Behavioral studies on language processing rely on the eye-mind assumption, which states that the time spent looking at text is an index of the time spent processing it. In most cases, relatively shorter reading times are interpreted as evidence of greater processing efficiency. However, previous evidence from L2 research indicates that non-native participants who present fast reading times are not always more efficient readers, but rather shallow parsers. Because earlier studies did not identify a reliable predictor of variability in L2 (...) processing, such uncertainty around the interpretation of reading times introduces a potential confound that undermines the credibility and the conclusions of online measures of processing. The present study proposes that a recently developed modulator of online processing efficiency, namely, chunking ability, may account for the observed variability in L2 online reading performance. L1 English – L2 Spanish learners’ eye movements were analyzed during natural reading. Chunking ability was predictive of overall reading speed. Target relative clauses contained L2 Verb-Noun multiword units, which were manipulated with regards to their L1-L2 congruency. The results indicated that processing of the L1-L2 incongruent units was modulated by an interaction of L2 chunking ability and level of knowledge of multiword units. Critically, the data revealed an inverse U-shaped pattern, with faster reading times in both learners with the highest and the lowest chunking ability scores, suggesting fast integration in the former, and lack of integration in the latter. Additionally, the presence of significant differences between conditions was correlated with individual chunking ability. The findings point at chunking ability as a significant modulator of general L2 processing efficiency, and of cross-language differences in particular, and add clarity to the interpretation of variability in the online reading performance of non-native speakers. (shrink)

In this review article, Christiansen provides a historical perspective on the two research traditions, implicit learning and statistical learning, thus nicely setting the scene for this special issue of Topics in Cognitive Science. In this “tale of two literatures”, he first traces the history of both literatures before sketching a framework that provides a basis for understanding implicit learning and statistical learning as a unified phenomenon.

Rule-like behaviour is found throughout human language, provoking a number of apparently conflicting explanations. This paper frames the topic in terms of Tinbergen’s four questions and works within the context of rule-like behaviour seen both in nature and the non-linguistic domain in humans. I argue for a minimal account of linguistic rules which relies on powerful domain-general cognition, has a communicative function allowing for multiple engineering solutions, and evolves mainly culturally, while leaving the door open for some genetic adaptation in (...) the form of learning biases. (shrink)

Evolutionary developmental systems theory stresses that selection pressures operate on entire developmental systems rather than just genes. This study extends this approach to language evolution, arguing that selection pressure may operate on two quasi-independent timescales. First, children clearly must acquire language successfully and evolution must equip them with the tools to do so. Second, while this is developing, they must also communicate with others in the moment using partially developed knowledge. These pressures may require different solutions, and their combination may (...) underlie the evolution of complex mechanisms for language development and processing. I present two case studies to illustrate how the demands of both real-time communication and language acquisition may be subtly different. The first case study examines infant-directed speech. A recent view is that IDS underwent cultural to statistical learning mechanisms that infants use to acquire the speech categories of their language. However, recent data suggest is it may not have evolved to enhance development, but rather to serve a more real-time communicative function. The second case study examines the argument for seemingly specialized mechanisms for learning word meanings. Both behavioral and computational work suggest that learning may be much slower and served by general-purpose mechanisms like associative learning. Fast-mapping, then, may be a real-time process meant to serve immediate communication, not learning, by augmenting incomplete vocabulary knowledge with constraints from the current context. Together, these studies suggest that evolutionary accounts consider selection pressure arising from both real-time communicative demands and from the need for accurate language development. (shrink)

