Dataset Information

Looking for Semantic Similarity: What a Vector-Space Model of Semantics Can Tell Us About Attention in Real-World Scenes.

ABSTRACT: The visual world contains more information than we can perceive and understand in any given moment. Therefore, we must prioritize important scene regions for detailed analysis. Semantic knowledge gained through experience is theorized to play a central role in determining attentional priority in real-world scenes but is poorly understood. Here, we examined the relationship between object semantics and attention by combining a vector-space model of semantics with eye movements in scenes. In this approach, the vector-space semantic model served as the basis for a concept map, an index of the spatial distribution of the semantic similarity of objects across a given scene. The results showed a strong positive relationship between the semantic similarity of a scene region and viewers' focus of attention; specifically, greater attention was given to more semantically related scene regions. We conclude that object semantics play a critical role in guiding attention through real-world scenes.

SUBMITTER: Hayes TR

PROVIDER: S-EPMC8726595 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Looking for Semantic Similarity: What a Vector-Space Model of Semantics Can Tell Us About Attention in Real-World Scenes.

Hayes Taylor R TR Henderson John M JM

Psychological science 20210712 8

The visual world contains more information than we can perceive and understand in any given moment. Therefore, we must prioritize important scene regions for detailed analysis. Semantic knowledge gained through experience is theorized to play a central role in determining attentional priority in real-world scenes but is poorly understood. Here, we examined the relationship between object semantics and attention by combining a vector-space model of semantics with eye movements in scenes. In this ...[more]

PMID: 34252325

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Project description:BackgroundThe Gene Ontology (GO) is a well known controlled vocabulary describing the biological process, molecular function and cellular component aspects of gene annotation. It has become a widely used knowledge source in bioinformatics for annotating genes and measuring their semantic similarity. These measures generally involve the GO graph structure, the information content of GO aspects, or a combination of both. However, only a few of the semantic similarity measures described so far can handle GO annotations differently according to their origin (i.e. their evidence codes).ResultsWe present here a new semantic similarity measure called IntelliGO which integrates several complementary properties in a novel vector space model. The coefficients associated with each GO term that annotates a given gene or protein include its information content as well as a customized value for each type of GO evidence code. The generalized cosine similarity measure, used for calculating the dot product between two vectors, has been rigorously adapted to the context of the GO graph. The IntelliGO similarity measure is tested on two benchmark datasets consisting of KEGG pathways and Pfam domains grouped as clans, considering the GO biological process and molecular function terms, respectively, for a total of 683 yeast and human genes and involving more than 67,900 pair-wise comparisons. The ability of the IntelliGO similarity measure to express the biological cohesion of sets of genes compares favourably to four existing similarity measures. For inter-set comparison, it consistently discriminates between distinct sets of genes. Furthermore, the IntelliGO similarity measure allows the influence of weights assigned to evidence codes to be checked. Finally, the results obtained with a complementary reference technique give intermediate but correct correlation values with the sequence similarity, Pfam, and Enzyme classifications when compared to previously published measures.ConclusionsThe IntelliGO similarity measure provides a customizable and comprehensive method for quantifying gene similarity based on GO annotations. It also displays a robust set-discriminating power which suggests it will be useful for functional clustering.AvailabilityAn on-line version of the IntelliGO similarity measure is available at: http://bioinfo.loria.fr/Members/benabdsi/intelligo_project/

Project description:Focal anterior temporal lobe degeneration often preferentially affects the left or right hemisphere. While patients with left-predominant anterior temporal lobe atrophy show severe anomia and verbal semantic deficits and meet criteria for semantic variant primary progressive aphasia and semantic dementia, patients with early right anterior temporal lobe atrophy are more difficult to diagnose as their symptoms are less well understood. Focal right anterior temporal lobe atrophy is associated with prominent emotional and behavioural changes, and patients often meet, or go on to meet, criteria for behavioural variant frontotemporal dementia. Uncertainty around early symptoms and absence of an overarching clinico-anatomical framework continue to hinder proper diagnosis and care of patients with right anterior temporal lobe disease. Here, we examine a large, well-characterized, longitudinal cohort of patients with right anterior temporal lobe-predominant degeneration and propose new criteria and nosology. We identified individuals from our database with a clinical diagnosis of behavioural variant frontotemporal dementia or semantic variant primary progressive aphasia and a structural MRI (n = 478). On the basis of neuroimaging criteria, we defined three patient groups: right anterior temporal lobe-predominant atrophy with relative sparing of the frontal lobes (n = 46), frontal-predominant atrophy with relative sparing of the right anterior temporal lobe (n = 79) and left-predominant anterior temporal lobe-predominant atrophy with relative sparing of the frontal lobes (n = 75). We compared the clinical, neuropsychological, genetic and pathological profiles of these groups. In the right anterior temporal lobe-predominant group, the earliest symptoms were loss of empathy (27%), person-specific semantic impairment (23%) and complex compulsions and rigid thought process (18%). On testing, this group exhibited greater impairments in Emotional Theory of Mind, recognition of famous people (from names and faces) and facial affect naming (despite preserved face perception) than the frontal- and left-predominant anterior temporal lobe-predominant groups. The clinical symptoms in the first 3 years of the disease alone were highly sensitive (81%) and specific (84%) differentiating right anterior temporal lobe-predominant from frontal-predominant groups. Frontotemporal lobar degeneration-transactive response DNA binding protein (84%) was the most common pathology of the right anterior temporal lobe-predominant group. Right anterior temporal lobe-predominant degeneration is characterized by early loss of empathy and person-specific knowledge, deficits that are caused by progressive decline in semantic memory for concepts of socioemotional relevance. Guided by our results, we outline new diagnostic criteria and propose the name, 'semantic behavioural variant frontotemporal dementia', which highlights the underlying cognitive mechanism and the predominant symptomatology. These diagnostic criteria will facilitate early identification and care of patients with early, focal right anterior temporal lobe degeneration as well as in vivo prediction of frontotemporal lobar degeneration-transactive response DNA binding protein pathology.

Dataset Information

Looking for Semantic Similarity: What a Vector-Space Model of Semantics Can Tell Us About Attention in Real-World Scenes.

Publications

Looking for Semantic Similarity: What a Vector-Space Model of Semantics Can Tell Us About Attention in Real-World Scenes.

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