Neurobiological Models of Sentence Processing

Neurobiological models of sentence processing Marvin Uhlmann The educational component of the doctoral training was provided by the International Max Planck Research School (IMPRS) for Language Sciences. The graduate school is a joint initiative between the Max Planck Institute for Psycholinguistics and two partner institutes at Radboud University – the Centre for Language Studies, and the Donders Institute for Brain, Cognition and Behaviour. The IMPRS curriculum, which is funded by the Max Planck Society for the Advancement of Science, ensures that each member receives interdisciplinary training in the language sciences and develops a well-rounded skill set in preparation for fulfilling careers in academia and beyond. More information can be found at www.mpi.nl/imprs ISBN: 978-94-92910-17-2 Cover design: Maria Otworowska & Marvin Uhlmann Printed by: Ipskamp printing Layout: This thesis was typeset with LATEX 2#. It is based on the Clean Thesis style, adapted by Maria Otworowska. © Marvin Uhlmann, 2020 All rights reserved. No part of this book may be reproduced, distributed, stored in a retrieval system, or transmitted in any form or by any means, without prior written permission of the author. The research reported in this thesis was conducted at the Max Planck Institute for Psycholinguistics, in Nijmegen, the Netherlands. Neurobiological models of sentence processing Proefschrift ter verkrijging van de graad van doctor aan de Radboud Universiteit Nijmegen op gezag van de rector magnificus prof. dr. J.H.J.M. van Krieken, volgens besluit van het college van decanen in het openbaar te verdedigen op dinsdag 29 september 2020 om 10.30 uur precies door Marvin Uhlmann geboren op 21 oktober 1988 te Lutherstadt Wittenberg (Duitsland) Promotor Prof. dr. P. Hagoort Copromotoren Dr. K.M. Petersson (MPI Nijmegen) Dr. H. Fitz (MPI Nijmegen) Manuscriptcommissie Prof. dr. M.A.J. van Gerven Prof. dr. T. Celikel Dr. A.E. Martin (MPI Nijmegen) Neurobiological models of sentence processing Doctoral Thesis to obtain the degree of doctor from Radboud University Nijmegen on the authority of the Rector Magnificus prof. dr. J.H.J.M. van Krieken, according to the decision of the Council of Deans to be defended in public on Tuesday, September 29, 2020 at 10.30 hours by Marvin Uhlmann Born on October 21, 1988 in Lutherstadt Wittenberg (Germany) Supervisor Prof. dr. P. Hagoort Co-supervisors Dr. K.M. Petersson (MPI Nijmegen) Dr. H. Fitz (MPI Nijmegen) Doctoral Thesis Committee Prof. dr. M.A.J. van Gerven Prof. dr. T. Celikel Dr. A.E. Martin (MPI Nijmegen) Contents Chapter 1 Introduction 3 Chapter 2 The NBL model, a neurobiological model of sentence processing 21 Chapter 3 Effects of input encoding on processing and memory 43 Chapter 4 Neuronal processing memory for language comprehension 67 Chapter 5 The NBL model and human sentence processing 85 Chapter 6 Binding in the NBL model 113 Chapter 7 General discussion 141 Bibliography 151 Chapter A Appendix 167 1 Introduction 1 An important aim of psycholinguistics is to understand how different aspects of language are implemented in the brain. These processes, including comprehension, production, and acquisition, are assumed to be computational. For each computation there can exist many different realizing algorithms and different ways to implement the algorithm in different computational systems, including the brain (Marr, 1982; Fodor, 1974). Cognitive models in psycholinguistics are based on empirical data from behavioral and neuroimaging experiments and use this data to derive an algorithmic description of language processing (Altmann, 1995). However, because they typically do not take into account the behavior and limitations of biological circuits, they have little explanatory power when it comes to understanding how such an algorithm may be implemented in the brain on the level of neurons, synapses and networks. In this dissertation, I introduce the notion of neurobiological models that take into account the biophysical constraints imposed by the neuronal infrastructure and through this connect cognitive data with the properties of neurons and networks. 