Humans have the ability to learn surprisingly complicated statistical information in a variety of modalities and situations, often based on relatively little input. These statistical learning (SL) skills appear to underlie many kinds of learning, but despite their ubiquity, we still do not fully understand precisely what SL is and what individual differences on SL tasks reflect. Here, we present experimental work suggesting that at least some individual differences arise from stimulus-specific variation in perceptual fluency: the ability to rapidly or (...) efficiently code and remember the stimuli that SL occurs over. Experiment 1 demonstrates that participants show improved SL when the stimuli are simple and familiar; Experiment 2 shows that this improvement is not evident for simple but unfamiliar stimuli; and Experiment 3 shows that for the same stimuli (Chinese characters), SL is higher for people who are familiar with them (Chinese speakers) than those who are not (English speakers matched on age and education level). Overall, our findings indicate that performance on a standard SL task varies substantially within the same (visual) modality as a function of whether the stimuli involved are familiar or not, independent of stimulus complexity. Moreover, test–retest correlations of performance in an SL task using stimuli of the same level of familiarity (but distinct items) are stronger than correlations across the same task with stimuli of different levels of familiarity. Finally, we demonstrate that SL performance is predicted by an independent measure of stimulus-specific perceptual fluency that contains no SL component at all. Our results suggest that a key component of SL performance may be related to stimulus-specific processing and familiarity. (shrink)

What is the relationship between complexity in the world and complexity in the mind? Intuitively, increasingly complex objects and events should give rise to increasingly complex mental representations (or perhaps a plateau in complexity after a certain point). However, a counterintuitive possibility with roots in information theory is an inverted U-shaped relationship between the “objective” complexity of some stimulus and the complexity of its mental representation, because excessively complex patterns might be characterized by surprisingly short computational descriptions (e.g., if they (...) are represented as having been generated “randomly”). Here, we demonstrate that this is the case, using a novel approach that takes the notion of “description” literally. Subjects saw static and dynamic visual stimuli whose objective complexity could be carefully manipulated, and they described these stimuli in their own words by giving freeform spoken descriptions of them. Across three experiments totaling over 10,000 speech clips, spoken descriptions of shapes (Experiment 1), dot-arrays (Experiment 2), and dynamic motion-paths (Experiment 3) revealed a striking quadratic relationship between the raw complexity of these stimuli and the length of their spoken descriptions. In other words, the simplest and most complex stimuli received the shortest descriptions, while those stimuli with a “medium” degree of complexity received the longest descriptions. Follow-up analyses explored the particular words used by subjects, allowing us to further explore how such stimuli were represented. We suggest that the mind engages in a kind of lossy compression for overly complex stimuli, and we discuss the utility of such freeform responses for exploring foundational questions about mental representation. (shrink)