Reliability of individual differences in distractor suppression driven by statistical learning.

A series of recent studies has demonstrated that attentional selection is modulated by statistical regularities, even when they concern task-irrelevant stimuli. Irrelevant distractors presented more frequently at one location interfere less with search than distractors presented elsewhere. To account for this finding, it has been proposed that through statistical learning, the frequent distractor location becomes suppressed relative to the other locations. Learned distractor suppression has mainly been studied at the group level, where individual differences are treated as unexplained error variance. Yet these individual differences may provide important mechanistic insights and could be predictive of cognitive and real-life outcomes. In the current study, we ask whether in an additional singleton task, the standard measures of attentional capture and learned suppression are reliable and stable at the level of the individual. In an online study, we assessed both the within- and between-session reliability of individual-level measures of attentional capture and learned suppression. We show that the measures of attentional capture, but not of distractor suppression, are moderately stable within the same session (i.e., split-half reliability). Test-retest reliability over a 2-month period was found to be moderate for attentional capture but weak or absent for suppression. RT-based measures proved to be superior to accuracy measures. While producing very robust findings at the group level, the predictive validity of these RT-based measures is still limited when it comes to individual-level performance. We discuss the implications for future research drawing on inter-individual variation in the attentional biases that result from statistical learning.

Beta-Band Activity Is a Signature of Statistical Learning.

Through statistical learning (SL), cognitive systems may discover the underlying regularities in the environment. Testing human adults (n = 35, 21 females), we document, in the context of a classical visual SL task, divergent rhythmic EEG activity in the interstimulus delay periods within patterns versus between patterns (i.e., pattern transitions). Our findings reveal increased oscillatory activity in the beta band (∼20 Hz) at triplet transitions that indexes learning: it emerges with increased pattern repetitions; and importantly, it is highly correlated with behavioral learning outcomes. These findings hold the promise of converging on an online measure of learning regularities and provide important theoretical insights regarding the mechanisms of SL and prediction.SIGNIFICANCE STATEMENT Statistical learning has become a major theoretical construct in cognitive science, providing the primary means by which organisms learn about regularities in the environment. As such, it is a critical building block for basic and higher-order cognitive functions. Here we identify, for the first time, a spectral neural index in the time window before stimulus presentation, which evolves with increased pattern exposure, and is predictive of learning performance. The manifestation of learning that is revealed, not in stimulus processing but in the blank interval between stimuli, makes a direct link between the fields of statistical learning on the one hand and either prediction or consolidation on the other hand, suggesting a possible mechanistic account of visual statistical learning.

Common neural basis of motor sequence learning and word recognition and its relation with individual differences in reading skill.

To investigate the neural basis of a common statistical learning mechanism involved in motor sequence learning and decoding, we recorded same participants' brain activation in a serial reaction time (SRT) and word reading task using functional magnetic resonance imaging. In the SRT, a manual response was made depending on the location of a visual cue, and the order of the locations was either fixed or random. In the word reading task, visual words were passively presented. Compared to less skilled readers, more skilled readers showed greater differences in activation in the inferior frontal gyrus pars triangularis (IFGpTr) and the insula between the ordered and random condition in the SRT task and greater activation in those regions in the word reading task. It suggests that extraction of statistically predictable patterns in the IFGpTr and insula contributes to both motor sequence learning and orthographic learning, and therefore predicts individual differences in decoding skill.

Measuring individual differences in statistical learning: Current pitfalls and possible solutions.