A large body of psycholinguistic research demonstrates that both language processing and language acquisition are sensitive to the distributions of linguistic constructions in usage. Here we investigate how statistical distributions at different linguistic levels – morphological and lexical (Experiments 1 and 2), and phrasal (Experiment 2) – contribute to the ease with which morphosyntax is processed and produced by second language learners. We analyze Chinese ESL learners’ knowledge of four English inflectional morphemes:-ed,-ing, and third-person-son verbs, and plural-son nouns. In Elicited (...) Imitation Tasks, participants listened to length- and difficulty-matched sentences each containing one target morpheme and typed the whole sentence as accurately as they could after a short delay. Experiment 1 investigated lexical and morphemic levels, testing the hypotheses that a morpheme is expected to be more easily processed when it is (1) highlyavailable(i.e., occurring in frequent word-forms), and (2) highlyreliable(i.e., occurring in lemma words that are consistently conjugated in the form containing this morpheme). Thirty sentences were made for each morpheme, divided into three Availability-Reliability Distribution (ARD) groups on the basis of corpus analysis in the Corpus of Contemporary American English (COCA;Davies, 2008-): 10 target words high in availability, 10 high in reliability, and 10 low in both reliability and availability. Responses were scored on whether the target morpheme was accurately reproduced given the provision of the correct lemma. A generalized linear mixed-effects logit model (GLMM) revealed fixed effects of morpheme type, availability, and reliability on the accuracy of morpheme provision. There were no effects of lemma frequency. Experiment 2 successfully replicated these results and extended the investigation to explore phrasal formulaicity by manipulating the frequency of the four-word strings in which the morpheme was embedded. GLMMs replicated the effects of word-form availability and reliability and additionally revealed independentphrase-superiority effectswhere morphemes were better reproduced in contexts of higher string-frequency. Taken together, these findings demonstrate that morpheme acquisition reflects the distributional properties of learners’ experience and the mappings therein between lexis, morphology, phraseology, and semantics. These conclusions support an emergentist view of the statistical symbolic learning of morphology where language acquisition involves the satisfaction of competing constraints across multiple grain-sizes of units. (shrink)

The computations involved in statistical learning have long been debated. Here, we build on work suggesting that a basic memory process, chunking, may account for the processing of statistical regularities into larger units. Drawing on methods from the memory literature, we developed a novel paradigm to test statistical learning by leveraging a robust phenomenon observed in serial recall tasks: that short‐term memory is fundamentally shaped by long‐term distributional learning. In the statistically induced chunking recall (SICR) task, participants are exposed to (...) an artificial language, using a standard statistical learning exposure phase. Afterward, they recall strings of syllables that either follow the statistics of the artificial language or comprise the same syllables presented in a random order. We hypothesized that if individuals had chunked the artificial language into word‐like units, then the statistically structured items would be more accurately recalled relative to the random controls. Our results demonstrate that SICR effectively captures learning in both the auditory and visual modalities, with participants displaying significantly improved recall of the statistically structured items, and even recall specific trigram chunks from the input. SICR also exhibits greater test–retest reliability in the auditory modality and sensitivity to individual differences in both modalities than the standard two‐alternative forced‐choice task. These results thereby provide key empirical support to the chunking account of statistical learning and contribute a valuable new tool to the literature. (shrink)

Events—the experiences we think we are having and recall having had—are constructed; they are not what actually occurs. What occurs is ongoing dynamic, multidimensional, sensory flow, which is somehow transformed via psychological processes into structured, describable, memorable units of experience. But what is the nature of the redescription processes that fluently render dynamic sensory streams as event representations? How do such processes cope with the ubiquitous novelty and variability that characterize sensory experience? How are event‐rendering skills acquired and how do (...) event representations change with development? This review considers emerging answers to these questions, beginning with evidence that an implicit tendency to monitor predictability structure via statistical learning is key to event rendering. That is, one way that the experience of bounded events (e.g., actions within behavior, words within speech) arises is with the detection of “troughs” in sensory predictability. Interestingly, such troughs in predictability are often predictable; these regions of predictable‐unpredictability provide articulation points to demarcate one event from another in representations derived from the actual streaming information. In our information‐optimization account, a fluent event‐processor predicts such troughs and selectively attends to them—while suppressing attention to other regions—as sensory streams unfold. In this way, usage of attentional resources is optimized for efficient sampling of the most relevant, information‐rich portions of the unfolding flow of sensation. Such findings point to the development of event‐processing fluency—whether in action, language, or other domains—depending crucially on rapid and continual cognitive reorganization. As knowledge of predictability grows, attention is adaptively redeployed. Accordingly, event experiences undergo continuous alteration. (shrink)

