Two computer simulations are investigated that model interaction of cultural evolution of language and biological evolution of adaptations to language. Both are agent-based models in which a population of agents imitates each other using realistic vowels. The agents evolve under selective pressure for good imitation. In one model, the evolution of the vocal tract is modeled; in the other, a cognitive mechanism for perceiving speech accurately is modeled. In both cases, biological adaptations to using and learning speech evolve, even (...) though the system of speech sounds itself changes at a more rapid time scale than biological evolution. However, the fact that the available acoustic space is used maximally is constant, and therefore biological evolution does have a stable target. This work shows that when cultural and biological traits are continuous, their co-evolution may lead to cognitive adaptations that are strong enough to detect empirically. (shrink)

Two computer simulations are investigated that model interaction of cultural evolution of language and biological evolution of adaptations to language. Both are agent‐based models in which a population of agents imitates each other using realistic vowels. The agents evolve under selective pressure for good imitation. In one model, the evolution of the vocal tract is modeled; in the other, a cognitive mechanism for perceiving speech accurately is modeled. In both cases, biological adaptations to using and learning speech evolve, even (...) though the system of speech sounds itself changes at a more rapid time scale than biological evolution. However, the fact that the available acoustic space is used maximally (a self‐organized result of cultural evolution) is constant, and therefore biological evolution does have a stable target. This work shows that when cultural and biological traits are continuous, their co‐evolution may lead to cognitive adaptations that are strong enough to detect empirically. (shrink)

Two decades of attempts to model the emergence of language as a collective cognitive activity have demonstrated a number of principles that might have been part of the historical process that led to language. Several models have demonstrated the emergence of structure in a symbolic medium, but none has demonstrated the emergence of the capacity for symbolic representation. The current shift in cognitive science toward theoretical frameworks based on embodiment is already furnishing computational models with additional mechanisms relevant to the (...) emergence of symbolic language. An analysis of embodied interaction among captive, but not human‐enculturated, bonobo chimpanzees reveals a number of additional features of embodiment that are relevant to the emergence of symbolic language, but that have not yet been explored in computational simulation models; for example, complementarity of action in addition to imitation, iconic in addition to indexical gesture, coordination among multiple sensory and perceptual modalities, and the orchestration of intra‐ and inter‐individual motor coordination. The bonobos provide an evolutionarily plausible intermediate stage in the development of symbolic expression that can inform efforts to model the emergence of symbolic language. (shrink)

It is my thesis that Renaissance classical translations and imitations were often works of political surrogacy in a literary environment characterized by harsh censorship. So, for instance, the works of Homer, Virgil, and Lucan were read as coded texts, that ranged across the political spectrum.

We focus on the evolution of action capabilities which set the stage for language, rather than analyzing how further brain evolution built on these capabilities to yield a language-ready brain. Our framework is given by the Mirror System Hypothesis, which charts a progression from a monkey-like mirror neuron system (MNS) to a chimpanzee-like mirror system that supports simple imitation and thence to a human-like mirror system that supports complex imitation and language. We present the MNS2 model, a new (...) model of action recognition learning by mirror neurons of the macaque brain and augmented competitive queuing, a model of opportunistic scheduling of action sequences as background for analysis of modeling strategies for simple imitation as seen in the great apes and complex/goal-directed imitation as seen in humans. Implications for the study of language are briefly noted. (shrink)

The aim of this paper is to grasp the relevant distinctions between various ways in which models and simulations in Artificial Intelligence (AI) relate to cognitive phenomena. In order to get a systematic picture, a taxonomy is developed that is based on the coordinates of formal versus material analogies and theory-guided versus pre-theoretic models in science. These distinctions have parallels in the computational versus mimetic aspects and in analytic versus exploratory types of computer simulation. The proposed taxonomy cuts across the (...) traditional dichotomies between symbolic and embodied AI, general intelligence and symbol and intelligence and cognitive simulation and human/non-human-like AI. -/- According to the taxonomy proposed here, one can distinguish between four distinct general approaches that figured prominently in early and classical AI, and that have partly developed into distinct research programs: first, phenomenal simulations (e.g., Turing’s “imitation game”); second, simulations that explore general-level formal isomorphisms in pursuit of a general theory of intelligence (e.g., logic-based AI); third, simulations as exploratory material models that serve to develop theoretical accounts of cognitive processes (e.g., Marr’s stages of visual processing and classical connectionism); and fourth, simulations as strictly formal models of a theory of computation that postulates cognitive processes to be isomorphic with computational processes (strong symbolic AI). -/- In continuation of pragmatic views of the modes of modeling and simulating world affairs, this taxonomy of approaches to modeling in AI helps to elucidate how available computational concepts and simulational resources contribute to the modes of representation and theory development in AI research—and what made that research program uniquely dependent on them. (shrink)