Most research in statistical learning (SL) has focused on the mean success rates of participants in detecting statistical contingencies at a group level. In recent years, however, researchers have shown increased interest in individual abilities in SL, either to predict other cognitive capacities or as a tool for understanding the mechanism underlying SL. Most if not all of this research enterprise has employed SL tasks that were originally designed for group-level studies. We argue that from an individual difference perspective, such tasks are psychometrically weak, and sometimes even flawed. In particular, the existing SL tasks have three major shortcomings: (1) the number of trials in the test phase is often too small (or, there is extensive repetition of the same targets throughout the test); (2) a large proportion of the sample performs at chance level, so that most of the data points reflect noise; and (3) the test items following familiarization are all of the same type and an identical level of difficulty. These factors lead to high measurement error, inevitably resulting in low reliability, and thereby doubtful validity. Here we present a novel method specifically designed for the measurement of individual differences in visual SL. The novel task we offer displays substantially superior psychometric properties. We report data regarding the reliability of the task and discuss the importance of the implementation of such tasks in future research.

The involvement of long-term serial-order memory in reading development: A longitudinal study.

Recent findings suggest that Hebb repetition learning-a paradigmatic example of long-term serial-order learning-is impaired in adults with dyslexia. The current study further investigated the link between serial-order learning and reading using a longitudinal developmental design. With this aim, verbal and visual Hebb repetition learning performance and reading skills were assessed in 96 Dutch-speaking children who we followed from first through second grade of primary school. We observed a positive association between order learning capacities and reading ability as well as weaker Hebb learning performance in early readers with poor reading skills even at the onset of reading instruction. Hebb learning further predicted individual differences in later (nonword) reading skills. Finally, Hebb learning was shown to explain a significant part of the variance in reading performance above and beyond phonological awareness. These findings highlight the role of serial-order memory in reading ability.

Increased susceptibility to proactive interference in adults with dyslexia?

Recent findings show that people with dyslexia have an impairment in serial-order memory. Based on these findings, the present study aimed to test the hypothesis that people with dyslexia have difficulties dealing with proactive interference (PI) in recognition memory. A group of 25 adults with dyslexia and a group of matched controls were subjected to a 2-back recognition task, which required participants to indicate whether an item (mis)matched the item that had been presented 2 trials before. PI was elicited using lure trials in which the item matched the item in the 3-back position instead of the targeted 2-back position. Our results demonstrate that the introduction of lure trials affected 2-back recognition performance more severely in the dyslexic group than in the control group, suggesting greater difficulty in resisting PI in dyslexia.

Sensitive periods in language development: Do children outperform adults on auditory word-form segmentation?

Children are more successful language learners than adults, yet the nature and cause of this phenomenon are still not well understood. Auditory statistical learning from speech has been a prominent focus of research in the field of language development because it is regarded as a fundamental learning mechanism underlying word segmentation in early language acquisition. However, a handful of studies that investigated developmental trajectories for auditory statistical learning found no clear child advantages. The degree to which the learning task measures explicit rather than implicit mechanisms might obscure a potential advantage for younger learners, as suggested by recent findings. In the present study, we compared children aged 7-12 years and young adults on an adapted version of the task that disentangles explicit and implicit contributions to learning. They were exposed to a continuous stream of speech sounds comprising four repeating trisyllabic pseudowords. Learning of the hidden words was tested (a) online through a target-detection task and (b) offline via a forced-choice word recognition test that included a memory judgement procedure. Both measures revealed comparable learning abilities. However, children's performance on the recognition task showed evidence for both explicit and implicit word knowledge while adults appeared primarily sensitive to explicit memory. Since implicit memory is more stable in time than explicit memory, we suggest that future work should focus more on developmental differences in the nature of the memory that is formed, rather than the strength of learning, when trying to understand child advantages in language acquisition.

No evidence for spatial suppression due to across-trial distractor learning in visual search.

Previous studies have shown that during visual search, participants are able to implicitly learn across-trial regularities regarding target locations and use these to improve search performance. The present study asks whether such across-trial visual statistical learning also extends to the location of salient distractors. In Experiments 1 and 2, distractor regularities were paired so that a specific distractor location was 100% predictive of another specific distractor location on the next trial. Unlike previous findings that employed target regularities, the current results show no difference in search times between predictable and unpredictable trials. In Experiments 3-5 the distractor location was presented in a structured order (a sequence) for one group of participants, while it was presented randomly for the other group. Again, there was no learning effect of the across-trial regularities regarding the salient distractor locations. Across five experiments, we demonstrated that participants were unable to exploit across-trial spatial regularities regarding the salient distractors. These findings point to important boundary conditions for the modulation of visual attention by statistical regularities and they highlight the need to differentiate between different types of statistical regularities.

