The Keys to the Future? An Examination of Statistical Versus Discriminative Accounts of Serial Pattern Learning.

Sequence learning is fundamental to a wide range of cognitive functions. Explaining how sequences-and the relations between the elements they comprise-are learned is a fundamental challenge to cognitive science. However, although hundreds of articles addressing this question are published each year, the actual learning mechanisms involved in the learning of sequences are rarely investigated. We present three experiments that seek to examine these mechanisms during a typing task. Experiments 1 and 2 tested learning during typing single letters on each trial. Experiment 3 tested for "chunking" of these letters into "words." The results of these experiments were used to examine the mechanisms that could best account for them, with a focus on two particular proposals: statistical transitional probability learning and discriminative error-driven learning. Experiments 1 and 2 showed that error-driven learning was a better predictor of response latencies than either n-gram frequencies or transitional probabilities. No evidence for chunking was found in Experiment 3, probably due to interspersing visual cues with the motor response. In addition, learning occurred across a greater distance in Experiment 1 than Experiment 2, suggesting that the greater predictability that comes with increased structure leads to greater learnability. These results shed new light on the mechanism responsible for sequence learning. Despite the widely held assumption that transitional probability learning is essential to this process, the present results suggest instead that the sequences are learned through a process of discriminative learning, involving prediction and feedback from prediction error.

Modal and amodal cognition: an overarching principle in various domains of psychology.

Accounting for how the human mind represents the internal and external world is a crucial feature of many theories of human cognition. Central to this question is the distinction between modal as opposed to amodal representational formats. It has often been assumed that one but not both of these two types of representations underlie processing in specific domains of cognition (e.g., perception, mental imagery, and language). However, in this paper, we suggest that both formats play a major role in most cognitive domains. We believe that a comprehensive theory of cognition requires a solid understanding of these representational formats and their functional roles within and across different domains of cognition, the developmental trajectory of these representational formats, and their role in dysfunctional behavior. Here we sketch such an overarching perspective that brings together research from diverse subdisciplines of psychology on modal and amodal representational formats so as to unravel their functional principles and their interactions.

Understanding the Phonetic Characteristics of Speech Under Uncertainty-Implications of the Representation of Linguistic Knowledge in Learning and Processing.

The uncertainty associated with paradigmatic families has been shown to correlate with their phonetic characteristics in speech, suggesting that representations of complex sublexical relations between words are part of speaker knowledge. To better understand this, recent studies have used two-layer neural network models to examine the way paradigmatic uncertainty emerges in learning. However, to date this work has largely ignored the way choices about the representation of inflectional and grammatical functions (IFS) in models strongly influence what they subsequently learn. To explore the consequences of this, we investigate how representations of IFS in the input-output structures of learning models affect the capacity of uncertainty estimates derived from them to account for phonetic variability in speech. Specifically, we examine whether IFS are best represented as outputs to neural networks (as in previous studies) or as inputs by building models that embody both choices and examining their capacity to account for uncertainty effects in the formant trajectories of word final [ɐ], which in German discriminates around sixty different IFS. Overall, we find that formants are enhanced as the uncertainty associated with IFS decreases. This result dovetails with a growing number of studies of morphological and inflectional families that have shown that enhancement is associated with lower uncertainty in context. Importantly, we also find that in models where IFS serve as inputs-as our theoretical analysis suggests they ought to-its uncertainty measures provide better fits to the empirical variance observed in [ɐ] formants than models where IFS serve as outputs. This supports our suggestion that IFS serve as cognitive cues during speech production, and should be treated as such in modeling. It is also consistent with the idea that when IFS serve as inputs to a learning network. This maintains the distinction between those parts of the network that represent message and those that represent signal. We conclude by describing how maintaining a "signal-message-uncertainty distinction" can allow us to reconcile a range of apparently contradictory findings about the relationship between articulation and uncertainty in context.

An exploration of error-driven learning in simple two-layer networks from a discriminative learning perspective.

