Towards a Computational Comparative Neuroprimatology: Framing the language-ready brain

We make the case for developing a Computational Comparative Neuroprimatology to inform the analysis of the function and evolution of the human brain.First, we update the mirror system hypothesis on the evolution of the language-ready brain by (i) modeling action and action recognition and opportunistic scheduling of macaque brains to hypothesize the nature of the last common ancestor of macaque and human (LCA-m); and then we (ii) introduce dynamic brain modeling to show how apes could acquire gesture through ontogenetic ritualization, hypothesizing the nature of evolution from LCA-m to the last common ancestor of chimpanzee and human (LCA-c). We then (iii) hypothesize the role of imitation, pantomime, protosign and protospeech in biological and cultural evolution from LCA-c to Homo sapiens with a language-ready brain.Second, we suggest how cultural evolution in Homo sapiens led from protolanguages to full languages with grammar and compositional semantics.Third, we assess the similarities and differences between the dorsal and ventral streams in audition and vision as the basis for presenting and comparing two models of language processing in the human brain: A model of (i) the auditory dorsal and ventral streams in sentence comprehension; and (ii) the visual dorsal and ventral streams in defining “what language is about” in both production and perception of utterances related to visual scenes provide the basis for (iii) a first step towards a synthesis and a look at challenges for further research.     

Interactions between cultural, social and biological explanations for language evolution

This is a reply to commentaries on a target article in this volume reviewing models for the cultural evolution of language. Many commentaries amplify positions taken in this article but they also cover novel issues in social evolution and biological evolution, which are briefly addressed here.     

Phase-Field Modelling in Extractive Metallurgy

The phase-field method has already proven its usefulness to simulate microstructural evolution for several applications, e.g., during solidification, solid-state phase transformations, fracture, etc. This wide variety of applications follows from its diffuse-interface approach. Moreover, it is straightforward to take different driving forces into account. The purpose of this paper is to give an introduction to the phase-field modelling technique with particular attention for models describing phenomena important in extractive metallurgy. The concept of diffuse interfaces, the phase-field variables, the thermodynamic driving force for microstructure evolution and the phase-field equations are discussed. Some of the possibilities to solve the equations describing microstructural evolution are also described, followed by possibilities to make the phase-field models quantitative and the phase-field modelling of the microstructural phenomena important in extractive metallurgy, i.e., multiphase field models. Finally, this paper illustrates how the phase-field method can be applied to simulate several processes taking place in extractive metallurgy and how the models can contribute to the further development or improvement of these processes.     

Musical emotions: Functions,origins, evolution

Theories of music origins and the role of musical emotions in the mind are reviewed. Most existing theories contradict each other, and cannot explain mechanisms or roles of musical emotions in workings of the mind, nor evolutionary reasons for music origins. Music seems to be an enigma. Nevertheless, a synthesis of cognitive science and mathematical models of the mind has been proposed describing a fundamental role of music in the functioning and evolution of the mind, consciousness, and cultures. The review considers ancient theories of music as well as contemporary theories advanced by leading authors in this field. It addresses one hypothesis that promises to unify the field and proposes a theory of musical origin based on a fundamental role of music in cognition and evolution of consciousness and culture. We consider a split in the vocalizations of proto-humans into two types: one less emotional and more concretely-semantic, evolving into language, and the other preserving emotional connections along with semantic ambiguity, evolving into music. The proposed hypothesis departs from other theories in considering specific mechanisms of the mind–brain, which required the evolution of music parallel with the evolution of cultures and languages. Arguments are reviewed that the evolution of language toward becoming the semantically powerful tool of today required emancipation from emotional encumbrances. The opposite, no less powerful mechanisms required a compensatory evolution of music toward more differentiated and refined emotionality. The need for refined music in the process of cultural evolution is grounded in fundamental mechanisms of the mind. This is why today's human mind and cultures cannot exist without today's music. The reviewed hypothesis gives a basis for future analysis of why different evolutionary paths of languages were paralleled by different evolutionary paths of music. Approaches toward experimental verification of this hypothesis in psychological and neuroimaging research are reviewed.     

Modelling language evolution: Examples and predictions

We survey recent computer modelling research of language evolution, focusing on a rule-based model simulating the lexicon–syntax coevolution and an equation-based model quantifying the language competition dynamics. We discuss four predictions of these models: (a) correlation between domain-general abilities (e.g. sequential learning) and language-specific mechanisms (e.g. word order processing); (b) coevolution of language and relevant competences (e.g. joint attention); (c) effects of cultural transmission and social structure on linguistic understandability; and (d) commonalities between linguistic, biological, and physical phenomena. All these contribute significantly to our understanding of the evolutions of language structures, individual learning mechanisms, and relevant biological and socio-cultural factors. We conclude the survey by highlighting three future directions of modelling studies of language evolution: (a) adopting experimental approaches for model evaluation; (b) consolidating empirical foundations of models; and (c) multi-disciplinary collaboration among modelling, linguistics, and other relevant disciplines.     

