How hierarchical is language use?

It is generally assumed that hierarchical phrase structure plays a central role in human language. However, considerations of simplicity and evolutionary continuity suggest that hierarchical structure should not be invoked too hastily. Indeed, recent neurophysiological, behavioural and computational studies show that sequential sentence structure has considerable explanatory power and that hierarchical processing is often not involved. In this paper, we review evidence from the recent literature supporting the hypothesis that sequential structure may be fundamental to the comprehension, production and acquisition of human language. Moreover, we provide a preliminary sketch outlining a non-hierarchical model of language use and discuss its implications and testable predictions. If linguistic phenomena can be explained by sequential rather than hierarchical structure, this will have considerable impact in a wide range of fields, such as linguistics, ethology, cognitive neuroscience, psychology and computer science.     

Co-evolution of learning complexity and social foraging strategies

Variation in learning abilities within populations suggests that complex learning may not necessarily be more adaptive than simple learning. Yet, the high cost of complex learning cannot fully explain this variation without some understanding of why complex learning is too costly for some individuals but not for others. Here we propose that different social foraging strategies can favor different learning strategies (that learn the environment with high or low resolution), thereby maintaining variable learning abilities within populations. Using a genetic algorithm in an agent-based evolutionary simulation of a social foraging game (the producer-scrounger game) we demonstrate how an association evolves between a strategy based on independent search for food (playing a producer) and a complex (high resolution) learning rule, while a strategy that combines independent search and following others (playing a scrounger) evolves an association with a simple (low resolution) learning rule. The reason for these associations is that for complex learning to have an advantage, a large number of learning steps, normally not achieved by scroungers, are necessary. These results offer a general explanation for persistent variation in cognitive abilities that is based on co-evolution of learning rules and social foraging strategies.     

The origins of gestures and language: history,current advances and proposed theories

Investigating in depth the mechanisms underlying human and non‐human primate intentional communication systems (involving gestures, vocalisations, facial expressions and eye behaviours) can shed light on the evolutionary roots of language. Reports on non‐human primates, particularly great apes, suggest that gestural communication would have been a crucial prerequisite for the emergence of language, mainly based on the evidence of large communication repertoires and their associated multifaceted nature of intentionality that are key properties of language. Such research fuels important debates on the origins of gestures and language. We review here three non‐mutually exclusive processes that can explain mainly great apes' gestural acquisition and development: phylogenetic ritualisation, ontogenetic ritualisation, and learning via social negotiation. We hypothesise the following scenario for the evolutionary origins of gestures: gestures would have appeared gradually through evolution via signal ritualisation following the principle of derived activities, with the key involvement of emotional expression and processing. The increasing level of complexity of socioecological lifestyles and associated daily manipulative activities might then have enabled the acquisition and development of different interactional strategies throughout the life cycle. Many studies support a multimodal origin of language. However, we stress that the origins of language are not only multimodal, but more broadly multicausal. We propose a multicausal theory of language origins which better explains current findings. It postulates that primates' communicative signalling is a complex trait continually shaped by a cost–benefit trade‐off of signal production and processing of interactants in relation to four closely interlinked categories of evolutionary and life cycle factors: species, individual and context‐related characteristics as well as behaviour and its characteristics. We conclude by suggesting directions for future research to improve our understanding of the evolutionary roots of gestures and language.     

The evolution of theory of mind on welfare tradeoff ratios

People seem to attribute beliefs and desires to another person when interacting with them. Such a “theory of mind” capacity is essential for complex and uniquely human behavior such as language, but its evolutionary origin remains elusive. Using the formal tools of evolutionary game theory, we asked what environmental properties are necessary to select for a basic form of theory of mind—the ability to infer the prosociality, quantified by the welfare tradeoff ratio, of another person toward oneself. We found that none of the environments studied in classical or evolutionary game theory give an advantage to this form of theory of mind capacities; theory of mind is advantageous only in a new class of environments with stable opponents and variable payoff structures. In two behavioral experiments (n = 91) we verified that people can, and do use theory of mind in such an environment. These results suggest that some features of early humans’ social environment that were previously neglected in evolutionary game theory may be responsible for the evolution of people’s complex social capacities.     

