Query-Based Memory (QBM) models are heavily used in machine learning, though their relevance to human cognition is unclear. In this paper, we explore QBM models through both formal exploration and a simulation study to address this question. We found that QBM models are theoretically motivated, as they approximate rational induction with neurally-plausible mechanisms. Additionally, a simple implementation of the model could readily reproduce four benchmark findings in infant statistical learning. These results provide an encouraging starting point for further research using these formal tools to understand cognition across development.

Many cognitive models assume that stimuli can be represented as points in a latent psychological space. However, it has been difficult to provide these spaces with a geometric structure where the distance between items accurately reflects their subjective dissimilarity. In this paper, we propose a new method to give psychological spaces a geometric structure by equating the amount of change undergone by a stimulus with the arc length of a curve in psychological space. We then assess our method with a categorization experiment where participants classified continuously changing visual stimuli according to their rate of change. Our results indicate that individuals’ judgements are well predicted by arc length, suggesting that it may be a promising geometric constraint for psychological spaces in other contexts.

Young children can generalize properties to novel stimuli, but the mechanism underlying these early inductions is stilldebated. Some researchers argue that from an early age induction relies on category information and undergoes littledevelopment, while others believe that early induction is similarity-based, and the use of categories emerges over time.This present study brings new evidence to the debate by exploring the kinds of features 4-year-old children and adults (N= 123) rely on in their induction. Our results indicate that induction undergoes dramatic development: young childrentend to rely on salient features when performing induction, whereas adults rely primarily on category information. Weargue that the reported findings present evidence challenging category-based accounts of early induction, while supportingsimilarity-based accounts.

Throughout development, humans infer unobserved properties of the objects they encounter. However, it is often ambigu-ous whether these inferences result from category-based reasoning or overall similarity to previously observed objects. Inthis study, we examined inferences about hidden properties in four-year-old children (N=36) and adults (N=44). We taughtparticipants three categories of artificial creatures. Each category had one critical feature, where one of its variations wasmore common to members of the category, while the other was more common overall. We found that, on average, bothgroups used within-category frequency to predict the value of an unseen critical feature. However, individual differencesrevealed distinctions between the groups. While adults who used within-category frequency for critical items used overallfrequency for other items, this correlation was qualitatively reversed in children. This suggests that some children weresensitive to category knowledge when predicting unseen features, but others likely used a novelty heuristic.

When making category decisions, humans sample features following their dynamic informativeness. Attention optimization models successfully predict these categorization behaviors, but optimization is not the only solution. Alternatively, categorization can be viewed as a Reinforcement Learning (RL) task in which learners sample information based on its expected utility. However, RL models of information sampling have high computational load, even though human learners solve this problem on the millisecond timescale. Therefore, we propose ATHENA-RSS, a model that implements reward-based information search in a more computationally efficient way, via the rapid sequential storage of memories and recurrent retrieval. To test the model, we conducted an experiment where participants (N = 99) learned hierarchically structured categories by uncovering stimulus features. We then conducted a simulation study, where ATHENA-RSS successfully reproduced all search patterns exhibited by participants. We conclude that rapid sequential storage and recurrent memory retrieval were sufficient to achieve human-like information sampling in this task.