Error-driven learning algorithms, which iteratively adjust expectations based on prediction error, are the basis for a vast array of computational models in the brain and cognitive sciences that often differ widely in their precise form and application: they range from simple models in psychology and cybernetics to current complex deep learning models dominating discussions in machine learning and artificial intelligence. However, despite the ubiquity of this mechanism, detailed analyses of its basic workings uninfluenced by existing theories or specific research goals are rare in the literature. To address this, we present an exposition of error-driven learning - focusing on its simplest form for clarity - and relate this to the historical development of error-driven learning models in the cognitive sciences. Although historically error-driven models have been thought of as associative, such that learning is thought to combine preexisting elemental representations, our analysis will highlight the discriminative nature of learning in these models and the implications of this for the way how learning is conceptualized. We complement our theoretical introduction to error-driven learning with a practical guide to the application of simple error-driven learning models in which we discuss a number of example simulations, that are also presented in detail in an accompanying tutorial.

How children learn to communicate discriminatively.

How do children learn to communicate, and what do they learn? Traditionally, most theories have taken an associative, compositional approach to these questions, supposing children acquire an inventory of form-meaning associations, and procedures for composing / decomposing them; into / from messages in production and comprehension. This paper presents an alternative account of human communication and its acquisition based on the systematic, discriminative approach embodied in psychological and computational models of learning, and formally described by communication theory. It describes how discriminative learning theory offers an alternative perspective on the way that systems of semantic cues are conditioned onto communicative codes, while information theory provides a very different view of the nature of the codes themselves. It shows how the distributional properties of languages satisfy the communicative requirements described in information theory, enabling language learners to align their expectations despite the vastly different levels of experience among language users, and to master communication systems far more abstract than linguistic intuitions traditionally assume. Topics reviewed include morphological development, the acquisition of verb argument structures, and the functions of linguistic systems that have proven to be stumbling blocks for compositional theories: grammatical gender and personal names.

Simulating the Acquisition of Verb Inflection in Typically Developing Children and Children With Developmental Language Disorder in English and Spanish.

Children with developmental language disorder (DLD) have significant deficits in language ability that cannot be attributed to neurological damage, hearing impairment, or intellectual disability. The symptoms displayed by children with DLD differ across languages. In English, DLD is often marked by severe difficulties acquiring verb inflection. Such difficulties are less apparent in languages with rich verb morphology like Spanish and Italian. Here we show how these differential profiles can be understood in terms of an interaction between properties of the input language, and the child's ability to learn predictive relations between linguistic elements that are separated within a sentence. We apply a simple associative learning model to sequential English and Spanish stimuli and show how the model's ability to associate cues occurring earlier in time with later outcomes affects the acquisition of verb inflection in English more than in Spanish. We relate this to the high frequency of the English bare form (which acts as a default) and the English process of question formation, which means that (unlike in Spanish) bare forms frequently occur in third-person singular contexts. Finally, we hypothesize that the pro-drop nature of Spanish makes it easier to associate person and number cues with the verb inflection than in English. Since the factors that conspire to make English verb inflection particularly challenging for learners with weak sequential learning abilities are much reduced or absent in Spanish, this provides an explanation for why learning Spanish verb inflection is relatively unaffected in children with DLD.

Paradigmatic enhancement of stem vowels in regular English inflected verb forms.

Many theories of word structure in linguistics and morphological processing in cognitive psychology are grounded in a compositional perspective on the (mental) lexicon in which complex words are built up during speech production from sublexical elements such as morphemes, stems, and exponents. When combined with the hypothesis that storage in the lexicon is restricted to the irregular, the prediction follows that properties specific to regular inflected words cannot co-determine the phonetic realization of these inflected words. This study shows that the stem vowels of regular English inflected verb forms that are more frequent in their paradigm are produced with more enhanced articulatory gestures in the midsaggital plane, challenging compositional models of lexical processing. The effect of paradigmatic probability dovetails well with the Paradigmatic Enhancement Hypothesis and is consistent with a growing body of research indicating that the whole is more than its parts.