Foraging Ecology Predicts Learning Performance in Insectivorous Bats

Bats are unusual among mammals in showing great ecological diversity even among closely related species and are thus well suited for studies of adaptation to the ecological background. Here we investigate whether behavioral flexibility and simple- and complex-rule learning performance can be predicted by foraging ecology. We predict faster learning and higher flexibility in animals hunting in more complex, variable environments than in animals hunting in more simple, stable environments. To test this hypothesis, we studied three closely related insectivorous European bat species of the genus Myotis that belong to three different functional groups based on foraging habitats: M. capaccinii, an open water forager, M. myotis, a passive listening gleaner, and M. emarginatus, a clutter specialist. We predicted that M. capaccinii would show the least flexibility and slowest learning reflecting its relatively unstructured foraging habitat and the stereotypy of its natural foraging behavior, while the other two species would show greater flexibility and more rapid learning reflecting the complexity of their natural foraging tasks. We used a purposefully unnatural and thus species-fair crawling maze to test simple- and complex-rule learning, flexibility and re-learning performance. We found that M. capaccinii learned a simple rule as fast as the other species, but was slower in complex rule learning and was less flexible in response to changes in reward location. We found no differences in re-learning ability among species. Our results corroborate the hypothesis that animals’ cognitive skills reflect the demands of their ecological niche.     

Chaos and language

Human language is a complex communication system with unlimited expressibility. Children spontaneously develop a native language by exposure to linguistic data from their speech community. Over historical time, languages change dramatically and unpredictably by accumulation of small changes and by interaction with other languages. We have previously developed a mathematical model for the acquisition and evolution of language in heterogeneous populations of speakers. This model is based on game dynamical equations with learning. Here, we show that simple examples of such equations can display complex limit cycles and chaos. Hence, language dynamical equations mimic complicated and unpredictable changes of languages over time. In terms of evolutionary game theory, we note that imperfect learning can induce chaotic switching among strict Nash equilibria.     

The evolutionary language game.

We explore how evolutionary game dynamics have to be modified to accomodate a mathematical framework for the evolution of language. In particular, we are interested in the evolution of vocabulary, that is associations between signals and objects. We assume that successful communication contributes to biological fitness: individuals who communicate well leave more offspring. Children inherit from their parents a strategy for language learning (a language acquisition device). We consider three mechanisms whereby language is passed from one generation to the next: (i) parental learning: children learn the language of their parents; (ii) role model learning: children learn the language of individuals with a high payoff; and (iii) random learning: children learn the language of randomly chosen individuals. We show that parental and role model learning outperform random learning. Then we introduce mistakes in language learning and study how this process changes language over time. Mistakes increase the overall efficacy of parental and role model learning: in a world with errors evolutionary adaptation is more efficient. Our model also provides a simple explanation why homonomy is common while synonymy is rare.     

A natural genetic polymorphism affects retroactive interference in Drosophila melanogaster

As environments change, animals update their internal representations of the external world. New information about the environment is learned and retained whereas outdated information is disregarded or forgotten. Retroactive interference (RI) occurs when the retrieval of previously learned information is less available owing to the acquisition of recently acquired information. Even though RI is thought to be a major cause of forgetting, its functional significance is still under debate. We find that natural allelic variants of the Drosophila melanogaster foraging gene known to affect rover and sitter behaviour differ in RI. More specifically, rovers who were previously shown to experience greater environmental heterogeneity while foraging display RI whereas sitters do not. Rover responses are biased towards more recent learning events. These results provide an ecological context to investigate the function of forgetting via RI and a suitable genetic model organism to address the evolutionary relevance of cognitive tasks.     

How Could Language Have Evolved?

