Social facilitation during the initial stage of motor learning: a re-examination of Martens' audience study

This study partially replicated Martens' (1969a) social-facilitation study of motor behavior. His very robust performance findings provided impressive confirmation for Zajonc's hypothesis, and his arousal findings have since been used as evidence for a nonlearned-drive basis for social facilitation. The present study also extended Martens' investigation by examining the separate and combined effects of an audience and videotape camera. The effects due to the presence of the audience and camera were not additive; instead, the audience detrimentally affected subjects' performance consistency and the camera resulted in more trials with errors greater than 30 msec after the performance criteria had been attained. Martens' most robust findings for constant error were not replicated, nor were some of his physiological arousal findings. His pattern of constant error results over all trials is atypical of known learning strategies that subjects use to reduce error over successive trials. Overall, audience effects accounted for only a very small portion of the variance.     

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.     

Implicit statistical learning is directly associated with the acquisition of syntax

This article reports on an individual differences study that investigated the role of implicit statistical learning in the acquisition of syntax in children. One hundred children ages 4 years 5 months through 6 years 11 months completed a test of implicit statistical learning, a test of explicit declarative learning, and standardized tests of verbal and nonverbal ability. They also completed a syntactic priming task, which provided a dynamic index of children's facility to detect and respond to changes in the input frequency of linguistic structure. The results showed that implicit statistical learning ability was directly associated with the long-term maintenance of the primed structure. The results constitute the first empirical demonstration of a direct association between implicit statistical learning and syntactic acquisition in children.     

Executive deficits appearing in the initial stage of Alzheimer's disease

The presence of executive deficits has been sought at a stage of Alzheimer's disease where currently used neuropsychological batteries could not yet distinguish Alzheimer's patients from normal age- and education-matched controls. This study shows that, at this early stage, those patients that 6 months later are found to show clear signs of Alzheimer's had been significantly worse than normal controls in an executive function task adapted from the Brown-Peterson procedure.     

Constraint on the semantic flexibility in visual statistical learning

We examined whether the expression of visual statistical learning (VSL) is flexible at the superordinate-categorical level. In the familiarization phase, participants viewed a sequence of line drawings. In the test phase, participants observed two test sequences (statistically related triplets versus unrelated foils) that consisted of the same objects as those presented during the familiarization phase (the same condition) or different objects that shared the same categorical information with drawings during the familiarization (the different condition). They then decided whether the first or the second sequence was more familiar. The results of Experiment 1 showed greater familiarity above chance levels for statistically related triplets only in the same condition. In Experiment 2, even where the word stimuli representing each superordinate-level category were included in the test phase (the categorical condition), the results showed VSL only in the same condition. Our findings suggest that the semantic flexibility of VSL is limited to the basic-level category.     

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.     

Context-gated statistical learning and its role in visual-saccadic decisions

Adaptive behavior in a nonstationary world requires humans to learn and track the statistics of the environment. We examined the mechanisms of adaptation in a nonstationary environment in the context of visual-saccadic inhibition of return (IOR). IOR is adapted to the likelihood that return locations will be refixated in the near future. We examined 2 potential learning mechanisms underlying adaptation: (a) a local tracking or priming mechanism that facilitates behavior that is consistent with recent experience and (b) a mechanism that supports retrieval of knowledge of the environmental statistics based on the contextual features of the environment. Participants generated sequences of 2 saccadic eye movements in conditions where the probability that the 2nd saccade was directed back to the previously fixated location varied from low (.17) to high (.50). In some conditions, the contingency was signaled by a contextual cue (the shape of the movement cue). Adaptation occurred in the absence of contextual signals but was more pronounced in the presence of contextual cues. Adaptation even occurred when different contingencies were randomly intermixed, showing the parallel formation of multiple associations between context and statistics. These findings are accounted for by an evidence accumulation framework in which the resting baseline of decision alternatives is adjusted on a trial-by-trial basis. This baseline tracks the subjective prior beliefs about the behavioral relevance of the different alternatives and is updated on the basis of the history of recent events and the contextual features of the current environment.     

Dissociation of sensory facilitation and decision bias in statistical learning

ABSTRACT

The detection of regularities in the sensory environment, known as statistical learning, is an important brain function that has been observed in many experimental contexts. In these experiments, statistical learning of patterned sensory stimulation leads to improvements in the speed and/or accuracy with which subsequent stimuli are recognized. That is, statistical learning facilitates the transformation of sensory stimuli into motor responses, but the mechanism by which this occurs is unclear. Statistical learning could improve the efficiency of sensory processing, or it could bias responses toward particular outcomes. The distinction is important, as these different hypotheses imply different functions and different neural substrates for statistical learning. Here we address this problem by studying statistical learning as a decision-making process, which allows us to leverage the extensive computational literature on this topic. Specifically we describe a method for applying the Diffusion Decision Model (DDM) to isolate different sensory and cognitive processes associated with decision-making. The results indicate that statistical learning improves performance on a visual learning task in two distinct ways: by altering the efficiency of sensory processing and by introducing biases in the decision-making process. By fitting the parameters of the DDM to data from individual subjects, we find that the prominence of these two factors differed substantially across the population, and that these differences were predictive of individual performance on the psychophysical task. Overall, these results indicate that different cognitive processes can be recruited by statistical learning, and that the DDM is a powerful framework for detecting these influences.     