Articulatory Variability is Reduced by Repetition and Predictability.

Repeating the movements associated with activities such as drawing or sports typically leads to improvements in kinematic behavior: these movements become faster, smoother, and exhibit less variation. Likewise, practice has also been shown to lead to faster and smoother movement trajectories in speech articulation. However, little is known about its effect on articulatory variability. To address this, we investigate the extent to which repetition and predictability influence the articulation of the frequent German word "sie" [zi] (they). We find that articulatory variability is proportional to speaking rate and the duration of [zi], and that overall variability decreases as [zi] is repeated during the experiment. Lower variability is also observed as the conditional probability of [zi] increases, and the greatest reduction in variability occurs during the execution of the vocalic target of [i]. These results indicate that practice can produce observable differences in the articulation of even the most common gestures used in speech.

How the Probabilistic Structure of Grammatical Context Shapes Speech.

Does systematic covariation in the usage patterns of forms shape the sublexical variance observed in conversational speech? We address this question in terms of a recently proposed discriminative theory of human communication that argues that the distribution of events in communicative contexts should maintain mutual predictability between language users, present evidence that the distributions of words in the empirical contexts in which they are learned and used are geometric, and thus support this. Here, we extend this analysis to a corpus of conversational English, showing that the distribution of grammatical regularities and the sub-distributions of tokens discriminated by them are also geometric. Further analyses reveal a range of structural differences in the distribution of types in parts of speech categories that further support the suggestion that linguistic distributions (and codes) are subcategorized by context at multiple levels of abstraction. Finally, a series of analyses of the variation in spoken language reveals that quantifiable differences in the structure of lexical subcategories appears in turn to systematically shape sublexical variation in speech signal.

Order Matters! Influences of Linear Order on Linguistic Category Learning.

Linguistic category learning has been shown to be highly sensitive to linear order, and depending on the task, differentially sensitive to the information provided by preceding category markers (premarkers, e.g., gendered articles) or succeeding category markers (postmarkers, e.g., gendered suffixes). Given that numerous systems for marking grammatical categories exist in natural languages, it follows that a better understanding of these findings can shed light on the factors underlying this diversity. In two discriminative learning simulations and an artificial language learning experiment, we identify two factors that modulate linear order effects in linguistic category learning: category structure and the level of abstraction in a category hierarchy. Regarding category structure, we find that postmarking brings an advantage for learning category diagnostic stimulus dimensions, an effect not present when categories are non-confusable. Regarding levels of abstraction, we find that premarking of super-ordinate categories (e.g., noun class) facilitates learning of subordinate categories (e.g., nouns). We present detailed simulations using a plausible candidate mechanism for the observed effects, along with a comprehensive analysis of linear order effects within an expectation-based account of learning. Our findings indicate that linguistic category learning is differentially guided by pre- and postmarking, and that the influence of each is modulated by the specific characteristics of a given category system.

Quantifying the speech-gesture relation with massive multimodal datasets: Informativity in time expressions.

The development of large-scale corpora has led to a quantum leap in our understanding of speech in recent years. By contrast, the analysis of massive datasets has so far had a limited impact on the study of gesture and other visual communicative behaviors. We utilized the UCLA-Red Hen Lab multi-billion-word repository of video recordings, all of them showing communicative behavior that was not elicited in a lab, to quantify speech-gesture co-occurrence frequency for a subset of linguistic expressions in American English. First, we objectively establish a systematic relationship in the high degree of co-occurrence between gesture and speech in our subset of expressions, which consists of temporal phrases. Second, we show that there is a systematic alignment between the informativity of co-speech gestures and that of the verbal expressions with which they co-occur. By exposing deep, systematic relations between the modalities of gesture and speech, our results pave the way for the data-driven integration of multimodal behavior into our understanding of human communication.

NDRA: A single route model of response times in the reading aloud task based on discriminative learning.

