The role of cross-modal associations in statistical learning

Our environment is richly structured, with objects producing correlated information within and across sensory modalities. A prominent challenge faced by our perceptual system is to learn such regularities. Here, we examined statistical learning and addressed learners’ ability to track transitional probabilities between elements in the auditory and visual modalities. Specifically, we investigated whether cross-modal information affects statistical learning within a single modality. Participants were familiarized with a statistically structured modality (e.g., either audition or vision) accompanied by different types of cues in a second modality (e.g., vision or audition). The results revealed that statistical learning within either modality is affected by cross-modal information, with learning being enhanced or reduced according to the type of cue provided in the second modality.     

Three Factors in L2 Reading Ability: Using Rauding Theory to Reanalyze a Study by Nassaji and Geva

This study investigated whether structured or unstructured questioning strategies, combined with two levels of domain knowledge and text coherence, significantly affect text memory and text learning. Results of this study suggest that high domain knowledge and structured questioning strategies are the most reliable predictors of text memory and text learning. Several main effects favoring high domain knowledge arose on text memory and, to a lesser extent, text learning measures. Significant results involving structured questioning techniques (i.e., signal words and generic question stems) were detected primarily through interaction effects and one main effect. While not always significantly superior, generic question stem participants outperformed their signal word and unguided questioning counterparts on the majority of text memory and text learning variables.     

Statistically Induced Chunking Recall: A Memory-Based Approach to Statistical Learning

The computations involved in statistical learning have long been debated. Here, we build on work suggesting that a basic memory process, chunking, may account for the processing of statistical regularities into larger units. Drawing on methods from the memory literature, we developed a novel paradigm to test statistical learning by leveraging a robust phenomenon observed in serial recall tasks: that short-term memory is fundamentally shaped by long-term distributional learning. In the statistically induced chunking recall (SICR) task, participants are exposed to an artificial language, using a standard statistical learning exposure phase. Afterward, they recall strings of syllables that either follow the statistics of the artificial language or comprise the same syllables presented in a random order. We hypothesized that if individuals had chunked the artificial language into word-like units, then the statistically structured items would be more accurately recalled relative to the random controls. Our results demonstrate that SICR effectively captures learning in both the auditory and visual modalities, with participants displaying significantly improved recall of the statistically structured items, and even recall specific trigram chunks from the input. SICR also exhibits greater test–retest reliability in the auditory modality and sensitivity to individual differences in both modalities than the standard two-alternative forced-choice task. These results thereby provide key empirical support to the chunking account of statistical learning and contribute a valuable new tool to the literature.     

Stimulus set size and statistical coverage of the grammar in artificial grammar learning

Adults and children acquire knowledge of the structure of their environment on the basis of repeated exposure to samples of structured stimuli. In the study of inductive learning, a straightforward issue is how much sample information is needed to learn the structure. The present study distinguishes between two measures for the amount of information in the sample: set size and the extent to which the set of exemplars statistically covers the underlying structure. In an artificial grammar learning experiment, learning was affected by the sample’s statistical coverage of the grammar, but not by its mere size. Our result suggests an alternative explanation of the set size effects on learning found in previous studies (McAndrews & Moscovitch, 1985; Meulemans & Van der Linden, 1997), because, as we argue, set size was confounded with statistical coverage in these studies. nt]mis|This research was supported by a grant from the Netherlands Organization for Scientific Research. We thank Jarry Porsius for his help with the data analyses.     

Statistical learning within and between modalities: pitting abstract against stimulus-specific representations

When learners encode sequential patterns and generalize their knowledge to novel instances, are they relying on abstract or stimulus-specific representations? Research on artificial grammar learning (AGL) has shown transfer of learning from one stimulus set to another, and such findings have encouraged the view that statistical learning is mediated by abstract representations that are independent of the sense modality or perceptual features of the stimuli. Using a novel modification of the standard AGL paradigm, we obtained data to the contrary. These experiments pitted abstract processing against stimulus-specific learning. The findings show that statistical learning results in knowledge that is stimulus-specific rather than abstract. They show furthermore that learning can proceed in parallel for multiple input streams along separate perceptual dimensions or sense modalities. We conclude that learning sequential structure and generalizing to novel stimuli inherently involve learning mechanisms that are closely tied to the perceptual characteristics of the input.     

