Melbourne School of Psychological Sciences - Research Publications
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ItemConstructing Word Meaning without Latent Representations using Spreading Activation(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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ItemBeyond Pattern Completion with Short-Term Plasticity(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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ItemNo Preview AvailableDigital Emotion Regulation in Everyday Life(ASSOC COMPUTING MACHINERY, 2022)
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Item‘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(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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ItemThe SIGTYP 2022 Shared Task on the Prediction of Cognate Reflexes(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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ItemInformation Retrieval and Survey Design for Two-Stage Customer Preference Modeling(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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ItemMorphology is not just a naive Bayes - UniMelb Submission to SIGMORPHON 2022 ST on Morphological Inflection(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.
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ItemThe SIGMORPHON 2022 Shared Task on Morpheme Segmentation(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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ItemSIGMORPHON-UniMorph 2022 Shared Task 0: Generalization and Typologically Diverse Morphological Inflection(Association for Computational Linguistics, 2022-01-01)The 2022 SIGMORPHON-UniMorph shared task on large scale morphological inflection generation included a wide range of typologically diverse languages: 33 languages from 11 top-level language families: Arabic (Modern Standard), Assamese, Braj, Chukchi, Eastern Armenian, Evenki, Georgian, Gothic, Gujarati, Hebrew, Hungarian, Itelmen, Karelian, Kazakh, Ket, Khalkha Mongolian, Kholosi, Korean, Lamahalot, Low German, Ludic, Magahi, Middle Low German, Old English, Old High German, Old Norse, Polish, Pomak, Slovak, Turkish, Upper Sorbian, Veps, and Xibe. We emphasize generalization along different dimensions this year by evaluating test items with unseen lemmas and unseen features separately under small and large training conditions. Across the six submitted systems and two baselines, the prediction of inflections with unseen features proved challenging, with average performance decreased substantially from last year. This was true even for languages for which the forms were in principle predictable, which suggests that further work is needed in designing systems that capture the various types of generalization required for the world's languages.
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ItemAssessing Psychological Flexibility with the Musician's Acceptance and Action Questionnaire (MAAQ).(ACBS, 2022-06-14)Performance anxiety, low motivation to practice, psychological distress, perfectionism, and having to endure adjudicated exams/auditions, are some of many stressors facing university musicians across the world. Existing research suggests that remaining psychologically flexible might enable students to cope more effectively with these challenges (Juncos et al., 2017). However, no specific measures of psychological flexibility (PF) exist for them yet, aside from general PF measures, i.e., AAQ-II (Bond et al., 2011). This study aimed to validate a self-report measure of PF for student musicians (Musician’s Acceptance and Action Questionnaire), and to determine its ability to predict outcomes of interest within their performances and practice, in particular, scores on an adjudicated music exam, avoidance of performances/practice, and a history of psychotherapy/medication due to music performance, e.g., treating performance anxiety. Two samples of university musicians were recruited from the Melbourne Conservatorium of Music (Nf64) and Butler University (Nf64). Students completed a pilot version of the MAAQ, along with measures of PF, music performance anxiety, perfectionism and flow. Demographic information about their training experiences were collected, and they were asked about a history of avoidance within their performances and practice. Results of analyses to determine the MAAQ's factor structure, internal consistency, and construct/discriminant validity will be reported. Also, the results of analyses to determine its incremental predictive validity when compared to the AAQ-II in predicting outcomes of interest for university musicians will be reported. The MAAQ's psychometric properties and overall ability to measure PF within music performance and practice settings will be discussed.