Primitive computations in speech processing

Neural Mechanisms of Learning and Consolidation of Morphologically Derived Words in a Novel Language: Evidence From Hebrew Speakers

We examined neural mechanisms associated with the learning of novel morphologically derived words in native Hebrew speakers within the Complementary Learning Systems (CLS) framework. Across four sessions, 28 participants were trained on an artificial language, which included two types of morphologically complex words: linear (root + suffix) with a salient structure, and non-linear (root interleaved with template), with a prominent derivational structure in participants' first language (L1). A third simple monomorphemic condition, which served as baseline, was also included. On the first and fourth sessions, training was followed by testing in an fMRI scanner. Our behavioural results showed decomposition of both types of complex words, with the linear structure more easily learned than the non-linear structure. Our fMRI results showed involvement of frontal areas, associated with decomposition, only for the non-linear condition, after just the first session. We also observed training-related increases in activation in temporal areas specifically for the non-linear condition, which was correlated with participants' L1 morphological awareness. These results demonstrate that morphological decomposition of derived words occurs in the very early stages of word learning, is influenced by L1 experience, and can facilitate word learning. However, in contrast to the CLS framework, we found no support for a shift from reliance on hippocampus to reliance on cortical areas in any of our conditions. Instead, our findings align more closely with recent theories showing a positive correlation between changes in hippocampus and cortical areas, suggesting that these representations co-exist and continue to interact with one another beyond initial learning.

Prosody outweighs statistics in 6-month-old German-learning infants' speech segmentation

It is well established that infants use various cues to find words within fluent speech from about 7 to 8 months of age. Research suggests that two main mechanisms support infants' speech segmentation: prosodic cues like the word stress patterns, and distributional cues like transitional probabilities (TPs). We tested 6-month-old German-learning infants' use of prosodic and statistical cues for speech segmentation in three experiments. In Experiment 1, infants were familiarized with an artificial language string where TPs signaled either word boundaries or iambic words-a stress pattern that is disfavored in German. Experiment 2 was a control and only the test phase was presented. In Experiment 3, prosodic cues were absent in the string and only TPs signaled word boundaries. All experiments included the same conditions at test: disyllabic words with high TPs in the string, words with low TPs and words with non-co-occurring syllables. Results showed that infants relied more strongly on prosodic cues than on TPs for word segmentation. Notably, no segmentation evidence emerged when prosodic cues were absent in the string. This finding underlines early impacts of language-specific structural properties on segmentation mechanisms.

Chunk boundaries disrupt dependency processing in an AG: Reconciling incremental processing and discrete sampling

Language is rooted in our ability to compose: We link words together, fusing their meanings. Links are not limited to neighboring words but often span intervening words. The ability to process these non-adjacent dependencies (NADs) conflicts with the brain's sampling of speech: We consume speech in chunks that are limited in time, containing only a limited number of words. It is unknown how we link words together that belong to separate chunks. Here, we report that we cannot-at least not so well. In our electroencephalography (EEG) study, 37 human listeners learned chunks and dependencies from an artificial grammar (AG) composed of syllables. Multi-syllable chunks to be learned were equal-sized, allowing us to employ a frequency-tagging approach. On top of chunks, syllable streams contained NADs that were either confined to a single chunk or crossed a chunk boundary. Frequency analyses of the EEG revealed a spectral peak at the chunk rate, showing that participants learned the chunks. NADs that cross boundaries were associated with smaller electrophysiological responses than within-chunk NADs. This shows that NADs are processed readily when they are confined to the same chunk, but not as well when crossing a chunk boundary. Our findings help to reconcile the classical notion that language is processed incrementally with recent evidence for discrete perceptual sampling of speech. This has implications for language acquisition and processing as well as for the general view of syntax in human language.