Domain experts regularly teach novice students how to perform a task. This often requires them to adjust their behavior to the less knowledgeable audience and, hence, to behave in a more didactic manner. Eye movement modeling examples (EMMEs) are a contemporary educational tool for displaying experts’ (natural or didactic) problem‐solving behavior as well as their eye movements to learners. While research on expert‐novice communication mainly focused on experts’ changes in explicit, verbal communication behavior, it is as yet unclear whether (...) and how exactly experts adjust their nonverbal behavior. This study first investigated whether and how experts change their eye movements and mouse clicks (that are displayed in EMMEs) when they perform a task naturally versus teach a task didactically. Programming experts and novices initially debugged short computer codes in a natural manner. We first characterized experts’ natural problem‐solving behavior by contrasting it with that of novices. Then, we explored the changes in experts’ behavior when being subsequently instructed to model their task solution didactically. Experts became more similar to novices on measures associated with experts’ automatized processes (i.e., shorter fixation durations, fewer transitions between code and output per click on the run button when behaving didactically). This adaptation might make it easier for novices to follow or imitate the expert behavior. In contrast, experts became less similar to novices for measures associated with more strategic behavior (i.e., code reading linearity, clicks on run button) when behaving didactically. (shrink)

Domain experts regularly teach novice students how to perform a task. This often requires them to adjust their behavior to the less knowledgeable audience and, hence, to behave in a more didactic manner. Eye movement modeling examples (EMMEs) are a contemporary educational tool for displaying experts’ (natural or didactic) problem‐solving behavior as well as their eye movements to learners. While research on expert‐novice communication mainly focused on experts’ changes in explicit, verbal communication behavior, it is as yet unclear whether (...) and how exactly experts adjust their nonverbal behavior. This study first investigated whether and how experts change their eye movements and mouse clicks (that are displayed in EMMEs) when they perform a task naturally versus teach a task didactically. Programming experts and novices initially debugged short computer codes in a natural manner. We first characterized experts’ natural problem‐solving behavior by contrasting it with that of novices. Then, we explored the changes in experts’ behavior when being subsequently instructed to model their task solution didactically. Experts became more similar to novices on measures associated with experts’ automatized processes (i.e., shorter fixation durations, fewer transitions between code and output per click on the run button when behaving didactically). This adaptation might make it easier for novices to follow or imitate the expert behavior. In contrast, experts became less similar to novices for measures associated with more strategic behavior (i.e., code reading linearity, clicks on run button) when behaving didactically. (shrink)

Deep neural networks (DNNs) have found many useful applications in recent years. Of particular interest have been those instances where their successes imitate human cognition and many consider artificial intelligences to offer a lens for understanding human intelligence. Here, we criticize the underlying conflation between the predictive and explanatory power of DNNs by examining the goals of modeling.

Existing research has identified the importance of role models in the imitation of cooperative behaviors. This Pre-Study attempted to explore the contagion effects of cooperative models. Drawing on goal contagion theory, we proposed that encountering cooperative models could catalyze participants’ cooperation when participants joined new groups without role models, and that moral elevation and calling would play a chain-mediating role in this process. To test the hypothesis, we designed a four-person public goods game consisting of two phases in which (...) participants were formed into teams with different people in each phase. We randomly assigned 108 participants to either a consistent contributor (CC) or control condition. The only difference was that participants in the CC condition encountered a cooperative role model (i.e., CC) in the first phase, while those in the control group did not. The results moderately supported all hypotheses. Briefly, our findings provide empirical evidence supporting the two processes of goal contagion theory: when individuals encounter a CC, they first make inferences about the CC’s goal, as reflected by moral elevation, and then adopt the model’s prosocial goals (i.e., calling), resulting in increased cooperative behaviors in new groups. These findings could extend our understanding of the contagion effect of cooperative modeling, but require high-powered replication studies before such conclusions can be drawn. (shrink)

Perception–action approaches are sometimes criticized because empathy takes cognitive forms and people do not overtly imitate or feel all observed states. These complaints reflect a misunderstanding of the framework, which we tried to clarify through a review that bridged social and neuroscientific views. Far from “simple fixes,” these misunderstandings appear to reflect deeply rooted differences in the way that each discipline conceptualizes science and the mind. We address the important points made by the commentators and reiterate the need to incorporate (...) rich, phenomenological descriptions into academic works so that we may prevent such conceptual cross-talk in the future. The open exchange of ideas across fields is often difficult, but essential to an integrated, scientific view of empathy. (shrink)

