Visually Grounded and Textual Semantic Models Differentially Decode Brain Activity Associated with Concrete and Abstract Nouns

Important advances have recently been made using computational semantic models to decode brain activity patterns associated with concepts; however, this work has almost exclusively focused on concrete nouns. How well these models extend to decoding abstract nouns is largely unknown. We address this question by applying state-of-the-art computational models to decode functional Magnetic Resonance Imaging (fMRI) activity patterns, elicited by participants reading and imagining a diverse set of both concrete and abstract nouns. One of the models we use is linguistic, exploiting the recent word2vec skipgram approach trained on Wikipedia. The second is visually grounded, using deep convolutional neural networks trained on Google Images. Dual coding theory considers concrete concepts to be encoded in the brain both linguistically and visually, and abstract concepts only linguistically. Splitting the fMRI data according to human concreteness ratings, we indeed observe that both models significantly decode the most concrete nouns; however, accuracy is significantly greater using the text-based models for the most abstract nouns. More generally this confirms that current computational models are sufficiently advanced to assist in investigating the representational structure of abstract concepts in the brain.Stephen Clark is supported by ERC Starting Grant DisCoTex (306920)

Multimodal Grounding for Language Processing

This survey discusses how recent developments in multimodal processing facilitate conceptual grounding of language. We categorize the information flow in multimodal processing with respect to cognitive models of human information processing and analyze different methods for combining multimodal representations. Based on this methodological inventory, we discuss the benefit of multimodal grounding for a variety of language processing tasks and the challenges that arise. We particularly focus on multimodal grounding of verbs which play a crucial role for the compositional power of language.Comment: The paper has been published in the Proceedings of the 27 Conference of Computational Linguistics. Please refer to this version for citations: https://www.aclweb.org/anthology/papers/C/C18/C18-1197

Experiential, Distributional and Dependency-based Word Embeddings have Complementary Roles in Decoding Brain Activity

We evaluate 8 different word embedding models on their usefulness for predicting the neural activation patterns associated with concrete nouns. The models we consider include an experiential model, based on crowd-sourced association data, several popular neural and distributional models, and a model that reflects the syntactic context of words (based on dependency parses). Our goal is to assess the cognitive plausibility of these various embedding models, and understand how we can further improve our methods for interpreting brain imaging data. We show that neural word embedding models exhibit superior performance on the tasks we consider, beating experiential word representation model. The syntactically informed model gives the overall best performance when predicting brain activation patterns from word embeddings; whereas the GloVe distributional method gives the overall best performance when predicting in the reverse direction (words vectors from brain images). Interestingly, however, the error patterns of these different models are markedly different. This may support the idea that the brain uses different systems for processing different kinds of words. Moreover, we suggest that taking the relative strengths of different embedding models into account will lead to better models of the brain activity associated with words.Comment: accepted at Cognitive Modeling and Computational Linguistics 201

Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey)

How does the brain represent different modes of information? Can we design a system that automatically understands what the user is thinking? Such questions can be answered by studying brain recordings like functional magnetic resonance imaging (fMRI). As a first step, the neuroscience community has contributed several large cognitive neuroscience datasets related to passive reading/listening/viewing of concept words, narratives, pictures and movies. Encoding and decoding models using these datasets have also been proposed in the past two decades. These models serve as additional tools for basic research in cognitive science and neuroscience. Encoding models aim at generating fMRI brain representations given a stimulus automatically. They have several practical applications in evaluating and diagnosing neurological conditions and thus also help design therapies for brain damage. Decoding models solve the inverse problem of reconstructing the stimuli given the fMRI. They are useful for designing brain-machine or brain-computer interfaces. Inspired by the effectiveness of deep learning models for natural language processing, computer vision, and speech, recently several neural encoding and decoding models have been proposed. In this survey, we will first discuss popular representations of language, vision and speech stimuli, and present a summary of neuroscience datasets. Further, we will review popular deep learning based encoding and decoding architectures and note their benefits and limitations. Finally, we will conclude with a brief summary and discussion about future trends. Given the large amount of recently published work in the `computational cognitive neuroscience' community, we believe that this survey nicely organizes the plethora of work and presents it as a coherent story.Comment: 16 pages, 10 figure

Learning semantic representations through multimodal graph neural networks

Proyecto de Graduación (Licenciatura en Ingeniería Mecatrónica) Instituto Tecnológico de Costa Rica. Área Académica de Ingeniería Mecatrónica, 2021Para proporcionar del conocimiento semántico sobre los objetos con los que van a interactuar los sistemas robóticos, se debe abordar el problema del aprendizaje de las representaciones semánticas a partir de las modalidades del lenguaje y la visión. El conocimiento semántico se refiere a la información conceptual, incluida la información semántica (significado) y léxica (palabra), y que proporciona la base para muchos de nuestros comportamientos no verbales cotidianos. Por lo tanto, es necesario desarrollar métodos que permitan a los robots procesar oraciones en un entorno del mundo real, por lo que este proyecto presenta un enfoque novedoso que utiliza Redes Convolucionales Gráficas para aprender representaciones de palabras basadas en el significado. El modelo propuesto consta de una primera capa que codifica representaciones unimodales y una segunda capa que integra estas representaciones unimodales en una para aprender una representación desde ambas modalidades. Los resultados experimentales muestran que el modelo propuesto supera al estado del arte en similitud semántica y que tiene la capacidad de simular juicios de similitud humana. Hasta donde sabemos, este enfoque es novedoso en el uso de Redes Convolucionales Gráficas para mejorar la calidad de las representaciones de palabras.To provide semantic knowledge about the objects that robotic systems are going to interact with, you must address the problem of learning semantic representations from modalities of language and vision. Semantic knowledge refers to conceptual information, including semantic (meaning) and lexical (word) information, and that provides the basis for many of our everyday non-verbal behaviors. Therefore, it is necessary to develop methods that enable robots to process sentences in a real-world environment, so this project introduces a novel approach that uses Graph Convolutional Networks to learn grounded meaning representations of words. The proposed model consists of a first layer that encodes unimodal representations, and a second layer that integrates these unimodal representations into one to learn a representation from both modalities. Experimental results show that the proposed model outperforms that state-of-the-art in semantic similarity and that can simulate human similarity judgments. To the best of our knowledge, this approach is novel in its use of Graph Convolutional Networks to enhance the quality of word representations