Hebbian learning can explain rhythmic neural entrainment to statistical regularities

In many domains, learners extract recurring units from continuous sequences. For example, in unknown languages, fluent speech is perceived as a continuous signal. Learners need to extract the underlying words from this continuous signal and then memorize them. One prominent candidate mechanism is statistical learning, whereby learners track how predictive syllables (or other items) are of one another. Syllables within the same word predict each other better than syllables straddling word boundaries. But does statistical learning lead to memories of the underlying words-or just to pairwise associations among syllables? Electrophysiological results provide the strongest evidence for the memory view. Electrophysiological responses can be time-locked to statistical word boundaries (e.g., N400s) and show rhythmic activity with a periodicity of word durations. Here, I reproduce such results with a simple Hebbian network. When exposed to statistically structured syllable sequences (and when the underlying words are not excessively long), the network activation is rhythmic with the periodicity of a word duration and activation maxima on word-final syllables. This is because word-final syllables receive more excitation from earlier syllables with which they are associated than less predictable syllables that occur earlier in words. The network is also sensitive to information whose electrophysiological correlates were used to support the encoding of ordinal positions within words. Hebbian learning can thus explain rhythmic neural activity in statistical learning tasks without any memory representations of words. Learners might thus need to rely on cues beyond statistical associations to learn the words of their native language. RESEARCH HIGHLIGHTS: Statistical learning may be utilized to identify recurring units in continuous sequences (e.g., words in fluent speech) but may not generate explicit memory for words. Exposure to statistically structured sequences leads to rhythmic activity with a period of the duration of the underlying units (e.g., words). I show that a memory-less Hebbian network model can reproduce this rhythmic neural activity as well as putative encodings of ordinal positions observed in earlier research. Direct tests are needed to establish whether statistical learning leads to declarative memories for words.

Of words and whistles: Statistical learning operates similarly for identical sounds perceived as speech and non-speech

Statistical learning is an ability that allows individuals to effortlessly extract patterns from the environment, such as sound patterns in speech. Some prior evidence suggests that statistical learning operates more robustly for speech compared to non-speech stimuli, supporting the idea that humans are predisposed to learn language. However, any apparent statistical learning advantage for speech could be driven by signal acoustics, rather than the subjective perception per se of sounds as speech. To resolve this issue, the current study assessed whether there is a statistical learning advantage for ambiguous sounds that are subjectively perceived as speech-like compared to the same sounds perceived as non-speech, thereby controlling for acoustic features. We first induced participants to perceive sine-wave speech (SWS)-a degraded form of speech not immediately perceptible as speech-as either speech or non-speech. After this induction phase, participants were exposed to a continuous stream of repeating trisyllabic nonsense words, composed of SWS syllables, and then completed an explicit familiarity rating task and an implicit target detection task to assess learning. Critically, participants showed robust and equivalent performance on both measures, regardless of their subjective speech perception. In contrast, participants who perceived the SWS syllables as more speech-like showed better detection of individual syllables embedded in speech streams. These results suggest that speech perception facilitates processing of individual sounds, but not the ability to extract patterns across sounds. Our findings suggest that statistical learning is not influenced by the perceived linguistic relevance of sounds, and that it may be conceptualized largely as an automatic, stimulus-driven mechanism.

Decomposition in early stages of learning novel morphologically derived words: The impact of linear vs. non-linear structure

We examined whether morphological decomposition takes place in early stages of learning a novel language, and whether morphological structure (linear vs. non-linear) influences decomposition. Across four sessions, 41 native-Hebrew speakers learned morphologically derived words in a novel morpho-lexicon, with two complex conditions: linear and non-linear; and a third simple condition with monomorphemic words. Participants showed faster learning of trained words in the linear condition, and better generalization to untrained words for both complex conditions compared to the simple condition, with better performance for linear than non-linear morphology. Learning the root morpheme, which provides a concrete meaning, was better than learning template/suffix morphemes, which are more abstract. Overall, our results suggest that saliency of discrete units plays an important role in decomposition in early stages of learning derived words, even for speakers highly familiar with the non-linear structure in their L1.