The purpose of this article is research of various aspects of imitation modeling as effective method of the analysis and optimization of the pricing process on the tourist enterprise. In article the methodology of imitation modeling of the pricing process for a tourist product is investigated. For formation and realization of model of pricing it is offered to use imitation modeling which allows to receive the expected size of the price under various operating conditions (...) of the tourist enterprise. It is shown that imitation modeling of economic process contains 7 main stages: the formulation of a problem and definition targets than imitation research; development of conceptual model of the modeling object, formalization of the imitation model, choice of software and control of the imitation model; complex testing of the model; planning and implementation of imitation experiment; analysis of results of modeling. The main purpose of imitation model consists in adjustment of level of the price depending on influence of pricing factors. In article the main advantages of the imitation modeling in comparison with other methods of modeling are marked out. Imitation modeling considers influence of a large number of uncertain factors, which cannot be precisely described by means of analytical models. The methodology of the imitation modeling allows to analysis expensive and not expensive elements of the price of a tourist product. Imitation modeling allows estimating efficiency and productivity of the prices of new level until their introduction at real process. The imitation model allows investigating system of pricing in dynamics and allows change of its functioning at any stage of modeling. It is possible to implementation new variables and conditions into model, to replace values of factors and so forth. The imitation model of pricing process allows uniting models of separate subsystems in one complex model, which considers influence of separate subsystems on behavior of the whole system. Based on the conducted researches advantage of a method of the imitation modeling to optimization of management of the pricing process at the tourist enterprise is proved. (shrink)

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

Computational modeling of the macaque brain grounds hypotheses on the brain of LCA-m. Elaborations thereof provide a brain model for LCA-c. The Mirror System Hypothesis charts further steps via imitation and pantomime to protosign and protolanguage on the path to a "language-ready brain" in Homo sapiens, with the path to speech being indirect. The material poses new challenges for both experimentation and modeling.

Es soll ein Beitrag zur epistemischen Charakterisierung von Computersimulationen als jenseits von Experiment und Theorie geleistet werden. Es wird argumentiert, dass die in der Simulationstechnik eingesetzten Verfahren nicht numerische Lösungen liefern, sondern deren Dynamik mittels generativer Mechanismen imitieren. Die Computersimulationen in der Klimatologie werden als systematisches wie historisches Fallbeispiel behandelt. Erst "Simulationsexperimente" gestatten es, mittels Modellen eine Dynamik zu imitieren, ohne deren Grundgleichungen zu "lösen". /// Computer simulations will be characterized in epistemic respect as a method between experiment and theory. (...) It will be argued that simulations do not provide numerical solutions, rather they use generative mechanisms to imitate a dynamics. Climate science will be considered as a case both systematically and historically. Only simulation experiments allow to build models that imitate a dynamics without solving the relevant equations. (shrink)

Es soll ein Beitrag zur epistemischen Charakterisierung von Computersimulationen als jenseits von Experiment und Theorie geleistet werden. Es wird argumentiert, dass die in der Simulationstechnik eingesetzten Verfahren nicht numerische Lösungen liefern, sondern deren Dynamik mittels generativer Mechanismen imitieren. Die Computersimulationen in der Klimatologie werden als systematisches wie historisches Fallbeispiel behandelt. Erst "Simulationsexperimente" gestatten es, mittels Modellen eine Dynamik zu imitieren, ohne deren Grundgleichungen zu "lösen". /// Computer simulations will be characterized in epistemic respect as a method between experiment and theory. (...) It will be argued that simulations do not provide numerical solutions, rather they use generative mechanisms to imitate a dynamics. Climate science will be considered as a case both systematically and historically. Only simulation experiments allow to build models that imitate a dynamics without solving the relevant equations. (shrink)

Preface --Part I: What is excellence? -- The making of a new meaning of excellence -- The making of a concept -- Divine imitation and excellence -- Excellence and subject -- Infinite longing -- A view of human nature -- Self-world correlation and excellence -- Exegeting excellence -- The grammar of excellence -- Excellence : technical and ontological -- Excellence as will-to-the-infinite -- Excellence as community of abstract-concrete and more -- Problematic standards of excellence -- Excellence and creativity -- (...) The core features of excellence -- An African conception of excellence -- New being : the construction of excellent self -- Conclusion: The features of excellence -- New being : participation and imitation -- Christology of excellence -- Imitation in Christian theology -- Justice, love and hope -- Excellence and justice -- Excellence and love -- Excellence and hope -- Part II: Social ethical framework -- A framework for social ethics -- Tillich's concept of ethics -- Creativity and theonomous ethics -- Governance and prophetic spirit -- A model of an excellentist social world -- Outline of governance ethics -- Excellence and the prophetic spirit -- The prophetic spirit and the law -- Social practice as boundary of possibilities -- Social practice : the tension between included and excluded possibilities -- The myth of goods : internal versus external -- Excellence and economic development -- The theological character of economic development -- Modeling excellence on factors of economic development -- Excellence and money -- Economic development : the role of work and monetary policy -- The good, truth, and beauty -- The depth and destiny of excellence -- The exit and tag line : Africa and excellence. (shrink)

