Whether adding songs to a playlist or groceries to a shopping basket, everyday decisions often require us to choose between an innumerable set of options. Laboratory studies of preferential choice have made considerable progress in describing how people navigate fixed sets of options. Yet, questions remain about how well this generalises to more complex, everyday choices. In this thesis, I ask how people navigate large option spaces, focusing particularly on how long-term memory supports decisions. In the first project, I explore how large option spaces are structured in the mind. A topic model trained on the purchasing patterns of consumers uncovered an intuitive set of themes that centred primarily around goals (e.g., tomatoes go well in a salad), suggesting that representations are geared to support action. In the second project, I explore how such representations are queried during memory-based decisions, where options must be retrieved from memory. Using a large dataset of over 100,000 online grocery shops, results revealed that consumers query multiple systems of associative memory when determining what choose next. Attending to certain knowledge sources, as estimated by a cognitive model, predicted important retrieval errors, such as the propensity to forget or add unwanted products. In the final project, I ask how preferences could be learned and represented in large option spaces, where most options are untried. A cognitive model of sequential decision making is proposed, which learns preferences over choice attributes, allowing for the generalisation of preferences to unseen options, by virtue of their similarity to previous choices. This model explains reduced exploration patterns behaviour observed in the supermarket and preferential choices in more controlled laboratory settings. Overall, this suggests that consumers depend on associative systems in long-term memory when navigating large spaces of options, enabling inferences about the conceptual properties and subjective value of novel options
