{ "types" : { "Bookmark" : { "pluralLabel" : "Bookmarks" }, "Publication" : { "pluralLabel" : "Publications" }, "GoldStandardPublication" : { "pluralLabel" : "GoldStandardPublications" }, "GoldStandardBookmark" : { "pluralLabel" : "GoldStandardBookmarks" }, "Tag" : { "pluralLabel" : "Tags" }, "User" : { "pluralLabel" : "Users" }, "Group" : { "pluralLabel" : "Groups" }, "Sphere" : { "pluralLabel" : "Spheres" } }, "properties" : { "count" : { "valueType" : "number" }, "date" : { "valueType" : "date" }, "changeDate" : { "valueType" : "date" }, "url" : { "valueType" : "url" }, "id" : { "valueType" : "url" }, "tags" : { "valueType" : "item" }, "user" : { "valueType" : "item" } }, "items" : [ { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/210e791a85e2b9de0d19f27ca7c09b69d/mariawirzberger", "tags" : [ "myown","learning","technology","performance","effort","digital","feedback","UTAUT","acceptance","academic","self-regulation","working","expectancy" ], "intraHash" : "10e791a85e2b9de0d19f27ca7c09b69d", "interHash" : "aa0c7aa833c1f78085582a4ea6384eda", "label" : "Performance expectancy benefits acceptance towards digital support for self-regulation", "user" : "mariawirzberger", "description" : "", "date" : "2025-09-12 20:29:24", "changeDate" : "2025-09-12 20:29:43", "count" : 3, "pub-type": "article", "journal": "Acta Psychologica","publisher":"Elsevier", "year": "2025", "url": "https://doi.org/10.1016/j.actpsy.2025.105273", "author": [ "Maria Wirzberger","Laura Bareiß","Veronika Herbst","Adrian Stock","Jule Kembitzky" ], "authors": [ {"first" : "Maria", "last" : "Wirzberger"}, {"first" : "Laura", "last" : "Bareiß"}, {"first" : "Veronika", "last" : "Herbst"}, {"first" : "Adrian", "last" : "Stock"}, {"first" : "Jule", "last" : "Kembitzky"} ], "volume": "258","pages": "105273","abstract": "Introduction\r\nThe persistent prevalence of distractions challenges people's capacities to study and work productively. Digital tools can support focusing on meaningful tasks with features like time tracking, feedback, or rewards. Evidence exists for benefits regarding behavioral focus, distraction management, and motivation, but also for hesitation to use digital support at all. Building on the Unified Theory of Acceptance and Use of Technology (UTAUT), this research explores which factors can foster or hinder the intention to use an exemplary software to support self-regulation.\r\nMethods\r\nA sample of 96 adult volunteers watched a short introductory video explaining how the software focUS fosters self-regulated studying and working. Subsequently, participants completed an online survey to capture their willingness to use focUS, expected gains and challenges, and individual characteristics.\r\nResults\r\nParticipants expressed stronger intentions to use focUS when they expected increased benefits in performance. By trend, lower levels of expected effort also hinted on stronger intentions to use focUS. Contrary to expectations, participants lacking previous experience with software in the scope described, who anticipated higher effort when using focUS, tended to express stronger intentions to use it.\r\nDiscussion\r\nTaken together, our evidence suggests that highlighting specific performance improvements may encourage the use of digital support for tasks that require self-regulation. Ensuring the use of digital support to be as effortless as possible could provide yet another compelling argument to use it. Particularly for inexperienced users, sparking curiosity for the challenges of the unknown might be a worthwhile strategy to reduce hesitation towards emerging technologies.", "doi" : "https://doi.org/10.1016/j.actpsy.2025.105273", "bibtexKey": "wirzberger2025performance" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2b08640837cf7043f0ae410d206b6316d/mariawirzberger", "tags" : [ "fNIRS","myown","llis","effort","multimodal","ML","classification" ], "intraHash" : "b08640837cf7043f0ae410d206b6316d", "interHash" : "b475960ec52f0838aa99c78d137c5ad9", "label" : "Decoding Mental Effort in a Quasi-Realistic Scenario: A Feasibility Study on Multimodal Data Fusion and Classification", "user" : "mariawirzberger", "description" : "", "date" : "2023-09-23 09:36:47", "changeDate" : "2023-09-23 09:36:47", "count" : 