Some talks at the CQL Lab

Ezequiel Koile, Max Planck Institute for the Science of Human History (October 4, 2018). Phylogeography of the Bantu Expansion.

  • Bantu expansion is among the most important and least understood human migrations. Bantu-speaking populations, which amount today to 240 million people, speaking around 500 languages, and spanning through 9 million square kilometers are the result of a huge migration originating in a homeland near the border of Nigeria and Cameroon between 4,000BP and 5,000BP.

Michael Zock, LIF-CNRS (July 13, 2017). Cognitive aspects of Natural Language Processing: Wheels for the mind of the language producer.

  • Languages are not only means of expression, but also vehicles of thought, allowing us to discover new ideas (brainstorming) or clarify existing ones by refining, expanding, illustrating more or less well specified thoughts. Of course, all this must be learned, and to this end we need resources, tools and knowledge on how to use them.

Stuart Semple, Roehampton University (December 15, 2016). Investigating communication in our primate relatives: from information content to linguistic laws.

  • Communication underpins the social behaviour of humans, and of our primate relatives. While language is unique to our own species, the other primates have complex repertoires of calls, which they use to convey diverse messages. In this seminar I will describe a range of studies on primate communication that I and my collaborators have conducted. I will talk about work investigating the information content and function of primates’ vocalisations, the role that bystanders can play in shaping the outcome of communicative interactions, and the correlates of primate vocal repertoire size. I will also describe our most recent work, testing whether patterns consistent with linguistic laws – specifically Zipf’s law of abbreviation and Menzerath’s law – are found in the vocal (and gestural) communication of monkeys and apes.

Iván González Torre, Universidad Politécnica de Madrid (December 15, 2016). Exploration of linguistic laws in human voice.

  • Despite great interest and research activity, the study of linguistic laws is usually restricted to written text where segmentation is explicit. However, this segmentation is not well defined in the case of oral corpora. I will present a mathematical method that we have proposed to transform generic acoustic signals into sequences of symbols describing speech energy fluctuations. With this transformation, we can explore linguistic laws such as Zipf’s law, Heap’s law and the law of abbreviation without requiring a transcription of the signals into symbols of some sort. The method is simple and general, and it allows one to perform comparative studies between human and animal communication and beyond.

Emília-Maria Garcia Casademont, Institut de Biologia Evolutiva, CSIC/UPF (November 24, 2016). Cultural emergence of recursive phrase structure.

  • Naming Game models have been widely used to study the emergence of purely lexical systems, consisting of word-meaning pairs, within a population of communicative agents. Following a similar methodology, I will discuss a model meant to study the emergence of a grammatical system exhibiting recursive phrase structure.

Vineeta Chand, University of Essex (June 14, 2016). Language dynamics in contemporary India.

  • In this talk I will introduce sociolinguistic aspects of contemporary India (language politics, changing fluencies, contemporary practices) as a situated case study with broader ramifications for sites of multilingualism and language contact across the globe. Specifically, I will focus on mixed practices (codeswitching) from two angles: (1) language shift dynamics in the Hindi Belt within a predator-prey modeling framework and (2) the quantitative characteristics of two mixed codes, Benglish and Hinglish, with respect to Zipf’s Law. There are theoretical linguistic, sociolinguistic and evolutionary linguistic conclusions to be drawn from such research, which will also be addressed.

Miquel Fernández (dijous 7 d’abril del 2016). L’ordre de constituents: una visió diacrònica.

  • La major part de treballs de tipologia lingüística es centren en els valors i freqüències observats a les llengües actuals, possiblement per la manca de dades lingüístiques històriques a la major part del món. En aquesta tesi s’han fet servir metodologies pròpies de la zoogeografia vicariant cladística per tal de trobar els escenaris més parsimoniosos de canvi d’ordre de constituents succeïts els darrers 50.000 anys, tot lligant-los a l’aparició dels factors socials i demogràfics propis del neolític.

Bruno Galantucci, Yeshiva University (March 14, 2016). A few things I learned about human communication.

