Linear Order in Language:an Error-Driven Learning Account

Learners of German often struggle with learning the grammatical gender of nouns and their correct articles, for example, that it should be “die Gabel” (the fork) and not “der Gabel”. Why is this so hard? And why do gender systems even exist?I taught participants differently structured artificial languages and found that it is especially difficult to learn a gender system, when gender is marked before the noun (e.g., in German: “die Gabel”, the fork, vs. “der Löffel”, the spoon) as compared to when gender is marked after the noun (e.g., in Albanian: “pirun-i”, the fork, vs. “lug-a”, the spoon). With computational simulations I could show that this effect arises because human learning is sensitive to the order of words.However, while gendered articles are hard to learn, they can facilitate communication because they can make following nouns more predictable and therefore easier to process: for example, after the German article “der”, “Löffel” is quite likely, “Gabel”, however, is very unlikely to follow. This is a function that gendered suffixes, as in Albanian, or genderless articles, as in English, cannot fulfill. In a language production study, I observed that speakers produce more articles that can make following nouns predictable, such as German articles, than articles that cannot fulfill this function, such as the English article “the”.I conclude that the order in which gender is marked in languages affects language learning as well as communication. This makes German gender hard to learn but useful for communication

Revealing the (predictive) code of top-down signals in the brain

Reciprocal connections are common in the brain, yet little is known about their functional role. Top-down connections, in particular, remain functionally obscure in both neuroscience and in the nascent field of deep learning. On the theoretical side, predictive coding has been put forward as a framework that assigns specific roles to top-down and bottom-up connections in sensory information processing. It remains unclear, however, if and how the brain implements this predictive code. This work examined top-down signals in the auditory cortex and in the corticostriatal system in macaques in order to validate the claims put forward by predictive coding. This theory suggests there are imbalances in message passing up and down the cortical hierarchy; these imbalances imply cross-frequency couplings should predominate top-down. It is unknown whether these asymmetries are expressed in cross-frequency interactions in the brain. This work examined cross-frequency interactions across four sectors of the macaque auditory cortex. Predictive coding also applies in decision making, where it allows for action selection based on predicted reward (or value). This is more commonly known as reinforcement learning (RL) and is supported by the fronto-striatal systems in the brain. The computational mechanisms that drive learning in this system are unknown, however. This work drew on a recurrent neural network (RNN) model of the dlPFC-dSTR circuit in the brain together with recordings from macaques from the same regions to answer this question. Altogether, the findings are largely consistent with the predictive coding framework.Open Acces

The Processing of Emotional Sentences by Young and Older Adults: A Visual World Eye-movement Study

Carminati MN, Knoeferle P. The Processing of Emotional Sentences by Young and Older Adults: A Visual World Eye-movement Study. Presented at the Architectures and Mechanisms of Language and Processing (AMLaP), Riva del Garda, Italy

Dating Victorians: an experimental approach to stylochronometry

A thesis submitted for the degree of Doctor of Philosophy ofthe University of LutonThe writing style of a number of authors writing in English was empirically investigated for the purpose of detecting stylistic patterns in relation to advancing age. The aim was to identify the type of stylistic markers among lexical, syntactical, phonemic, entropic, character-based, and content ones that would be most able to discriminate between early, middle, and late works of the selected authors, and the best classification or prediction algorithm most suited for this task. Two pilot studies were initially conducted. The first one concentrated on Christina Georgina Rossetti and Edgar Allan Poe from whom personal letters and poetry were selected as the genres of study, along with a limited selection of variables. Results suggested that authors and genre vary inconsistently. The second pilot study was based on Shakespeare's plays using a wider selection of variables to assess their discriminating power in relation to a past study. It was observed that the selected variables were of satisfactory predictive power, hence judged suitable for the task. Subsequently, four experiments were conducted using the variables tested in the second pilot study and personal correspondence and poetry from two additional authors, Edna St Vincent Millay and William Butler Yeats. Stepwise multiple linear regression and regression trees were selected to deal with the first two prediction experiments, and ordinal logistic regression and artificial neural networks for two classification experiments. The first experiment revealed inconsistency in accuracy of prediction and total number of variables in the final models affected by differences in authorship and genre. The second experiment revealed inconsistencies for the same factors in terms of accuracy only. The third experiment showed total number of variables in the model and error in the final model to be affected in various degrees by authorship, genre, different variable types and order in which the variables had been calculated. The last experiment had all measurements affected by the four factors. Examination of whether differences in method within each task play an important part revealed significant influences of method, authorship, and genre for the prediction problems, whereas all factors including method and various interactions dominated in the classification problems. Given the current data and methods used, as well as the results obtained, generalizable conclusions for the wider author population have been avoided