3, "pub-type": "article", "journal": "Sensors", "year": "2023", "url": "https://www.mdpi.com/1424-8220/23/14/6546", "author": [ "Sabrina Gado","Katharina Lingelbach","Maria Wirzberger","Mathias Vukelić" ], "authors": [ {"first" : "Sabrina", "last" : "Gado"}, {"first" : "Katharina", "last" : "Lingelbach"}, {"first" : "Maria", "last" : "Wirzberger"}, {"first" : "Mathias", "last" : "Vukelić"} ], "volume": "23","number": "14","abstract": "Humans\u2019 performance varies due to the mental resources that are available to successfully pursue a task. To monitor users\u2019 current cognitive resources in naturalistic scenarios, it is essential to not only measure demands induced by the task itself but also consider situational and environmental influences. We conducted a multimodal study with 18 participants (nine female, M = 25.9 with SD = 3.8 years). In this study, we recorded respiratory, ocular, cardiac, and brain activity using functional near-infrared spectroscopy (fNIRS) while participants performed an adapted version of the warship commander task with concurrent emotional speech distraction. We tested the feasibility of decoding the experienced mental effort with a multimodal machine learning architecture. The architecture comprised feature engineering, model optimisation, and model selection to combine multimodal measurements in a cross-subject classification. Our approach reduces possible overfitting and reliably distinguishes two different levels of mental effort. These findings contribute to the prediction of different states of mental effort and pave the way toward generalised state monitoring across individuals in realistic applications.", "pubmedid" : "37514840", "issn" : "1424-8220", "article-number" : "6546", "doi" : "10.3390/s23146546", "bibtexKey": "s23146546" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/22ce5ad32ad7833994d19cb437d723614/mariawirzberger", "tags" : [ "myown","modeling","cognition","mental","effort" ], "intraHash" : "2ce5ad32ad7833994d19cb437d723614", "interHash" : "e9712d1a773253a50b60b6f1436dfef4", "label" : "Mental effort: One construct, many faces?", "user" : "mariawirzberger", "description" : "", "date" : "2020-08-16 17:33:37", "changeDate" : "2023-09-23 09:33:58", "count" : 3, "pub-type": "inproceedings", "booktitle": "Proceedings of the 42nd Annual Conference of the Cognitive Science Society","publisher":"Cognitive Science Society", "year": "2020", "url": "https://cognitivesciencesociety.org/cogsci20/papers/0001/0001.pdf", "author": [ "Sebastian Musslick","Maria Wirzberger","Ivan Grahek","Laura Bustamante","Amitai Shenhav","Johnathan D. Cohen" ], "authors": [ {"first" : "Sebastian", "last" : "Musslick"}, {"first" : "Maria", "last" : "Wirzberger"}, {"first" : "Ivan", "last" : "Grahek"}, {"first" : "Laura", "last" : "Bustamante"}, {"first" : "Amitai", "last" : "Shenhav"}, {"first" : "Johnathan D.", "last" : "Cohen"} ], "editor": [ "Stephanie Denison","Michael Mack","Yang Xu","Blair C. Armstrong" ], "editors": [ {"first" : "Stephanie", "last" : "Denison"}, {"first" : "Michael", "last" : "Mack"}, {"first" : "Yang", "last" : "Xu"}, {"first" : "Blair C.", "last" : "Armstrong"} ], "pages": "1-2","abstract": "We can all feel exhausted after a day of work, even if we have spent it sitting at a desk. The intuitive concept of mental effort pervades virtually all domains of human information processing and has become an indispensable ingredient for general theories of cognition (Anderson, 2007; Shenhav et al., 2017; Lieder & Griffiths, 2015). However, inconsistent use of the term across cognitive sciences, including cognitive psychology, education, human-factors engineering and artificial intelligence, makes it one of the least well-defined theoretical constructs across fields. A number of recent approaches lay the foundation for a consensus by offering formal accounts of mental effort. Yet, reaching a multifield-wide consensus on the operationalization of mental effort will require cross-talk between different empirical and computational approaches, including symbolic architectures, non-parametric Bayesian statistics and neural networks. The purpose of this full-day workshop is to review and integrate these emerging perspectives.", "bibtexKey": "musslick2020mental" } ] }