Dynamic Motion and Human Agents Facilitate Visual Nonadjacent Dependency Learning

Many events that humans and other species experience contain regularities in which certain elements within an event predict certain others. While some of these regularities involve tracking the co-occurrences between temporally adjacent stimuli, others involve tracking the co-occurrences between temporally distant stimuli (i.e., nonadjacent dependencies, NADs). Prior research shows robust learning of adjacent dependencies in humans and other species, whereas learning NADs is more difficult, and often requires support from properties of the stimulus to help learners notice the NADs. Here, we report on seven experiments that examined NAD learning from various types of visual stimuli, exploring the effects of dynamic motion on adults' NAD learning from visual sequences involving human and nonhuman agents. We tested adults' NAD learning from visual sequences of human actions, object transformations, static images of human postures, and static images of an object in different postures. We found that dynamic motion aids the acquisition of NADs. We also found that learning NADs in sequences involving human agents is more robust compared to sequences involving nonhuman objects. We propose that dynamic motion and human agents both independently result in richer representations that provide a stronger signal for NAD learning.

Visual Implicit Learning Abilities in Infants at Familial Risk for Language and Learning Impairments

The ability of infants to track transitional probabilities (Statistical Learning—SL) and to extract and generalize high-order rules (Rule Learning—RL) from sequences of items have been proposed as being pivotal for the acquisition of language and reading skills. Although there is ample evidence of specific associations between SL and RL abilities and, respectively, vocabulary and grammar skills, research exploring SL and RL as early markers of language and learning (dis)abilities is still scarce. Here we investigated the efficiency of visual SL and RL skills in typically developing (TD) seven-month-old infants and in seven-month-old infants at high risk (HR) for language learning impairment. Infants were tested in two visual-habituation tasks aimed to measure their ability to extract transitional probabilities (SL task) or high-order, repetition-based rules (RL task) from sequences of visual shapes. Post-habituation looking time preferences revealed that both TD and HR infants succeeded in learning the statistical structure (SL task), while only TD infants, but not HR infants, were able to learn and generalize the high-order rule (RL task). These findings suggest that SL and RL may contribute differently to the emergence of language learning impairment and support the hypothesis that a mechanism linked to the extraction of grammar structures may contribute to the disorder.

Cognitive mechanisms of statistical learning and segmentation of continuous sensory input

Two classes of cognitive mechanisms have been proposed to explain segmentation of continuous sensory input into discrete recurrent constituents: clustering and boundary-finding mechanisms. Clustering mechanisms are based on identifying frequently co-occurring elements and merging them together as parts that form a single constituent. Bracketing (or boundary-finding) mechanisms work by identifying rarely co-occurring elements that correspond to the boundaries between discrete constituents. In a series of behavioral experiments, I tested which mechanisms are at play in the visual modality both during segmentation of a continuous syllabic sequence into discrete word-like constituents and during recognition of segmented constituents. Additionally, I explored conscious awareness of the products of statistical learning—whole constituents versus merged clusters of smaller subunits. My results suggest that both online segmentation and offline recognition of extracted constituents rely on detecting frequently co-occurring elements, a process likely based on associative memory. However, people are more aware of having learnt whole tokens than of recurrent composite clusters.

Integrating when and what information in the left parietal lobe allows language rule generalization