1.1 Neurobiological models One of the aims of a neurobiological model is to reflect the constraints imposed by known neurophysiology and neurobiological infrastructure. Based on these constraints, the model processes information which can then be related to aspects of cognition, behavioral or cognitive neuroscience data. Such a model is built using experimentally attained insights about the structure and function of neurons, and the connectivity of neural networks in the brain. Thus, the aim is to capture relevant properties of the biological system that implements the information processing. This allows for the investigation of constraints on processing imposed by the neurobiology 3 of the underlying system and provides the basis for a mechanistic understanding of language processing. Cognitive and neurobiological models can be of different levels of complexity. For cognitive models, simplicity is often preferred if this does not sacrifice coverage and precision in the phenomena the model is attempting to capture. This is typically not the case for neurobiological models which aim to reflect properties of the underlying biological system. Because the biological system consists of large networks of neurons with interconnecting synapses, supporting a variety of biophysical processes, neurobiological models can have a large number of independent components and parameters that determine how they respond to input. However, the characteristics of the biological system impose constraints on those parameters and therefore determine the model properties. This means that the values of model parameters are provided by independent experimental data from fundamental neuroscience. Implementing such neurobiological models implies, among other things, that the model parameters also have physical units of measurement that need to fall within physiological bounds. Another implication is that these models operate physical time that can be directly related to temporal aspects of behavioral and neuroimaging experiments. However, the goal of neurobiological modeling is not to reproduce the entire complexity of the biological system, but rather to understand the system and its functionally relevant aspects in models of reduced complexity. Hence, the goal of such models is to reflect relevant characteristics of the neurobiological system, while at the same time relying on some level of abstraction or reduction of the full details of the neural system, in particular, at the neuronal level. These reductions are derived from experimental data and attempt to capture critical properties that have signifi- cant effects on the behavior of neurons and networks in which they are integrated. Neurobiological models can then be used to investigate the causal relationship between biophysical properties and the processing characteristics of neural networks in an attempt to relate these to cognition. The explicit model descriptions (typically a large set of coupled differential equations) are implemented as computer simula- tions which provides complete control and manipulation opportunities of the various components and parameters of the model. Models with different implementational features can be compared to establish relationships between these aspects of a model and its processing characteristics. Since the models reflect properties of the actual neurobiology, these causal relationships are not only relevant within the models but also for the functional interpretation of the behavior of actual biological subsystems of the brain. Ultimately, neurobiological modeling aims to bridge the description 4 Chapter 1 Introduction Figure 1.1.: Image based on silver staining from Ramón y Cajal (around 1905). The capital letters indicate different morphological neuron classes within the pyramidal neurons (A to G) and other morphological neuron classes (H to M). Small a indicate the axon protruding out from each cell. The cortical layer structure is indicated by numbers on the left. 1.1 Neurobiological models 5 of brain operations across levels of explanation in order to provide a mechanistic understanding of higher-level cognitive functions, including language processing. 1.2 Intersecting disciplines The work presented in this dissertation applies methods from computational neuroscience and neurophysiology to address questions in psycholinguistics and cognitive neuroscience. To give a comprehensive introduction to these fields goes beyond the scope of this chapter. However, I will introduce the findings and concepts that this dissertation is based on and that are used later to argue for or against particular design choices in modeling (chapter2). 1.2.1 Neurophysiology The brain is a large network of interconnected neurons. To understand how information is processed in this system, it is important to understand the properties of neurons as developed experimentally in the field of neurophysiology. Single neurons Biological neurons are complex molecular machines and their experimental study was initiated in the late 19th century by Ramón y Cajal, see Figure 1.1. Based on his morphological characterizations, he suggested that the neurons in the brain are fundamental computational units that communicate with each other. Neurons typically consist of three basic structural components: (i) a dendritic tree, receiving and accumulating electric signals, (ii) a soma or cell body with the nucleus and

Load more