Domain-specific learning of grammatical structure in musical and phonological sequences

Artificial grammar learning depends on acquisition of abstract structural representations rather than domain-specific representational constraints, or so many studies tell us. Using an artificial grammar task, we compared learning performance in two stimulus domains in which respondents have differing tacit prior knowledge. We found that despite grammatically identical sequence structures, learning was better for harmonically related chord sequences than for letter name sequences or harmonically unrelated chord sequences. We also found transfer effects within the musical and letter name tasks, but not across the domains. We conclude that knowledge acquired in implicit learning depends not only on abstract features of structured stimuli, but that the learning of regularities is in some respects domain-specific and strongly linked to particular features of the stimulus domain.     

On the automaticity of pure perceptual sequence learning

We investigated the automaticity of implicit sequence learning by varying perceptual load in a pure perceptual sequence learning paradigm. Participants responded to the randomly changing identity of a target, while the irrelevant target location was structured. In Experiment 1, the target was presented under low or high perceptual load during training, whereas testing occurred without load. Unexpectedly, no sequence learning was observed. In Experiment 2, perceptual load was introduced during the test phase to determine whether load is required to express perceptual knowledge. Learning itself was unaffected by visuospatial demands, but more learning was expressed under high load test conditions. In Experiment 3, we demonstrated that perceptual load is not required for the acquisition of perceptual sequence knowledge. These findings suggest that perceptual load does not mediate the perceptual sequence learning process itself, supporting the automaticity of implicit learning, but is mandatory for the expression of pure perceptual sequence knowledge.     

Object representations are biased toward each other through statistical learning

The visual system is remarkably efficient at extracting regularities from the environment through statistical learning. While such extraction has extensive consequences on cognition, it is unclear how statistical learning shapes the representations of the individual objects that comprise the regularities. Here we examine how statistical learning alters object representations. In three experiments, participants were exposed to either random arrays containing objects in a random order, or structured arrays containing object pairs where two objects appeared next to each other in fixed spatial or temporal configurations. After exposure, one object in each pair was briefly presented and participants judged the location or the orientation of the object without seeing the other object in the pair. We found that when an object reliably appeared next to another object in space, it was judged as being closer to the other object in space even though the other object was never presented (Experiments 1 and 2). Likewise, when an object reliably preceded another object in time, its orientation was biased toward the orientation of the other object even though the other object was never presented (Experiment 3). These results demonstrated that statistical learning fundamentally shapes how individual objects are represented in visual memory, by biasing the representation of one object toward its co-occurring partner. Importantly, participants in all experiments were not explicitly aware of the regularities. Thus, the bias in object representations was implicit. The current study reveals a novel impact of statistical learning on object representation: spatially co-occurring objects are represented as being closer in space, and temporally co-occurring objects are represented as having more similar features.     

Learning From Electronic Texts: Effects of Interactive Overviews for Information Access

This research investigated the effects of structured and unstructured overviews on learning from electronic text. It compared review strategies and text representations developed for traditional continuous text and for electronic text with a structured overview (a hierarchically organized map of the text content) and an unstructured overview (a menu-like content listing). Both overviews produced better memory for text topics and better breadth of recall compared with traditional text. However, when readers lacked specific study goals, the unstructured overview produced a more fragmented knowledge structure. This suggests that minimally structured overviews may be better for specific learning tasks than for gaining familiarity with new topics. The structured overview was easier for readers to recall and use. It also increased the amount of review when readers lacked specific study goals, suggesting that the overview design influences the extent to which readers take advantage of the flexibility provided by electronic text overviews.     

Estimating causal strength: the role of structural knowledge and processing effort.

The strength of causal relations typically must be inferred on the basis of statistical relations between observable events. This article focuses on the problem that there are multiple ways of extracting statistical information from a set of events. In causal structures involving a potential cause, an effect and a third related event, the assumed causal role of this third event crucially determines whether it is appropriate to control for this event when making causal assessments between the potential cause and the effect. Three experiments show that prior assumptions about the causal roles of the learning events affect the way contingencies are assessed with otherwise identical learning input. However, prior assumptions about causal roles is only one factor influencing contingency estimation. The experiments also demonstrate that processing effort affects the way statistical information is processed. These findings provide further evidence for the interaction between bottom-up and top-down influences in the acquisition of causal knowledge. They show that, apart from covariation information or knowledge about mechanisms, abstract assumptions about causal structures also may affect the learning process.     

