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- Department Linguistik (59) (remove)
Deep learning is a sub-field of machine learning that has recently gained substantial popularity in various domains such as computer vision, automatic speech recognition, natural language processing, and bioinformatics. Deep-learning techniques are able to learn complex feature representations from raw signals and thus also have potential to improve signal processing in the context of brain-computer interfaces (BCIs). However, they typically require large amounts of data for training - much more than what can often be provided with reasonable effort when working with brain activity recordings of any kind. In order to still leverage the power of deep-learning techniques with limited available data, special care needs to be taken when designing the BCI task, defining the structure of the deep model, and choosing the training method. This chapter presents example approaches for the specific scenario of music-based brain-computer interaction through electroencephalography - in the hope that these will prove to be valuable in different settings as well. We explain important decisions for the design of the BCI task and their impact on the models and training techniques that can be used. Furthermore, we present and compare various pre-training techniques that aim to improve the signal-to-noise ratio. Finally, we discuss approaches to interpret the trained models.
In a preferential looking paradigm, we studied how children's looking behavior and pupillary response were modulated by the degree of phonological mismatch between the correct label of a target referent and its manipulated form. We manipulated degree of mismatch by introducing one or more featural changes to the target label. Both looking behavior and pupillary response were sensitive to degree of mismatch, corroborating previous studies that found differential responses in one or the other measure. Using time-course analyses, we present for the first time results demonstrating full separability among conditions (detecting difference not only between one vs. more, but also between two and three featural changes). Furthermore, the correct labels and small featural changes were associated with stable target preference, while large featural changes were associated with oscillating looking behavior, suggesting significant shifts in looking preference over time. These findings further support and extend the notion that early words are represented in great detail, containing subphonemic information.
Encountering a cataphoric pronoun triggers a search for a suitable referent. Previous research indicates that this search is constrained by binding Condition C, which prohibits coreference between a cataphoric pronoun and a referential expression within its c-command domain. We report the results from a series of eye-movement monitoring and questionnaire experiments investigating cataphoric pronoun resolution in German. Given earlier findings suggesting that the application of structure-sensitive constraints on reference resolution may be delayed in non-native language processing, we tested both native and proficient non-native speakers of German. Our results show that cataphoric pronouns trigger an active search in both native and non-native comprehenders. Whilst both participant groups demonstrated awareness of Condition C in an offline task, we found Condition C effects to be restricted to later processing measures during online reading. This indicates that during natural reading, Condition C applies as a relatively late filter on potential coreference assignments.
This tutorial analyzes voice onset time (VOT) data from Dongbei (Northeastern) Mandarin Chinese and North American English to demonstrate how Bayesian linear mixed models can be fit using the programming language Stan via the R package brms. Through this case study, we demonstrate some of the advantages of the Bayesian framework: researchers can (i) flexibly define the underlying process that they believe to have generated the data; (ii) obtain direct information regarding the uncertainty about the parameter that relates the data to the theoretical question being studied; and (iii) incorporate prior knowledge into the analysis. Getting started with Bayesian modeling can be challenging, especially when one is trying to model one’s own (often unique) data. It is difficult to see how one can apply general principles described in textbooks to one’s own specific research problem. We address this barrier to using Bayesian methods by providing three detailed examples, with source code to allow easy reproducibility. The examples presented are intended to give the reader a flavor of the process of model-fitting; suggestions for further study are also provided. All data and code are available from: https://osf.io/g4zpv.
Time reference, which has been found to be selectively impaired in agrammatic aphasia, is often interwoven with grammatical aspect. A recent study on Russian aphasia found that time reference and aspect interact: Past reference was less impaired when tested within a perfective aspect context (compared to when tested within an imperfective aspect context), and reference to the non-past was less impaired when tested within an imperfective aspect context (compared to when tested within a perfective aspect context). To explain this pattern, the authors argued that there are prototypical associations between time frames and aspectual values. The present study explores the relationship between time reference and aspect focusing on Greek aphasia and healthy ageing and using a sentence completion task that crosses time reference and aspect. The findings do not support prototypical matches between different time frames and aspectual values. Building on relevant studies, we propose that patterns of performance of healthy or language-impaired speakers on constrained tasks tapping different combinations of time frames with aspectual values should reflect the relative frequency of these combinations in a given language. The analysis of the results at the individual level revealed a double dissociation, which indicates that a given time frame-aspectual value combination may be relatively easy to process for some persons with aphasia but demanding for some others.
