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. (shrink)

Some embodied theories of concepts state that concepts are represented in a sensorimotor manner, typically via simulation in sensorimotor cortices. Fred Adams (2010) has advanced an empirical argument against embodied concepts reasoning as follows. If concepts are embodied, then patients with certain sensorimotor impairments should perform worse on categorization tasks involving those concepts. Adams cites a study with Moebius Syndrome patients that shows typical categorization performance in face-based emotion recognition. Adams concludes that their typical performance shows that embodiment is false. (...) Moebius patients must draw on amodal (non-embodied) emotion concepts. In this paper, I review face-based emotion recognition studies with Moebius patients yielding conflicting results and diagnose these conflicts as a difference in experimental design. When emotion labels are provided, patients have typical performance, but when labels are not provided patients are severely deficient. I then show how an embodied, psychological constructionist view of emotions predicts and explains these performance differences. The upshot is that embodied theories of concepts are vindicated. (shrink)

Our target article proposed that language production and comprehension are interwoven, with speakers making predictions of their own utterances and comprehenders making predictions of other people's utterances at different linguistic levels. Here, we respond to comments about such issues as cognitive architecture and its neural basis, learning and development, monitoring, the nature of forward models, communicative intentions, and dialogue.

Cross-situational word learning, like any statistical learning problem, involves tracking the regularities in the environment. However, the information that learners pick up from these regularities is dependent on their learning mechanism. This article investigates the role of one type of mechanism in statistical word learning: competition. Competitive mechanisms would allow learners to find the signal in noisy input and would help to explain the speed with which learners succeed in statistical learning tasks. Because cross-situational word learning provides information at multiple (...) scales—both within and across trials/situations—learners could implement competition at either or both of these scales. A series of four experiments demonstrate that cross-situational learning involves competition at both levels of scale, and that these mechanisms interact to support rapid learning. The impact of both of these mechanisms is considered from the perspective of a process-level understanding of cross-situational learning. (shrink)

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. (shrink)

Currently, production and comprehension are regarded as quite distinct in accounts of language processing. In rejecting this dichotomy, we instead assert that producing and understanding are interwoven, and that this interweaving is what enables people to predict themselves and each other. We start by noting that production and comprehension are forms of action and action perception. We then consider the evidence for interweaving in action, action perception, and joint action, and explain such evidence in terms of prediction. Specifically, we assume (...) that actors construct forward models of their actions before they execute those actions, and that perceivers of others' actions covertly imitate those actions, then construct forward models of those actions. We use these accounts of action, action perception, and joint action to develop accounts of production, comprehension, and interactive language. Importantly, they incorporate well-defined levels of linguistic representation. We show how speakers and comprehenders use covert imitation and forward modeling to make predictions at these levels of representation, how they interweave production and comprehension processes, and how they use these predictions to monitor the upcoming utterances. We show how these accounts explain a range of behavioral and neuroscientific data on language processing and discuss some of the implications of our proposal. (shrink)

We welcome the proposal to use forward models to understand predictive processes in language processing. However, Pickering & Garrod (P&G) miss the opportunity to provide a strong framework for future work. Forward models need to be pursued in the context of learning. This naturally leads to questions about what prediction error these models aim to minimize.

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. (shrink)

Why are children better language learners than adults despite being worse at a range of other cognitive tasks? Here, we explore the role of multiword sequences in explaining L1–L2 differences in learning. In particular, we propose that children and adults differ in their reliance on such multiword units in learning, and that this difference affects learning strategies and outcomes, and leads to difficulty in learning certain grammatical relations. In the first part, we review recent findings that suggest that MWUs play (...) a facilitative role in learning. We then discuss the implications of these findings for L1–L2 differences: We hypothesize that adults are both less likely to extract MWUs and less capable of benefiting from them in the process of learning. In the next section, we draw on psycholinguistic, developmental, and computational findings to support these predictions. We end with a discussion of the relation between this proposal and other accounts of L1–L2 difficulty. (shrink)