Causal Reasoning and Event Cognition as Evolutionary Determinants of Language Structure

The aim of this article is to provide an evolutionarily grounded explanation of central aspects of the structure of language. It begins with an account of the evolution of human causal reasoning. A comparison between humans and non-human primates suggests that human causal cognition is based on reasoning about the underlying forces that are involved in events, while other primates hardly understand external forces. This is illustrated by an analysis of the causal cognition required for early hominin tool use. Second, the thinking concerning forces in causation is used to motivate a model of human event cognition. A mental representation of an event contains two vectors representing a cause as well as a result but also entities such as agents, patients, instruments and locations. The fundamental connection between event representations and language is that declarative sentences express events (or states). The event structure also explains why sentences are constituted of noun phrases and verb phrases. Finally, the components of the event representation show up in language, where causes and effects are expressed by verbs, agents and patients by nouns (modified by adjectives), locations by prepositions, etc. Thus, the evolution of the complexity of mental event representations also provides insight into the evolution of the structure of language.     

The origins of language and the evolution of music: A comparative perspective

According to Darwin [Darwin, CR. The descent of man, and selection in relation to sex. London: John Murray; 1871], the human musical faculty 'must be ranked amongst the most mysterious with which he is endowed'. Music is a human cultural universal that serves no obvious adaptive purpose, making its evolution a puzzle for evolutionary biologists. This review examines Darwin's hypothesis of similarities between language and music indicating a shared evolutionary history. In particular, the fact that both are human universals, have phrase structure, and entail learning and cultural transmission, suggests that any theory of the evolution of language will have implications for the evolution of music, and vice versa. The argument starts by describing variable predispositional musical capabilities and the ontogeny of prosodic communication in human infants and young children, presenting comparative data regarding communication systems commonly present in living nonhuman primate species. Like language, the human music faculty is based on a suite of abilities, some of which are shared with other primates and some of which appear to be uniquely human. Each of these subcomponents may have a different evolutionary history, and should be discussed separately. After briefly considering possible functions of human music for language acquisition, the review ends by discussing the phylogenetic history of music. It concludes that many strands of evidence support Darwin's hypothesis of an intermediate stage of human evolutionary history, characterized by a communication system that resembled music more closely than language, but was identical to neither. This pre-linguistic system, which could probably referred to as "prosodic protolanguage", provided a precursor for both modern language and music.     

Organizational Development as Generative Entrenchment

A critical task for organizations is how to best structure themselves to efficiently allocate information and resources to individuals tasked with solving sub-components of the organization’s central problems. Despite this criticality, the processes by which organizational structures form remain largely opaque within organizational theory, with most approaches focused on how structure is influenced by individual managerial heuristics, normative cultural perceptions, and trial-and-error. Here, we propose that a broad understanding of organizational formation can be aided by appealing to generative entrenchment, a theory from developmental biology that helps explain why phylogenetically diverse animals appear similar as embryos. Drawing inferences from generative entrenchment and applying it to organizational differentiation, we argue that the reason many organizations appear structurally similar is due to core informational restraints on individual actors beginning at the top and descending to the bottom of these informational hierarchies, which reinforces these structures via feedback between separate levels. We further argue that such processes can lead to the emergence of a variety of group-level traits, an important but undertheorized class of phenomena in cultural evolution.     

An evolutionary framework for cultural change: Selectionism versus communal exchange

Dawkins? replicator-based conception of evolution has led to widespread mis-application of selectionism across the social sciences because it does not address the paradox that necessitated the theory of natural selection in the first place: how do organisms accumulate change when traits acquired over their lifetime are obliterated? This is addressed by von Neumann?s concept of a self-replicating automaton (SRA). A SRA consists of a self-assembly code that is used in two distinct ways: (1) actively deciphered during development to construct a self-similar replicant, and (2) passively copied to the replicant to ensure that it can reproduce. Information that is acquired over a lifetime is not transmitted to offspring, whereas information that is inherited during copying is transmitted. In cultural evolution there is no mechanism for discarding acquired change. Acquired change can accumulate orders of magnitude faster than, and quickly overwhelm, inherited change due to differential replication of variants in response to selection. This prohibits a selectionist but not an evolutionary framework for culture and the creative processes that fuel it. The importance non-Darwinian processes in biological evolution is increasingly recognized. Recent work on the origin of life suggests that early life evolved through a non-Darwinian process referred to as communal exchange that does not involve a self-assembly code, and that natural selection emerged from this more haphazard, ancestral evolutionary process. It is proposed that communal exchange provides an evolutionary framework for culture that enables specification of cognitive features necessary for a (real or artificial) societies to evolve culture. This is supported by a computational model of cultural evolution and a conceptual network based program for documenting material cultural history, and it is consistent with high levels of human cooperation.     