Recent theoretical work in developmental psychology suggests that humans are predisposed to align their mental states with those of other individuals. One way this manifests is in cooperative communication ; that is, intentional communication aimed at aligning individuals’ mental states with respect to events in their shared environment. This idea has received strong empirical support. The purpose of this paper is to extend this account by proposing an integrative model of the biobehavioral dynamics of cooperative communication. Our formulation is based (...) on active inference. Active inference suggests that action-perception cycles operate to minimize uncertainty and optimize an individual’s internal model of the world. We propose that humans are characterized by an evolved adaptive prior belief that their mental states are aligned with, or similar to, those of conspecifics (i.e., that ‘we are the same sort of creature, inhabiting the same sort of niche’). The use of cooperative communication emerges as the principal means to gather evidence for this belief, allowing for the development of a shared narrative that is used to disambiguate interactants’ (hidden and inferred) mental states. Thus, by using cooperative communication, individuals effectively attune to a hermeneutic niche composed, in part, of others’ mental states; and, reciprocally, attune the niche to their own ends via epistemic niche construction. This means that niche construction enables features of the niche to encode precise, reliable cues about the deontic or shared value of certain action policies (e.g., the utility of using communicative constructions to disambiguate mental states, given expectations about shared prior beliefs). In turn, the alignment of mental states (prior beliefs) enables the emergence of a novel, contextualizing scale of cultural dynamics that encompasses the actions and mental states of the ensemble of interactants and their shared environment. The dynamics of this contextualizing layer of cultural organization feedback, across scales, to constrain the variability of the prior expectations of the individuals who constitute it. Our theory additionally builds upon the active inference literature by introducing a new set of neurobiologically plausible computational hypotheses for cooperative communication. We conclude with directions for future research. (shrink)

How linguistic structures evolve so as to become easier to process is addressed by Isbilen and Christiansen for the Now‐or‐Never bottleneck. The authors suggest that this fundamental challenge in language processing is coped with by rapid compression of the transient linguistic input into chunks then to be passed on. As linguistic structures that can be chunked more easily tend to stabilize and proliferate, language evolves to fit learners’ cognitive capabilities.

Working memory has been shown to be strongly related to fluid intelligence; however, our goal is to shed further light on the process of information compression in working memory as a determining factor of fluid intelligence. Our main hypothesis was that compression in working memory is an excellent indicator for studying the relationship between working-memory capacity and fluid intelligence because both depend on the optimization of storage capacity. Compressibility of memoranda was estimated using an algorithmic complexity metric. The results showed (...) that compressibility can be used to predict working-memory performance and that fluid intelligence is well predicted by the ability to compress information. We conclude that the ability to compress information in working memory is the reason why both manipulation and retention of information are linked to intelligence. This result offers a new concept of intelligence based on the idea that compression and intelligence are equivalent problems. (shrink)

The ability to convey our thoughts using an infinite number of linguistic expressions is one of the hallmarks of human language. Understanding the nature of the psychological mechanisms and representations that give rise to this unique productivity is a fundamental goal for the cognitive sciences. A long-standing hypothesis is that single words and rules form the basic building blocks of linguistic productivity, with multiword sequences being treated as units only in peripheral cases such as idioms. The new millennium, however, has (...) seen a shift toward construing multiword linguistic units not as linguistic rarities, but as important building blocks for language acquisition and processing. This shift—which originated within theoretical approaches that emphasize language learning and use—has far-reaching implications for theories of language representation, processing, and acquisition. Incorporating multiword units as integral building blocks blurs the distinction between grammar and lexicon; calls for models of production and comprehension that can accommodate and give rise to the effect of multiword information on processing; and highlights the importance of such units to learning. In this special topic, we bring together cutting-edge work on multiword sequences in theoretical linguistics, first-language acquisition, psycholinguistics, computational modeling, and second-language learning to present a comprehensive overview of the prominence and importance of such units in language, their possible role in explaining differences between first- and second-language learning, and the challenges the combined findings pose for theories of language. (shrink)

