Visual statistical learning in the newborn infant

Statistical learning – implicit learning of statistical regularities within sensory input – is a way of acquiring structure within continuous sensory environments. Statistics computation, initially shown to be involved in word segmentation, has been demonstrated to be a general mechanism that operates across domains, across time and space, and across species. Recently, statistical leaning has been reported to be present even at birth when newborns were tested with a speech stream. The aim of the present study was to extend this finding, by investigating whether newborns’ ability to extract statistics operates in multiple modalities, as found for older infants and adults. Using the habituation procedure, two experiments were carried out in which visual sequences were presented. Results demonstrate that statistical learning is a general mechanism that extracts statistics across domain since the onset of sensory experience. Intriguingly, present data reveal that newborn learner’s limited cognitive resources constrain the functioning of statistical learning, narrowing the range of what can be learned.     

Timing is everything: Changes in presentation rate have opposite effects on auditory and visual implicit statistical learning

Implicit statistical learning (ISL) is exclusive to neither a particular sensory modality nor a single domain of processing. Even so, differences in perceptual processing may substantially affect learning across modalities. In three experiments, statistically equivalent auditory and visual familiarizations were presented under different timing conditions that either facilitated or disrupted temporal processing (fast or slow presentation rates). We find an interaction of rate and modality of presentation: At fast rates, auditory ISL was superior to visual. However, at slow presentation rates, the opposite pattern of results was found: Visual ISL was superior to auditory. Thus, we find that changes to presentation rate differentially affect ISL across sensory modalities. Additional experiments confirmed that this modality-specific effect was not due to cross-modal interference or attentional manipulations. These findings suggest that ISL is rooted in modality-specific, perceptually based processes.     

Experience and sentence processing: statistical learning and relative clause comprehension

Many explanations of the difficulties associated with interpreting object relative clauses appeal to the demands that object relatives make on working memory. MacDonald and Christiansen [MacDonald, M. C., & Christiansen, M. H. (2002). Reassessing working memory: Comment on Just and Carpenter (1992) and Waters and Caplan (1996). Psychological Review, 109, 35-54] pointed to variations in reading experience as a source of differences, arguing that the unique word order of object relatives makes their processing more difficult and more sensitive to the effects of previous experience than the processing of subject relatives. This hypothesis was tested in a large-scale study manipulating reading experiences of adults over several weeks. The group receiving relative clause experience increased reading speeds for object relatives more than for subject relatives, whereas a control experience group did not. The reading time data were compared to performance of a computational model given different amounts of experience. The results support claims for experience-based individual differences and an important role for statistical learning in sentence comprehension processes.     

The link between statistical segmentation and word learning in adults

Many studies have shown that listeners can segment words from running speech based on conditional probabilities of syllable transitions, suggesting that this statistical learning could be a foundational component of language learning. However, few studies have shown a direct link between statistical segmentation and word learning. We examined this possible link in adults by following a statistical segmentation exposure phase with an artificial lexicon learning phase. Participants were able to learn all novel object-label pairings, but pairings were learned faster when labels contained high probability (word-like) or non-occurring syllable transitions from the statistical segmentation phase than when they contained low probability (boundary-straddling) syllable transitions. This suggests that, for adults, labels inconsistent with expectations based on statistical learning are harder to learn than consistent or neutral labels. In contrast, a previous study found that infants learn consistent labels, but not inconsistent or neutral labels.     

Irrelevant speech effects and statistical learning

Immediate serial recall of visually presented verbal stimuli is impaired by the presence of irrelevant auditory background speech, the so-called irrelevant speech effect. Two of the three main accounts of this effect place restrictions on when it will be observed, limiting its occurrence either to items processed by the phonological loop (the phonological loop hypothesis) or to items that are not too dissimilar from the irrelevant speech (the feature model). A third, the object-oriented episodic record (O-OER) model, requires only that the memory task involves seriation. The present studies test these three accounts by examining whether irrelevant auditory speech will interfere with a task that does not involve the phonological loop, does not use stimuli that are compatible with those to be remembered, but does require seriation. Two experiments found that irrelevant speech led to lower levels of performance in a visual statistical learning task, offering more support for the O-OER model and posing a challenge for the other two accounts.     