  • In this talk I present a synopsis of the research on human communication that I conducted over the last few years. In the first part of the talk I focus on the linguistic side of communication, which I investigated through a methodology—Experimental Semiotics—that allows us to study in the laboratory the emergence of novel forms of human communication. In particular, I present a set of related studies aimed at investigating how communication systems acquire a combinatorial design. In the second part of the talk I focus on the psychological and social sides of communication, starting from the observation that humans exhibit important limitations when they are asked to perform tasks that require communicative sophistication. This raises the question of how individuals who have limited communicative skills manage to develop sophisticated forms of communication. I discuss three non-mutually exclusive hypotheses to address the question and present some empirical evidence relevant to one of them.

William Schueller, INRIA & ENSTA ParisTech (July 27, 2015). Active learning and active control of growth complexity in naming games.

  • Naming Games are models of the dynamic formation of lexical conventions in populations of agents. In this work we introduce new Naming Game strategies, using developmental and active learning mechanisms to control the growth of complexity. An information theoretical measure to compare those strategies is introduced, and used to study their impact on the dynamics of the Naming Game.

Chris Kello, UC Merced (April 23, 2015). Adaptive critical branching networks.

  • Biological neural networks exhibit ongoing, spatiotemporal patterns of spiking activity. Evidence shows that spike dynamics shift from one transient attractor to another, i.e. they appear to be metastable. Metastability is theorized to be adaptive for neural and cognitive function, but learning must somehow remain stable in the context of highly variable spike dynamics. Stable learning is challenging in part because it appears that functions of homeostatic regulation and learning are both expressed through potentiation and de-potentiation of synapses. In this talk, Prof. Kello will present a spiking neural network model that integrates homeostatic regulation with learning via a local, biological plausible process of synaptic modulation. Homeostatic regulation towards the critical branching point results in power law spike dynamics, while learning shapes those dynamics to maximize reward and minimize punishment. The model is shown to simulate intrinsic fluctuations in neural and behavioral activity, and the efficacy of learning is demonstrated using time-delayed XOR classification as a simple test function, and real-time phoneme recognition in naturalistic speech as a more challenging test.

Cynthia Siew, University of Kansas (June 5, 2014). Community structure in the phonological network.

  • Community structure, which refers to the presence of densely connected groups within a larger network, is a common feature of several real-world networks from a variety of domains such as the human brain, social networks of hunter-gatherers and business organizations, and the World Wide Web (Porter et al., 2009). Using a community detection technique known as the Louvain optimization method, 17 communities were extracted from the giant component of the phonological network described in Vitevitch (2008). Additional analyses comparing the lexical and phonological characteristics of words in these communities against words in randomly generated communities revealed several novel discoveries. Larger communities tend to consist of short, frequent words of high degree and low age of acquisition ratings, and smaller communities tend to consist of longer, less frequent words of low degree and high age of acquisition ratings. Real communities also contained fewer different phonological segments compared to random communities, although the number of occurrences of phonological segments found in real communities was much higher than that of the same phonological segments in random communities. Interestingly, the observation that relatively few biphones occur very frequently and a large number of biphones occur rarely within communities mirrors the pattern of the overall frequency of words in a language (Zipf, 1935). The present findings have important implications for understanding the dynamics of activation spread among words in the phonological network that are relevant to lexical processing, as well as understanding the mechanisms that underlie language acquisition and the evolution of language.

Cynthia Siew, University of Kansas (June 11, 2014). Spoken word recognition and serial recall of words from components in the phonological network.

  • Network science uses mathematical techniques to study complex systems such as the phonological lexicon (Vitevitch, 2008). The phonological network consists of a giant component (the largest connected component of the network) and lexical islands (smaller groups of words that are connected to each other but not to the giant component). To determine if the component that a word resided in influenced lexical processing, three language-related tasks (naming, lexical decision, and serial recall) were used to compare the processing of words from the giant component and from lexical islands. Results showed that words from lexical islands were recognized more quickly and recalled more accurately than words from the giant component. These findings can be accounted for via a spreading activation framework. Implications for network science and for models of spoken word recognition are also discussed.