Melbourne School of Psychological Sciences - Research Publications

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    Semantic Shifts in Mental Health-Related Concepts
    Baes, N ; Haslam, N ; Vylomova, E ; Tahmasebi, N ; Montariol, S ; Dubossarsky, H ; Kutuzov, A ; Hengchen, S ; Alfter, D ; Periti, F ; Cassotti, P (Association for Computational Linguistics, 2023)
    The present study evaluates semantic shifts in mental health-related concepts in two diachronic corpora spanning 1970-2016, one academic and one general. It evaluates whether their meanings have broadened to encompass less severe phenomena and whether they have become more pathology related. It applies a recently proposed methodology (Baes et al., 2023) to examine whether words collocating with a sample of mental health concepts have become less emotionally intense and develops a new way to examine whether the concepts increasingly co-occur with pathology-related terms. In support of the first hypothesis, mental health-related concepts became associated with less emotionally intense language in the psychology corpus (addiction, anger, stress, worry) and in the general corpus (addiction, grief, stress, worry). In support of the second hypothesis, mental health-related concepts came to be more associated with pathology-related language in psychology (addiction, grief, stress, worry) and in the general corpus (grief, stress). Findings demonstrate that some mental health concepts have become normalized and/or pathologized, a conclusion with important social and cultural implications.
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    Constructing Word Meaning without Latent Representations using Spreading Activation
    Shabahang, KD ; Yim, H ; Dennis, SJ (Cognitive Science Society, 2022-01-01)
    Models of word meaning, like the Topics model (Griffiths et al., 2007) and word2vec (Mikolov et al., 2013), condense word-by-context co-occurrence statistics to induce representations that organize words along semantically relevant dimensions (e.g., synonymy, antonymy, hyponymy etc.). However, their reliance on latent representations leaves them vulnerable to interference and makes them slow learners. We show how it is possible to construct the meaning of words online during retrieval to avoid these limitations. We implement our spreading activation account of word meaning in an associative net, a one-layer highly recurrent network of associations, called a Dynamic-Eigen-Net, that we developed to address the limitations of earlier variants of associative nets when scaling up to deal with unstructured input domains such as natural language text. After fixing the corpus across models, we show that spreading activation using a Dynamic-Eigen-Net outperforms the Topics model and word2vec in several cases when predicting human free associations and word similarity ratings. We argue in favour of the Dynamic-Eigen-Net as a fast learner that is not subject to catastrophic interference, and present it as an example of delegating the induction of latent relationships to process assumptions instead of assumptions about representation.
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    Beyond Pattern Completion with Short-Term Plasticity
    Shabahang, KD ; Yim, H ; Dennis, SJ (Cognitive Science Society, 2020-01-01)
    In a Linear Associative Net (LAN), all input settles to a single pattern, therefore Anderson, Silverstein, Ritz, and Jones (1977) introduced saturation to force the system to reach other steady-states in the Brain-State-in-a-Box (BSB). Unfortunately, the BSB is limited in its ability to generalize because its responses are restricted to previously stored patterns. We present simulations showing how a Dynamic-Eigen-Net (DEN), a LAN with Short-Term Plasticity (STP), overcomes the single-response limitation. Critically, a DEN also accommodates novel patterns by aligning them with encoded structure. We train a two-slot DEN on a text corpus, and provide an account of lexical decision and judgement-of-grammaticality (JOG) tasks showing how grammatical bi-grams yield stronger responses relative to ungrammatical bi-grams. Finally, we present a simulation showing how a DEN is sensitive to syntactic violations introduced in novel bi-grams. We propose DENs as associative nets with greater promise for generalization than the classic alternatives.
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    The Musician’s Acceptance and Action Questionnaire (MAAQ): A New Tool for Measuring Psychological Flexibility as it relates to Music Performance Anxiety in Student and Professional Singers
    Zenobi, D ; Juncos, D ; Roman, J ; Osborne, M (The Voice Foundation, 2023)
    Singers in all genres, and at all levels of study struggle with Music Performance Anxiety (MPA). MPA can manifest behaviorally as avoidance of auditions and other high-stakes performances, avoidance of practice, the inability to experience flow while practicing/performing, and it also correlates with maladaptive perfectionism. Acceptance and Commitment Therapy (ACT), a mindfulness and acceptance-based intervention, teaches that if singers remain “psychologically flexible” in the presence of MPA, we may cope with it more effectively. These findings are relevant to voice professionals because ACT coaching can ethically be administered within the lesson by voice teachers/vocal coaches to help singers cope better with MPA and related challenges. This study is the first to measure levels of psychological flexibility in both student and professional musicians. It tested a new musician-specific questionnaire, the Musician’s Acceptance and Action Questionnaire (MAAQ), adapted from the Acceptance and Action Questionnaire - Version II (AAQ-II). Students were recruited from an Australian conservatory and a School of Music within a private liberal arts university in the Midwestern United States. Professional participants included an international sample of musicians in all genres/instruments. Participants filled out the AAQII, the MAAQ, the Frost Multidimensional Perfectionism Scale (FMPS), the revised Kenny Music Performance Anxiety Inventory (KMPAI-R), short measures of flow within performance and practice (SDFS-2), and they answered questions about their performance and practice-related behaviors. The MAAQ was successfully constructed and showed good reliability and invariance of its factor structure and good convergent/divergent validity with established questionnaires. It outperformed the AAQ-II as a predictor of important outcomes in music performance and practice. In the student samples, the MAAQ better predicted avoidant behavior in one’s practice and in performances, flow states in practice and performances, and grades on a recent, adjudicated music exam. In the professional sample, the MAAQ better predicted performance-related avoidance and whether one competes in professional music competitions after completing their education. Despite the small samples, these results offer preliminary support for the MAAQ’s utility in measuring and predicting problematic behaviors associated with MPA.