-/- The aim of this paper is to grasp the relevant distinctions between various ways in which models and simulations in Artificial Intelligence (AI) relate to cognitive phenomena. In order to get a systematic picture, a taxonomy is developed that is based on the coordinates of formal versus material analogies and theory-guided versus pre-theoretic models in science. These distinctions have parallels in the computational versus mimetic aspects and in analytic versus exploratory types of computer simulation. The proposed taxonomy cuts across (...) the traditional dichotomies between symbolic and embodied AI, general intelligence and symbol and intelligence and cognitive simulation and human/non-human-like AI. -/- According to the taxonomy proposed here, one can distinguish between four distinct general approaches that figured prominently in early and classical AI, and that have partly developed into distinct research programs: first, phenomenal simulations (e.g., Turing’s “imitation game”); second, simulations that explore general-level formal isomorphisms in pursuit of a general theory of intelligence (e.g., logic-based AI); third, simulations as exploratory material models that serve to develop theoretical accounts of cognitive processes (e.g., Marr’s stages of visual processing and classical connectionism); and fourth, simulations as strictly formal models of a theory of computation that postulates cognitive processes to be isomorphic with computational processes (strong symbolic AI). -/- In continuation of pragmatic views of the modes of modeling and simulating world affairs, this taxonomy of approaches to modeling in AI helps to elucidate how available computational concepts and simulational resources contribute to the modes of representation and theory development in AI research—and what made that research program uniquely dependent on them. (shrink)

How do conventions of communication emerge? How do sounds or gestures take on a semantic meaning, and how do pragmatic conventions emerge regarding the passing of adequate, reliable, and relevant information? My colleagues and I have attempted in earlier work to extend spatialized game theory to questions of semantics. Agent-based simulations indicate that simple signaling systems emerge fairly naturally on the basis of individual information maximization in environments of wandering food sources and predators. Simple signaling emerges by means of any (...) of various forms of updating on the behavior of immediate neighbors: imitation, localized genetic algorithms, and partial training in neural nets. Here the goal is to apply similar techniques to questions of pragmatics. The motivating idea is the same: the idea that important aspects of pragmatics, like important aspects of semantics, may fall out as a natural results of information maximization in informational networks. The attempt below is to simulate fundamental elements of the Gricean picture: in particular, to show within networks of very simple agents the emergence of behavior in accord with the Gricean maxims. What these simulations suggest is that important features of pragmatics, like important aspects of semantics, don't have to be added in a theory of informational networks. They come for free. (shrink)

Many questions about the fundamentals of some area take the form “What is …?” It does not come as a surprise then that, at the dawn of Western philosophy, Socrates asked the questions of what piety, courage, and justice are. Nor is it a wonder that the philosophical preoccupation with computer simulations centered, among other things, about the question of what computer simulations are. Very often, this question has been answered by stating that computer simulation is a species of a (...) well-known method, e.g., experimentation. Other answers claim at least a close relationship between computer simulation and another method. In any case, correct answers to the question of what a computer simulation is should help us to better understand what validation of simulations is. The aim of this chapter is to discuss the most important proposals to understand computer simulation in terms of another method and to trace consequences for validation. Although it has sometimes been claimed that computer simulations are experiments, there are strong reasons to reject this view. A more appropriate proposal is to say that computer simulations often model experiments. This implies that the simulation scientists should to some extent imitate the validation of an experiment. But the validation of computer simulations turns out to be more comprehensive. Computer simulations have also been conceptualized as thought experimentsThought experiment or close cousins of the latter. This seems true, but not very telling since thought experiments are not a standardStandard method and since it is controversial how they contribute to our acquisition of knowledge. I thus consider a specific view on thought experiments to make some progress on understanding simulations and their validation. There is finally a close connection between computer simulation and modeling, and it can be shown that the validation of a computer simulation is the validation of a specific model, which may either be thought to be mathematical or fictional. (shrink)

A biophysical and biochemical perspective of Brahmajnaana will be advanced by viewing Upanishads and related books as “Texts of Science on human mind”. A biological and cognitive science insight of Atman and Maya, the results of breathing process; constituting and responsible for human consciousness and mental functions will be developed. The Advaita and Dvaita phases of human mind, its cognitive and functional states will be discussed. These mental activities will be modeled as brain-wave modulation and demodulation processes. The energy-forms and (...) their transformations as ideas/moods/experiences/thoughts/feelings/utterances/knowing/perception/experience/mood; and a theory of human cognition and communication will be advanced. The sameness of these and processes taking place and steps involved in human language acquisition and communication processes will be highlighted taking ideas from Sabdabrahma Siddhanta and Sphota Vaada, for which the basis is Brahmajnaana only. In fine, a physiological psychological and neurological model of human consciousness and function of mind based on Indian spiritual thought will be derived and discussed using concepts from modern science and technology. The application of these derivations in the fields of physiological psychology, mind-machine modeling, natural language comprehension branch of artificial intelligence and neurology to model and imitate human mental functions will be hinted. -/- . (shrink)