Insight and strategy in multiple-cue learning

In multiple-cue learning (also known as probabilistic category learning) people acquire information about cue-outcome relations and combine these into predictions or judgments. Previous researchers claimed that people can achieve high levels of performance without explicit knowledge of the task structure or insight into their own judgment policies. It has also been argued that people use a variety of suboptimal strategies to solve such tasks. In three experiments the authors reexamined these conclusions by introducing novel measures of task knowledge and self-insight and using "rolling regression" methods to analyze individual learning. Participants successfully learned a four-cue probabilistic environment and showed accurate knowledge of both the task structure and their own judgment processes. Learning analyses suggested that the apparent use of suboptimal strategies emerges from the incremental tracking of statistical contingencies in the environment.     

Verbal knowledge, working memory, and processing speed as predictors of verbal learning in older adults

The present study aimed at modeling individual differences in a verbal learning task by means of a latent structured growth curve approach based on an exponential function that yielded 3 parameters: initial recall, learning rate, and asymptotic performance. Three cognitive variables-speed of information processing, verbal knowledge, working memory-and the participant's age were included in the model in order to explain individual differences in the learning parameters. The data come from the second wave of the Zurich Longitudinal Study on Cognitive Aging (D. Zimprich, Martin, et al., 2008) comprising 334 participants ranging in age from 66 to 81 years (M = 74.43, SD = 4.41). Among the logistic, the Gompertz, and the hyperbolic function, the exponential function described the data best. Reliable individual differences were found in all 3 learning parameters. The cognitive predictor variables affected the verbal learning parameters differentially: All 3 predictors affected positively initial recall, the asymptotic performance increased with better working memory and faster processing speed, and the learning rate was positively associated with verbal knowledge only. Age did not affect the learning parameters but correlated negatively with working memory and processing speed. The finding of large and reliable individual differences in learning is seen as evidence that the potential for positive change, or plasticity in adulthood is maintained and that it is worthwhile to enhance the determinants of learning or learning itself.     

All words are not created equal: expectations about word length guide infant statistical learning

Infants have been described as ‘statistical learners’ capable of extracting structure (such as words) from patterned input (such as language). Here, we investigated whether prior knowledge influences how infants track transitional probabilities in word segmentation tasks. Are infants biased by prior experience when engaging in sequential statistical learning? In a laboratory simulation of learning across time, we exposed 9- and 10-month-old infants to a list of either disyllabic or trisyllabic nonsense words, followed by a pause-free speech stream composed of a different set of disyllabic or trisyllabic nonsense words. Listening times revealed successful segmentation of words from fluent speech only when words were uniformly disyllabic or trisyllabic throughout both phases of the experiment. Hearing trisyllabic words during the pre-exposure phase derailed infants’ abilities to segment speech into disyllabic words, and vice versa. We conclude that prior knowledge about word length equips infants with perceptual expectations that facilitate efficient processing of subsequent language input.     

How to build a connectionist idiot (savant)

This paper describes the development of a connectionist model of an idiot savant date calculator using a multi-layer back-propagation network. The model mimics the behaviour of idiot savant date calculators in learning to perform the date calculation task without the need either to develop arithmetical skills or to encode explicit knowledge about regularities in the structure of dates. However, the performance of the model highlights limitations on the use of back-propagation as a general-purpose learning algorithm in complex problem domains. Even with a relatively simple problem like date calculation back-propagation can only succeed when forced to take a highly structured and modular approach to learning.     

Equal Sharing and Shared Meaning: The Development of Fraction Concepts in a First-Grade Classroom

Work on learning from a situative point of view suggests that to understand how children's thinking about a domain such as fractions develops, research needs to take into account the socially organized ways of thinking in which children participate during instruction. The aim of the study reported in this article was to explore children's fraction learning in a 1st-grade classroom in which the teacher elicited and built on children's informal knowledge of fractions. Instruction revolved around children's solutions and discussions of equal-sharing tasks. The study provides an account of children's learning that examines the relation between classroom talk and children's evolving fraction concepts, with a focus on the analysis of several key classroom interactions that resulted in cognitive change. Pretests and posttests indicated that children's understanding of fractions changed in important ways. The results suggest that how children think about fractions is influenced not only by how their own knowledge is structured but, perhaps more profoundly, by how the context for thinking about and discussing fractions is structured.     