The evolution of the faculty of language largely remains an enigma. In this essay, we ask why. Language''s evolutionary analysis is complicated because it has no equivalent in any nonhuman species. There is also no consensus regarding the essential nature of the language “phenotype.” According to the “Strong Minimalist Thesis,” the key distinguishing feature of language (and what evolutionary theory must explain) is hierarchical syntactic structure. The faculty of language is likely to have emerged quite recently in evolutionary terms, some 70,000–100,000 years ago, and does not seem to have undergone modification since then, though individual languages do of course change over time, operating within this basic framework. The recent emergence of language and its stability are both consistent with the Strong Minimalist Thesis, which has at its core a single repeatable operation that takes exactly two syntactic elements a and b and assembles them to form the set {a, b}.It is uncontroversial that language has evolved, just like any other trait of living organisms. That is, once—not so long ago in evolutionary terms—there was no language at all, and now there is, at least in Homo sapiens. There is considerably less agreement as to how language evolved. There are a number of reasons for this lack of agreement. First, “language” is not always clearly defined, and this lack of clarity regarding the language phenotype leads to a corresponding lack of clarity regarding its evolutionary origins. Second, there is often confusion as to the nature of the evolutionary process and what it can tell us about the mechanisms of language. Here we argue that the basic principle that underlies language''s hierarchical syntactic structure is consistent with a relatively recent evolutionary emergence.     

Neurophysiological mechanisms involved in language learning in adults

Little is known about the brain mechanisms involved in word learning during infancy and in second language acquisition and about the way these new words become stable representations that sustain language processing. In several studies we have adopted the human simulation perspective, studying the effects of brain-lesions and combining different neuroimaging techniques such as event-related potentials and functional magnetic resonance imaging in order to examine the language learning (LL) process. In the present article, we review this evidence focusing on how different brain signatures relate to (i) the extraction of words from speech, (ii) the discovery of their embedded grammatical structure, and (iii) how meaning derived from verbal contexts can inform us about the cognitive mechanisms underlying the learning process. We compile these findings and frame them into an integrative neurophysiological model that tries to delineate the major neural networks that might be involved in the initial stages of LL. Finally, we propose that LL simulations can help us to understand natural language processing and how the recovery from language disorders in infants and adults can be accomplished.     

The challenge of challenge: Can problem solving opportunities enhance animal welfare?

Cognitive mechanisms are an important part of the organization of the behavior systems of animals. In the wild, animals regularly face problems that they must overcome in order to survive and thrive. Solving such problems often requires animals to process, store, retrieve, and act upon information from the environment—in other words, to use their cognitive skills. For example, animals may have to use navigational, tool-making or cooperative social skills in order to procure their food. However, many enrichment programs for captive animals do not include the integration of these types of cognitive challenges. Thus, foraging enrichments typically are designed to facilitate the physical expression of feeding behaviors such as food-searching and food consumption, but not to facilitate complex problem solving behaviors related to food acquisition. Challenging animals by presenting them with problems is almost certainly a source of frustration and stress. However, we suggest here that this is an important, and even necessary, feature of an enrichment program, as long as animals also possess the skills and resources to effectively solve the problems with which they are presented. We discuss this with reference to theories about the emotional consequences of coping with challenge, the association between lack of challenge and the development of abnormal behavior, and the benefits of stress (arousal) in facilitating learning and memory of relevant skills. Much remains to be done to provide empirical support for these theories. However, they do point the way to a practical approach to improving animal welfare—to design enrichments to facilitate the cognitive mechanisms which underlie the performance of complex behaviors that cannot be performed due to the restrictions inherent to the captive environment.     