Multiple constraints, including semantic, lexical, and usage-based factors, have been shown to influence dative alternation across different languages. This work explores whether fine-grained statistics and semantic properties of the verb affect the acceptability of dative constructions in Spanish. First, a corpus analysis reveals that verbs of different semantic classes occur naturally in alternative dative constructions, a pattern quite different from English. The fact that dative alternation appears independent of semantic classes challenges traditional semantic-based approaches. Second, acceptability rating tasks reveal that (...) dative constructions containing highly entrenched word combinations and frequent word order are judged more acceptable regardless of the verbs' semantics. The results are interpreted in favor of usage-based accounts: Acceptability of dative constructions may be language-specific and depend on patterns of use and conventionalization. (shrink)

In the generative tradition, the language faculty has been shrinking—perhaps to include only the mechanism of recursion. This paper argues that even this view of the language faculty is too expansive. We first argue that a language faculty is difficult to reconcile with evolutionary considerations. We then focus on recursion as a detailed case study, arguing that our ability to process recursive structure does not rely on recursion as a property of the grammar, but instead emerges gradually by piggybacking on (...) domain-general sequence learning abilities. Evidence from genetics, comparative work on non-human primates, and cognitive neuroscience suggests that humans have evolved complex sequence learning skills, which were subsequently pressed into service to accommodate language. Constraints on sequence learning therefore have played an important role in shaping the cultural evolution of linguistic structure, including our limited abilities for processing recursive structure. Finally, we re-evaluate some of the key considerations that have often been taken to require the postulation of a language faculty. (shrink)

General principles of human cognition can help to explain why languages are more likely to have certain characteristics than others: structures that are difficult to process or produce will tend to be lost over time. One aspect of cognition that is implicated in language use is working memory—the component of short‐term memory used for temporary storage and manipulation of information. In this study, we consider the relationship between working memory and regularization of linguistic variation. Regularization is a well‐documented process whereby (...) languages become less variable (on some dimension) over time. This process has been argued to be driven by the behavior of individual language users, but the specific mechanism is not agreed upon. Here, we use an artificial language learning experiment to investigate whether limitations in working memory during either language learning or language production drive regularization behavior. We find that taxing working memory during production results in the loss of all types of variation, but the process by which random variation becomes more predictable is better explained by learning biases. A computational model offers a potential explanation for the production effect using a simple self‐priming mechanism. (shrink)

Cognitive Science, Volume 46, Issue 1, January 2022.

A key component of research on human sentence processing is to characterize the processing difficulty associated with the comprehension of words in context. Models that explain and predict this difficulty can be broadly divided into two kinds, expectation‐based and memory‐based. In this work, we present a new model of incremental sentence processing difficulty that unifies and extends key features of both kinds of models. Our model, lossy‐context surprisal, holds that the processing difficulty at a word in context is proportional to (...) the surprisal of the word given a lossy memory representation of the context—that is, a memory representation that does not contain complete information about previous words. We show that this model provides an intuitive explanation for an outstanding puzzle involving interactions of memory and expectations: language‐dependent structural forgetting, where the effects of memory on sentence processing appear to be moderated by language statistics. Furthermore, we demonstrate that dependency locality effects, a signature prediction of memory‐based theories, can be derived from lossy‐context surprisal as a special case of a novel, more general principle called information locality. (shrink)