统计学习的认知神经机制及其与语言的关系

统计学习是指个体在连续刺激流中发现转移概率等统计规律的过程, 在Saffran等(1996)的经典婴儿语音切分研究中首次被提出。大量研究证实了统计学习的普遍存在, 近期学界开始关注统计学习的特异性及其对认知的影响, 尤其是从学习过程及其特异性两个方面阐述统计学习的认知神经机制并揭示其和语言的交互作用。未来应从脑和行为的多模态数据视角, 丰富统计学习结果的行为和神经指标, 考察不同类型统计学习过程的动态神经活动模式, 建立统计学习行为和脑的关联, 深化对统计学习认知神经机制的认识, 在统计学习与语言交互作用的基础上, 从成人二语学习切入结合音乐统计学习训练探讨促进语言学习的统计学习干预手段。     

Electrophysiological evidence of heterogeneity in visual statistical learning in young children with ASD

Statistical learning is characterized by detection of regularities in one's environment without an awareness or intention to learn, and it may play a critical role in language and social behavior. Accordingly, in this study we investigated the electrophysiological correlates of visual statistical learning in young children with autism spectrum disorder (ASD) using an event‐related potential shape learning paradigm, and we examined the relation between visual statistical learning and cognitive function. Compared to typically developing (TD) controls, the ASD group as a whole showed reduced evidence of learning as defined by N1 (early visual discrimination) and P300 (attention to novelty) components. Upon further analysis, in the ASD group there was a positive correlation between N1 amplitude difference and non‐verbal IQ, and a positive correlation between P300 amplitude difference and adaptive social function. Children with ASD and a high non‐verbal IQ and high adaptive social function demonstrated a distinctive pattern of learning. This is the first study to identify electrophysiological markers of visual statistical learning in children with ASD. Through this work we have demonstrated heterogeneity in statistical learning in ASD that maps onto non‐verbal cognition and adaptive social function.     

Testing the limits of statistical learning for word segmentation

Past research has demonstrated that infants can rapidly extract syllable distribution information from an artificial language and use this knowledge to infer likely word boundaries in speech. However, artificial languages are extremely simplified with respect to natural language. In this study, we ask whether infants’ ability to track transitional probabilities between syllables in an artificial language can scale up to the challenge of natural language. We do so by testing both 5.5‐ and 8‐month‐olds’ ability to segment an artificial language containing four words of uniform length (all CVCV) or four words of varying length (two CVCV, two CVCVCV). The transitional probability cues to word boundaries were held equal across the two languages. Both age groups segmented the language containing words of uniform length, demonstrating that even 5.5‐month‐olds are extremely sensitive to the conditional probabilities in their environment. However, neither age group succeeded in segmenting the language containing words of varying length, despite the fact that the transitional probability cues defining word boundaries were equally strong in the two languages. We conclude that infants’ statistical learning abilities may not be as robust as earlier studies have suggested.     

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.     

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.     

The role of partial knowledge in statistical word learning

A critical question about the nature of human learning is whether it is an all-or-none or a gradual, accumulative process. Associative and statistical theories of word learning rely critically on the later assumption: that the process of learning a word’s meaning unfolds over time. That is, learning the correct referent for a word involves the accumulation of partial knowledge across multiple instances. Some theories also make an even stronger claim: Partial knowledge of one word–object mapping can speed up the acquisition of other word–object mappings. We present three experiments that test and verify these claims by exposing learners to two consecutive blocks of cross-situational learning, in which half of the words and objects in the second block were those that participants failed to learn in Block 1. In line with an accumulative account, Re-exposure to these mis-mapped items accelerated the acquisition of both previously experienced mappings and wholly new word–object mappings. But how does partial knowledge of some words speed the acquisition of others? We consider two hypotheses. First, partial knowledge of a word could reduce the amount of information required for it to reach threshold, and the supra-threshold mapping could subsequently aid in the acquisition of new mappings. Alternatively, partial knowledge of a word’s meaning could be useful for disambiguating the meanings of other words even before the threshold of learning is reached. We construct and compare computational models embodying each of these hypotheses and show that the latter provides a better explanation of the empirical data.     

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.     

An analysis of learning data which distinguishes between initial preference and learning ability

Several sets of learning data furnished by I. Krechevsky have been analyzed in terms of meaningful parameters of the learning curve, and the changes in the frequency distributions of these parameters with changes in the experimental conditions have been studied. One of the parameters represents the animal's initial preference for the light or dark, the other represents learning ability. The analysis shows that destruction of about ten or fifteen per cent. of the cortex, increases the animal's preference for the light and decreases the learning ability slightly. By ordinary methods of analysis, it is not possible to discover thatboth initial preference and learning ability have been changed by any given factor.The author wishes to acknowledge financial assistance from the Social Science Research Committee of the University of Chicago in the completion of this study.     

Working memory load in the initial learning phase facilitates relearning: a study of vocabulary learning

In this study, the effect of working memory load in the initial learning phase on the relearning phase was examined. Exp. 1 examined the effect of articulatory suppression in paired associative learning with a relearning method. In the learning phase, 28 participants (M age=21.8 yr., SD=2.1) learned all word-nonword associations under conditions of articulatory suppression or simple tapping. After a delay, they answered a cued recall task in a nonsuppressed condition and a relearning task in a simple tapping condition. The subjects who learned under the simple tapping condition in the learning phase required significantly more trials in the relearning phase. In Exp. 2, 28 participants (M age=20.8 yr., SD=1.5) participated, and the result was replicated. These results suggested that working memory load facilitates relearning. Results are best explained by contextual interference (Battig) rather than by the less-is-more hypothesis of Newport.