Corpus ID: 220830873

Characterizing the Effect of Sentence Context on Word Meanings: Mapping Brain to Behavior

@article{AguirreCelis2020CharacterizingTE,
  title={Characterizing the Effect of Sentence Context on Word Meanings: Mapping Brain to Behavior},
  author={Nora E. Aguirre-Celis and Risto Miikkulainen},
  journal={ArXiv},
  year={2020},
  volume={abs/2007.13840}
}
Semantic feature models have become a popular tool for prediction and interpretation of fMRI data. In particular, prior work has shown that differences in the fMRI patterns in sentence reading can be explained by context-dependent changes in the semantic feature representations of the words. However, whether the subjects are aware of such changes and agree with them has been an open question. This paper aims to answer this question through a human-subject study. Subjects were asked to judge how… Expand

Figures and Tables from this paper

Characterizing Dynamic Word Meaning Representations in the Brain
During sentence comprehension, humans adjust word meanings according to the combination of the concepts that occur in the sentence. This paper presents a neural network model called CEREBRAExpand

References

SHOWING 1-10 OF 20 REFERENCES
In defense of abstract conceptual representations
  • J. Binder
  • Psychology, Medicine
  • Psychonomic bulletin & review
  • 2016
TLDR
The evidence supports a hierarchical model of knowledge representation in which modal systems provide a mechanism for concept acquisition and serve to ground individual concepts in external reality, whereas broadly conjunctive, supramodal representations play an equally important role in concept association and situation knowledge. Expand
Quantifying the Conceptual Combination Effect on Word Meanings
TLDR
A neural network is trained with backpropagation to map attributebased semantic representations to fMRI images of subjects reading everyday sentences, demonstrating how word meanings change in different contexts and could be included in natural language processing systems to encode rich contextual embeddings to mirror human performance more accurately. Expand
Combining fMRI Data and Neural Networks to Quantify Contextual Effects in the Brain
TLDR
This paper quantifies for the first time how attribute weightings for the same word are modified by context, allowing them to mirror human performance more accurately in future natural language processing systems. Expand
Semantic projection: recovering human knowledge of multiple, distinct object features from word embeddings
TLDR
A powerful, domain-general solution: "semantic projection" of word-vectors onto lines that represent various object features, like size, intelligence, and danger, which recovers human judgments across a range of object categories and properties. Expand
From Words to Sentences & Back: Characterizing Context-dependent Meaning Rep in the Brain
  • Proceedings of the 39th Annual Conference of the Cognitive Science Society, London,
  • 2017
Predicting Neural Activity Patterns Associated with Sentences Using a Neurobiologically Motivated Model of Semantic Representation
TLDR
The results show how a neurobiologically motivated semantic model can decompose sentence-level fMRI data into activation features for component words, which can be recombined to predict activation patterns for new sentences. Expand
Evaluating semantic models with word-sentence relatedness
TLDR
A data set for evaluating semantic models was developed consisting of 775 English word-sentence pairs annotated for semantic relatedness by human raters engaged in a Maximum Difference Scaling (MDS) task, as well as a faster alternative task. Expand
Toward a brain-based componential semantic representation
TLDR
This study proposes a basic set of approximately 65 experiential attributes based on neurobiological considerations, comprising sensory, motor, spatial, temporal, affective, social, and cognitive experiences, and discusses how this representation might deal with various longstanding problems in semantic theory, such as feature selection and weighting, representation of abstract concepts, effects of context on semantic retrieval, and conceptual combination. Expand
Conceptual representations in mind and brain: Theoretical developments, current evidence and future directions
TLDR
It is argued that an embodiment view of conceptual representations realized as distributed sensory and motor cell assemblies that are complemented by supramodal integration brain circuits may serve as a theoretical framework to guide future research on concrete and abstract concepts. Expand
The neurobiology of semantic memory
TLDR
It is shown that large brain regions that participate in comprehension tasks but are not modality-specific lie at convergences of multiple perceptual processing streams, which enable increasingly abstract, supramodal representations of perceptual experience that support a variety of conceptual functions including object recognition, social cognition, language, and the remarkable human capacity to remember the past and imagine the future. Expand
...
1
2
...