Dissociable behavioural outcomes of visual statistical learning

Acute Stress Effects on Statistical Learning and Episodic Memory

Stress is widely considered to negatively impact hippocampal function, thus impairing episodic memory. However, the hippocampus is not merely the seat of episodic memory. Rather, it also (via distinct circuitry) supports statistical learning. On the basis of rodent work suggesting that stress may impair the hippocampal pathway involved in episodic memory while sparing or enhancing the pathway involved in statistical learning, we developed a behavioral experiment to investigate the effects of acute stress on both episodic memory and statistical learning in humans. Participants were randomly assigned to one of three conditions: stress (socially evaluated cold pressor) immediately before learning, stress ∼15 min before learning, or no stress. In the learning task, participants viewed a series of trial-unique scenes (allowing for episodic encoding of each image) in which certain scene categories reliably followed one another (allowing for statistical learning of associations between paired categories). Memory was assessed 24 hr later to isolate stress effects on encoding/learning rather than retrieval. We found modest support for our hypothesis that acute stress can amplify statistical learning: Only participants stressed ∼15 min in advance exhibited reliable evidence of learning across multiple measures. Furthermore, stress-induced cortisol levels predicted statistical learning retention 24 hr later. In contrast, episodic memory did not differ by stress condition, although we did find preliminary evidence that acute stress promoted memory for statistically predictable information and attenuated competition between statistical and episodic encoding. Together, these findings provide initial insights into how stress may differentially modulate learning processes within the hippocampus.

What sticks after statistical learning: The persistence of implicit versus explicit memory traces

Statistical learning is a powerful mechanism that extracts even subtle regularities from our information-dense worlds. Recent theories argue that statistical learning can occur through multiple mechanisms-both the conventionally assumed automatic process that precipitates unconscious learning, and an attention-dependent process that brings regularities into conscious awareness. While this view has gained popularity, there are few empirical dissociations of the hypothesized implicit and explicit forms of statistical learning. Here we provide strong evidence for this dissociation in two ways. First, we show in healthy adults (N = 60) that implicit and explicit traces have divergent consolidation trajectories, with implicit knowledge of structure strengthened over a 24-h period, while precise explicit representations tend to decay. Second, we demonstrate that repeated testing strengthens the retention of explicit representations but that implicit statistical learning is uninfluenced by testing. Together these dissociations provide much needed support for the reconceptualization of statistical learning as a multi-component construct.

Superior learning in synesthetes: Consistent grapheme-color associations facilitate statistical learning

In synesthesia activation in one sensory domain, such as smell or sound, triggers an involuntary and unusual secondary sensory or cognitive experience. In the present study, we ask whether the added sensory experience of synesthesia can aid statistical learning-the ability to track environmental regularities in order to segment continuous information. To investigate this, we measured statistical learning outcomes, using an aurally presented artificial language, in two groups of synesthetes alongside controls and simulated the multimodal experience of synesthesia in non-synesthetes. One group of synesthetes exclusively had grapheme-color (GC) synesthesia, in which the experience of color is automatically triggered by exposure to written or spoken graphemes. The other group had both grapheme-color and sound-color (SC+) synesthesia, in which the experience of color is also triggered by the waveform properties of a voice, such as pitch, timbre, and/or musical chords. Unlike GC-only synesthetes, the experience of color in the SC+ group is not perfectly consistent with the statistics that signal word boundaries. We showed that GC-only synesthetes outperformed both non-synesthetes and SC+ synesthetes, likely because the visual concurrents for GC-only synesthetes are highly consistent with the artificial language. We further observed that our simulations of GC synesthesia, but not SC+ synesthesia produced superior statistical learning, showing that synesthesia likely boosts learning outcomes by providing a consistent secondary cue. Findings are discussed with regard to how multimodal experience can improve learning, with the present data indicating that this boost is more likely to occur through explicit, as opposed to implicit, learning systems.

Tasks determine what is learned in visual statistical learning

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.

