Visual statistical learning is modulated by arbitrary and natural categories

Abstract

Visual statistical learning (VSL) describes the unintentional extraction of statistical regularities from visual environments across time or space, and is typically studied using novel stimuli (e.g., symbols unfamiliar to participants) and using familiarization procedures that are passive or require only basic vigilance. The natural visual world, however, is rich with a variety of complex visual stimuli, and we experience that world in the presence of goal-driven behavior including overt learning of other kinds. To examine how VSL responds to such contexts, we exposed subjects to statistical contingencies as they learned arbitrary categorical mappings of unfamiliar stimuli (fractals, Experiment 1) or familiar stimuli with preexisting categorical boundaries (faces and scenes, Experiment 2). In a familiarization stage, subjects learned by trial and error the arbitrary mappings between stimuli and one of two responses. Unbeknownst to participants, items were paired such that they always appeared together in the stream. Pairs were equally likely to be of the same or different category. In a pair recognition stage to assess VSL, subjects chose between a target pair and a foil pair. In both experiments, subjects' VSL was shaped by arbitrary categories: same-category pairs were learned better than different-category pairs. Natural categories (Experiment 2) also played a role, with subjects learning samenatural-category pairs at higher rates than different-category pairs, an effect that did not interact with arbitrary mappings. We conclude that learning goals of the observer and preexisting knowledge about the structure of the world play powerful roles in the incidental learning of novel statistical information.

Estimating Sentence-like Structure in Synthetic Languages Using Information Topology

“…The Shannon self-information due to the occurrence of a single probabilistic symbolic event is defined according to Equation (2). We can generalize this to permit the occurrence of an event at a particular time n, where the probability is a function of both the particular symbol and the time (or context) in which it may occur.…”

“…In human communications, language is generally understood in chunks [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. In spoken language, the idea of a sentence is not a straightforward notion due to the lack of textual clues, punctuation or morphological information [ 14 ].…”

Similarity Benefits Visual Statistical Learning

“…The results were interpreted as indicating a strong role of task on VSL, generally, and as indicating a potentially important role of categories in VSL. Rogers, Park, and Vickery (2021) built upon this work. In one experiment, statistically paired items were from the same natural category (e.g., two faces) or different natural categories (e.g., a face and a scene).…”

“…Learning in the real-world however, is affected by many contextual factors, including similarity (Ashby et al, 2020;Wammes et al, 2022), categorization (Hutter & Wilson, 2018;Lau et al, 2020;Reed et al, 1999;Rogers et al, 2021;Smith, 2008), as well as task demands.…”

The Effects of Load on Spatial Statistical Learning

“…In the absence of additional demands temporal SL can operate robustly (Musz et al, 2015) but is somewhat limited under additional demands (Tal et al, 2017). Recent findings point out that task demands during the learning phase affect temporal SL (Vickery et al, 2018;Rogers et al, 2021) but critically, it is not well known how concurrent demands affect the learning in both the temporal and spatial SL forms.…”