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To optimize the sharing and reuse of existing data, many funding organizations now require researchers to specify a management plan for research data. In such a plan, researchers are supposed to describe the entire life cycle of the research data they are going to produce, from data creation to formatting, interpretation, documentation, short-term storage, long-term archiving and data re-use. To support researchers with this task, we built DMPTY, a wizard that guides researchers through the essential aspects of managing data, elicits information from them, and finally, generates a document that can be further edited and linked to the original research proposal.
Two very reliable influences on eye fixation durations in reading are word frequency, as measured by corpus counts, and word predictability, as measured by cloze norming. Several studies have reported strictly additive effects of these 2 variables. Predictability also reliably influences the amplitude of the N400 component in event-related potential studies. However, previous research suggests that while frequency affects the N400 in single-word tasks, it may have little or no effect on the N400 when a word is presented with a preceding sentence context. The present study assessed this apparent dissociation between the results from the 2 methods using a coregistration paradigm in which the frequency and predictability of a target word were manipulated while readers’ eye movements and electroencephalograms were simultaneously recorded. We replicated the pattern of significant, and additive, effects of the 2 manipulations on eye fixation durations. We also replicated the predictability effect on the N400, time-locked to the onset of the reader’s first fixation on the target word. However, there was no indication of a frequency effect in the electroencephalogram record. We suggest that this pattern has implications both for the interpretation of the N400 and for the interpretation of frequency and predictability effects in language comprehension.
Hierarchical predictive coding has been identified as a possible unifying principle of brain function, and recent work in cognitive neuroscience has examined how it may be affected by age–related changes. Using language comprehension as a test case, the present study aimed to dissociate age-related changes in prediction generation versus internal model adaptation following a prediction error. Event-related brain potentials (ERPs) were measured in a group of older adults (60–81 years; n = 40) as they read sentences of the form “The opposite of black is white/yellow/nice.” Replicating previous work in young adults, results showed a target-related P300 for the expected antonym (“white”; an effect assumed to reflect a prediction match), and a graded N400 effect for the two incongruous conditions (i.e. a larger N400 amplitude for the incongruous continuation not related to the expected antonym, “nice,” versus the incongruous associated condition, “yellow”). These effects were followed by a late positivity, again with a larger amplitude in the incongruous non-associated versus incongruous associated condition. Analyses using linear mixed-effects models showed that the target-related P300 effect and the N400 effect for the incongruous non-associated condition were both modulated by age, thus suggesting that age-related changes affect both prediction generation and model adaptation. However, effects of age were outweighed by the interindividual variability of ERP responses, as reflected in the high proportion of variance captured by the inclusion of by-condition random slopes for participants and items. We thus argue that – at both a neurophysiological and a functional level – the notion of general differences between language processing in young and older adults may only be of limited use, and that future research should seek to better understand the causes of interindividual variability in the ERP responses of older adults and its relation to cognitive performance.
This paper investigates evidence for linguistic coherence in new urban dialects that evolved in multiethnic and multilingual urban neighbourhoods. We propose a view of coherence as an interpretation of empirical observations rather than something that would be ‘‘out there in the data’’, and argue that this interpretation should be based on evidence of systematic links between linguistic phenomena, as established by patterns of covariation between phenomena that can be shown to be related at linguistic levels. In a case study, we present results from qualitative and quantitative analyses for a set of phenomena that have been described for Kiezdeutsch, a new dialect from multilingual urban Germany. Qualitative analyses point to linguistic relationships between different phenomena and between pragmatic and linguistic levels. Quantitative analyses, based on corpus data from KiDKo (www.kiezdeutschkorpus.de), point to systematic advantages for the Kiezdeutsch data from a multiethnic and multilingual context provided by the main corpus (KiDKo/Mu), compared to complementary corpus data from a mostly monoethnic and monolingual (German) context (KiDKo/Mo). Taken together, this indicates patterns of covariation that support an interpretation of coherence for this new dialect: our findings point to an interconnected linguistic system, rather than to a mere accumulation of individual features. In addition to this internal coherence, the data also points to external coherence: Kiezdeutsch is not disconnected on the outside either, but fully integrated within the general domain of German, an integration that defies a distinction of ‘‘autochthonous’’ and ‘‘allochthonous’’ German, not only at the level of speakers, but also at the level of linguistic systems.
Neologismen
(2015)
Social perception studies have revealed that smiling individuals are perceived more favourably on many communion dimensions in comparison to nonsmiling individuals. Research on gender differences in smiling habits showed that women smile more than men. In our study, we investigated this phenomena further and hypothesised that women perceive smiling individuals as more honest than men. An experiment conducted in seven countries (China, Germany, Mexico, Norway, Poland, Republic of South Africa and USA) revealed that gender may influence the perception of honesty in smiling individuals. We compared ratings of honesty made by male and female participants who viewed photos of smiling and nonsmiling people. While men and women did not differ on ratings of honesty in nonsmiling individuals, women assessed smiling individuals as more honest than men did. We discuss these results from a social norms perspective.
In this article, we explore the feasibility of extracting suitable and unsuitable food items for particular health conditions from natural language text. We refer to this task as conditional healthiness classification. For that purpose, we annotate a corpus extracted from forum entries of a food-related website. We identify different relation types that hold between food items and health conditions going beyond a binary distinction of suitability and unsuitability and devise various supervised classifiers using different types of features. We examine the impact of different task-specific resources, such as a healthiness lexicon that lists the healthiness status of a food item and a sentiment lexicon. Moreover, we also consider task-specific linguistic features that disambiguate a context in which mentions of a food item and a health condition co-occur and compare them with standard features using bag of words, part-of-speech information and syntactic parses. We also investigate in how far individual food items and health conditions correlate with specific relation types and try to harness this information for classification.
We examine the combination of pattern-based and distributional similarity for the induction of semantic categories. Pattern-based methods are precise and sparse while distributional methods have a higher recall. Given these particular properties we use the prediction of distributional methods as a back-off to pattern-based similarity. Since our pattern-based approach is embedded into a semi-supervised graph clustering algorithm, we also examine how distributional information is best added to that classifier. Our experiments are carried out on 5 different food categorization tasks.