Fine-grained sensitivity to statistical information in adult word learning

A language learner trying to acquire a new word must often sift through many potential relations between particular words and their possible meanings. In principle, statistical information about the distribution of those mappings could serve as one important source of data, but little is known about whether learners can in fact track multiple word-referent mappings, and, if they do, the precision with which they can represent those statistics. To test this, two experiments contrasted a pair of possibilities: that learners encode the fine-grained statistics of mappings in the input - both high- and low-frequency mappings - or, alternatively, that only high frequency mappings are represented. Participants were briefly trained on novel word-novel object pairs combined with varying frequencies: some objects were paired with one word, other objects with multiple words with differing frequencies (ranging from 10% to 80%). Results showed that participants were exquisitely sensitive to very small statistical differences in mappings. The second experiment showed that word learners' representation of low frequency mappings is modulated as a function of the variability in the environment. Implications for Mutual Exclusivity and Bayesian accounts of word learning are discussed.     

Visual learning of statistical relations among nonadjacent features: Evidence for structural encoding

Recent results suggest that observers can learn, unsupervised, the co-occurrence of independent shape features in viewed patterns (e.g., Fiser & Aslin, 2001). A critical question with regard to these findings is whether learning is driven by a structural, rule-based encoding of spatial relations between distinct features or by a pictorial, template-like encoding, in which spatial configurations of features are embedded in a “holistic” fashion. In two experiments, we test whether observers can learn combinations of features when the paired features are separated by an intervening spatial “gap”, in which other, unrelated features can appear. This manipulation both increases task difficulty and makes it less likely that the feature combinations are encoded simply as larger unitary features. Observers exhibited learning consistent with earlier studies, suggesting that unsupervised learning of compositional structure is based on the explicit encoding of spatial relations between separable visual features. More generally, these results provide support for compositional structure in visual representation.     

Dissociable behavioural outcomes of visual statistical learning

ABSTRACT

Statistical learning refers to the extraction of probabilistic relationships between stimuli and is increasingly used as a method to understand learning processes. However, numerous cognitive processes are sensitive to the statistical relationships between stimuli and any one measure of learning may conflate these processes; to date little research has focused on differentiating these processes. To understand how multiple processes underlie statistical learning, here we compared, within the same study, operational measures of learning from different tasks that may be differentially sensitive to these processes. In Experiment 1, participants were visually exposed to temporal regularities embedded in a stream of shapes. Their task was to periodically detect whether a shape, whose contrast was staircased to a threshold level, was present or absent. Afterwards, they completed a search task, where statistically predictable shapes were found more quickly. We used the search task to label shape pairs as “learned” or “non-learned”, and then used these labels to analyse the detection task. We found a dissociation between learning on the search task and the detection task where only non-learned pairs showed learning effects in the detection task. This finding was replicated in further experiments with recognition memory (Experiment 2) and associative learning tasks (Experiment 3). Taken together, these findings are consistent with the view that statistical learning may comprise a family of processes that can produce dissociable effects on different aspects of behaviour.     

音乐学习对语言加工的促进作用

音乐学习可以增强个体的艺术素养,同时,音乐学习对其他认知加工过程尤其是语言学习还有正面的促进作用。近年来国内外学者主要围绕着音乐家、音乐学习障碍个体开展了一系列相关工作,同时还进行了相关的追踪研究。值得注意的是,有关音乐学习促进语言加工的实证依据主要来自横断研究,还需要更多的纵向追踪研究来进一步提供因果关系的证据。这方面的工作将最终促进音乐学习与训练在教育与医疗领域的广泛应用。     

Effects of visual information on adults' and infants' auditory statistical learning

Infant and adult learners are able to identify word boundaries in fluent speech using statistical information. Similarly, learners are able to use statistical information to identify word-object associations. Successful language learning requires both feats. In this series of experiments, we presented adults and infants with audio-visual input from which it was possible to identify both word boundaries and word-object relations. Adult learners were able to identify both kinds of statistical relations from the same input. Moreover, their learning was actually facilitated by the presence of two simultaneously present relations. Eight-month-old infants, however, do not appear to benefit from the presence of regular relations between words and objects. Adults, like 8-month-olds, did not benefit from regular audio-visual correspondences when they were tested with tones, rather than linguistic input. These differences in learning outcomes across age and input suggest that both developmental and stimulus-based constraints affect statistical learning.     