Speech is a perceptuo-motor system. A natural computational modeling framework is provided by cognitive robotics, or more precisely speech robotics, which is also based on embodiment, multimodality, development, and interaction. This paper describes the bases of a virtual baby robot which consists in an articulatory model that integrates the non-uniform growth of the vocal tract, a set of sensors, and a learning model. The articulatory model delivers sagittal contour, lip shape and acoustic formants from seven input parameters that characterize (...) the configurations of the jaw, the tongue, the lips and the larynx. To simulate the growth of the vocal tract from birth to adulthood, a process modifies the longitudinal dimension of the vocal tract shape as a function of age. The auditory system of the robot comprises a “phasic” system for event detection over time, and a “tonic” system to track formants. The model of visual perception specifies the basic lips characteristics: height, width, area and protrusion. The orosensorial channel, which provides the tactile sensation on the lips, the tongue and the palate, is elaborated as a model for the prediction of tongue-palatal contacts from articulatory commands. Learning involves Bayesian programming, in which there are two phases: specification of the variables, decomposition of the joint distribution and identification of the free parameters through exploration of a learning set, and utilization which relies on questions about the joint distribution. Two studies were performed with this system. Each of them focused on one of the two basic mechanisms, which ought to be at work in the initial periods of speech acquisition, namely vocal exploration and vocal imitation. The first study attempted to assess infants’ motor skills before and at the beginning of canonical babbling. It used the model to infer the acoustic regions, the articulatory degrees of freedom and the vocal tract shapes that are the likeliest explored by actual infants according to their vocalizations. Subsequently, the aim was to simulate data reported in the literature on early vocal imitation, in order to test whether and how the robot was able to reproduce them and to gain some insights into the actual cognitive representations that might be involved in this behavior. Speech modeling in a robotics framework should contribute to a computational approach of sensori-motor interactions in speech communication, which seems crucial for future progress in the study of speech and language ontogeny and phylogeny. (shrink)

Understanding the beliefs held by the learners about learning a language, and the way they utilize their thoughts about knowledge and learning seem essential for planning a constructive language program. Following this line of research, this paper aims at testing a hypothetical model of the relationship between epistemic beliefs and subscales of language-learning strategies through the mediating role of learners' self-efficacy. To this end, a sample of 300 Iranian high school students, taking regular courses, completed three survey questionnaires. At this (...) stage, correlational analysis and structural equation modeling were employed to probe the interconnections, analyze the model, and outline the conceptual framework. The results revealed that the LSE framework can adequately account for the learners' LLSs. In particular, the results indicated that efforts, persistence, and imitation positively and significantly influenced LLSs. However, EBs with the mediating role of LSE were known to be a significant factor in demoting the LLSs. Notably, knowledge and learning agents were the negative predictors of LLSs. This paper suggests that LSE has higher explanatory power than EBs in predicting LLSs. The findings of this study suggest that teachers and material developers should pay serious attention to the learners' self-efficacy as they were known to influence LLSs. (shrink)

What is it for a sound or gesture to have a meaning, and how does it come to have one? In this paper, a range of simulations are used to extend the tradition of theories of meaning as use. The authors work throughout with large spatialized arrays of sessile individuals in an environment of wandering food sources and predators. Individuals gain points by feeding and lose points when they are hit by a predator and are not hiding. They can also (...) make sounds heard by immediate neighbours in the array, and can respond to sounds from immediate neighbours. No inherent meaning for these sounds is built into the simulation; under what circumstances they are sent, if any, and what the response to them is, if any, vary initially with the strategies randomized across the array. These sounds do take on a specific function for communities of individuals, however, with any of three forms of strategy change: direct imitation of strategies of successful neighbours, a localized genetic algorithm in which strategies are ‘crossed’ with those of successful neighbours, and neural net training on the behaviour of successful neighbours. Starting from an array randomized across a large number of strategies, and using any of these modes of strategy change, communities of ‘communicators’ emerge. Within these evolving communities the sounds heard from immediate neighbours, initially arbitrary across the array, come to be used for very specific communicative functions. ‘Communicators’ make a particular sound on feeding and respond to that same sound from neighbours by opening their mouths; they make a different sound when hit with a predator and respond to that sound by hiding. Robustly and persistently, even in simple computer models of communities of self-interested agents, something suggestively like signalling emerges and spreads. Keywords: meaning, communication, genetic algorithms, neural networks. (shrink)