We present the Naive Discriminative Reading Aloud (ndra) model. The ndra differs from existing models of response times in the reading aloud task in two ways. First, a single lexical architecture is responsible for both word and non-word naming. As such, the model differs from dual-route models, which consist of both a lexical route and a sub-lexical route that directly maps orthographic units onto phonological units. Second, the linguistic core of the ndra exclusively operates on the basis of the equilibrium equations for the well-established general human learning algorithm provided by the Rescorla-Wagner model. The model therefore does not posit language-specific processing mechanisms and avoids the problems of psychological and neurobiological implausibility associated with alternative computational implementations. We demonstrate that the single-route discriminative learning architecture of the ndra captures a wide range of effects documented in the experimental reading aloud literature and that the overall fit of the model is at least as good as that of state-of-the-art dual-route models.

Alternative Solutions to a Language Design Problem: The Role of Adjectives and Gender Marking in Efficient Communication.

A central goal of typological research is to characterize linguistic features in terms of both their functional role and their fit to social and cognitive systems. One long-standing puzzle concerns why certain languages employ grammatical gender. In an information theoretic analysis of German noun classification, Dye, Milin, Futrell, and Ramscar (2017) enumerated a number of important processing advantages gender confers. Yet this raises a further puzzle: If gender systems are so beneficial to processing, what does this mean for languages that make do without them? Here, we compare the communicative function of gender marking in German (a deterministic system) to that of prenominal adjectives in English (a probabilistic one), finding that despite their differences, both systems act to efficiently smooth information over discourse, making nouns more equally predictable in context. We examine why evolutionary pressures may favor one system over another and discuss the implications for compositional accounts of meaning and Gricean principles of communication.

Are baboons learning "orthographic" representations? Probably not.

The ability of Baboons (papio papio) to distinguish between English words and nonwords has been modeled using a deep learning convolutional network model that simulates a ventral pathway in which lexical representations of different granularity develop. However, given that pigeons (columba livia), whose brain morphology is drastically different, can also be trained to distinguish between English words and nonwords, it appears that a less species-specific learning algorithm may be required to explain this behavior. Accordingly, we examined whether the learning model of Rescorla and Wagner, which has proved to be amazingly fruitful in understanding animal and human learning could account for these data. We show that a discrimination learning network using gradient orientation features as input units and word and nonword units as outputs succeeds in predicting baboon lexical decision behavior-including key lexical similarity effects and the ups and downs in accuracy as learning unfolds-with surprising precision. The models performance, in which words are not explicitly represented, is remarkable because it is usually assumed that lexicality decisions, including the decisions made by baboons and pigeons, are mediated by explicit lexical representations. By contrast, our results suggest that in learning to perform lexical decision tasks, baboons and pigeons do not construct a hierarchy of lexical units. Rather, they make optimal use of low-level information obtained through the massively parallel processing of gradient orientation features. Accordingly, we suggest that reading in humans first involves initially learning a high-level system building on letter representations acquired from explicit instruction in literacy, which is then integrated into a conventionalized oral communication system, and that like the latter, fluent reading involves the massively parallel processing of the low-level features encoding semantic contrasts.

The Mismeasurement of Mind: Life-Span Changes in Paired-Associate-Learning Scores Reflect the "Cost" of Learning, Not Cognitive Decline.

The age-related declines observed in scores on paired-associate-learning (PAL) tests are widely taken as support for the idea that human cognitive capacities decline across the life span. In a computational simulation, we showed that the patterns of change in PAL scores are actually predicted by the models that formalize the associative learning process in other areas of behavioral and neuroscientific research. These models also predict that manipulating language exposure can reproduce the experience-related performance differences erroneously attributed to age-related decline in age-matched adults. Consistent with this, results showed that older bilinguals outperformed native speakers in a German PAL test, an advantage that increased with age. These analyses and results show that age-related PAL performance changes reflect the predictable effects of learning on the associability of test items, and indicate that failing to control for these effects is distorting the understanding of cognitive and brain development in adulthood.