We examined how language supports the expression of temporality within sentence boundaries in English, which has a rich inventory of grammatical means to express temporality. Using a computational model that mimics how humans learn from exposure we explored what the use of different tense and aspect combinations reveals about the interaction between our experience of time and the cognitive demands that talking about time puts on the language user. Our model was trained on n-grams extracted from the BNC to select (...) the TA combination that fits the context best. It revealed the existence of two different sub-systems within the set of TA combinations, a “simplex” one that is supported lexically and is easy to learn, and a “complex” one that is supported contextually and is hard to learn. The finding that some TA combinations are essentially lexical in nature necessitates a rethink of tense and aspect as grammatical categories that form the axes of the temporal system. We argue that the system of temporal reference may be more fruitfully thought of as the result of learning a system that is steeped in experience and organised along a number of functional principles. (shrink)

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. (shrink)

Ramscar and colleagues (2010, this volume) describe the “feature-label-order” (FLO) effect on category learning and characterize it as a constraint on symbolic learning. I argue that FLO is neither a constraint on symbolic learning in the sense of “learning elements of a symbol system” (instead, it is an effect on nonsymbolic, association learning) nor is it, more than any other constraint on category learning, a constraint on symbolic learning in the sense of “solving the symbol grounding problem.”.

Learning in natural environments is often characterized by a degree of inconsistency from an input. These inconsistencies occur, for example, when learning from more than one source, or when the presence of environmental noise distorts incoming information; as a result, the task faced by the learner becomes ambiguous. In this study, we investigate how learners handle such situations. We focus on the setting where a learner receives and processes a sequence of utterances to master associations between objects and their labels, (...) where the source is inconsistent by design: It uses both “correct” and “incorrect” object‐label pairings. We hypothesize that depending on the order of presentation, the result of the learning may be different. To this end, we consider two types of symbolic learning procedures: the Object‐Label (OL) and the Label‐Object (LO) process. In the OL process, the learner is first exposed to the object, and then the label. In the LO process, this order is reversed. We perform experiments with human subjects, and also construct a computational model that is based on a nonlinear stochastic reinforcement learning algorithm. It is observed experimentally that OL learners are generally better at processing inconsistent input compared to LO learners. We show that the patterns observed in the learning experiments can be reproduced in the simulations if the model includes (a) an ability to regularize the input (and also to do the opposite, i.e., undermatch) and (b) an ability to take account of implicit negative evidence (i.e., interactions among different objects/labels). The model suggests that while both types of learners utilize implicit negative evidence in a similar way, there is a difference in regularization patterns: OL learners regularize the input, whereas LO learners undermatch. As a result, OL learners are able to form a more consistent system of image‐utterance associations, despite the ambiguous learning task. (shrink)

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 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 to that of prenominal adjectives in English, 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. (shrink)

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. (shrink)

Cognitive Science, Volume 46, Issue 2, February 2022.

Over the past 10 years, Cognitive Linguistics has taken a quantitative turn. Yet, concerns have been raised that this preoccupation with quantification and modelling may not bring us any closer to understanding how language works. We show that this objection is unfounded, especially if we rely on modelling techniques based on biologically and psychologically plausible learning algorithms. These make it possible to take a quantitative approach, while generating and testing specific hypotheses that will advance our understanding of how knowledge of (...) language emerges from exposure to usage. (shrink)

One of the great challenges in word learning is that words are typically uttered in a context with many potential referents. Children's tendency to associate novel words with novel referents, which is taken to reflect a mutual exclusivity (ME) bias, forms a useful disambiguation mechanism. We study semantic learning in pragmatic agents—combining the Rational Speech Act model with gradient‐based learning—and explore the conditions under which such agents show an ME bias. This approach provides a framework for investigating a pragmatic account (...) of the ME bias in humans but also for building artificial agents that display an ME bias. A series of analyses demonstrates striking parallels between our model and human word learning regarding several aspects relevant to the ME bias phenomenon: online inference, long‐term learning, and developmental effects. By testing different implementations, we find that two components, pragmatic online inference and incremental collection of evidence for one‐to‐one correspondences between words and referents, play an important role in modeling the developmental trajectory of the ME bias. Finally, we outline an extension of our model to a deep neural network architecture that can process more naturalistic visual and linguistic input. Until now, in contrast to children, deep neural networks have needed indirect access to (supposed to be novel) test inputs during training to display an ME bias. Our model is the first one to do so without using this manipulation. (shrink)