A large portion of mathematics education centers heavily around imitative reasoning and rote learning, raising concerns about students’ lack of deeper and conceptual understanding of mathematics. To address these concerns, there has been a growing focus on students learning and teachers teaching methods that aim to enhance conceptual understanding and problem-solving skills. One suggestion is allowing students to construct their own solution methods using creative mathematical reasoning, a method that in previous studies has been contrasted against algorithmic reasoning with positive (...) effects on test tasks. Although previous studies have evaluated the effects of CMR, they have ignored if and to what extent intrinsic cognitive motivation play a role. This study investigated the effects of intrinsic cognitive motivation to engage in cognitive strenuous mathematical tasks, operationalized through Need for Cognition, and working memory capacity. Two independent groups, consisting of upper secondary students, practiced non-routine mathematical problem solving with CMR and AR tasks and were tested 1 week later. An initial t-test confirmed that the CMR group outperformed the AR group. Structural equation modeling revealed that NFC was a significant predictor of math performance for the CMR group but not for the AR group. The results also showed that WMC was a strong predictor of math performance independent of group. These results are discussed in terms of allowing for time and opportunities for struggle with constructing own solution methods using CMR, thereby enhancing students conceptual understanding. (shrink)

Whether “biomimetic” or “bioinspired,” the projects of bioengineering tend to refer their devices or inventions to the biological systems that provide models or originals for detachable functionalities. And yet, they do not satisfy the picturing relation of original and copy. They are mimetic or imitative in the sense of reenacting a function in a different setting with its own principles of composition or its own parameters that select for salience. The taking up of salient features for the purposes of producing (...) a performance of functionality results not in the copy of an original but in its parody. Parodies are not judged for their veracity but for their effectiveness. They have a heuristic value in the context of design and for knowing the world through making and building. In somewhat experimental fashion, this paper seeks to develop a vocabulary for the parodistic qualities of Synthetic Biology, genome editing, or other bioengineering practices. In order to do so, it introduces categories from aesthetics to qualify modeling relations, one of these categories being the notion of “parody” itself. (shrink)

Social cognition differs from general cognition in its focus on understanding, perceiving, and interpreting social information. However, we argue that the significance of domain‐general processes for controlling cognition has been historically undervalued in social cognition and social neuroscience research. We suggest much of social cognition can be characterized as specialized feature representations supported by domain‐general cognitive control systems. To test this proposal, we develop a comprehensive working model, based on an interactive activation and competition architecture and applied to the control (...) of action. As such, we label the model “ICON” (interactive activation and competition model for the control of action). We used the ICON model to simulate human performance across various laboratory tasks. Our simulations emphasize that many laboratory‐based social tasks do not require socially specific control systems, such as those that are argued to rely on neural networks associated with theory‐of‐mind. Moreover, our model clarifies that perceived disruptions in social cognition, even in what appears to be disruption to the control of social cognition, can stem from deficits in social representation instead. We advocate for a “default stance” in social cognition, where control is usually general, but representation is specific. This study underscores the importance of integrating social cognition within the broader realm of domain‐general control processing, offering a unified perspective on task processing. (shrink)

In this paper, we study the performance of baseline hidden Markov model (HMM) for segmentation of speech signals. It is applied on single-speaker segmentation task, using Hindi speech database. The automatic phoneme segmentation framework evolved imitates the human phoneme segmentation process. A set of 44 Hindi phonemes were chosen for the segmentation experiment, wherein we used continuous density hidden Markov model (CDHMM) with a mixture of Gaussian distribution. The left-to-right topology with no skip states has been selected as it is (...) effective in speech recognition due to its consistency with the natural way of articulating the spoken words. This system accepts speech utterances along with their orthographic “transcriptions” and generates segmentation information of the speech. This corpus was used to develop context-independent hidden Markov models (HMMs) for each of the Hindi phonemes. The system was trained using numerous sentences that are relevant to provide information to the passengers of the Metro Rail. The system was validated against a few manually segmented speech utterances. The evaluation of the experiments shows that the best performance is obtained by using a combination of two Gaussians mixtures and five HMM states. A category-wise phoneme error analysis has been performed, and the performance of the phonetic segmentation has been reported. The modeling of HMMs has been implemented using Microsoft Visual Studio 2005 (C++), and the system is designed to work on Windows operating system. The goal of this study is automatic segmentation of speech at phonetic level. (shrink)