Language cannot be reduced to biology: Perspectives from neuro-developmental disorders affecting language learning

The study of language knowledge guided by a purely biological perspective prioritizes the study of syntax. The essential process of syntax is recursion — the ability to generate an infinite array of expressions from a limited set of elements. Researchers working within the biological perspective argue that this ability is possible only because of an innately specified genetic makeup that is specific to human beings. Such a view of language knowledge may be fully justified in discussions on biolinguistics, and in evolutionary biology. However, it is grossly inadequate in understanding language-learning problems, particularly those experienced by children with neurodevelopmental disorders such as developmental dyslexia, Williams syndrome, specific language impairment and autism spectrum disorders. Specifically, syntax-centered definitions of language knowledge completely ignore certain crucial aspects of language learning and use, namely, that language is embedded in a social context; that the role of envrironmental triggering as a learning mechanism is grossly underestimated; that a considerable extent of visuo-spatial information accompanies speech in day-to-day communication; that the developmental process itself lies at the heart of knowledge acquisition; and that there is a tremendous variation in the orthographic systems associated with different languages. All these (socio-cultural) factors can influence the rate and quality of spoken and written language acquisition resulting in much variation in phenotypes associated with disorders known to have a genetic component. Delineation of such phenotypic variability requires inputs from varied disciplines such as neurobiology, neuropsychology, linguistics and communication disorders. In this paper, I discuss published research that questions cognitive modularity and emphasises the role of the environment for understanding linguistic capabilities of children with neuro-developmental disorders. The discussion pertains to two specific disorders, developmental dyslexia and Williams syndrome.     

Biological hierarchies and the nature of extinction

Hierarchy theory recognises that ecological and evolutionary units occur in a nested and interconnected hierarchical system, with cascading effects occurring between hierarchical levels. Different biological disciplines have routinely come into conflict over the primacy of different forcing mechanisms behind evolutionary and ecological change. These disconnects arise partly from differences in perspective (with some researchers favouring ecological forcing mechanisms while others favour developmental/historical mechanisms), as well as differences in the temporal framework in which workers operate. In particular, long‐term palaeontological data often show that large‐scale (macro) patterns of evolution are predominantly dictated by shifts in the abiotic environment, while short‐term (micro) modern biological studies stress the importance of biotic interactions. We propose that thinking about ecological and evolutionary interactions in a hierarchical framework is a fruitful way to resolve these conflicts. Hierarchy theory suggests that changes occurring at lower hierarchical levels can have unexpected, complex effects at higher scales due to emergent interactions between simple systems. In this way, patterns occurring on short‐ and long‐term time scales are equally valid, as changes that are driven from lower levels will manifest in different forms at higher levels. We propose that the dual hierarchy framework fits well with our current understanding of evolutionary and ecological theory. Furthermore, we describe how this framework can be used to understand major extinction events better. Multi‐generational attritional loss of reproductive fitness (MALF) has recently been proposed as the primary mechanism behind extinction events, whereby extinction is explainable solely through processes that result in extirpation of populations through a shutdown of reproduction. While not necessarily explicit, the push to explain extinction through solely population‐level dynamics could be used to suggest that environmentally mediated patterns of extinction or slowed speciation across geological time are largely artefacts of poor preservation or a coarse temporal scale. We demonstrate how MALF fits into a hierarchical framework, showing that MALF can be a primary forcing mechanism at lower scales that still results in differential survivorship patterns at the species and clade level which vary depending upon the initial environmental forcing mechanism. Thus, even if MALF is the primary mechanism of extinction across all mass extinction events, the primary environmental cause of these events will still affect the system and result in differential responses. Therefore, patterns at both temporal scales are relevant.     

If at first you don't succeed… Studies of ontogeny shed light on the cognitive demands of habitual tool use

Many species use tools, but the mechanisms underpinning the behaviour differ between species and even among individuals within species, depending on the variants performed. When considering tool use ‘as adaptation’, an important first step is to understand the contribution made by fixed phenotypes as compared to flexible mechanisms, for instance learning. Social learning of tool use is sometimes inferred based on variation between populations of the same species but this approach is questionable. Specifically, alternative explanations cannot be ruled out because population differences are also driven by genetic and/or environmental factors. To better understand the mechanisms underlying routine but non-universal (i.e. habitual) tool use, we suggest focusing on the ontogeny of tool use and individual variation within populations. For example, if tool-using competence emerges late during ontogeny and improves with practice or varies with exposure to social cues, then a role for learning can be inferred. Experimental studies help identify the cognitive and developmental mechanisms used when tools are used to solve problems. The mechanisms underlying the route to tool-use acquisition have important consequences for our understanding of the accumulation in technological skill complexity over the life course of an individual, across generations and over evolutionary time.     

