep, with increased noise and reduced synapses, the network can “dream” by creatively combining sequences, exploiting features shared by different episodes. Finally, an irrational behavior (erroneous superimposition of features in various episodes, like “delusion”) occurs after pathological-like reduction in fast inhibitory synapses. The model can represent a straightforward and innovative tool to help mechanistically understand the theta-gamma code in different mental states.

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A feature-based neurocomputational model of semantic memory

Cited by 11 publications

References 66 publications

Emoji-based semantic representations for abstract and concrete concepts

Emoji-based semantic representations for abstract and concrete concepts

Distinctive semantic features in the healthy adult brain

Modeling the contribution of theta-gamma coupling to sequential memory, imagination, and dreaming

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