The interactive effects of prior knowledge and text structure on memory for cognitive psychology texts

Background: Interest in the interactive effects of prior knowledge and text structure on learning from text is increasing but experimental manipulations of knowledge and structure variables often produce findings that do not help teachers to select expository texts for students. Aims: We aimed to extend the ecological validity of previous findings by asking students with a high or low level of discipline‐relevant knowledge to read texts characteristic of those they would normally encounter. A compensation effect was hypothesized, where high prior knowledge would compensate for a lack of text structure and text structure would compensate for a lack of prior knowledge. Samples: One hundred and ninety‐five undergraduate psychology students (144 Year 1 students and 51 Year 3 students) were allocated to a high knowledge (HiPK) or low knowledge (LoPK) group on the basis of their performance on a word association test. Methods: Participants were randomly assigned to one of five text structure groups (compare/contrast, sequence, classification, enumeration, generalization) and asked to study two cognitive psychology passages before recalling the main points of the text immediately afterwards and after a delay of 2 weeks. The five text structures were placed on an ‘organizational continuum’ according to the degree of structure/organization in the 10 passages. Results: A compensation effect did not emerge. Recall was high when texts were well structured and readers had prior knowledge, but recall was poor when texts were less structured, regardless of the level of prior knowledge. Conclusions: Readers benefit most from texts that challenge pre‐existing mental representations.     

How does prior knowledge affect implicit and explicit concept learning?

Two experiments investigated the effect of prior knowledge on implicit and explicit learning. Implicit as opposed to explicit learning is sometimes characterized as unselective or purely statistical. During training, one group of participants was presented with category exemplars whose features could be tied together by integrative knowledge, whereas another group saw category exemplars with unrelated feature combinations. Half of the participants in each group learned these categories under a secondary-task condition (meant to discourage explicit learning), and the remaining half performed the categorization task under a single-task condition (meant to favour explicit learning). In a test phase, participants classified only the individual features of the training exemplars. Secondary- as opposed to single-task conditions impaired explicit but not implicit knowledge (as determined by subjective measures). Importantly, prior knowledge resulted in increased amounts of both implicit and explicit knowledge.     

Implicit learning and statistical learning: one phenomenon, two approaches

The domain-general learning mechanisms elicited in incidental learning situations are of potential interest in many research fields, including language acquisition, object knowledge formation and motor learning. They have been the focus of studies on implicit learning for nearly 40 years. Stemming from a different research tradition, studies on statistical learning carried out in the past 10 years after the seminal studies by Saffran and collaborators, appear to be closely related, and the similarity between the two approaches is strengthened further by their recent evolution. However, implicit learning and statistical learning research favor different interpretations, focusing on the formation of chunks and statistical computations, respectively. We examine these differing approaches and suggest that this divergence opens up a major theoretical challenge for future studies.     

Integrating physical constraints in statistical inference by 11-month-old infants

Much research on cognitive development focuses either on early-emerging domain-specific knowledge or domain-general learning mechanisms. However, little research examines how these sources of knowledge interact. Previous research suggests that young infants can make inferences from samples to populations (Xu & Garcia, 2008) and 11- to 12.5-month-old infants can integrate psychological and physical knowledge in probabilistic reasoning (Teglas, Girotto, Gonzalez, & Bonatti, 2007; Xu & Denison, 2009). Here, we ask whether infants can integrate a physical constraint of immobility into a statistical inference mechanism. Results from three experiments suggest that, first, infants were able to use domain-specific knowledge to override statistical information, reasoning that sometimes a physical constraint is more informative than probabilistic information. Second, we provide the first evidence that infants are capable of applying domain-specific knowledge in probabilistic reasoning by using a physical constraint to exclude one set of objects while computing probabilities over the remaining sets.     

Reflecting talks: a pedagogical model in the learning organization

The aim of the article is to deepen the understanding of how a pedagogical model for reflecting talks can be used in order to make sustainable learning part of the daily work in the learning organization. From an interactive research approach, we have together with a project management group in a European Social Fund project worked with sustainable learning and knowledge development. Empirical data has been collected at the implementation of ten reflecting talks about sustainable equality. The results of the study lead to a strategy for how sustainable learning can become part of the daily work at a workplace. The strategy is constituted by a pedagogical model for reflecting talks, which clearly shows how sustainable learning in an organization can be structured. The core of the pedagogical model for the reflecting talks where both practically applied and theoretically anchored knowledge are important components. The learning process is based on observation, reflection, analysis and discussion of concrete situations/events. The models rests on four basic conditions; pedagogical competence, a delimited problem area, the learning group and timeframes. The model can be used in the daily work at short dialogues or at more penetrating discussions.