Planning-while-acting: addressing the continuous dynamics of planning and action in a virtually embodied task
Notice bibliographique
Résumé
Abstract Everyday tasks, such as selecting routes when driving or preparing meals, require making sequences of embodied decisions, in which planning and action processes are intertwined. In this study, we address how people make sequential embodied decisions, requiring balancing be-tween immediate affordances and long-term utilities of alternative action plans. We designed a novel virtually embodied task in which participants controlled an avatar tasked with “crossing rivers” by jumping across rocks. The task permitted us to assess how participants balanced between immediate jumping affordances (“safe” versus “risky” jumps) and the utility (length) of the ensuing paths to the goal. Behavioral and computational analyses revealed that partici-pants planned ahead their path to the goal rather than simply focusing on the most immediate jumping affordances. Furthermore, spatial and embodied components of the task influenced participants’ decision strategies, as participants’ current direction of movement and momen-tum influenced their choice between safe and risky jumps. Additionally, participants showed (pre)planning before making the first jump, but they continued deliberating during it, with movement speed decreasing at decision points and when approaching them. Computational modeling indicates that farsighted participants who assigned greater weight to the utility of future jumps showed a better performance, highlighting the usefulness of planning in embodied settings. Finally, analysing participants’ performance across trials indicates that during the experiment, they become faster in moving and deciding but they do not change their overall strategy. Our findings underscore the importance of studying decision-making and planning in ecologically valid, embodied settings, providing new insights into the interplay between action and cognition in real-world planning-while-acting scenarios. Significance statement Everyday activities, such as navigating a crowded street or assembling objects, often involve contin-uous interactions between planning and action. Yet, it is unclear how people balance opportunities for action (affordances) with long-term planning in embodied settings. In a novel virtually embod-ied task, in which people have to “cross rivers” by making a series of jumps across rocks, we found that they plan ahead their path to the goal rather than simply focusing on the immediate jumping affordances - and they continue deliberating during movement. Crucially, spatial and embodied constraints, such as movement direction and momentum, influenced decision-making, challenging traditional models that treat decision and action as distinct stages and highlighting the importance of studying decisions in ecologically valid, embodied settings.
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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 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,002 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».