TMS and Working Memory and Programming
Notice bibliographique
Résumé
The goal of this project is to better understand the role of working memory in writing programs (or program synthesis). Working memory can be defined as the small amount of information that can be held in an especially accessible state and used in cognitive tasks. Program synthesis refers to the ability of a programmer to construct a program that provably satisfies a given high-level formal specification. This is a key component in programming. By better understanding the cognitive processes of programming, we can better develop educational interventions and software programming support. A previously published study from our lab used MRI (magnetic resonance imaging) and TMS (Transcranial magnetic stimulation) to examine the relationship between spatial visualization and program comprehension tasks [1] leveraging previous correlative findings [2]. Our study aims to use a similar approach to examine the existence of a causal relationship between previous correlative findings of working-memory related brain regions with code system thesis. In this study, we propose to investigate the causal link of working memory and programming using only TMS for disrupting working-memory-associated regions Research Question: Is there a causal relationship between working memory load and programming ability? Significance: Confirming a causal link between working memory and program comprehension at the neurological level could spur changes in introductory programming education (e.g., including spatial visualization training, presenting material with a focus on spatial diagrams, etc.) and improve student success. Furthermore, as the second TMS study of programming (to the best our knowledge), this project would further the confidence of using TMS in computer science research. Methods: TMS will be used to temporarily disrupt brain areas that are associated with working memory. To localize these brain regions, we will use Montreal Neurological Institute (MNI) coordinates derived from previous studies who sampled more than 700 participants [3]. Participants will be brought in for two TMS sessions, where TMS is used to stimulate a working-memory brain region, and one where TMS is used to stimulate a non-working-memory associated region as an active control. In each session, participants will respond to a series of working memory and programming-related stimuli. We will check to see if there is a difference in accuracy, response time, and/or keystrokes between the sessions. Informally, if disrupting the X region of the brain interferes with the programming task but disrupting the Y region does not, we gain evidence that X activity is causally linked to performing the programming task. [1] Ahmad, H., Endres, M., Newman, K., Santiesteban, P., Shedden, E., & Weimer, W. Causal Relationships and Programming Outcomes: A Transcranial Magnetic Stimulation Experiment. In the International Conference on Software Engineering (ICSE): 2024 [2] Madeline Endres, Zachary Karas, Xiaosu Hu, Ioulia Kovelman, Westley Weimer: Relating Reading, Visualization, and Coding for New Programmers: A Neuroimaging Study: International Conference on Software Engineering (ICSE): 2021 [3] Schicktanz, N., Fastenrath, M., Milnik, A., Spalek, K., Auschra, B., Nyffeler, T., ... & Schwegler, K. (2015). Continuous theta burst stimulation over the left dorsolateral prefrontal cortex decreases medium load working memory performance in healthy humans. PloS one, 10(3), e0120640.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».