Christopher M. Conway

Statistical structures and our ability to exploit them are a ubiquitous component of daily life. Yet, we still do not fully understand how we track these sophisticated statistics and the role they play in sensory processing. Predictive coding frameworks hypothesize that for stimuli that can be accurately anticipated based on prior experience, we rely more strongly on our internal model of the sensory world and are more "surprised" when that expectation is unmet. The present study used this phenomenon to probe listeners' sensitivity to probabilistic structures generated using rapid 50 ms tone-pip sequences that precluded conscious prediction of upcoming stimuli. Over three experiments, we measured listeners' sensitivity and response time to deviants of a frequency outside the expected range. Predictable sequences were generated using either a triplet-based or network-style structure, and deviant detection contrasted against the same set of tones but in a random, unpredictable order. All experiments found structured sequences enhanced deviant detection relative to random sequences. Additionally, Experiment 2 used three different instantiations of the community structure to demonstrate that the level of uncertainty in the structured sequences modulated deviant saliency. Finally, Experiment 3 placed the deviant within an established community or immediately after a transition between communities, where the perceptual boundary should generate momentary uncertainty. However, this manipulation did not impact performance. Together, these results demonstrate that probabilistic contexts generated from statistical structures modulate the processing of an ongoing auditory signal, leading to an improved ability to detect unexpected deviant stimuli, consistent with the predictive coding framework. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Research into the construct of statistical learning, the ability to learn structured patterns in the environment, has been exploding in recent years. Despite the plethora of studies, critical reviews have highlighted challenges facing this area of research, including confusion about what statistical learning is, what cognitive and neural mechanisms support it, how these mechanisms map onto different laboratory tasks, and how it relates to other forms of learning, memory, and cognition. These challenges are not unique to statistical learning research but in fact are difficulties confronting the psychological sciences more broadly. In this paper, we use recent conceptual advances regarding the nature of the “theory crisis” in psychology to provide a roadmap for developing a theoretical framework that can provide meaningful and valid conceptualizations of the various phenomena that statistical learning touches upon. First, we describe three important challenges to statistical learning research: a lack of robust phenomena to constrain theories, issues with construct validity, and challenges with establishing causality. Next, we discuss each issue in relation to several research phenomena that have been examined in some detail in the statistical learning research. Finally, we offer recommendations that we believe can help address the theory crisis in statistical learning research to help move the field forward in fruitful directions.

Statistical structures and our ability to exploit them are a ubiquitous component of daily life. Yet, we still do not fully understand how we track these sophisticated statistics and the role they play in sensory processing. Predictive coding frameworks hypothesize that for stimuli that can be accurately anticipated based on prior experience, we rely more strongly on our internal model of the sensory world and are more “surprised” when that expectation is unmet. The current study used this phenomenon to probe listeners’ sensitivity to probabilistic structures generated using rapid 50 milli-second tone-pip sequences that precluded conscious prediction of upcoming stimuli. Over three experiments we measured listeners’ sensitivity and response time to deviants of a frequency outside the expected range. Predictable sequences were generated using either a triplet-based or community structure and deviance detection contrasted against the same set of tones but in a random, unpredictable order. All experiments found structured sequences enhanced deviant detection relative to random sequences. Additionally, Experiment 2 used three different instantiations of the community structure to demonstrate that the level of uncertainty in the structured sequences modulated deviant saliency. Finally, Experiment 3 placed the deviant within an established community or immediately after a transition between communities, where the perceptual boundary should generate momentary uncertainty. However, this manipulation did not impact performance. Together these results demonstrate that probabilistic contexts generated from statistical structures modulate the processing of an ongoing auditory signal, leading to an improved ability to detect unexpected deviant stimuli, consistent with the predictive coding framework. Public significance statement As we navigate through the world our brain must rapidly detect and process auditory information. Many of these auditory sources contain predictable patterns. Being able to learn predictable patterns allows us to anticipate upcoming sounds, and more efficiently process information. In addition, if we have strong expectations about a sound, we should be surprised if that expectation is not met. Therefore, this predictive mechanism is important for helping us to detect unexpected events or changes in our environment. In this study, we explored the brain’s capacity to detect and exploit various predictable patterns and use this information to detect surprising events.

One important aspect of human cognition involves the learning of structured information encountered in our environment, a phenomenon known as statistical learning. A growing body of research suggests that learning to read print is partially guided by learning the statistical contingencies existing between the letters within a word, and also between the letters and sounds to which the letters refer. Research also suggests that impairments to statistical learning ability may at least partially explain the difficulties experienced by individuals diagnosed with dyslexia. However, the findings regarding impaired learning are not consistent, perhaps partly due to the varied use of methodologies across studies – such as differences in the learning paradigms, stimuli used, and the way that learning is assessed – as well as differences in participant samples such as age and extent of the learning disorder. In this review, we attempt to examine the purported link between statistical learning and dyslexia by assessing a set of the most recent and relevant studies in both adults and children. Based on this review, we conclude that although there is some evidence for a statistical learning impairment in adults with dyslexia, the evidence for an impairment in children is much weaker. We discuss several suggestive trends that emerge from our examination of the research, such as issues related to task heterogeneity, possible age effects, the role of publication bias, and other suggestions for future research such as the use of neural measures and a need to better understand how statistical learning changes across typical development. We conclude that no current theoretical framework of dyslexia fully captures the extant research findings on statistical learning.

Language is acquired in part through statistical learning abilities that encode environmental regularities. Language development is also heavily influenced by social environmental factors such as socioeconomic status. However, it is unknown to what extent statistical learning interacts with SES to affect language outcomes. We measured event-related potentials in 26 children aged 8–12 while they performed a visual statistical learning task. Regression analyses indicated that children’s learning performance moderated the relationship between socioeconomic status and both syntactic and vocabulary language comprehension scores. For children demonstrating high learning, socioeconomic status had a weaker effect on language compared to children showing low learning. These results suggest that high statistical learning ability can provide a buffer against the disadvantages associated with being raised in a lower socioeconomic status household.

We investigated the extent to which touch, vision, and audition are similar in the ways they mediate the processing of statistical regularities within sequential input. While previous research has examined statistical/sequential learning in the visual and auditory domains, few researchers have conducted rigorous comparisons across sensory modalities; in particular, the sense of touch has been virtually ignored in such research. Our data reveal commonalities between the ways in which these three modalities afford the learning of sequential information. However, the data also suggest that in terms of sequential learning, audition is superior to the other two senses. We discuss these findings in terms of whether statistical/sequential learning is likely to consist of a single, unitary mechanism or multiple, modality-constrained ones.