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"Fitch, W. Tecumseh"
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Animal cognition and the evolution of human language: why we cannot focus solely on communication
Studies of animal communication are often assumed to provide the ‘royal road’ to understanding the evolution of human language. After all, language is the pre-eminent system of human communication: doesn't it make sense to search for its precursors in animal communication systems? From this viewpoint, if some characteristic feature of human language is lacking in systems of animal communication, it represents a crucial gap in evolution, and evidence for an evolutionary discontinuity. Here I argue that we should reverse this logic: because a defining feature of human language is its ability to flexibly represent and recombine concepts, precursors for many important components of language should be sought in animal cognition rather than animal communication. Animal communication systems typically only permit expression of a small subset of the concepts that can be represented and manipulated by that species. Thus, if a particular concept is not expressed in a species' communication system this is not evidence that it lacks that concept. I conclude that if we focus exclusively on communicative signals, we sell the comparative analysis of language evolution short. Therefore, animal cognition provides a crucial (and often neglected) source of evidence regarding the biology and evolution of human language.
This article is part of the theme issue ‘What can animal communication teach us about human language?’
Journal Article
This idea is brilliant : lost, overlooked, and underappreciated scientific concepts everyone should know
Presents essays responding to a question about what scientific term or concept ought to be more widely known, written by such authors as Jared Diamond, Richard Thaler, Richard Dawkins, Lisa Randall, Steven Pinker, and Carlo Roveri.
BOOK
The “Domestication Syndrome” in Mammals: A Unified Explanation Based on Neural Crest Cell Behavior and Genetics
Charles Darwin, while trying to devise a general theory of heredity from the observations of animal and plant breeders, discovered that domesticated mammals possess a distinctive and unusual suite of heritable traits not seen in their wild progenitors. Some of these traits also appear in domesticated birds and fish. The origin of Darwin’s “domestication syndrome” has remained a conundrum for more than 140 years. Most explanations focus on particular traits, while neglecting others, or on the possible selective factors involved in domestication rather than the underlying developmental and genetic causes of these traits. Here, we propose that the domestication syndrome results predominantly from mild neural crest cell deficits during embryonic development. Most of the modified traits, both morphological and physiological, can be readily explained as direct consequences of such deficiencies, while other traits are explicable as indirect consequences. We first show how the hypothesis can account for the multiple, apparently unrelated traits of the syndrome and then explore its genetic dimensions and predictions, reviewing the available genetic evidence. The article concludes with a brief discussion of some genetic and developmental questions raised by the idea, along with specific predictions and experimental tests.
Journal Article
Finding the beat: a neural perspective across humans and non-human primates
Humans possess an ability to perceive and synchronize movements to the beat in music (‘beat perception and synchronization’), and recent neuroscientific data have offered new insights into this beat-finding capacity at multiple neural levels. Here, we review and compare behavioural and neural data on temporal and sequential processing during beat perception and entrainment tasks in macaques (including direct neural recording and local field potential (LFP)) and humans (including fMRI, EEG and MEG). These abilities rest upon a distributed set of circuits that include the motor cortico-basal-ganglia–thalamo-cortical (mCBGT) circuit, where the supplementary motor cortex (SMA) and the putamen are critical cortical and subcortical nodes, respectively. In addition, a cortical loop between motor and auditory areas, connected through delta and beta oscillatory activity, is deeply involved in these behaviours, with motor regions providing the predictive timing needed for the perception of, and entrainment to, musical rhythms. The neural discharge rate and the LFP oscillatory activity in the gamma- and beta-bands in the putamen and SMA of monkeys are tuned to the duration of intervals produced during a beat synchronization–continuation task (SCT). Hence, the tempo during beat synchronization is represented by different interval-tuned cells that are activated depending on the produced interval. In addition, cells in these areas are tuned to the serial-order elements of the SCT. Thus, the underpinnings of beat synchronization are intrinsically linked to the dynamics of cell populations tuned for duration and serial order throughout the mCBGT. We suggest that a cross-species comparison of behaviours and the neural circuits supporting them sets the stage for a new generation of neurally grounded computational models for beat perception and synchronization.
Journal Article
Four principles of bio-musicology
As a species-typical trait of Homo sapiens, musicality represents a cognitively complex and biologically grounded capacity worthy of intensive empirical investigation. Four principles are suggested here as prerequisites for a successful future discipline of bio-musicology. These involve adopting: (i) a multicomponent approach which recognizes that musicality is built upon a suite of interconnected capacities, of which none is primary; (ii) a pluralistic Tinbergian perspective that addresses and places equal weight on questions of mechanism, ontogeny, phylogeny and function; (iii) a comparative approach, which seeks and investigates animal homologues or analogues of specific components of musicality, wherever they can be found; and (iv) an ecologically motivated perspective, which recognizes the need to study widespread musical behaviours across a range of human cultures (and not focus solely on Western art music or skilled musicians). Given their pervasiveness, dance and music created for dancing should be considered central subcomponents of music, as should folk tunes, work songs, lullabies and children's songs. Although the precise breakdown of capacities required by the multicomponent approach remains open to debate, and different breakdowns may be appropriate to different purposes, I highlight four core components of human musicality—song, drumming, social synchronization and dance—as widespread and pervasive human abilities spanning across cultures, ages and levels of expertise. Each of these has interesting parallels in the animal kingdom (often analogies but in some cases apparent homologies also). Finally, I suggest that the search for universal capacities underlying human musicality, neglected for many years, should be renewed. The broad framework presented here illustrates the potential for a future discipline of bio-musicology as a rich field for interdisciplinary and comparative research.