A crucial aspect when learning a language is discovering the rules that govern how words are combined in order to convey meanings. Because rules are characterized by sequential co-occurrences between elements (e.g., “These cupcakes are unbelievable”), tracking the statistical relationships between these elements is fundamental. However, purely bottom-up statistical learning alone cannot fully account for the ability to create abstract rule representations that can be generalized, a paramount requirement of linguistic rules. Here, we provide evidence that, after the statistical relations between words have been extracted, the engagement of goal-directed attention is key to enable rule generalization. Incidental learning performance during a rule-learning task on an artificial language revealed a progressive shift from statistical learning to goal-directed attention. In addition, and consistent with the recruitment of attention, functional MRI (fMRI) analyses of late learning stages showed left parietal activity within a broad bilateral dorsal frontoparietal network. Critically, repetitive transcranial magnetic stimulation (rTMS) on participants’ peak of activation within the left parietal cortex impaired their ability to generalize learned rules to a structurally analogous new language. No stimulation or rTMS on a nonrelevant brain region did not have the same interfering effect on generalization. Performance on an additional attentional task showed that this rTMS on the parietal site hindered participants’ ability to integrate “what” (stimulus identity) and “when” (stimulus timing) information about an expected target. The present findings suggest that learning rules from speech is a two-stage process: following statistical learning, goal-directed attention—involving left parietal regions—integrates “what” and “when” stimulus information to facilitate rapid rule generalization.

This study uses repetitive transcranial stimulation to show that learning language rules from speech is a two-stage process; following statistical learning, goal-directed attention (involving left parietal regions) integrates "what" and "when" stimulus information to facilitate rapid rule generalization.

Statistical learning and memory

Learners often need to identify and remember recurring units in continuous sequences, but the underlying mechanisms are debated. A particularly prominent candidate mechanism relies on distributional statistics such as Transitional Probabilities (TPs). However, it is unclear what the outputs of statistical segmentation mechanisms are, and if learners store these outputs as discrete chunks in memory. We critically review the evidence for the possibility that statistically coherent items are stored in memory and outline difficulties in interpreting past research. We use Slone and Johnson's (2018) experiments as a case study to show that it is difficult to delineate the different mechanisms learners might use to solve a learning problem. Slone and Johnson (2018) reported that 8-month-old infants learned coherent chunks of shapes in visual sequences. Here, we describe an alternate interpretation of their findings based on a multiple-cue integration perspective. First, when multiple cues to statistical structure were available, infants' looking behavior seemed to track with the strength of the strongest one - backward TPs, suggesting that infants process multiple cues simultaneously and select the strongest one. Second, like adults, infants are exquisitely sensitive to chunks, but may require multiple cues to extract them. In Slone and Johnson's (2018) experiments, these cues were provided by immediate chunk repetitions during familiarization. Accordingly, infants showed strongest evidence of chunking following familiarization sequences in which immediate repetitions were more frequent. These interpretations provide a strong argument for infants' processing of multiple cues and the potential importance of multiple cues for chunk recognition in infancy.

Mark my words: High frequency marker words impact early stages of language learning

High frequency words have been suggested to benefit both speech segmentation and grammatical categorization of the words around them. Despite utilizing similar information, these tasks are usually investigated separately in studies examining learning. We determined whether including high frequency words in continuous speech could support categorization when words are being segmented for the first time. We familiarized learners with continuous artificial speech comprising repetitions of target words, which were preceded by high-frequency marker words. Crucially, marker words distinguished targets into 2 distributionally defined categories. We measured learning with segmentation and categorization tests and compared performance against a control group that heard the artificial speech without these marker words (i.e., just the targets, with no cues for categorization). Participants segmented the target words from speech in both conditions, but critically when the marker words were present, they influenced acquisition of word-referent mappings in a subsequent transfer task, with participants demonstrating better early learning for mappings that were consistent (rather than inconsistent) with the distributional categories. We propose that high-frequency words may assist early grammatical categorization, while speech segmentation is still being learned. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Influence of metrical structure on learning of positional regularities in movement sequences