Discrimination in lexical decision.

In this study we present a novel set of discrimination-based indicators of language processing derived from Naive Discriminative Learning (ndl) theory. We compare the effectiveness of these new measures with classical lexical-distributional measures-in particular, frequency counts and form similarity measures-to predict lexical decision latencies when a complete morphological segmentation of masked primes is or is not possible. Data derive from a re-analysis of a large subset of decision latencies from the English Lexicon Project, as well as from the results of two new masked priming studies. Results demonstrate the superiority of discrimination-based predictors over lexical-distributional predictors alone, across both the simple and primed lexical decision tasks. Comparable priming after masked corner and cornea type primes, across two experiments, fails to support early obligatory segmentation into morphemes as predicted by the morpho-orthographic account of reading. Results fit well with ndl theory, which, in conformity with Word and Paradigm theory, rejects the morpheme as a relevant unit of analysis. Furthermore, results indicate that readers with greater spelling proficiency and larger vocabularies make better use of orthographic priors and handle lexical competition more efficiently.

Exploratory decision-making as a function of lifelong experience, not cognitive decline.

Older adults perform worse than younger adults in some complex decision-making scenarios, which is commonly attributed to age-related declines in striatal and frontostriatal processing. Recently, this popular account has been challenged by work that considered how older adults' performance may differ as a function of greater knowledge and experience, and by work showing that, in some cases, older adults outperform younger adults in complex decision-making tasks. In light of this controversy, we examined the performance of older and younger adults in an exploratory choice task that is amenable to model-based analyses and ostensibly not reliant on prior knowledge. Exploration is a critical aspect of decision-making poorly understood across the life span. Across 2 experiments, we addressed (a) how older and younger adults differ in exploratory choice and (b) to what extent observed differences reflect processing capacity declines. Model-based analyses suggested that the strategies used by the 2 groups were qualitatively different, resulting in relatively worse performance for older adults in 1 decision-making environment but equal performance in another. Little evidence was found that differences in processing capacity drove performance differences. Rather the results suggested that older adults' performance might result from applying a strategy that may have been shaped by their wealth of real-word decision-making experience. While this strategy is likely to be effective in the real world, it is ill suited to some decision environments. These results underscore the importance of taking into account effects of experience in aging studies, even for tasks that do not obviously tap past experiences.

The myth of cognitive decline: non-linear dynamics of lifelong learning.

As adults age, their performance on many psychometric tests changes systematically, a finding that is widely taken to reveal that cognitive information-processing capacities decline across adulthood. Contrary to this, we suggest that older adults'; changing performance reflects memory search demands, which escalate as experience grows. A series of simulations show how the performance patterns observed across adulthood emerge naturally in learning models as they acquire knowledge. The simulations correctly identify greater variation in the cognitive performance of older adults, and successfully predict that older adults will show greater sensitivity to fine-grained differences in the properties of test stimuli than younger adults. Our results indicate that older adults'; performance on cognitive tests reflects the predictable consequences of learning on information-processing, and not cognitive decline. We consider the implications of this for our scientific and cultural understanding of aging.

Children value informativity over logic in word learning.

The question of how children learn the meanings of words has long puzzled philosophers and psychologists. As Quine famously pointed out, simply hearing a word in context reveals next to nothing about its meaning. How then do children learn to understand and use words correctly? Here, we show how learning theory can offer an elegant solution to this seemingly intractable puzzle in language acquisition. From it, we derived formal predictions about word learning in situations of Quinean ambiguity, and subsequently tested our predictions on toddlers, undergraduates, and developmental psychologists. The toddlers' performance was consistent both with our predictions and with the workings of implicit mechanisms that can facilitate the learning of meaningful lexical systems. Adults adopted a markedly different and likely suboptimal strategy. These results suggest one explanation for why early word learning can appear baffling: Adult intuitions may be a poor source of insight into how children learn.