7 lectures, Torquay, UK, August 12-20, 1924 (CW 311) These seven intimate, aphoristic talks were presented to a small group on Steiner's final visit to England. Because they were given to "pioneers" dedicated to opening a new Waldorf school, these talks are often considered one of the best introductions to Waldorf education. Steiner shows the necessity for teachers to work on themselves first, in order to transform their own inherent gifts. He explains the need to use humor to keep their (...) teaching lively and imaginative. Above all, he stresses the tremendous importance of doing everything in the knowledge that children are citizens of both the spiritual and the earthly worlds. And, throughout these lectures, he continually returns to the practical value of Waldorf education. These talks are filled with practical illustrations and revolve around certain themes--the need for observation in teachers; the dangers of stressing the intellect too early; children's need for teaching that is concrete and pictorial; the education of children's souls through wonder and reverence; the importance of first presenting the "whole," then the parts, to the children's imagination. Here is one of the best introductions to Waldorf education, straight from the man who started it all. German source: Die Kunst des Erziehens aus dem Erfassen der Menschenwesenhiet (GA 311). ∞ ∞ ∞ SYNOPSIS OF THE LECTURES LECTURE 1: The need for a new art of education. The whole of life must be considered. Process of incarnation as a stupendous task of the spirit. Fundamental changes at seven and fourteen. At seven, the forming of the "new body" out of the "model body" inherited at birth. After birth, the bodily milk as sole nourishment. The teacher's task to give "soul milk" at the change of teeth and "spiritual milk" at puberty. LECTURE 2: In first epoch of life child is wholly sense organ. Nature of child's environment and conduct of surrounding adults of paramount importance. Detailed observation of children and its significance. In second epoch, seven to fourteen, fantasy and imagination as life blood of all education, e.g., in teaching of writing and reading, based on free creative activity of each teacher. The child as integral part of the environment until nine. Teaching about nature must be based on this. The "higher truths" in fairy tales and myths. How the teacher can guide the child through the critical moment of the ninth year. LECTURE 3: How to teach about plants and animals (seven to fourteen). Plants must always be considered, not as specimens, but growing in the soil. The plant belongs to the earth. This is the true picture and gives the child an inward joy. Animals must be spoken of always in connection with humans. All animal qualities and physical characteristics are to be found, in some form, in the human being. Humans as synthesis of the whole animal kingdom. Minerals should not be introduced until twelfth year. History should first be presented in living, imaginative pictures, through legends, myths, and stories. Only at eleven or twelve should any teaching be based on cause and effect, which is foreign to the young child's nature. Some thoughts on punishment, with examples. LECTURE 4: Development of imaginative qualities in the teacher. The story of the violet and the blue sky. Children's questions. Discipline dependent on the right mood of soul. The teacher's own preparation for this. Seating of children according to temperament. Retelling of stories. Importance of imaginative stories that can be recalled in later school life. Drawing of diagrams, from ninth year. Completion and metamorphosis of simple figures, to give children feeling of form and symmetry. Concentration exercises to awaken an active thinking as basis of wisdom for later life. Simple color exercises. A Waldorf school timetable. The "main lesson." LECTURE 5: All teaching matter must be intimately connected with life. In counting, each different number should be connected with the child or what the child sees in the environment. Counting and stepping in rhythm. The body counts. The head looks on. Counting with fingers and toes is good (also writing with the feet). The ONE is the whole. Other numbers proceed from it. Building with bricks is against the child's nature, whose impulse is to proceed from whole to parts, as in medieval thinking. Contrast atomic theory. In real life we have first a basket of apples, a purse of coins. In teaching addition, proceed from the whole. In subtraction, start with minuend and remainder; in multiplication, with product and one factor. Theorem of Pythagoras (eleven-twelve years). Details given of a clear, visual proof, based on practical thinking. This will arouse fresh wonder every time. LECTURE 6: In first seven years etheric body is an inward sculptor. After seven, child has impulse to model and to paint. Teacher must learn anatomy by modeling the organs. Teaching of physiology (nine to twelve years) should be based on modeling. Between seven and fourteen astral body gradually draws into physical body, carrying the breathing by way of nerves, as playing on a lyre. Importance of singing. Child's experience of well being like that of cows chewing the cud. Instrumental music from beginning of school life, wind or strings. Teaching of languages; up to nine through imitation, then beginnings of grammar, as little translation as possible. Vowels are expression of feeling, consonants are imitation of external processes. Each language expresses a different conception. Compare head, Kopf, testa. The parts of speech in relation to the life after death. If language is rightly taught, out of feeling, eurythmy will develop naturally, expressing inner and outer experiences in ordered movements--"visible speech." Finding relationship to space in gymnastics. LECTURE 7: Between seven and fourteen soul qualities are paramount. Beginnings of science teaching from twelfth year only, and connected with real phenomena of life. The problem of fatigue. Wrong conceptions of psychologists. The rhythmic system, predominant in second period, never tires. Rhythm and fantasy. Composition. Sums from real life, not abstractions. Einstein's theory. The kindergarten--imitation of life. Teachers' meetings, the heart of the school. Every child to be in the right class for its age. Importance of some knowledge of trades, e.g., shoemaking, handwork, and embroidery. Children's reports-- characterization, but no grading. Contact with the parents. QUESTIONS AND ANSWERS: The close relationship of Multiplication and Division. How to deal with both together. Transition from the concrete to the abstract in Arithmetic. Not before the ninth year. Healthiness of English weights and measures as related to real life. Decimal system as an intellectual abstraction. Drawing. Lines have no reality in drawing and painting, only boundaries. How to teach children to draw a tree in shading, speaking only of light and color. (Illustration). Line drawing belongs only to geometry. Gymnastics and Sport. Sport is of no educational value, but necessary as belonging to English life. Gymnastics should be taught by demonstration. Religious Instruction. Religion lessons in the Waldorf school given by Catholic priest and Protestant pastor. "Free" religion lessons provided for the other children. Plan of such teaching described, of which the fundamental aim is an understanding of Christianity. The Sunday services. Modern Language Lessons. Choice of languages must be guided by the demands of English life. These can be introduced at an early age. Direct method in language teaching. Closing words by Dr. Steiner on the seriousness of this first attempt to found a school in England. (shrink)