Sequential stimuli are usually perceived to have hierarchical temporal structures. However, some of these structures are only investigated in one type of sequence, regardless of the existing evidence, showing the domain-generality of the representation of these structures. Here, we assess whether the hierarchical representation of regularly segmented action sequences resembles the perceived metrical patterns that organize the representation of events hierarchically in temporally regular sequences. In all our experiments, we presented the participants with sequences of human movements and tested the perception of metrical pattern by segmenting the movement streams into temporally equal groups containing four movements. In Experiment 1, we found that a movement sequence with temporally equal groupings improves the learning of positional regularities inherent within each group of movements. To further clarify the degree to which this learning mechanism is affected by the perceived metrical patterns, we conducted Experiments 2a and 2b, in which the relative saliencies of the first and last positions in the movement groups, respectively, were studied. The results showed that, although in the learning of positional regularities, the rule-conforming first positions are as effective as when both first and last positions are legal, the last positions are not as influential. Based on these findings we conclude that, in grouped sequences, learning of positional regularities may be modulated by the metrical saliency patterns that are imposed by the temporal regularity of the sequential grouping pattern.

Developmental changes in automatic rule-learning mechanisms across early childhood

Infants' ability to learn complex linguistic regularities from early on has been revealed by electrophysiological studies indicating that 3-month-olds, but not adults, can automatically detect non-adjacent dependencies between syllables. While different ERP responses in adults and infants suggest that both linguistic rule learning and its link to basic auditory processing undergo developmental changes, systematic investigations of the developmental trajectories are scarce. In the present study, we assessed 2- and 4-year-olds' ERP indicators of pitch discrimination and linguistic rule learning in a syllable-based oddball design. To test for the relation between auditory discrimination and rule learning, ERP responses to pitch changes were used as predictor for potential linguistic rule-learning effects. Results revealed that 2-year-olds, but not 4-year-olds, showed ERP markers of rule learning. Although, 2-year-olds' rule learning was not dependent on differences in pitch perception, 4-year-old children demonstrated a dependency, such that those children who showed more pronounced responses to pitch changes still showed an effect of rule learning. These results narrow down the developmental decline of the ability for automatic linguistic rule learning to the age between 2 and 4 years, and, moreover, point towards a strong modification of this change by auditory processes. At an age when the ability of automatic linguistic rule learning phases out, rule learning can still be observed in children with enhanced auditory responses. The observed interrelations are plausible causes for age-of-acquisition effects and inter-individual differences in language learning.

What's statistical about learning? Insights from modelling statistical learning as a set of memory processes

Statistical learning has been studied in a variety of different tasks, including word segmentation, object identification, category learning, artificial grammar learning and serial reaction time tasks (e.g. Saffran et al. 1996 Science 274: , 1926-1928; Orban et al. 2008 Proceedings of the National Academy of Sciences 105: , 2745-2750; Thiessen & Yee 2010 Child Development 81: , 1287-1303; Saffran 2002 Journal of Memory and Language 47: , 172-196; Misyak & Christiansen 2012 Language Learning 62: , 302-331). The difference among these tasks raises questions about whether they all depend on the same kinds of underlying processes and computations, or whether they are tapping into different underlying mechanisms. Prior theoretical approaches to statistical learning have often tried to explain or model learning in a single task. However, in many cases these approaches appear inadequate to explain performance in multiple tasks. For example, explaining word segmentation via the computation of sequential statistics (such as transitional probability) provides little insight into the nature of sensitivity to regularities among simultaneously presented features. In this article, we will present a formal computational approach that we believe is a good candidate to provide a unifying framework to explore and explain learning in a wide variety of statistical learning tasks. This framework suggests that statistical learning arises from a set of processes that are inherent in memory systems, including activation, interference, integration of information and forgetting (e.g. Perruchet & Vinter 1998 Journal of Memory and Language 39: , 246-263; Thiessen et al. 2013 Psychological Bulletin 139: , 792-814). From this perspective, statistical learning does not involve explicit computation of statistics, but rather the extraction of elements of the input into memory traces, and subsequent integration across those memory traces that emphasize consistent information (Thiessen and Pavlik 2013 Cognitive Science 37: , 310-343).This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'.

