Computational Mechanisms of Language Understanding and Use in the Brain and Behaviour

Linguistic communication is a unique characteristic of intelligent behaviour that distinguishes humans from non-human animals. Natural language is a structured, complex communication system supported by a variety of cognitive functions, realized by hundreds of millions of neurons in the brain. Artificial neural networks typically used in natural language processing (NLP) are often designed to focus on benchmark performance, where one of the main goals is reaching the state-of-the-art performance on a set of language tasks. Although the advances in NLP have been tremendous in the past decade, such networks provide only limited insights into biological mechanisms underlying linguistic processing in the brain. In this thesis, we propose an integrative approach to the study of computational mechanisms underlying fundamental language processes, spanning biologically plausible neural networks, and learning of basic communicative abilities through environmentally grounded behaviour. In doing so, we argue for the usage-based approach to language, where language is supported by a variety of cognitive functions and learning mechanisms. Thus, we focus on the three following questions: How are basic linguistic units, such as words, represented in the brain? Which neural mechanisms operate on those representations in cognitive tasks? How can aspects of such representations, such as associative similarity and structure, be learned in a usage-based framework? To answer the first two questions, we build novel, biologically realistic models of neural function that perform different semantic processing tasks: the Remote Associates Test (RAT) and the semantic fluency task. Both tasks have been used in experimental and clinical environments to study organizational principles and retrieval mechanisms from semantic memory. The models we propose realize the mental lexicon and cognitive retrieval processes operating on that lexicon using associative mechanisms in a biologically plausible manner. We argue that such models are the first and only biologically plausible models that propose specific mechanisms as well as reproduce a wide range of human behavioural data on those tasks, further corroborating their plausibility. To address the last question, we use an interactive, collaborative agent-based reinforcement learning setup in a navigation task where agents learn to communicate to solve the task. We argue that agents in such a setup learn to jointly coordinate their actions, and develop a communication protocol that is often optimal for the performance on the task, while exhibiting some core properties of language, such as representational similarity structure and compositionality, essential for associative mechanisms underlying cognitive representations

Over-communicate no more: Situated RL agents learn concise communication protocols

While it is known that communication facilitates cooperation in multi-agent settings, it is unclear how to design artificial agents that can learn to effectively and efficiently communicate with each other. Much research on communication emergence uses reinforcement learning (RL) and explores unsituated communication in one-step referential tasks -- the tasks are not temporally interactive and lack time pressures typically present in natural communication. In these settings, agents may successfully learn to communicate, but they do not learn to exchange information concisely -- they tend towards over-communication and an inefficient encoding. Here, we explore situated communication in a multi-step task, where the acting agent has to forgo an environmental action to communicate. Thus, we impose an opportunity cost on communication and mimic the real-world pressure of passing time. We compare communication emergence under this pressure against learning to communicate with a cost on articulation effort, implemented as a per-message penalty (fixed and progressively increasing). We find that while all tested pressures can disincentivise over-communication, situated communication does it most effectively and, unlike the cost on effort, does not negatively impact emergence. Implementing an opportunity cost on communication in a temporally extended environment is a step towards embodiment, and might be a pre-condition for incentivising efficient, human-like communication

Processed Ngram data

Processed Ngram matrices containing bi-gram association strengths<br

Spiking data

Spiking data produced by the model in an example run. This data was used for the spike raster plot.<br

Rhizobium croatiense sp. nov. and Rhizobium redzepovicii sp. nov., two new species isolated from nodules of Phaseolus vulgaris in Croatia

7 páginas, 6 tablas, 3 figurasPhaseolus vulgaris is a legume indigenous to America which is nodulated by strains of genus Rhizobium in Croatia. Four of these strains, 13TT, 9T, 18TT and 8Z are phylogenetically close to the species from the Rhizobium leguminosarum phylogenetic complex in the 16S rRNA gene analysis. The results of both the analyses of the concatenated recA and atpD genes and whole genomes revealed that the strains 13TT and 9T clustered with Rhizobium sophoriradicis CCBAU 03470T and the strains 18TT and 8Z with Rhizobium ecuadorense CNPSO 671T. Whole genome average nucleotide identity blast (ANIb) and dDDH values between the strains 13TT and the type strain of R. sophoriradicis and between the strains 18TT and the type strain of R. ecuadorense were lower than 95% and 70%, respectively, which are the threshold values recommended for bacterial species differentiation. These results combined with those of chemotaxonomic and phenotypic analyses support the affiliation of these strains to two novel species within the genus Rhizobium for which we propose the names Rhizobium croatiense sp. nov. 13TT (=LMG 32397T, = HAMBI 3740T) as type strain and Rhizobium redzepovicii sp. nov. 18TT (=LMG 32398T, = HAMBI 3741T) as type strain.The authors also thank the Strategic Research Programs for Units of Excellence CLU-2O18-04 (University of Salamanca) and CLU-2019-05 (IRNASA/CSIC) co-funded by the Junta de Castilla y León and European Union (ERDF ‘‘Europe drives our growth”)Peer reviewe