Neural networks have long been used to study linguistic phenomena spanning the domains of phonology, morphology, syntax, and semantics. Of these domains, semantics is somewhat unique in that there is little clarity concerning what a model needs to be able to do in order to provide an account of how the meanings of complex linguistic expressions, such as sentences, are understood. We argue that one thing such models need to be able to do is generate predictions about which further sentences (...) are likely to follow from a given sentence; these define the sentence's “inferential role.” We then show that it is possible to train a tree-structured neural network model to generate very simple examples of such inferential roles using the recently released Stanford Natural Language Inference (SNLI) dataset. On an empirical front, we evaluate the performance of this model by reporting entailment prediction accuracies on a set of test sentences not present in the training data. We also report the results of a simple study that compares human plausibility ratings for both human-generated and model-generated entailments for a random selection of sentences in this test set. On a more theoretical front, we argue in favor of a revision to some common assumptions about semantics: understanding a linguistic expression is not only a matter of mapping it onto a representation that somehow constitutes its meaning; rather, understanding a linguistic expression is mainly a matter of being able to draw certain inferences. Inference should accordingly be at the core of any model of semantic cognition. (shrink)

In this article, I present a substantive proposal about the timing and nature of the final stage of the evolution of full human language, the transition from so-called “protolanguage” to language, and on the origins of a simple protolanguage with structure and displaced reference; a proposal that depends on the idea that the initial expansion of communicative powers in our lineage involved a much expanded role for gesture and mime. But though it defends a substantive proposal, the article also defends (...) and illustrates a methodological proposal too. I argue that language is a special case of a more general phenomenon—cumulative cultural evolution—and while we rarely have direct information about communication, we have more direct information about the cumulative cultural evolution of technical skill, ecological strategies, and social complexity. These same factors also enable us to make a reasonable estimate of the intergenerational social learning capacities of these communities and of the communicative demands these communities face. For example, we can, at least tentatively, identify forms of cooperation that are stable only if third party information is transmitted widely, cheaply, and accurately. So we can use these more direct markers of information accumulation to locate, in broad terms, the period in our evolutionary history during which we became lingual. (shrink)

This paper has two aims. One is to defend an incrementalist view of the evolution of language, not from those who think that syntax could not evolve incrementally, but from those who defend a fundamental distinction between Gricean communication or ostensive inferential communication and code-based communication. The paper argues against this dichotomy, and sketches ways in which a code-based system could evolve into Gricean communication. The second is to assess the merits of the Sender–Receiver Framework, originally formulated by David Lewis, (...) and much elaborated and set into an evolutionary context by Brian Skyrms and colleagues, as a framework for thinking about the evolution of language. Despite the great strengths of that framework, and despite the great value of a framework that is both general and formally tractable, I argue that there are critical features of language that it fails to capture. (shrink)

Inspired by early proposals in philosophy, dominant accounts of language posit a central role for mutual knowledge, either encoded directly in common ground, or approximated through other cognitive mechanisms. Using existing empirical evidence from language and memory, we challenge this tradition, arguing that mutual knowledge captures only a subset of the mental states needed to support communication. In a novel theoretical proposal, we argue for a cognitive architecture that includes separate, distinct representations of the self and other, and a cognitive (...) process that compares these representations continuously during conversation, outputting both similarities and differences in perspective. Our theory accounts for existing data, interfaces with findings from other cognitive domains, and makes novel predictions about the role of perspective in language use. We term this new account the Multiple Perspectives Theory of mental states in communication. (shrink)

Statistical learning (SL) involving sensitivity to distributional regularities in the environment has been suggested to be an important factor in many aspects of cognition, including language. However, the degree to which statistically-learned information is retained over time is not well understood. To establish whether or not learners are able to preserve such regularities over time, we examined performance on an artificial second language learning task both immediately after training and also at a follow-up session 2 weeks later. Participants were exposed (...) to an artificial language (Brocanto2), half of them receiving simplified training items in which only 20% of sequences contained complex structures, whereas the other half were exposed to a training set in which 80% of the items were composed of complex sequences. Overall, participants showed signs of learning at the first session and retention at the second, but the degree of learning was affected by the nature of the training they received. Participants exposed to the simplified input outperformed those in the more complex training condition. A GLMM was used to model the relationship between stimulus properties and participants’ endorsement strategies across both sessions. The results indicate that participants in the complex training condition relied more on an item’s chunk strength than those in the simple training condition. Taken together, this set of findings shows that statistically learned regularities are retained over the course of 2 weeks. The results also demonstrate that training on input featuring simple items leads to improved learning and retention of grammatical regularities. (shrink)