On Models of Nonlinear Evolution Paths in Adiabatic Quantum Algorithms

In this paper,we study two different nonlinear interpolating paths in adiabatic evolution algorithms for solving a particular class of quantum search problems where both the initial and final Hamiltonian are one-dimensional projector Hamiltonians on the corresponding ground state.If the overlap between the initial state and final state of the quantum system is not equal to zero,both of these models can provide a constant time speedup over the usual adiabatic algorithms by increasing some another corresponding "complexity".But when the initial state has a zero overlap with the solution state in the problem,the second model leads to an infinite time complexity of the algorithm for whatever interpolating functions being applied while the first one can still provide a constant running time.However,inspired by a related reference,a variant of the first model can be constructed which also fails for the problem when the overlap is exactly equal to zero if we want to make up the "intrinsic" fault of the second model - an increase in energy.Two concrete theorems are given to serve as explanations why neither of these two models can improve the usual adiabatic evolution algorithms for the phenomenon above.These just tell us what should be noted when using certain nonlinear evolution paths in adiabatic quantum algorithms for some special kind of problems.     

A New Method for the Solution of Models of Biological Evolution: Derivation of Exact Steady-State Distributions

We investigate well-known models of biological evolution and address the open problem of how construct a correct continuous analog of mutations in discrete sequence space. We deal with models where the fitness is a function of a Hamming distance from the reference sequence. The mutation-selection master equation in the discrete sequence space is replaced by a Hamilton-Jacobi equation for the logarithm of relative frequencies of different sequences. The steady-state distribution, mean fitness and the variance of fitness are derived. All our results are asymptotic in the large genome limit. A variety of important biological and biochemical models can be solved by this new approach. PACS numbers: 87.10.+e, 87.15.Aa, 87.23.Kg, 02.50.-r     

The evolution of opinions on scale-free networks

An opinion evolution model without “bounded confidence” is proposed in this paper. Computer simulation shows that our model can figure out the breakage of the coexistence of majority and minority after a period’s evolution. With further analysis, our model shows that, without the influence of the external field, the opinions will finally die out to a limited small value no matter what the initial condition of the system is. On the other hand, we simulate the evolution of the opinions under the influence of an external field, and get some meaningful and instructional results.     

基于改进差分进化算法的相机标定研究

目前相机标定多采用Tsai和Weng的基于非线性成像模型的分步标定法。在研究标定算法的过程中发现,待优化的目标函数具有多个局部极值点,采用传统的局部优化算法会导致迅速收敛到局部极值点,而非全局最优解,影响了标定精度。差分进化是一种收敛速度很快的进化算法,但当初始种群分布不够理想时,它可能快速收敛到局部极值点。将其改进后用于相机标定,提出了分步法与改进的差分进化算法相结合的相机标定方法。数据实验表明:标定精度比直接采用分步法有所提高。     

Still a bridge too far? Biolinguistic questions for grounding language on brains

In this paper we discuss how Fibonacci growth patterns are apparent in the structure of human language. We moreover show how familiar dynamics yielding these sorts of patterns in nature may be taken to apply, at some level of abstraction, for the human faculty of language. The overall picture casts doubts on any simplistic treatment of language behavior, of the sort stemming from classical behaviorism in psychology (which is again popular in contemporary computational models). Instead, it appears to be more profitable to study language as a complex dynamic system, emerging in human brains for physical reasons which are yet to be fully comprehended, but which in the end disfavor any dualistic approach to the study of mind in general, and the human mind in particular.     

Ladderpath Approach: How Tinkering and Reuse Increase Complexity and Information

The notion of information and complexity are important concepts in many scientific fields such as molecular biology, evolutionary theory and exobiology. Many measures of these quantities are either difficult to compute, rely on the statistical notion of information, or can only be applied to strings. Based on assembly theory, we propose the notion of a ladderpath, which describes how an object can be decomposed into hierarchical structures using repetitive elements. From the ladderpath, two measures naturally emerge: the ladderpath-index and the order-index, which represent two axes of complexity. We show how the ladderpath approach can be applied to both strings and spatial patterns and argue that all systems that undergo evolution can be described as ladderpaths. Further, we discuss possible applications to human language and the origin of life. The ladderpath approach provides an alternative characterization of the information that is contained in a single object (or a system) and could aid in our understanding of evolving systems and the origin of life in particular.     

