Recent studies of visual statistical learning (VSL) have demonstrated that statistical regularities in sequences of visual stimuli can be automatically extracted, even without intent or awareness. Despite much work on this topic, however, several fundamental questions remain about the nature of VSL. In particular, previous experiments have not explored the underlying units over which VSL operates. In a sequence of colored shapes, for example, does VSL operate over each feature dimension independently, or over multidimensional objects in which color and shape are bound together? The studies reported here demonstrate that VSL can be both object-based and feature-based, in systematic ways based on how different feature dimensions covary. For example, when each shape covaried perfectly with a particular color, VSL was object-based: Observers expressed robust VSL for colored-shape sub-sequences at test but failed when the test items consisted of monochromatic shapes or color patches. When shape and color pairs were partially decoupled during learning, however, VSL operated over features: Observers expressed robust VSL when the feature dimensions were tested separately. These results suggest that VSL is object-based, but that sensitivity to feature correlations in multidimensional sequences (possibly another form of VSL) may in turn help define what counts as an object.

Visual statistical learning of shape sequences was examined in the context of occluded object trajectories. In a learning phase, participants viewed a sequence of moving shapes whose trajectories and speed profiles elicited either a bouncing or a streaming percept: the sequences consisted of a shape moving toward and then passing behind an occluder, after which two different shapes emerged from behind the occluder. At issue was whether statistical learning linked both object transitions equally, or whether the percept of either bouncing or streaming constrained the association between pre- and postocclusion objects. In familiarity judgments following the learning, participants reliably selected the shape pair that conformed to the bouncing or streaming bias that was present during the learning phase. A follow-up experiment demonstrated that differential eye movements could not account for this finding. These results suggest that sequential statistical learning is constrained by the spatiotemporal perceptual biases that bind two shapes moving through occlusion, and that this constraint thus reduces the computational complexity of visual statistical learning. Human sensory systems handle enormous amounts of complex information during every waking moment. An increasing number of studies supports the idea that the processing of this information is implemented by an unsupervised form of continuous observational learning, which organizes both old and new sensory information into maximally efficient codes (Fiser & Aslin, 2005). This statistical learning mechanism has been demonstrated using speech streams in adults (Saffran, Newport, & Aslin,

Statistical topic models provide a general data-driven framework for automated discovery of high-level knowledge from large collections of text documents. Although topic models can potentially discover a broad range of themes in a data set, the interpretability of the learned topics is not always ideal. Human-defined concepts, however, tend to be semantically richer due to careful selection of words that define the concepts, but they may not span the themes in a data set exhaustively. In this study, we review a new probabilistic framework for combining a hierarchy of human-defined semantic concepts with a statistical topic model to seek the best of both worlds. Results indicate that this combination leads to systematic improvements in generalization performance as well as enabling new techniques for inferring and visualizing the content of a document.

Statistical topic models provide a general data-driven framework for automated discovery of high-level knowledge from large collections of text documents. While topic models can potentially discover a broad range of themes in a data set, the interpretability of the learned topics is not always ideal. Human-defined concepts, on the other hand, tend to be semantically richer due to careful selection of words that define the concepts but they may not span the themes in a data set exhaustively. In this paper, we review a new probabilistic framework for combining a hierarchy of human-defined semantic concepts with a statistical topic model to seek the best of both worlds. Results indicate that this combination leads to systematic improvements in generalization performance as well as enabling new techniques for inferring and visualizing the content of a document. 2

In the present review we discuss an extension of classical perceptual learning called the observational learning paradigm. We propose that studying the process how humans develop internal representation of their environment requires modifications of the original perceptual learning paradigm which lead to observational learning. We relate observational learning to other types of learning, mention some recent developments that enabled its emergence, and summarize the main empirical and modeling findings that observational learning studies obtained. We conclude by suggesting that observational learning studies have the potential of providing a unified framework to merge human statistical learning, chunk learning and rule learning.

humans and animals

Visual statistical learning allows observers to extract high-level structure from visual scenes (Fiser & Aslin, 2001). Previous work has explored the types of statistical computations afforded but has not addressed to what extent learning results in unbound versus spatially bound representations of element cooccurrences. We explored these two possibilities using an unsupervised learning task with adult participants who observed complex multi-element scenes embedded with consistently paired elements. If learning is mediated by unconstrained associative learning mechanisms, then learning the element pairings may depend only on the co-occurrence of the elements in the scenes, without regard to their specific spatial arrangements. If learning is perceptually constrained, cooccurring elements ought to form perceptual units specific to their observed spatial arrangements. Results showed that participants learned the statistical structure of element cooccurrences in a spatial-specific manner, showing that visual statistical learning is perceptually constrained by spatial grouping principles.

■ Several studies report a right hemisphere advantage for visuospatial integration and a left hemisphere advantage for inferring conceptual knowledge from patterns of covariation. The present study examined hemispheric asymmetry in the implicit learning of new visual feature combinations. A split-brain patient and normal control participants viewed multishape scenes presented in either the right or the left visual fields. Unbeknownst to the participants, the scenes were composed from a random combination of fixed pairs of shapes. Subsequent testing found that control participants could discriminate fixed-pair shapes from randomly combined shapes when presented in either visual field. The split-brain patient performed at chance except when both the practice and the test displays were presented in the left visual field (right hemisphere). These results suggest that the statistical learning of new visual features is dominated by visuospatial processing in the right hemisphere and provide a prediction about how fMRI activation patterns might change during unsupervised statistical learning. ■