Dissociation of rapid response learning and facilitation in perceptual and conceptual networks of person recognition

Repetition priming is a prominent example of non‐declarative memory, and it increases the accuracy and speed of responses to repeatedly processed stimuli. Major long‐hold memory theories posit that repetition priming results from facilitation within perceptual and conceptual networks for stimulus recognition and categorization. Stimuli can also be bound to particular responses, and it has recently been suggested that this rapid response learning, not network facilitation, provides a sound theory of priming of object recognition. Here, we addressed the relevance of network facilitation and rapid response learning for priming of person recognition with a view to advance general theories of priming. In four experiments, participants performed conceptual decisions like occupation or nationality judgments for famous faces. The magnitude of rapid response learning varied across experiments, and rapid response learning co‐occurred and interacted with facilitation in perceptual and conceptual networks. These findings indicate that rapid response learning and facilitation in perceptual and conceptual networks are complementary rather than competing theories of priming. Thus, future memory theories need to incorporate both rapid response learning and network facilitation as individual facets of priming.     

Linking sounds to meanings: Infant statistical learning in a natural language

The processes of infant word segmentation and infant word learning have largely been studied separately. However, the ease with which potential word forms are segmented from fluent speech seems likely to influence subsequent mappings between words and their referents. To explore this process, we tested the link between the statistical coherence of sequences presented in fluent speech and infants’ subsequent use of those sequences as labels for novel objects. Notably, the materials were drawn from a natural language unfamiliar to the infants (Italian). The results of three experiments suggest that there is a close relationship between the statistics of the speech stream and subsequent mapping of labels to referents. Mapping was facilitated when the labels contained high transitional probabilities in the forward and/or backward direction (Experiment 1). When no transitional probability information was available (Experiment 2), or when the internal transitional probabilities of the labels were low in both directions (Experiment 3), infants failed to link the labels to their referents. Word learning appears to be strongly influenced by infants’ prior experience with the distribution of sounds that make up words in natural languages.     

Comparison of decision learning models using the generalization criterion method

It is a hallmark of a good model to make accurate a priori predictions to new conditions ( Busemeyer & Wang, 2000 ). This study compared 8 decision learning models with respect to their generalizability. Participants performed 2 tasks (the Iowa Gambling Task and the Soochow Gambling Task), and each model made a priori predictions by estimating the parameters for each participant from 1 task and using those same parameters to predict on the other task. Three methods were used to evaluate the models at the individual level of analysis. The first method used a post hoc fit criterion, the second method used a generalization criterion for short-term predictions, and the third method again used a generalization criterion for long-term predictions. The results suggest that the models with the prospect utility function can make generalizable predictions to new conditions, and different learning models are needed for making short-versus long-term predictions on simple gambling tasks.     

The acquisition of allophonic rules: statistical learning with linguistic constraints

Phonological rules relate surface phonetic word forms to abstract underlying forms that are stored in the lexicon. Infants must thus acquire these rules in order to infer the abstract representation of words. We implement a statistical learning algorithm for the acquisition of one type of rule, namely allophony, which introduces context-sensitive phonetic variants of phonemes. This algorithm is based on the observation that different realizations of a single phoneme typically do not appear in the same contexts (ideally, they have complementary distributions). In particular, it measures the discrepancies in context probabilities for each pair of phonetic segments. In Experiment 1, we test the algorithm's performances on a pseudo-language and show that it is robust to statistical noise due to sampling and coding errors, and to non-systematic rule application. In Experiment 2, we show that a natural corpus of semiphonetically transcribed child-directed speech in French presents a very large number of near-complementary distributions that do not correspond to existing allophonic rules. These spurious allophonic rules can be eliminated by a linguistically motivated filtering mechanism based on a phonetic representation of segments. We discuss the role of a priori linguistic knowledge in the statistical learning of phonology.     

Redundant sensory information does not enhance sequence learning in the serial reaction time task

In daily life we encounter multiple sources of sensory information at any given moment. Unknown is whether such sensory redundancy in some way affects implicit learning of a sequence of events. In the current paper we explored this issue in a serial reaction time task. Our results indicate that redundant sensory information does not enhance sequence learning when all sensory information is presented at the same location (responding to the position and/or color of the stimuli; Experiment 1), even when the distinct sensory sources provide more or less similar baseline response latencies (responding to the shape and/or color of the stimuli; Experiment 2). These findings support the claim that sequence learning does not (necessarily) benefit from sensory redundancy. Moreover, transfer was observed between various sets of stimuli, indicating that learning was predominantly response-based.     