Gleaning Structure from Sound: The Role of Prosodic Contrast in Learning Non-adjacent Dependencies

The ability to detect non-adjacent dependencies (i.e. between a and b in aXb) in spoken input may support the acquisition of morpho-syntactic dependencies (e.g. The princess is kiss ing the frog). Functional morphemes in morpho-syntactic dependencies are often marked by perceptual cues that render them distinct from lexical elements. We use an artificial grammar learning experiment with adults to investigate the role of perceptual cues in non-adjacent dependency learning, by manipulating the perceptual/prosodic properties of the a / b elements in aXb strings and testing participants' incidental learning of these dependencies. Our results show that non-adjacent dependencies are learned both when the dependent elements are perceptually prominent, and when they are perceptually reduced compared to the intervening material (in the same way that functional words are reduced compared to lexical words), but only if integrated into a natural prosodic contour. This result supports the idea that the prosodic properties of natural languages facilitate non-adjacent dependency learning.

Transitional probabilities count more than frequency, but might not be used for memorization

Learners often need to extract recurring items from continuous sequences, in both vision and audition. The best-known example is probably found in word-learning, where listeners have to determine where words start and end in fluent speech. This could be achieved through universal and experience-independent statistical mechanisms, for example by relying on Transitional Probabilities (TPs). Further, these mechanisms might allow learners to store items in memory. However, previous investigations have yielded conflicting evidence as to whether a sensitivity to TPs is diagnostic of the memorization of recurring items. Here, we address this issue in the visual modality. Participants were familiarized with a continuous sequence of visual items (i.e., arbitrary or everyday symbols), and then had to choose between (i) high-TP items that appeared in the sequence, (ii) high-TP items that did not appear in the sequence, and (iii) low-TP items that appeared in the sequence. Items matched in TPs but differing in (chunk) frequency were much harder to discriminate than items differing in TPs (with no significant sensitivity to chunk frequency), and learners preferred unattested high-TP items over attested low-TP items. Contrary to previous claims, these results cannot be explained on the basis of the similarity of the test items. Learners thus weigh within-item TPs higher than the frequency of the chunks, even when the TP differences are relatively subtle. We argue that these results are problematic for distributional clustering mechanisms that analyze continuous sequences, and provide supporting computational results. We suggest that the role of TPs might not be to memorize items per se, but rather to prepare learners to memorize recurring items once they are presented in subsequent learning situations with richer cues.

Learning multiple rules simultaneously: Affixes are more salient than reduplications

Language learners encounter numerous opportunities to learn regularities, but need to decide which of these regularities to learn, because some are not productive in their native language. Here, we present an account of rule learning based on perceptual and memory primitives (Endress, Dehaene-Lambertz, & Mehler, Cognition, 105(3), 577–614, 2007; Endress, Nespor, & Mehler, Trends in Cognitive Sciences, 13(8), 348–353, 2009), suggesting that learners preferentially learn regularities that are more salient to them, and that the pattern of salience reflects the frequency of language features across languages. We contrast this view with previous artificial grammar learning research, which suggests that infants “choose” the regularities they learn based on rational, Bayesian criteria (Frank & Tenenbaum, Cognition, 120(3), 360–371, 2013; Gerken, Cognition, 98(3)B67–B74, 2006, Cognition, 115(2), 362–366, 2010). In our experiments, adult participants listened to syllable strings starting with a syllable reduplication and always ending with the same “affix” syllable, or to syllable strings starting with this “affix” syllable and ending with the “reduplication”. Both affixation and reduplication are frequently used for morphological marking across languages. We find three crucial results. First, participants learned both regularities simultaneously. Second, affixation regularities seemed easier to learn than reduplication regularities. Third, regularities in sequence offsets were easier to learn than regularities at sequence onsets. We show that these results are inconsistent with previous Bayesian rule learning models, but mesh well with the perceptual or memory primitives view. Further, we show that the pattern of salience revealed in our experiments reflects the distribution of regularities across languages. Ease of acquisition might thus be one determinant of the frequency of regularities across languages.