Levelt, reviewing the AGL field from a psycholinguistic perspective, identifies various gaps and makes a number of concrete suggestions for improving several currently used experimental designs. He raises the question whether artificial (and natural) grammar learning is about detecting ‘rules’, as is commonly assumed, or rather the detection of a set of ‘constraints’. He cautions the community to not ignore ‘semantics’, and recommends to consider less artificial tasks, that may be needed for learning more complex rules by human or nonhuman (...) animals. (shrink)

It has been suggested that external and/or internal limitations paradoxically may lead to superior learning, that is, the concepts of starting small and less is more (Elman, ; Newport, ). In this paper, we explore the type of incremental ordering during training that might help learning, and what mechanism explains this facilitation. We report four artificial grammar learning experiments with human participants. In Experiments 1a and 1b we found a beneficial effect of starting small using two types of simple recursive (...) grammars: right‐branching and center‐embedding, with recursive embedded clauses in fixed positions and fixed length. This effect was replicated in Experiment 2 (N = 100). In Experiment 3 and 4, we used a more complex center‐embedded grammar with recursive loops in variable positions, producing strings of variable length. When participants were presented an incremental ordering of training stimuli, as in natural language, they were better able to generalize their knowledge of simple units to more complex units when the training input “grew” according to structural complexity, compared to when it “grew” according to string length. Overall, the results suggest that starting small confers an advantage for learning complex center‐embedded structures when the input is organized according to structural complexity. (shrink)

A classic problem in cognitive science concerns how listeners perceive and understand speech as comprised of discrete words. We propose a Syllable Inference account of spoken word recognition and segmentation, under which alternative hierarchical models of syllables, words, and phonemes are dynamically posited from cues that include current and past speech rate, with a goal of maximal prediction of sensory input. Three experiments using the Visual World eye‐tracking paradigm provide evidence supporting our proposal.

The general principles of perceptuo-motor processing and memory give rise to theNow-or-Never bottleneckconstraint imposed on the organization of the language processing system. In particular, the Now-or-Never bottleneck demands an appropriate structure of linguistic input and rapid incorporation of both linguistic and multisensory contextual information in a progressive, integrative manner. I argue that the emerging predictive processing framework is well suited for the task of providing a comprehensive account of language processing under the Now-or-Never constraint. Moreover, this framework presents a stronger (...) alternative to theChunk-and-Passaccount proposed by Christiansen and Chater (2016), as it better accommodates the available evidence concerning the role of context (in both the narrow and wider senses) in language comprehension at various levels of linguistic representation. Furthermore, the predictive processing approach allows for treating language as a special case of domain-general processing strategies, suggesting deep parallels with other cognitive processes such as vision. (shrink)

The present event‐related potential (ERP) study used picture–sentence verification to investigate the neurolinguistic correlates of the online processing of compositional‐semantic information. To this end, we examined context effects on sentences involving temporal adverbial quantification likeJana war jeden Morgen schwimmen an den Arbeitstagen (“Jana went for a swim every morning during the working week”). We tested whether the conceptual complexity associated with quantifying over time intervals leads to delayed predictions regarding the upcoming words in a sentence. The present study replicated previous (...) results relating to quantification over individuals, which are conceptually less complex than time intervals. Analogous to previous studies, false vs. true sentences elicited an N400 whenever contextual cues did not permit a potential revision of a locally assigned truth value. The present results are compatible with an approach under which contextual cues are immediately considered for predicting how a sentence continues. The fact that the contextual complexity did not lead to processing delays indicates that the processing system quickly abstracts away from the conceptual complexity associated with the linguistic input if such an abstraction is possible. (shrink)