Journal Article
Birds have primate-like numbers of neurons in the forebrain
Some birds achieve primate-like levels of cognition, even though their brains tend to be much smaller in absolute size. This poses a fundamental problem in comparative and computational neuroscience, because small brains are expected to have a lower information-processing capacity. Using the isotropic fractionator to determine numbers of neurons in specific brain regions, here we show that the brains of parrots and songbirds contain on average twice as many neurons as primate brains of the same mass, indicating that avian brains have higher neuron packing densities than mammalian brains. Additionally, corvids and parrots have much higher proportions of brain neurons located in the pallial telencephalon compared with primates or other mammals and birds. Thus, large-brained parrots and corvids have forebrain neuron counts equal to or greater than primates with much larger brains. We suggest that the large numbers of neurons concentrated in high densities in the telencephalon substantially contribute to the neural basis of avian intelligence.
Journal Article
Dance as mindful movement: a perspective from motor learning and predictive coding
Defining “dance” is challenging, because many distinct classes of human movement may be considered dance in a broad sense. Although the most obvious category is rhythmic dancing to a musical beat, other categories of expressive movement such as dance improvisation, pantomime, tai chi, or Japanese
butoh
suggest that a more inclusive conception of human dance is needed. Here we propose that a specific type of conscious awareness plays an overarching role in most forms of expressive movement and can be used to define dance (in the broad sense). We can briefly summarize this broader notion of dance as “mindful movement.” However, to make this conception explicit and testable, we need an empirically verifiable characterization of “mindful movement.” We propose such a characterization in terms of predictive coding and procedural learning theory: mindful movement involves a “suspension” of automatization. When first learning a new motor skill, we are highly conscious of our movements, and this is reflected in neural activation patterns. As skill increases, automatization and overlearning occurs, involving a progressive suppression of conscious awareness. Overlearned, habitual movement patterns become mostly unconscious, entering consciousness only when mistakes or surprising outcomes occur. In mindful movement, this automatization process is essentially inverted or suspended, reactivating previously unconscious details of movement in the conscious workspace, and crucially enabling a renewed aesthetic attention to such details. This wider perspective on dance has important implications for potential animal analogs of human dance and leads to multiple lines of experimental exploration.
Journal Article
Artificial grammar learning meets formal language theory: an overview
Formal language theory (FLT), part of the broader mathematical theory of computation, provides a systematic terminology and set of conventions for describing rules and the structures they generate, along with a rich body of discoveries and theorems concerning generative rule systems. Despite its name, FLT is not limited to human language, but is equally applicable to computer programs, music, visual patterns, animal vocalizations, RNA structure and even dance. In the last decade, this theory has been profitably used to frame hypotheses and to design brain imaging and animal-learning experiments, mostly using the ‘artificial grammar-learning’ paradigm. We offer a brief, non-technical introduction to FLT and then a more detailed analysis of empirical research based on this theory. We suggest that progress has been hampered by a pervasive conflation of distinct issues, including hierarchy, dependency, complexity and recursion. We offer clarifications of several relevant hypotheses and the experimental designs necessary to test them. We finally review the recent brain imaging literature, using formal languages, identifying areas of convergence and outstanding debates. We conclude that FLT has much to offer scientists who are interested in rigorous empirical investigations of human cognition from a neuroscientific and comparative perspective.
Journal Article
Cellular computation and cognition
Contemporary neural network models often overlook a central biological fact about neural processing: that single neurons are themselves complex, semi-autonomous computing systems. Both the information processing and information storage abilities of actual biological neurons vastly exceed the simple weighted sum of synaptic inputs computed by the “units” in standard neural network models. Neurons are eukaryotic cells that store information not only in synapses, but also in their dendritic structure and connectivity, as well as genetic “marking” in the epigenome of each individual cell. Each neuron computes a complex nonlinear function of its inputs, roughly equivalent in processing capacity to an entire 1990s-era neural network model. Furthermore, individual cells provide the biological interface between gene expression, ongoing neural processing, and stored long-term memory traces. Neurons in all organisms have these properties, which are thus relevant to all of neuroscience and cognitive biology. Single-cell computation may also play a particular role in explaining some unusual features of human cognition. The recognition of the centrality of cellular computation to “natural computation” in brains, and of the constraints it imposes upon brain evolution, thus has important implications for the evolution of cognition, and how we study it.
Journal Article