Visual statistical learning (VSL), the unsupervised learning of statistical contingencies across time and space, may play a key role in efficient and predictive encoding of the perceptual world. How VSL capabilities vary as a function of ongoing task demands is still poorly understood. VSL is modulated by selective attention and faces interference from some secondary tasks, but there is little evidence that the types of contingencies learned in VSL are sensitive to task demands. We found a powerful effect of task on what is learned in VSL. Participants first completed a visual familiarization task requiring judgments of face gender (female/male) or scene location (interior/exterior). Statistical regularities were embedded between stimulus pairs. During a surprise recognition phase, participants showed less recognition for pairs that had required a change in response key (e.g., female followed by male) or task (e.g., female followed by indoor) during familiarization. When familiarization required detection of "flicker" or "jiggle" events unrelated to image content, there was weaker, but uniform, VSL across pair types. These results suggest that simple task manipulations play a strong role in modulating the distribution of learning over different pair combinations. Such variations may arise from task and response conflict or because the manner in which images are processed is altered.

Humans are adept at learning regularities in a visual environment, even without explicit cues to structure and in the absence of instruction-this has been termed "visual statistical learning" (VSL). The nature of the representations resulting from VSL are still poorly understood. In five experiments, we examined the specificity of temporal VSL representations. In Experiments 1A, 1B, and 2, we compared recognition rates of triplets and all embedded pairs to chance. Robust learning of all structures was evident, and even pairs of non-adjacent items in a sequentially presented triplet (AC extracted from a triplet composed of ABC) were recognized at above-chance levels. In Experiment 3, we asked whether people could recognize rearranged pairs to examine the flexibility of learned representations. Recognition of all possible orders of target triplets and pairs was significantly higher than chance, and there were no differences between canonical orderings and their corresponding randomized orderings, suggesting that learners were not dependent upon originally experienced stimulus orderings to recognize co-occurrence. Experiment 4 demonstrates the essential role of an interstitial item in VSL representations. By comparing the learning of quadruplet sets (e.g., ABCD) and triplet sets (e.g., ABC), we found learning of AC and BD in ABCD (quadruplet) sets were better than the learning of AC in ABC (triplet) sets. This pattern of results might result from the critical role of interstitial items in statistical learning. In short, our work supports the idea of generalized representation in VSL and provides evidence about how this representation is structured.

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.

Brain corticostriatal circuits are important for understanding chronic pain and highly relevant to motivation and cognitive processes. It has been demonstrated that in patients with chronic back pain, altered nucleus accumbens (NAcc)—medial prefrontal cortex (MPFC) circuit fMRI-based activity is predictive of patient outcome. We evaluated the NAcc-MPFC circuit in patients with another chronic pain condition, fibromyalgia, to extend these important findings. First, we compared fMRI-based NAcc-MPFC resting-state functional connectivity in patients with fibromyalgia (N = 32) vs. healthy controls (N = 37). Compared to controls, the NAcc-MPFC circuit’s connectivity was significantly reduced in fibromyalgia. In addition, within the fibromyalgia group, NAcc-MPFC connectivity was significantly correlated with trait anxiety. Our expanded connectivity analysis of the NAcc to subcortical brain regions showed reduced connectivity of the right NAcc with mesolimbic circuit regions (putamen, thalamus, and ventral pallidum) in fibromyalgia. Lastly, in an exploratory analysis comparing our fibromyalgia and healthy control cohorts to a separate publicly available dataset from patients with chronic back pain, we identified reduced NAcc-MPFC connectivity across both the patient groups with unique alterations in NAcc-mesolimbic connectivity. Together, expanding upon prior observed alterations in brain corticostriatal circuits, our results provide novel evidence of altered corticostriatal and mesolimbic circuits in chronic pain.