Supervised versus unsupervised categorization: Two sides of the same coin?

Supervised and unsupervised categorization have been studied in separate research traditions. A handful of studies have attempted to explore a possible convergence between the two. The present research builds on these studies, by comparing the unsupervised categorization results of Pothos et al. (submitted; 2008) with the results from two procedures of supervised categorization. In two experiments, we tested 375 participants with nine different stimulus sets, and examined the relation between ease of learning of a classification, memory for a classification, and spontaneous preference for a classification. After taking into account the role of the number of category labels (clusters) in supervised learning, we found the three variables to be closely associated with each other. Our results provide encouragement for researchers seeking unified theoretical explanations for supervised and unsupervised categorization, but raise a range of challenging theoretical questions

Don’t Stop ‘Til You Get Enough: Adaptive Information Sampling in a Visuomotor Estimation Task

We investigated how subjects sample information in order to improve performance in a visuomotor estimation task. Subjects were rewarded for touching a hidden circular target based on visual cues to the target’s location. The cues were 'dots ' drawn from a Gaussian distribution centered on the middle of the target. Subjects could sample as many cues as they wished, but the potential reward for hitting the target decreased by a fixed amount for each additional cue requested. The subjects ' objective was to balance the benefits of increased information against the costs incurred in acquiring it. We compared human performance to ideal and found that subjects sampled more cues than dictated by the optimal stopping rule that tries to maximize expected gain. We contrast our results with recent reports in the literature that subjects typically under-sample

Exploring and exploiting uncertainty: Statistical learning ability affects how we learn to process language along multiple dimensions of experience

While the effects of pattern learning on language processing are well known, the way in which pattern learning shapes exploratory behavior has long gone unnoticed. We report on the way in which individual differences in statistical pattern learning affect performance in the domain of language along multiple dimensions. Analyzing data from healthy monolingual adults’ performance on a serial reaction time task and a self-paced reading task we show how individual differences in statistical pattern learning are reflected in readers’ knowledge of linguistic co-occurrence patterns and in their exploration and exploitation of content-specific and task-general information. First, we investigated the extent to which an individual’s pattern learning correlates with their sensitivity to systematic morphological and syntactic co-occurrences, as evidenced while reading authentic sentences. We found that the stream of morphological and syntactic information has a more pronounced effect on the reading speed of, as we will label them, content-sensitive learners in that the more probable the co-occurrence pattern, the faster their reading of that pattern will be. Next, we investigated how differences in pattern learning are reflected in the ways in which individuals approach the reading task itself and adapt to it. Casting this relation in terms of exploration/exploitation strategies, known from Reinforcement Learning, we conclude that content-sensitive learners are also more likely to initially probe (explore) a wider range of directly relevant patterns, which they can later use (exploit) to optimize their reading performance further. By affecting exploratory behavior, pattern learning influences the information that is gathered and becomes available for exploitation, thereby increasing the effect pattern learning has on language cognition