Behavioral and neural analysis of associative learning in the honeybee: a taste from the magic well

Equipped with a mini brain smaller than one cubic millimeter and containing only 950,000 neurons, honeybees could be indeed considered as having rather limited cognitive abilities. However, bees display a rich and interesting behavioral repertoire, in which learning and memory play a fundamental role in the framework of foraging activities. We focus on the question of whether adaptive behavior in honeybees exceeds simple forms of learning and whether the neural mechanisms of complex learning can be unraveled by studying the honeybee brain. Besides elemental forms of learning, in which bees learn specific and univocal links between events in their environment, bees also master different forms of non-elemental learning, including categorization, contextual learning and rule abstraction, both in the visual and in the olfactory domain. Different protocols allow accessing the neural substrates of some of these learning forms and understanding how complex problem solving can be achieved by a relatively simple neural architecture. These results underline the enormous richness of experience-dependent behavior in honeybees, its high flexibility, and the fact that it is possible to formalize and characterize in controlled laboratory protocols basic and higher-order cognitive processing using an insect as a model. This paper is dedicated to the memory of Guillermo ‘Willy’ Zaccardi (1972–2007), disciple and friend beyond time and distance, who will always be remembered with a smile.     

Foraging errors play a role in resource exploration by bumble bees (Bombus terrrestris)

If the cognitive performance of animals reflects their particular ecological requirements, how can we explain appreciable variation in learning ability amongst closely related individuals (e.g. foraging workers within a bumble bee colony)? One possibility is that apparent ‘errors’ in a learning task actually represent an alternative foraging strategy. In this study we investigate the potential relationship between foraging ‘errors’ and foraging success among bumble bee (Bombus terrestris) workers. Individual foragers were trained to choose yellow, rewarded flowers and ignore blue, unrewarded flowers. We recorded the number of errors (visits to unrewarded flowers) each bee made during training, then tested them to determine how quickly they discovered a more profitable food source (either familiar blue flowers, or novel green flowers). We found that error prone bees discovered the novel food source significantly faster than accurate bees. Furthermore, we demonstrate that the time taken to discover the novel, more profitable, food source is positively correlated with foraging success. These results suggest that foraging errors are part of an ‘exploration’ foraging strategy, which could be advantageous in changeable foraging environments. This could explain the observed variation in learning performance amongst foragers within social insect colonies.     

Evolutionary Connectionism: Algorithmic Principles Underlying the Evolution of Biological Organisation in Evo-Devo,Evo-Eco and Evolutionary Transitions

The mechanisms of variation, selection and inheritance, on which evolution by natural selection depends, are not fixed over evolutionary time. Current evolutionary biology is increasingly focussed on understanding how the evolution of developmental organisations modifies the distribution of phenotypic variation, the evolution of ecological relationships modifies the selective environment, and the evolution of reproductive relationships modifies the heritability of the evolutionary unit. The major transitions in evolution, in particular, involve radical changes in developmental, ecological and reproductive organisations that instantiate variation, selection and inheritance at a higher level of biological organisation. However, current evolutionary theory is poorly equipped to describe how these organisations change over evolutionary time and especially how that results in adaptive complexes at successive scales of organisation (the key problem is that evolution is self-referential, i.e. the products of evolution change the parameters of the evolutionary process). Here we first reinterpret the central open questions in these domains from a perspective that emphasises the common underlying themes. We then synthesise the findings from a developing body of work that is building a new theoretical approach to these questions by converting well-understood theory and results from models of cognitive learning. Specifically, connectionist models of memory and learning demonstrate how simple incremental mechanisms, adjusting the relationships between individually-simple components, can produce organisations that exhibit complex system-level behaviours and improve the adaptive capabilities of the system. We use the term “evolutionary connectionism” to recognise that, by functionally equivalent processes, natural selection acting on the relationships within and between evolutionary entities can result in organisations that produce complex system-level behaviours in evolutionary systems and modify the adaptive capabilities of natural selection over time. We review the evidence supporting the functional equivalences between the domains of learning and of evolution, and discuss the potential for this to resolve conceptual problems in our understanding of the evolution of developmental, ecological and reproductive organisations and, in particular, the major evolutionary transitions.     