Statistical learning of distractor locations is dependent on task context.

Through statistical learning, humans can learn to suppress visual areas that often contain distractors. Recent findings suggest that this form of learned suppression is insensitive to context, putting into question its real-life relevance. The current study presents a different picture: we show context-dependent learning of distractor-based regularities. Unlike previous studies which typically used background cues to differentiate contexts, the current study manipulated task context. Specifically, the task alternated from block to block between a compound search and a detection task. In both tasks, participants searched for a unique shape, while ignoring a uniquely colored distractor item. Crucially, a different high-probability distractor location was assigned to each task context in the training blocks, and all distractor locations were made equiprobable in the testing blocks. In a control experiment, participants only performed a compound search task such that the contexts were made indistinguishable, but the high-probability locations changed in exactly the same way as in the main experiment. We analyzed response times for different distractor locations and show that participants can learn to suppress a location in a context-dependent way, but suppression from previous task contexts lingers unless a new high-probability location is introduced.

What to expect where and when: how statistical learning drives visual selection.

While the visual environment contains massive amounts of information, we should not and cannot pay attention to all events. Instead, we need to direct attention to those events that have proven to be important in the past and suppress those that were distracting and irrelevant. Experiences molded through a learning process enable us to extract and adapt to the statistical regularities in the world. While previous studies have shown that visual statistical learning (VSL) is critical for representing higher order units of perception, here we review the role of VSL in attentional selection. Evidence suggests that through VSL, attentional priority settings are optimally adjusted to regularities in the environment, without intention and without conscious awareness.

Statistical learning of across-trial regularities during serial search.

Previous studies have shown that attention becomes biased toward those locations that frequently contain a target and is biased away from locations that have a high probability to contain a distractor. A recent study showed that participants also learned regularities that exist across trials: Participants were faster to find the singleton when its location was predicted by the location of the target singleton on the previous trial. Note, however, that this across-trial statistical learning was only demonstrated for parallel search involving "pop-out" singleton targets. The current study investigated whether there is also learning of across-trial regularities when search is serial, using a T-among-Ls task. In Experiment 1, using search displays with a gray T-target among gray Ls, we found that participants did not learn the existing across-trial regularities. In Experiment 2 we used the same display and same regularities except that during the first half of the experiment the targets were colored red, allowing feature search. Critically, now participants did learn the across-trial regularities during pop-out feature search and the learned biases persisted when search was serial again. Participants were not aware of these regularities suggesting that learning was automatic and implicit. We propose that across-trial target-target associations learned during feature search shape a flexible priority map whereby the selection of the predicting location results in up-weighting of the predicted location on the next trial. This flexible priority map remained active even when search task changed dramatically from parallel to serial search. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Is there such a thing as a 'good statistical learner'?

A growing body of research investigates individual differences in the learning of statistical structure, tying them to variability in cognitive (dis)abilities. This approach views statistical learning (SL) as a general individual ability that underlies performance across a range of cognitive domains. But is there a general SL capacity that can sort individuals from 'bad' to 'good' statistical learners? Explicating the suppositions underlying this approach, we suggest that current evidence supporting it is meager. We outline an alternative perspective that considers the variability of statistical environments within different cognitive domains. Once we focus on learning that is tuned to the statistics of real-world sensory inputs, an alternative view of SL computations emerges with a radically different outlook for SL research.

Does it help to expect distraction? Attentional capture is attenuated by high distractor frequency but not by trial-to-trial predictability.