Words, rules, and mechanisms of language acquisition

We review recent artificial language learning studies, especially those following Endress and Bonatti (Endress AD, Bonatti LL. Rapid learning of syllable classes from a perceptually continuous speech stream. Cognition 2007, 105:247-299), suggesting that humans can deploy a variety of learning mechanisms to acquire artificial languages. Several experiments provide evidence for multiple learning mechanisms that can be deployed in fluent speech: one mechanism encodes the positions of syllables within words and can be used to extract generalization, while the other registers co-occurrence statistics of syllables and can be used to break a continuum into its components. We review dissociations between these mechanisms and their potential role in language acquisition. We then turn to recent criticisms of the multiple mechanisms hypothesis and show that they are inconsistent with the available data. Our results suggest that artificial and natural language learning is best understood by dissecting the underlying specialized learning abilities, and that these data provide a rare opportunity to link important language phenomena to basic psychological mechanisms. For further resources related to this article, please visit the WIREs website.

Simultaneous segmentation and generalisation of non-adjacent dependencies from continuous speech

Language learning requires mastering multiple tasks, including segmenting speech to identify words, and learning the syntactic role of these words within sentences. A key question in language acquisition research is the extent to which these tasks are sequential or successive, and consequently whether they may be driven by distinct or similar computations. We explored a classic artificial language learning paradigm, where the language structure is defined in terms of non-adjacent dependencies. We show that participants are able to use the same statistical information at the same time to segment continuous speech to both identify words and to generalise over the structure, when the generalisations were over novel speech that the participants had not previously experienced. We suggest that, in the absence of evidence to the contrary, the most economical explanation for the effects is that speech segmentation and grammatical generalisation are dependent on similar statistical processing mechanisms.

Individual Differences in Statistical Learning Predict Children's Comprehension of Syntax

Variability in children's language acquisition is likely due to a number of cognitive and social variables. The current study investigated whether individual differences in statistical learning (SL), which has been implicated in language acquisition, independently predicted 6- to 8-year-old's comprehension of syntax. Sixty-eight (N = 68) English-speaking children completed a test of comprehension of four syntactic structures, a test of SL utilizing nonlinguistic visual stimuli, and several additional control measures. The results revealed that SL independently predicted comprehension of two syntactic structures that show considerable variability in this age range: passives and object relative clauses. These data suggest that individual differences in children's capacity for SL are associated with the acquisition of the syntax of natural languages.

Finding words and word structure in artificial speech: the development of infants' sensitivity to morphosyntactic regularities

To achieve language proficiency, infants must find the building blocks of speech and master the rules governing their legal combinations. However, these problems are linked: words are also built according to rules. Here, we explored early morphosyntactic sensitivity by testing when and how infants could find either words or within-word structure in artificial speech snippets embodying properties of morphological constructions. We show that 12-month-olds use statistical relationships between syllables to extract words from continuous streams, but find word-internal regularities only if the streams are segmented. Seven-month-olds fail both tasks. Thus, 12-month-olds infants possess the resources to analyze the internal composition of words if the speech contains segmentation information. However, 7-month-old infants may not possess them, although they can track several statistical relations. This developmental difference suggests that morphosyntactic sensitivity may require computational resources extending beyond the detection of simple statistics.

Verbal positional memory in 7-month-olds

Verbal memory is a fundamental prerequisite for language learning. This study investigated 7-month-olds' (N = 62) ability to remember the identity and order of elements in a multisyllabic word. The results indicate that infants detect changes in the order of edge syllables, or the identity of the middle syllables, but fail to encode the order of middle syllables. This suggests that the representational format of multisyllabic words is determined by core mnemonic biases, which favor accurate encoding of edges and limits the encoding of temporal order for internal segments. The studies support accounts proposing that content and order are encoded separately; in addition, the data show that this dissociation occurs early in development.