Heyes argues that human metacognitive strategies (“cognitive gadgets” or “mills”) are the products of cultural evolution based on domain‐general cognition with few simple biases. Although like Heyes, we believe that the evolution of domain‐general cognitive processes played a crucial role in the evolution of human cognition, we argue that Heyes' distinction between mills and grist is too sharp, that associative learning evolved gradually to become more complex and hierarchical, something that is not captured by the system 1/system 2 distinction, and (...) that human cognitive plasticity required the genetic accommodation of gadget‐specific processes that led to more plasticity‐enhancing human‐specific biases. (shrink)

This thesis proposes a perspective on language and its development by starting from two approaches. The first is the ecological-enactive approach to cognition. In opposition to the widespread idea that cognition is information-processing in the brain, the ecological-enactive approach explains human cognition in relational terms, as skilful interactions with a sociomaterial environment shaped by practices. The second is the metalinguistic approach to language, which holds that reflexive or metalinguistic language use – talking about talking – is crucial for understanding language (...) and its development. In particular, I defend two theses: 1. A child’s initial communicative behaviour can be explained in terms of attentional actions: social actions that function by directing someone else’s attention. 2. In order for the child’s communicative behaviour to be sensitive to key properties of language, such as semantic content and normativity, she needs to learn metalinguistic skills. The development of this ecological-enactive perspective on language serves two functions. First, the ecological-enactive approach started by considering basic behaviour, such as locomotion and grasping. An approach in the cognitive sciences, however, should be able to account for the full gamut of human cognition. If the perspective developed in this thesis is viable, this is a contribution to extending the ecological-enactive approach to typically human forms of cognition. Second, this perspective throws new light on philosophical problems concerning language. In the different chapters, I deal with questions concerning the nature of linguistic knowledge, explanations of communicative behaviour, and the origins of semantic content and linguistic normativity. (shrink)

This paper outlines how the focus on how communicative signals might emerge and how the capacity to interpret them might develop, does not yet explain what type of motivation is required to actually deal with those signals. Without the consistent production of appropriate responses to the production of communicative signals, there would be no point in producing any signal. If language is a tool to accomplish things with others, we need to understand what would lead to cooperation. The first step (...) consists in avoiding the blind belief that all cooperation requires some prosocial attitude. A great deal of cooperation can occur while each participant in the interaction is selfishly attempting to maximize their own benefits or minimizing damaging consequences.I describe how different types of turn-taking can be achieved via different levels of cognitive complexity and how interpretive turn-taking requires a great deal of cognitive abilities that great apes possess. Finally, I provide empirical evidence of social manipulation in non-human primates. Given our awareness of the occurrence of social manipulation during cooperation among human adults, it seems necessary to reconsider to what degree human communication and language evolution require unique prosocial motivations. (shrink)

We trained a computational model (the Chunk-Based Learner; CBL) on a longitudinal corpus of child–caregiver interactions in English to test whether one proposed statistical learning mechanism—backward transitional probability—is able to predict children's speech productions with stable accuracy throughout the first few years of development. We predicted that the model less accurately reconstructs children's speech productions as they grow older because children gradually begin to generate speech using abstracted forms rather than specific “chunks” from their speech environment. To test this idea, (...) we trained the model on both recently encountered and cumulative speech input from a longitudinal child language corpus. We then assessed whether the model could accurately reconstruct children's speech. Controlling for utterance length and the presence of duplicate chunks, we found no evidence that the CBL becomes less accurate in its ability to reconstruct children's speech with age. (shrink)

Beller, Bender, & Jordan [Intro]. Which factors have triggered, constrained, or shaped the course of cognitive evolution is a question of key interest to cognitive science. The topic introduced here highlights the relevance of culture as a driving force in this process. It provides an overview of current empirical and theoretical work leading this field, and it investigates the potential for integrating multiple perspectives across several timescales and levels of analysis.