Evolution of Brains and Computers: The Roads Not Taken

When computers started to become a dominant part of technology around the 1950s, fundamental questions about reliable designs and robustness were of great relevance. Their development gave rise to the exploration of new questions, such as what made brains reliable (since neurons can die) and how computers could get inspiration from neural systems. In parallel, the first artificial neural networks came to life. Since then, the comparative view between brains and computers has been developed in new, sometimes unexpected directions. With the rise of deep learning and the development of connectomics, an evolutionary look at how both hardware and neural complexity have evolved or designed is required. In this paper, we argue that important similarities have resulted both from convergent evolution (the inevitable outcome of architectural constraints) and inspiration of hardware and software principles guided by toy pictures of neurobiology. Moreover, dissimilarities and gaps originate from the lack of major innovations that have paved the way to biological computing (including brains) that are completely absent within the artificial domain. As it occurs within synthetic biocomputation, we can also ask whether alternative minds can emerge from A.I. designs. Here, we take an evolutionary view of the problem and discuss the remarkable convergences between living and artificial designs and what are the pre-conditions to achieve artificial intelligence.     

Spatial evolution character of multi-objective evolutionary algorithm based on self-organized criticality theory

This paper analyzes the spatial evolution character of multi-objective evolutionary algorithms using self-organized criticality theory. The spatial evolution character is modeled by the statistical property of crowding distance, which displays a scale-free feature and a power-law distribution. We propose that the evolutional rule of multi-objective optimization algorithms is a self-organized state transition from an initial scale-free state to a final scale-free state. The target is to get close to a critical state representing the true Pareto-optimal front. Besides, the anti-Matthew effect is the internal incentive factor of most strategies. The final scale-free state reflects the quality of the final Pareto-optimal front. The speed of the state transition reflects the efficiency of the algorithm. We simulate the spatial evolution characters of three typical multi-objective evolutionary algorithms representing three fields, i.e., Genetic Algorithm, Differential Evolution and the Artificial Immune System algorithm. The results prove that the model and the explanation are effective for analyzing the evolutional rule of multi-objective evolutionary algorithms.     

A theorem allowing to derive deterministic evolution equations from stochastic evolution equations

The deterministic evolution equations of classical as well as quantum mechanical models are derived from a set of stochastic evolution equations after taking an average over realizations using a theorem. Examples are given that show that deterministic quantum mechanical evolution equations, obtained initially by R.P. Feynman and subsequently studied by Boghosian and Taylor IV [B.M. Boghosian, W. Taylor IV, Phys. Rev. E 57 (1998) 54. See also arXiv:quant-ph/9904035] and Meyer [D.A. Meyer, Phys. Rev. E 55 (1997) 5261], among others, are derived from a set of stochastic evolution equations. In addition, a deterministic classical evolution equation for the diffusion of monomers, similar to the second Fick law, is also obtained.     

Dislocation and defect density-based micromechanical modeling of the mechanical behavior of fcc metals under neutron irradiation

A rate-independent dislocation and defect density-based evolution model is presented that captures the pre- and post-yield material behavior of fcc metals subjected to different doses of neutron radiation. Unlike previously developed phenomenological models, this model is capable of capturing the salient features of irradiation-induced hardening, including increase in yield stress followed by yield drop and non-zero stress offset from the unirradiated stress–strain curve. The key contribution is a model for the critical resolved slip resistance that depends on both dislocation and defect densities, which are governed by evolution equations based on physical observations. The result is an orientation-dependent non-homogeneous deformation model, which accounts for defect annihilation on active slip planes. Results for both single and polycrystalline simulations of OFHC copper are presented and are observed to be in reasonably good agreement with experimental data. Extension of the model to other fcc metals is straightforward and is currently being developed for bcc metals.     

壁面静止水滴冻结过程形状变化

低温表面上的液滴冻结时会形成具有尖顶的形状。针对这一现象开展了理论与实验研究,建立了新的动态曲形相界面模型用来模拟水滴冻结过程中的形状变化。模型考虑重力和成核再辉效应的影响,将冻结过程中的冰水相界面近似为球冠形曲面,并在三相点处引入动态生长角和直角关系。对壁面上20μL静止水滴进行了冻结实验,记录水滴三相点高度的演化过程,以此拟合得到了其随时间变化的关联式,基于该关联式求解理论模型,得到了水滴最终冻结形状。模拟结果与实验结果在水滴初始轮廓、成核再辉轮廓和最终冻结轮廓以及冻结时间上均吻合良好。曲面模型的计算结果表明,固液相界面上不同位置处的冻结速率不同;随着相界面向上推移,冻结速率逐渐减小。