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    Digital Emotion Regulation in Everyday Life
    Smith, W ; Wadley, G ; Webber, S ; Tag, B ; Kostakos, V ; Koval, P ; Gross, JJ (ASSOC COMPUTING MACHINERY, 2022)
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    ‘It’s not so scary anymore. It’s actually exhilarating’: A proof-of-concept study using virtual reality technology for music performance training under pressure
    Osborne, M ; Glasser, S ; Loveridge, B (ASCILITE Publications, 2022-11-18)
    Extended lockdowns associated with the COVID-19 pandemic severely ruptured the capacity of performing artists to connect with peers and colleagues in professional and educational contexts. In this study we test a protocol for the use of immersive performance technologies in tertiary institutions to provide developing and early career musicians to connect with pedagogues and access safe, realistic spaces within which to practice performing under varying degrees of pressure. We investigated the affordances of a VR environment to trigger performance anxiety, and the effectiveness of a synchronous digitally mediated environment for tertiary educators to train a key performance psychology skill. Heart rate, self-reported anxiety and confidence measurements were taken over four levels of situational stress and performance demands. Results revealed that the technology enabled a pre-performance routine to be effectively taught online by an instructor to a musician wearing a VR headset. Notably, this was achieved by both participants being in separate locations without detriment to the teacher-student relationship. This study provides encouraging insight into the capacity for immersive technologies to help students effectively manage the stresses of live performance in both virtual and real worlds.
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    The SIGTYP 2022 Shared Task on the Prediction of Cognate Reflexes
    List, JM ; Vylomova, E ; Forkel, R ; Hill, NW ; Cotterell, RD (ACL, 2022-01-01)
    This study describes the structure and the results of the SIGTYP 2022 shared task on the prediction of cognate reflexes from multilingual wordlists. We asked participants to submit systems that would predict words in individual languages with the help of cognate words from related languages. Training and surprise data were based on standardized multilingual wordlists from several language families. Four teams submitted a total of eight systems, including both neural and non-neural systems, as well as systems adjusted to the task and systems using more general settings. While all systems showed a rather promising performance, reflecting the overwhelming regularity of sound change, the best performance throughout was achieved by a system based on convolutional networks originally designed for image restoration.
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    Information Retrieval and Survey Design for Two-Stage Customer Preference Modeling
    Xiao, Y ; Cui, Y ; Raut, N ; Januar, JH ; Koskinen, J ; Contractor, N ; Chen, W ; Sha, Z (Cambridge University Press, 2022-05-01)
    Customer survey data is critical to supporting customer preference modeling in engineering design. We present a framework of information retrieval and survey design to ensure the collection of quality customer survey data for analyzing customers' preferences in their consideration-then-choice decision-making and the related social impact. The utility of our approach is demonstrated through the survey design for customers in the vacuum cleaner market. Based on the data, we performed descriptive analysis and network-based modeling to understand customers' preferences in consideration and choice.
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    The SIGMORPHON 2022 Shared Task on Morpheme Segmentation
    Batsuren, K ; Bella, G ; Arora, A ; Martinovic, V ; Gorman, K ; Žabokrtský, Z ; Ganbold, A ; Dohnalová, Š ; Ševčíková, M ; Pelegrinová, K ; Giunchiglia, F ; Cotterell, R ; Vylomova, E (Association for Computational Linguistics, 2022-01-01)
    The SIGMORPHON 2022 shared task on morpheme segmentation challenged systems to decompose a word into a sequence of morphemes and covered most types of morphology: compounds, derivations, and inflections. Subtask 1, word-level morpheme segmentation, covered 5 million words in 9 languages (Czech, English, Spanish, Hungarian, French, Italian, Russian, Latin, Mongolian) and received 13 system submissions from 7 teams and the best system averaged 97.29% F1 score across all languages, ranging English (93.84%) to Latin (99.38%). Subtask 2, sentence-level morpheme segmentation, covered 18,735 sentences in 3 languages (Czech, English, Mongolian), received 10 system submissions from 3 teams, and the best systems outperformed all three state-of-the-art subword tokenization methods (BPE, ULM, Morfessor2) by 30.71% absolute. To facilitate error analysis and support any type of future studies, we released all system predictions, the evaluation script, and all gold standard datasets.
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    Morphology is not just a naive Bayes - UniMelb Submission to SIGMORPHON 2022 ST on Morphological Inflection
    Scherbakov, A ; Vylomova, E (Association for Computational Linguistics, 2022-01-01)
    The paper describes the Flexica team's submission to the SIGMORPHON 2022 Shared Task 1 Part 1: Typologically Diverse Morphological Inflection. Our team submitted a non-neural system that extracted transformation patterns from alignments between a lemma and inflected forms. For each inflection category, we chose a pattern based on its abstractness score. The system outperformed the non-neural baseline, the extracted patterns covered a substantial part of possible inflections. However, we discovered that such score that does not account for all possible combinations of string segments as well as morphosyntactic features is not sufficient for a certain proportion of inflection cases.