Salient distractors such as color singletons typically capture attention. Recent studies have shown that probabilistic expectations of color singletons' occurrence-even when their location and features are unpredictable-can eliminate attentional capture. Here we ask whether this effect, referred to as "second-order distractor suppression," (a) could be merely a result of repetition priming, and (b) is also observed when distractor occurrences are predictable within a sequence of trials? Experiment 1 introduces a novel approach for manipulating the frequency of distractor occurrence while controlling for intertrial priming by design, by embedding identical trial sequences in the to-be-compared conditions. We observed no elimination but significant attenuation of capture in the condition with a higher distractor frequency. In Experiments 2 and 3 we investigated the effect of the trial-to-trial predictability of distractor presence. Repeating regular distractor absent/present patterns did not result in attenuated capture compared with a random condition, not even when upcoming distractor presence was cued. Taken together, the results demonstrate that second-order distractor suppression is not merely a result of repetition priming. However, it is not a response to any type of expectation; this nonspecific type of suppression is almost instantly elicited by environments characterized by a high likelihood of distractors but not by distractor presence that can be anticipated on a trial-by-trial basis. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Surprisingly inflexible: Statistically learned suppression of distractors generalizes across contexts.

The present study investigates the flexibility of statistically learned distractor suppression between different contexts. Participants performed the additional singleton task searching for a unique shape, while ignoring a uniquely colored distractor. Crucially, we created two contexts within the experiments, and each context was assigned its own high-probability distractor location, so that the location where the distractor was most likely to appear depended on the context. Experiment 1 signified context through the color of the background. In Experiment 2, we aimed to more strongly differentiate between the contexts using an auditory or visual cue to indicate the upcoming context. In Experiment 3, context determined the appropriate response ensuring that participants engaged the context in order to be able to perform the task. Across all experiments, participants learned to suppress both high-probability locations, even if they were not aware of these spatial regularities. However, these suppression effects occurred independent of context, as the pattern of suppression reflected a de-prioritization of both high-probability locations which did not change with the context. We employed Bayesian analyses to statistically quantify the absence of context-dependent suppression effects. We conclude that statistically learned distractor suppression is robust and generalizes across contexts.

When the "Tabula" is Anything but "Rasa:" What Determines Performance in the Auditory Statistical Learning Task?

How does prior linguistic knowledge modulate learning in verbal auditory statistical learning (SL) tasks? Here, we address this question by assessing to what extent the frequency of syllabic co-occurrences in the learners' native language determines SL performance. We computed the frequency of co-occurrences of syllables in spoken Spanish through a transliterated corpus, and used this measure to construct two artificial familiarization streams. One stream was constructed by embedding pseudowords with high co-occurrence frequency in Spanish ("Spanish-like" condition), the other by embedding pseudowords with low co-occurrence frequency ("Spanish-unlike" condition). Native Spanish-speaking participants listened to one of the two streams, and were tested in an old/new identification task to examine their ability to discriminate the embedded pseudowords from foils. Our results show that performance in the verbal auditory SL (ASL) task was significantly influenced by the frequency of syllabic co-occurrences in Spanish: When the embedded pseudowords were more "Spanish-like," participants were better able to identify them as part of the stream. These findings demonstrate that learners' task performance in verbal ASL tasks changes as a function of the artificial language's similarity to their native language, and highlight how linguistic prior knowledge biases the learning of regularities.

Statistical Learning and Language Impairments: Toward More Precise Theoretical Accounts.

Statistical-learning (SL) theory offers an experience-based account of typical and atypical spoken and written language acquisition. Recent work has provided initial support for this view, tying individual differences in SL abilities to linguistic skills, including language impairments. In the current article, we provide a critical review of studies testing SL abilities in participants with and without developmental dyslexia and specific language impairment and discuss the directions that this field of research has taken so far. We identify substantial vagueness in the demarcation lines between different theoretical constructs (e.g., "statistical learning," "implicit learning," and "procedural learning") as well as in the mappings between experimental tasks and these theoretical constructs. Moreover, we argue that current studies are not designed to contrast different theoretical approaches but rather test singular confirmatory predictions without including control tasks showing normal performance. We end by providing concrete suggestions for how to advance research on SL deficits in language impairments.

