In many languages, grammatical gender is an inherent property of nouns and, as such, forms a basis for agreement relations between nouns and their dependent elements (e.g., adjectives, determiners). Mental gender representation is traditionally assumed to be categorial, with categorial gender nodes corresponding to the given gender specifications in a certain language (e.g., [masculine], [feminine], [neuter] in German). In alternative models, inspired by accounts put forward in theoretical linguistics, it has been argued that mental gender representations consist of sets of binary features which might be fully specified (e.g., masc [+ m, − f], fem [− m, + f], neut [− m, − f]) or underspecified (e.g., masc [+ m], fem [+ f], neut [] or masc [+ m, − f], fem [], neut [− f]). We have conducted two experiments to test these controversial accounts. Native speakers of German were asked to decide on the (un-)grammaticality of gender agreement of visually presented combinations of I) definite determiners and nouns, and II) anaphoric personal pronouns and nouns in an implicit nominative singular setting. Overall, agreement violations with neuter das / es increased processing costs compared to violations with die / sie or der / er for masculine or feminine target nouns, respectively. The observed pattern poses a challenge for models involving categorial gender representation. Rather, it is consistent with feature-based representations of grammatical gender in the mental lexicon.
Pretty flattering to be identified being part of the Spatial Computing element of the #Metaverse - our psychospatial data layer is designed to enrich conversations, identify a range of psycholinguistic aspects, and inform the Human Experience
https://masto.ai/@HumanAI/109692540603411614
ChatGPTs greatest contribution seems to be the enablement of theft but isn't too far off the hype of Crypto as the narrative is pushed by fan-techies
Attached: 1 image As we build psycholinguistic technologies, we have many VCs ask us about ChatGPT First, we assessed it by trying it on our own code but it seems to only work on code that everyone else uses as it looks to be a scrape of GitHub rather than anything unique or custom. Second, it rips off original thinkers and creators and much of the information it provides as fact is actually false - as evidenced by ripping off our work & effort in psychological imagery as a dream interpretation method. 1/2
Most current affect scales and sentiment analysis on written text focus on quantifying valence/sentiment, the primary dimension of emotion. Distinguishing broader, more complex negative emotions of similar valence is key to evaluating mental health. We propose a semi-supervised machine learning model, DASentimental, to extract depression, anxiety, and stress from written text. We trained DASentimental to identify how N = 200 sequences of recalled emotional words correlate with recallers’ depression, anxiety, and stress from the Depression Anxiety Stress Scale (DASS-21). Using cognitive network science, we modeled every recall list as a bag-of-words (BOW) vector and as a walk over a network representation of semantic memory—in this case, free associations. This weights BOW entries according to their centrality (degree) in semantic memory and informs recalls using semantic network distances, thus embedding recalls in a cognitive representation. This embedding translated into state-of-the-art, cross-validated predictions for depression (R = 0.7), anxiety (R = 0.44), and stress (R = 0.52), equivalent to previous results employing additional human data. Powered by a multilayer perceptron neural network, DASentimental opens the door to probing the semantic organizations of emotional distress. We found that semantic distances between recalls (i.e., walk coverage), was key for estimating depression levels but redundant for anxiety and stress levels. Semantic distances from “fear” boosted anxiety predictions but were redundant when the “sad–happy” dyad was considered. We applied DASentimental to a clinical dataset of 142 suicide notes and found that the predicted depression and anxiety levels (high/low) corresponded to differences in valence and arousal as expected from a circumplex model of affect. We discuss key directions for future research enabled by artificial intelligence detecting stress, anxiety, and depression in texts.
In the past I've approached this in different ways, either collaborating on a #psycholinguistic experiment on #ReferringExpression production (https://aclanthology.org/W17-3522/) or #crowdsourcing variations on an existing #corpus (https://davehowcroft.com/publication/2017-08_interspeech_extended-sparky-restaurant-corpus/).
At the moment, I'm working to collect #dialogue/s grounded in a given information source (a description of a museum exhibit) to develop a dataset for training #ScottishGaelic / #Gàidhlig #chatbots for #QuestionAnswering (https://blogs.ed.ac.uk/garg/2022/08/23/scottish-gaelic-chatbots-for-museum-exhibits/).
Iraide Ibarretxe-Antuñano
Sprachwissenschaft Uni Erfurt
Human AI
Neil D Fogarty
Massimo Stella
Dave in 🏴