Bayesian learning and the psychology of rule induction

In recent years, Bayesian learning models have been applied to an increasing variety of domains. While such models have been criticized on theoretical grounds, the underlying assumptions and predictions are rarely made concrete and tested experimentally. Here, I use Frank and Tenenbaum's (2011) Bayesian model of rule-learning as a case study to spell out the underlying assumptions, and to confront them with the empirical results Frank and Tenenbaum (2011) propose to simulate, as well as with novel experiments. While rule-learning is arguably well suited to rational Bayesian approaches, I show that their models are neither psychologically plausible nor ideal observer models. Further, I show that their central assumption is unfounded: humans do not always preferentially learn more specific rules, but, at least in some situations, those rules that happen to be more salient. Even when granting the unsupported assumptions, I show that all of the experiments modeled by Frank and Tenenbaum (2011) either contradict their models, or have a large number of more plausible interpretations. I provide an alternative account of the experimental data based on simple psychological mechanisms, and show that this account both describes the data better, and is easier to falsify. I conclude that, despite the recent surge in Bayesian models of cognitive phenomena, psychological phenomena are best understood by developing and testing psychological theories rather than models that can be fit to virtually any data.

Learning melodies from non-adjacent tones

Language acquisition might heavily rely on statistical learning mechanisms. This has led many researchers to investigate the computational constraints that limit such learning. In particular, it has been argued that statistical relations among non-adjacent items cannot be tracked, as this might lead to a "computational explosion" making statistical learning intractable. In line with this view, previous research suggests that listeners cannot track relations among non-adjacent musical tones (Creel, Newport, & Aslin, 2004). Here I show that participants readily track non-adjacent tone relations when these are implemented in a musically meaningful way. Specifically, participants readily track non-adjacent tone relations in tonal melodies, but find it more difficult to track non-adjacent tone relations in random melodies, suggesting that non-adjacent relations are easier to track when listeners face "ecological", musically meaningful stimuli.

The apes' edge: positional learning in chimpanzees and humans

A wide variety of organisms produce actions and signals in particular temporal sequences, including the motor actions recruited during tool-mediated foraging, the arrangement of notes in the songs of birds, whales and gibbons, and the patterning of words in human speech. To accurately reproduce such events, the elements that comprise such sequences must be memorized. Both memory and artificial language learning studies have revealed at least two mechanisms for memorizing sequences, one tracking co-occurrence statistics among items in sequences (i.e., transitional probabilities) and the other one tracking the positions of items in sequences, in particular those of items in sequence-edges. The latter mechanism seems to dominate the encoding of sequences after limited exposure, and to be recruited by a wide array of grammatical phenomena. To assess whether humans differ from other species in their reliance on one mechanism over the other after limited exposure, we presented chimpanzees (Pan troglodytes) and human adults with brief exposure to six items, auditory sequences. Each sequence consisted of three distinct sound types (X, A, B), arranged according to two simple temporal rules: the A item always preceded the B item, and the sequence-edges were always occupied by the X item. In line with previous results with human adults, both species primarily encoded positional information from the sequences; that is, they kept track of the items that occurred in the sequence-edges. In contrast, the sensitivity to co-occurrence statistics was much weaker. Our results suggest that a mechanism to spontaneously encode positional information from sequences is present in both chimpanzees and humans and may represent the default in the absence of training and with brief exposure. As many grammatical regularities exhibit properties of this mechanism, it may be recruited by language and constrain the form that certain grammatical regularities take.

Perceptual and memory constraints on language acquisition

A wide variety of organisms employ specialized mechanisms to cope with the demands of their environment. We suggest that the same is true for humans when acquiring artificial grammars, and at least some basic properties of natural grammars. We show that two basic mechanisms can explain many results in artificial grammar learning experiments, and different linguistic regularities ranging from stress assignment to interfaces between different components of grammar. One mechanism is sensitive to identity relations, whereas the other uses sequence edges as anchor points for extracting positional regularities. This piecemeal approach to mental computations helps to explain otherwise perplexing data, and offers a working hypothesis on how statistical and symbolic accounts of cognitive processes could be bridged.