Automating the Morning Outfit Decision: A Cognitive-Load Framework for Product Experience Design in Everyday Dress

Choosing what to wear is one of the first and most frequently repeated decisions of the day, yet product design treats it either as a matter of taste or as a pretext for selling new garments. This paper argues that the morning outfit decision is a well-specified product experience problem: a bundle of interdependent micro-decisions performed under time pressure, over a large and poorly indexed choice set, with consequences for how the wearer feels and performs. The argument is built on three literatures and is explicit about the strength of each. First, the decision-fatigue literature, whose strong "limited resource" account remains contested after large preregistered replications (Hagger et al., 2016; Vohs et al., 2021; Dang et al., 2025), but whose weaker behavioural claim — that accumulating effortful decisions pushes people towards less effortful, more conservative choices — is consistently reported across domains (Maier et al., 2025; Choudhury & Saravanan, 2026). Second, the choice-overload literature, whose meta-analytic moderators — choice-set complexity, decision-task difficulty, preference uncertainty and decision goal (Chernev et al., 2015) — map closely onto the structure of a personal wardrobe. Third, research on dress as embodied practice and on enclothed cognition, including the 2025 meta-analysis that supports post-2015 effects while questioning earlier ones (Horton et al., 2025). On this basis the paper (a) decomposes the dressing decision into a taxonomy of seven operations; (b) maps each operation onto an implementable product mechanism and a level of delegation; (c) proposes a "Constrain–Curate–Confirm" interaction model that reduces cognitive load while preserving the choice autonomy that consumers demonstrably value (Husairi & Rossi, 2024; Wertenbroch et al., 2020); and (d) specifies a measurement framework and a preregistrable field-experiment design. Market developments of 2025–2026, including the rapid adoption of generative AI in fashion discovery, are reviewed as context rather than as evidence of effectiveness.