Structure Learning in a Sensorimotor Association Task

Learning is often understood as an organism''s gradual acquisition of the association between a given sensory stimulus and the correct motor response. Mathematically, this corresponds to regressing a mapping between the set of observations and the set of actions. Recently, however, it has been shown both in cognitive and motor neuroscience that humans are not only able to learn particular stimulus-response mappings, but are also able to extract abstract structural invariants that facilitate generalization to novel tasks. Here we show how such structure learning can enhance facilitation in a sensorimotor association task performed by human subjects. Using regression and reinforcement learning models we show that the observed facilitation cannot be explained by these basic models of learning stimulus-response associations. We show, however, that the observed data can be explained by a hierarchical Bayesian model that performs structure learning. In line with previous results from cognitive tasks, this suggests that hierarchical Bayesian inference might provide a common framework to explain both the learning of specific stimulus-response associations and the learning of abstract structures that are shared by different task environments.     

Coevolution of learning and data-acquisition mechanisms: a model for cognitive evolution

A fundamental and frequently overlooked aspect of animal learning is its reliance on compatibility between the learning rules used and the attentional and motivational mechanisms directing them to process the relevant data (called here data-acquisition mechanisms). We propose that this coordinated action, which may first appear fragile and error prone, is in fact extremely powerful, and critical for understanding cognitive evolution. Using basic examples from imprinting and associative learning, we argue that by coevolving to handle the natural distribution of data in the animal's environment, learning and data-acquisition mechanisms are tuned jointly so as to facilitate effective learning using relatively little memory and computation. We then suggest that this coevolutionary process offers a feasible path for the incremental evolution of complex cognitive systems, because it can greatly simplify learning. This is illustrated by considering how animals and humans can use these simple mechanisms to learn complex patterns and represent them in the brain. We conclude with some predictions and suggested directions for experimental and theoretical work.     

Parasitoidism, not sociality, is associated with the evolution of elaborate mushroom bodies in the brains of hymenopteran insects

The social brain hypothesis posits that the cognitive demands of social behaviour have driven evolutionary expansions in brain size in some vertebrate lineages. In insects, higher brain centres called mushroom bodies are enlarged and morphologically elaborate (having doubled, invaginated and subcompartmentalized calyces that receive visual input) in social species such as the ants, bees and wasps of the aculeate Hymenoptera, suggesting that the social brain hypothesis may also apply to invertebrate animals. In a quantitative and qualitative survey of mushroom body morphology across the Hymenoptera, we demonstrate that large, elaborate mushroom bodies arose concurrent with the acquisition of a parasitoid mode of life at the base of the Euhymenopteran (Orussioidea + Apocrita) lineage, approximately 90 Myr before the evolution of sociality in the Aculeata. Thus, sociality could not have driven mushroom body elaboration in the Hymenoptera. Rather, we propose that the cognitive demands of host-finding behaviour in parasitoids, particularly the capacity for associative and spatial learning, drove the acquisition of this evolutionarily novel mushroom body architecture. These neurobehavioural modifications may have served as pre-adaptations for central place foraging, a spatial learning-intensive behaviour that is widespread across the Aculeata and may have contributed to the multiple acquisitions of sociality in this taxon.