Ramscar, Yarlett, Dye, Denny, and Thorpe (2010) showed how, consistent with the predictions of error‐driven learning models, the order in which stimuli are presented in training can affect category learning. Specifically, learners exposed to artificial language input where objects preceded their labels learned the discriminating features of categories better than learners exposed to input where labels preceded objects. We sought to replicate this finding in two online experiments employing the same tests used originally: A four pictures test (match a label (...) to one of four pictures) and a four labels test (match a picture to one of four labels). In our study, only findings from the four pictures test were consistent with the original result. Additionally, the effect sizes observed were smaller, and participants over‐generalized high‐frequency category labels more than in the original study. We suggest that although Ramscar, Yarlett, Dye, Denny, and Thorpe (2010) feature‐label order predictions were derived from error‐driven learning, they failed to consider that this mechanism also predicts that performance in any training paradigm must inevitably be influenced by participant prior experience. We consider our findings in light of these factors, and discuss implications for the generalizability and replication of training studies. (shrink)

A word often expresses many different morphological functions. Which part of a word contributes to which part of the overall meaning is not always clear, which raises the question as to how such functions are learned. While linguistic studies tacitly assume the co-occurrence of cues and outcomes to suffice in learning these functions (Baer-Henney, Kügler, & van de Vijver, 2015; Baer-Henney & van de Vijver, 2012), error-driven learning suggests that contingency rather than contiguity is crucial (Nixon, 2020; Ramscar, Yarlett, Dye, (...) Denny, & Thorpe, 2010). In error-driven learning, cues gain association strength if they predict a certain outcome, and they lose strength if the outcome is absent. This reduction of association strength is called unlearning. So far, it is unclear if such unlearning has consequences for cue–outcome associations beyond the ones that get reduced. To test for such consequences of unlearning, we taught participants morphophonological patterns in an artificial language learning experiment. In one block, the cues to two morphological outcomes—plural and diminutive—co-occurred within the same word forms. In another block, a single cue to only one of these two outcomes was presented in a different set of word forms. We wanted to find out, if participants unlearn this cue's association with the outcome that is not predicted by the cue alone, and if this allows the absent cue to be associated with the absent outcome. Our results show that if unlearning was possible, participants learned that the absent cue predicts the absent outcome better than if no unlearning was possible. This effect was stronger if the unlearned cue was more salient. This shows that unlearning takes place even if no alternative cues to an absent outcome are provided, which highlights that learners take both positive and negative evidence into account—as predicted by domain general error-driven learning. (shrink)

In the 1990s, language acquisition researchers and theoretical linguists developed an interest in learning mechanisms, and learning theorists rediscovered the verbal learning tradition. Nonetheless, learning theory and language acquisition continued to develop largely independently, which has stymied progress in both fields. However, exciting progress is happening in applying learning theory to language, and, more recently, in using language learning data to advance domain‐general learning theory. These developments raise hopes for a bidirectional flow of information between the fields. The importance of (...) language data for learning theory and of learning theory for understanding language is briefly discussed. (shrink)

The literature on human and animal learning suggests that individuals attend to and act on cues differently based on the order in which they were learned. Recent studies have proposed that one specific type of learning outcome, the highlighting effect, can serve as a framework for understanding a number of early cognitive milestones. However, little is known how this learning effect itself emerges among children, whose memory and attention are much more limited compared to adults. Two experiments were conducted using (...) different versions of the general highlighting paradigm: Experiment 1 tested 3 to 6 year olds with a newly developed image-based version of the paradigm, which was designed specifically to test young children. Experiment 2 tested the validity of an image-based implementation of the highlighting paradigm with adult participants. The results from Experiment 1 provide evidence for the highlighting effect among children 3–6 years old, and they suggest age-related differences in dividing attention among multiple cues during learning. Experiment 2 replicated results from previous studies by showing robust biases for both image-based and text-based versions of the highlighting task. This study suggests that sensitivity to learning order emerges early through the process of cued attention, and the role of the highlighting effect in early language learning is discussed. (shrink)