Visual statistical learning deficits in memory-impaired individuals

Visual statistical learning (VSL) refers to the learning of environmental regularities. Classically considered an implicit process, one patient with isolated hippocampal damage is severely impaired at VSL tasks, suggesting involvement of explicit memory. Here, we asked whether memory impairment (MI) alone, absent of clear hippocampal pathology, predicted deficits across different VSL tasks. A classic VSL task revealed no learning in MI participants (Exp. 1), while imposing attentional demands (Exp. 2: flicker detection, Exp. 3: gender/location categorization) during familiarization revealed modest residual VSL. MI with nonspecific neural correlates predicted impaired VSL overall, but attentional processes may be harnessed for rehabilitation.

Prefrontal and Hippocampal Structure Predict Statistical Learning Ability in Early Childhood

Statistical learning can be used to gain sensitivity to many important regularities in our environment, including structure that is foundational to language and visual perception. As yet, little is known about how statistical learning takes place in the human brain, especially in children's developing brains and with regard to the broader neurobiology of learning and memory. We therefore explored the relationship between statistical learning and the thickness and volume of structures that are traditionally implicated in declarative and procedural memory, focusing specifically on the left inferior PFC, the hippocampus, and the caudate during early childhood (ages 5-8.5 years). We found that the thickness of the left inferior frontal cortex and volume of the right hippocampus predicted statistical learning ability in young children. Importantly, these regions did not change in thickness or volume with age, but the relationship between learning and the right hippocampus interacted with age such that older children's hippocampal structure more strongly predicted performance. Overall, the data show that children's statistical learning is supported by multiple neural structures that are more broadly implicated in learning and memory, especially declarative memory (hippocampus) and attention/top-down control (the PFC).

Statistical Learning Creates Novel Object Associations via Transitive Relations

A remarkable ability of the cognitive system is to make novel inferences on the basis of prior experiences. What mechanism supports such inferences? We propose that statistical learning is a process through which transitive inferences of new associations are made between objects that have never been directly associated. After viewing a continuous sequence containing two base pairs (e.g., A-B, B-C), participants automatically inferred a transitive pair (e.g., A-C) where the two objects had never co-occurred before (Experiment 1). This transitive inference occurred in the absence of explicit awareness of the base pairs. However, participants failed to infer the transitive pair from three base pairs (Experiment 2), showing the limits of the transitive inference (Experiment 3). We further demonstrated that this transitive inference can operate across the categorical hierarchy (Experiments 4-7). The findings revealed a novel consequence of statistical learning in which new transitive associations between objects are implicitly inferred.

Redefining "Learning" in Statistical Learning: What Does an Online Measure Reveal About the Assimilation of Visual Regularities?

From a theoretical perspective, most discussions of statistical learning (SL) have focused on the possible "statistical" properties that are the object of learning. Much less attention has been given to defining what "learning" is in the context of "statistical learning." One major difficulty is that SL research has been monitoring participants' performance in laboratory settings with a strikingly narrow set of tasks, where learning is typically assessed offline, through a set of two-alternative-forced-choice questions, which follow a brief visual or auditory familiarization stream. Is that all there is to characterizing SL abilities? Here we adopt a novel perspective for investigating the processing of regularities in the visual modality. By tracking online performance in a self-paced SL paradigm, we focus on the trajectory of learning. In a set of three experiments we show that this paradigm provides a reliable and valid signature of SL performance, and it offers important insights for understanding how statistical regularities are perceived and assimilated in the visual modality. This demonstrates the promise of integrating different operational measures to our theory of SL.

The multi-component nature of statistical learning

The central argument presented in this paper is that statistical learning (SL) is an ability comprised of multiple components that operate largely implicitly. Components relating to the stimulus encoding, retention and abstraction required for SL may include, but are not limited to, certain types of attention, processing speed and memory. It is likely that individuals vary in terms of the efficiency of these underlying components, and in patterns of connectivity among these components, and that SL tasks differ from one another in how they draw on certain underlying components more than others. This theoretical framework is of value because it can assist in gaining a clearer understanding of how SL is linked with individual differences in complex mental activities such as language processing. Variability in language processing across individuals is of central concern to researchers interested in child development, including those interested in neurodevelopmental disorders where language can be affected such as autism spectrum disorders (ASD). This paper discusses the link between SL and individual differences in language processing in the context of age-related changes in SL during infancy and childhood, and whether SL is affected in ASD. Viewing SL as a multi-component ability may help to explain divergent findings from previous empirical research in these areas and guide the design of future studies.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'.