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)

Cognitive Science, Volume 45, Issue 11, November 2021.

In this paper, I develop an ecological-enactive perspective on the role rules play in linguistic behaviour. I formulate and motivate the hypothesis that metalinguistic reflexivity – our ability to talk about talking – is constitutive of linguistic normativity. On first sight, this hypothesis might seem to fall prey to a regress objection. By discussing the work of Searle, I show that this regress objection originates in the idea that learning language involves learning to follow rules from the very start. I (...) propose an ecological-enactive response to the regress objection. The key move is to deny that language learning consists initially in learning rules. A child first engages in regular communicative behaviour, by learning first-order linguistic skills, and then retroactively interprets her own behaviour in normative metalinguistic terms, i.e., as being guided by rules by relying on reflexive or second-order linguistic skills. On this view, metalinguistic reflexivity enables regulation of already regular communicative behaviour, and thereby constitutes linguistic normativity. Finally, I argue that linguistic rules are resources: they are available to participants in order to (re)negotiate properties of situated language behaviour and thereby reorganize linguistic practices. The account developed in this paper thus allows us to understand the constitutive role of metalinguistic reflexivity for linguistic normativity without falling prey to the regress objection. (shrink)

Healey et al. use experiments with chat dialogues to test the hypothesis that language co‐ordination is driven by ‘running repairs’. They replace signals of understanding such as “okay” with weaker, ‘spoof’ signals like “ummm”, and replace specific requests for clarification like “on the left?” with signals that suggest a higher degree of misunderstanding like “what?”. The latter manipulation causes participants to switch rapidly to more abstract forms of referring expression.

The language parasite approach refers to the view that language, like a parasite, is an adaptive system that evolves to fit its human hosts. Supported by recent computer simulations, LPA proponents claim that the reason that humans can use languages with ease is not because we have evolved with genetically specified linguistic instincts but because languages have adapted to the preexisting brain structures of humans. This article examines the LPA. It argues that, while the LPA has advantages over its rival, (...) Chomskyan nativism, there are additional factors that may limit linguistic variety that have yet to be identified by its insightful proponents. This article suggests abandoning the search for a decisive cause of language capacity and argues that language evolution is more likely to arise from balancing multiple engineering constraints. (shrink)

Previous work on individual differences has revealed limitations in the ability of existing measures (e.g., working memory) to predict language processing. Recent evidence suggests that an individual's sensitivity to detect the statistical regularities present in language (i.e., “chunk sensitivity”) may significantly modulate online sentence processing. We investigated whether individual chunk sensitivity predicted the online processing of gender cues, a core linguistic feature of Spanish. In a self‐paced reading task, we examined native speakers’ processing of relative clauses in which gender cues (...) were variably exploited to induce processing costs. Even after considering the effect of working memory and cognitive control, the results revealed a significant effect of chunking ability in modulating online sentence processing. Critically, higher‐chunking ability speakers’ reading times showed online sensitivity to core linguistic cues online; while low‐chunking ability readers showed no sensitivity to manipulations, indicating shallow real‐time processing of their native language. (shrink)

Identifying wordlike units in language is typically done by applying a battery of criteria, though how to weight these criteria with respect to one another is currently unknown. We address this question by investigating whether certain criteria are also used as cues for learning an artificial language—if they are, then perhaps they can be relied on more as trustworthy top‐down diagnostics. The two criteria for grammatical wordhood that we consider are a unit's free mobility and its internal immutability. These criteria (...) also map to two cognitive mechanisms that could underlie successful statistical learning: learners might orient themselves around the low transitional probabilities at unit boundaries, or they might seek chunks with high internal transitional probabilities. We find that each criterion has its own facilitatory effect, and learning is best where they both align. This supports the battery‐of‐criteria approach to diagnosing wordhood, and also suggests that the mechanism behind statistical learning may not be a question of either/or; perhaps the two mechanisms do not compete, but mutually reinforce one another. (shrink)