The effect of statistical learning on internal stimulus representations: Predictable items are enhanced even when not predicted

Statistical learning is the automatic and unconscious learning of environmental regularities and is a basic mechanism of learning in a variety of human perceptual and cognitive domains. Previous studies have mainly focused on the associative mechanisms of statistical learning. However, an unexplored question is whether the internal representations of individual stimuli are altered as their associations are learned. Using a temporal statistical learning paradigm, we examine this question across three experiments and find clear evidence that the internal representations of individual stimuli are differentially altered according to their degree of temporal predictability. These findings complement previous accounts of statistical learning and reveal an enriched mechanism of human learning, such that learning to associate items also enhances the representations of certain items relative to others.     

Modelling unsupervised online-learning of artificial grammars: Linking implicit and statistical learning

Humans rapidly learn complex structures in various domains. Findings of above-chance performance of some untrained control groups in artificial grammar learning studies raise questions about the extent to which learning can occur in an untrained, unsupervised testing situation with both correct and incorrect structures. The plausibility of unsupervised online-learning effects was modelled with n-gram, chunking and simple recurrent network models. A novel evaluation framework was applied, which alternates forced binary grammaticality judgments and subsequent learning of the same stimulus. Our results indicate a strong online learning effect for n-gram and chunking models and a weaker effect for simple recurrent network models. Such findings suggest that online learning is a plausible effect of statistical chunk learning that is possible when ungrammatical sequences contain a large proportion of grammatical chunks. Such common effects of continuous statistical learning may underlie statistical and implicit learning paradigms and raise implications for study design and testing methodologies.     

Instance‐based learning in dynamic decision making

This paper presents a learning theory pertinent to dynamic decision making (DDM) called instancebased learning theory (IBLT). IBLT proposes five learning mechanisms in the context of a decision‐making process: instance‐based knowledge, recognition‐based retrieval, adaptive strategies, necessity‐based choice, and feedback updates. IBLT suggests in DDM people learn with the accumulation and refinement of instances, containing the decision‐making situation, action, and utility of decisions. As decision makers interact with a dynamic task, they recognize a situation according to its similarity to past instances, adapt their judgment strategies from heuristic‐based to instance‐based, and refine the accumulated knowledge according to feedback on the result of their actions. The IBLT's learning mechanisms have been implemented in an ACT‐R cognitive model. Through a series of experiments, this paper shows how the IBLT's learning mechanisms closely approximate the relative trend magnitude and performance of human data. Although the cognitive model is bounded within the context of a dynamic task, the IBLT is a general theory of decision making applicable to other dynamic environments.     

Influences of credibility of testimony and strength of statistical evidence on children’s and adolescents’ reasoning

According to dual-process models that include analytic and heuristic modes of processing, analytic processing is often expected to become more common with development. Consistent with this view, on reasoning problems, adolescents are more likely than children to select alternatives that are backed by statistical evidence. It is shown here that this pattern depends on the quality of the statistical evidence and the quality of the testimonial that is the typical alternative to statistical evidence. In Experiment 1, 9- and 13-year-olds (N = 64) were presented with scenarios in which solid statistical evidence was contrasted with casual or expert testimonial evidence. When testimony was casual, children relied on it but adolescents did not; when testimony was expert, both children and adolescents relied on it. In Experiment 2, 9- and 13-year-olds (N = 83) were presented with scenarios in which casual testimonial evidence was contrasted with weak or strong statistical evidence. When statistical evidence was weak, children and adolescents relied on both testimonial and statistical evidence; when statistical evidence was strong, most children and adolescents relied on it. Results are discussed in terms of their implications for dual-process accounts of cognitive development.     

The time course of gaze bias in visual decision tasks

In three experiments, we used eyetracking to investigate the time course of biases in looking behaviour during visual decision making. Our study replicated and extended prior research by Shimojo, Simion, Shimojo, and Scheier (2003), and Simion and Shimojo (2006). Three groups of participants performed forced-choice decisions in a two-alternative free-viewing condition (Experiment 1a), a two-alternative gaze-contingent window condition (Experiment 1b), and an eight-alternative free-viewing condition (Experiment 1c). Participants viewed photographic art images and were instructed to select the one that they preferred (preference task), or the one that they judged to be photographed most recently (recency task). Across experiments and tasks, we demonstrated robust bias towards the chosen item in either gaze duration, gaze frequency or both. The present gaze bias effect was less task specific than those reported previously. Importantly, in the eight-alternative condition we demonstrated a very early gaze bias effect, which rules out a postdecision response-related explanation.