In a large ( N = 300), pre-registered experiment and data analysis model, we find that individual variation in overall performance on Raven’s Progressive Matrices is substantially driven by differential strategizing in the face of difficulty. Some participants choose to spend more time on hard problems while others choose to spend less and these differences explain about 42% of the variance in overall performance. In a data analysis jointly predicting participants’ reaction times and accuracy on each item, we find that the Raven’s task captures at most half of participants’ variation in time-controlled ability (48%) down to almost none (3%), depending on which notion of ability is assumed. Our results highlight the role that confounding factors such as motivation play in explaining individuals’ differential performance in IQ testing.

Words that are more surprising given context take longer to process. However, no incremental parsing algorithm has been shown to directly predict this phenomenon. In this work, we focus on a class of algorithms whose runtime does naturally scale in surprisal—those that involve repeatedly sampling from the prior. Our first contribution is to show that simple examples of such algorithms predict runtime to increase superlinearly with surprisal, and also predict variance in runtime to increase. These two predictions stand in contrast with literature on surprisal theory (Hale, 2001 ; Levy, 2008a ) which assumes that the expected processing cost increases linearly with surprisal, and makes no prediction about variance. In the second part of this paper, we conduct an empirical study of the relationship between surprisal and reading time, using a collection of modern language models to estimate surprisal. We find that with better language models, reading time increases superlinearly in surprisal, and also that variance increases. These results are consistent with the predictions of sampling-based algorithms.

Many social and legal conflicts hinge on semantic disagreements. Understanding the origins and implications of these disagreements necessitates novel methods for identifying and quantifying variation in semantic cognition between individuals. We collected conceptual similarity ratings and feature judgements from a variety of words in two domains. We analyzed this data using a non-parametric clustering scheme, as well as an ecological statistical estimator, in order to infer the number of different variants of common concepts that exist in the population. Our results show at least ten to thirty quantifiably different variants of word meanings exist for even common nouns. Further, people are unaware of this variation, and exhibit a strong bias to erroneously believe that other people share their semantics. This highlights conceptual factors that likely interfere with productive political and social discourse.

The extent to which word learning is delayed by maturation as opposed to accumulating data is a longstanding question in language acquisition. Further, the precise way in which data influence learning on a large scale is unknown—experimental results reveal that children can rapidly learn words from single instances as well as by aggregating ambiguous information across multiple situations. We analyze Wordbank, a large cross-linguistic dataset of word acquisition norms, using a statistical waiting time model to quantify the role of data in early language learning, building off Hidaka ( 2013 ). We find that the model both fits and accurately predicts the shape of children’s growth curves. Further analyses of model parameters suggest a primarily data-driven account of early word learning. The parameters of the model directly characterize both the amount of data required and the rate at which informative data occurs. With high statistical certainty, words require on the order of ∼ 10 learning instances, which occur on average once every two months. Our method is extremely simple, statistically principled, and broadly applicable to modeling data-driven learning effects in development.