Detection of regularities in a random environment.

Regularity detection, or statistical learning, is regarded as a fundamental component of our cognitive system. To test the ability of human participants to detect regularity in a more ecological situation (i.e., mixed with random information), we used a simple letter-naming paradigm in which participants were instructed to name single letters presented one at a time on a computer screen. The regularity consisted of a triplet of letters that were systematically presented in that order. Participants were not told about the presence of this regularity. A variable number of random letters were presented between two repetitions of the regular triplet, making this paradigm similar to a Hebb repetition task. Hence, in this Hebb-naming task, we predicted that if any learning of the triplet occurred, naming times for the predictable letters in the triplet would decrease as the number of triplet repetitions increased. Surprisingly, across four experiments, detection of the regularity only occurred under very specific experimental conditions and was far from a trivial task. Our study provides new evidence regarding the limits of statistical learning and the critical role of contextual information in the detection (or not) of repeated patterns.

Implicit Statistical Learning Across Modalities and Its Relationship With Reading in Childhood.

Implicit statistical learning (ISL) describes our ability to tacitly pick up regularities from our environment therefore, shaping our behavior. A broad understanding of ISL incorporates a great range of possible computations, which render it highly relevant to reading. In the light of this hypothesized relationship, ISL performance was explored in young (M = 8.47 years) typical readers (N = 31) across three different modalities (i.e., visual, auditory, and tactile) using the Artificial Grammar Learning (AGL) paradigm. Adopting repeated measures and correlational designs, the obtained data revealed modality constraints: (1) above-chance performance was observed on the visual and tactile tasks but not on the auditory task, (2) there was no significant correlation of ISL performance across modalities, and (3) split-half reliability of visual and auditory tasks was reasonably high, yet for the tactile task it was close to zero. Evaluating the relation between ISL ability and language skills, we observed a positive correlation between visual ISL performance and phonological awareness. We discuss these findings in view of current perspectives on the nature of ISL and its potential involvement in mastering successful (i.e., accurate and fluent) reading.

What Determines Visual Statistical Learning Performance? Insights From Information Theory.

In order to extract the regularities underlying a continuous sensory input, the individual elements constituting the stream have to be encoded and their transitional probabilities (TPs) should be learned. This suggests that variance in statistical learning (SL) performance reflects efficiency in encoding representations as well as efficiency in detecting their statistical properties. These processes have been taken to be independent and temporally modular, where first, elements in the stream are encoded into internal representations, and then the co-occurrences between them are computed and registered. Here, we entertain a novel hypothesis that one unifying construct-the rate of information in the sensory input-explains learning performance. This theoretical approach merges processes related to encoding of events and those related to learning their regularities into a single computational principle. We present data from two large-scale experiments with over 800 participants tested in support for this hypothesis, showing that rate of information in a visual stream clearly predicts SL performance, and that similar rate of information values leads to similar SL performance. We discuss the implications for SL theory and its relation to regularity learning.

What exactly is learned in visual statistical learning? Insights from Bayesian modeling.

It is well documented that humans can extract patterns from continuous input through Statistical Learning (SL) mechanisms. The exact computations underlying this ability, however, remain unclear. One outstanding controversy is whether learners extract global clusters from the continuous input, or whether they are tuned to local co-occurrences of pairs of elements. Here we adopt a novel framework to address this issue, applying a generative latent-mixture Bayesian model to data tracking SL as it unfolds online using a self-paced learning paradigm. This framework not only speaks to whether SL proceeds through computations of global patterns versus local co-occurrences, but also reveals the extent to which specific individuals employ these computations. Our results provide evidence for inter-individual mixture, with different reliance on the two types of computations across individuals. We discuss the implications of these findings for understanding the nature of SL and individual-differences in this ability.