Second language speakers often struggle to apply grammatical constraints such as subject-verb agreement. One hypothesis for this difficulty is that it results from problems suppressing syntactically unlicensed constituents in working memory. We investigated which properties of these constituents make them more likely to elicit errors: their grammatical distance to the subject head or their linear distance to the verb. We used double modifier constructions (e.g., the smell of the stables of the farmers), where the errors of native speakers are modulated by the linguistic relationships between the nouns in the subject phrase: second plural nouns, which are syntactically and semantically closer to the subject head, elicit more errors than third plural nouns, which are linearly closer to the verb (2nd-3rd-noun asymmetry). In order to dissociate between grammatical and linear distance, we compared embedded and coordinated modifiers, which were linearly identical but differed in grammatical distance. Using an attraction paradigm, we showed that German native speakers and proficient Russian speakers of German exhibited similar attraction rates and that their errors displayed a 2nd-3rd-noun asymmetry, which was more pronounced in embedded than in coordinated constructions. We suggest that both native and second language learners prioritize linguistic structure over linear distance in their agreement computations.
Background: Event-related potentials (ERPs) are increasingly used in cognitive science. With their high temporal resolution, they offer a unique window into cognitive processes and their time course. In this paper, we focus on ERP experiments whose designs involve selecting participants and stimuli amongst many. Recently, Westfall et al. (2017) highlighted the drastic consequences of not considering stimuli as a random variable in fMRI studies with such designs. Most ERP studies in cognitive psychology suffer from the same drawback. New method: We advocate the use of the Quasi-F or Mixed-effects models instead of the classical ANOVA/by-participant F1 statistic to analyze ERP datasets in which the dependent variable is reduced to one measure per trial (e.g., mean amplitude). We combine Quasi-F statistic and cluster mass tests to analyze datasets with multiple measures per trial. Doing so allows us to treat stimulus as a random variable while correcting for multiple comparisons. Results: Simulations show that the use of Quasi-F statistics with cluster mass tests allows maintaining the family wise error rates close to the nominal alpha level of 0.05. Comparison with existing methods: Simulations reveal that the classical ANOVA/F1 approach has an alarming FWER, demonstrating the superiority of models that treat both participant and stimulus as random variables, like the Quasi-F approach. Conclusions: Our simulations question the validity of studies in which stimulus is not treated as a random variable. Failure to change the current standards feeds the replicability crisis.
We present a computational evaluation of three hypotheses about sources of deficit in sentence comprehension in aphasia: slowed processing, intermittent deficiency, and resource reduction. The ACT-R based Lewis and Vasishth (2005) model is used to implement these three proposals. Slowed processing is implemented as slowed execution time of parse steps; intermittent deficiency as increased random noise in activation of elements in memory; and resource reduction as reduced spreading activation. As data, we considered subject vs. object relative sentences, presented in a self-paced listening modality to 56 individuals with aphasia (IWA) and 46 matched controls. The participants heard the sentences and carried out a picture verification task to decide on an interpretation of the sentence. These response accuracies are used to identify the best parameters (for each participant) that correspond to the three hypotheses mentioned above. We show that controls have more tightly clustered (less variable) parameter values than IWA; specifically, compared to controls, among IWA there are more individuals with slow parsing times, high noise, and low spreading activation. We find that (a) individual IWA show differential amounts of deficit along the three dimensions of slowed processing, intermittent deficiency, and resource reduction, (b) overall, there is evidence for all three sources of deficit playing a role, and (c) IWA have a more variable range of parameter values than controls. An important implication is that it may be meaningless to talk about sources of deficit with respect to an abstract verage IWA; the focus should be on the individual's differential degrees of deficit along different dimensions, and on understanding the causes of variability in deficit between participants.
A close call
(2018)
The present study investigated how lexical selection is influenced by the number of semantically related representations (semantic neighbourhood density) and their similarity (semantic distance) to the target in a speeded picture-naming task. Semantic neighbourhood density and similarity as continuous variables were used to assess lexical selection for which competitive and noncompetitive mechanisms have been proposed. Previous studies found mixed effects of semantic neighbourhood variables, leaving this issue unresolved. Here, we demonstrate interference of semantic neighbourhood similarity with less accurate naming responses and a higher likelihood of producing semantic errors and omissions over accurate responses for words with semantically more similar (closer) neighbours. No main effect of semantic neighbourhood density and no interaction between semantic neighbourhood density and similarity was found. We assessed further whether semantic neighbourhood density can affect naming performance if semantic neighbours exceed a certain degree of semantic similarity. Semantic similarity between the target and each neighbour was used to split semantic neighbourhood density into two different density variables: The number of semantically close neighbours versus distant neighbours. The results showed a significant effect of close, but not of distant, semantic neighbourhood density: Naming pictures of targets with more close semantic neighbours led to longer naming latencies, less accurate responses, and a higher likelihood for the production of semantic errors and omissions over accurate responses. The results show that word inherent semantic attributes such as semantic neighbourhood similarity and the number of coactivated close semantic neighbours modulate lexical selection supporting theories of competitive lexical processing.