A view of evolution is presented in this paper (a two paper series), intended as a methodological infrastructure for modeling spatio-cultural systems (the design outline of such a model is presented in paper II). A motivation for the re-articulation of evolution as information dynamics is the phenomenologically discovered prerequisite of embedding a meaning-attributing apparatus in any and all models of spatio-cultural systems. An evolution is construed as the dynamics of a complex system comprised of memory devices, connected in an (...) ordered fashion (not randomly) by information-exchanges. An information-exchange transpires when the recipient system adopts a strategy (a continuum of events) that eventually changes its structure; namely, after the exchange, it contains and conveys different information. These memory devices—sub-systems—are also similarly constructed complex system. Only a part of the information is retained by a system in its physical-memory storage, which eventually loses this function too, when the ability to retrieve a common enough structure is lost. The entire amount of information is a system’s structure of connections (information exchanges); it is contained (apparently stored) dynamically when a system is observed in a temporarily stable state. This temporary permanence—robustness and resilience—is attained dynamically; namely, enabled by changes taking place in its sub-systems and in each of their sub-systems. Therefore, for modeling such a system a multi-layer/multi-scale approach is preferable. It enables the addition and subtraction of an interim scale and the consideration of such a scale as a micro or a macro (thus initiating a maneuver up or down scale) according to an ad-hoc requirement of the model, which imitates an envisaged sub system (called an ‘Inner World’), to which a certain range of decisions is relegated. This dynamics is driven (in time) by dynamically originated information growth, as defined by Shannon; i.e. by the fact that each of certain state transitions have occurred sometime in the past of the system just so and not otherwise, and by the fact that they have occurred in a certain order. Therefore, each ‘history’ and memory retrieval availability of information is unique, and thus can be used to differentiate meanings. Hence, there cannot be a comprehensive solution to the meaning attribution model-design challenge. However, the observation that at the core of each envisaged complex system, moving in time according to a rounded logic, there is an information manipulating device, operating necessarily according to a Boolean logic, can be copied into the design of a model. This observation enables, therefore, the embedding of a specific, locally fitted, meaning attributing device, which is an information manipulating mechanism (it splices/attaches one segment of information to another—its meaning). However, this is just a framework; the actual solution has still to be found locally—for each subject system. Such a solution is demonstrated for the change in location or layout in the Israeli city in paper II. (shrink)

Moral virtue is, for Aristotle, famously acquired through the practice of virtuous actions. But how should we understand the activity of Aristotle’s moral learner, and how does her activity result in the acquisition of virtue? I argue that by understanding Aristotle’s learner as engaged in the emulative imitation of a virtuous agent, we can best account for her development. Such activity crucially involves the adoption of the virtuous agent’s perspective, from which I argue the learner is positioned so as (...) to appreciate the value of virtuous action—its fineness—and what it would be to act finely herself. (shrink)

To the extent that language is conventional, non-verbal individuals, including human infants, must participate in conventions in order to learn to use even simple utterances of words. This raises the question of which varieties of learning could make this possible. In this paper I defend Tomasello’s (The cultural origins of human cognition. Harvard UP, Cambridge, 1999, Origins of human communication. MIT, Cambridge, 2008) claim that knowledge of linguistic conventions could be learned through imitation. This is possible because Lewisian accounts (...) of convention have overstated what one must know to participate in conventions; and because the required knowledge could be learned imitatively. The imitation claim that I defend is consistent with what we know about both the proliferation of conventional behaviours in human children, who are skilful imitators, and the comparative absence of such behaviours in non-human great apes, who are poor at imitative learning. (shrink)

This chapter contains sections titled: Introduction I Peters on Ritual in Education II R. S. Peters on Ritual and Imitation: An Assessment Future Directions References.

Modeling is an important scientific practice, yet it raises significant philosophical puzzles. Models are typically idealized, and they are often explored via imaginative engagement and at a certain “distance” from empirical reality. These features raise questions such as what models are and how they relate to the world. Recent years have seen a growing discussion of these issues, including a number of views that treat modeling in terms of indirect representation and analysis. Indirect views treat the model as (...) a bona fide object, specified by the modeler and used to represent and reason about some portion of the concrete empirical world. On some indirect views, model systems are abstract entities, such as mathematical structures, while on other views they are concrete hypothetical things. Here I assess these views and offer a novel account of models. I argue that regarding models as abstracta results in some significant tensions with the practice of modeling, especially in areas where non-mathematical models are common. Furthermore, viewing models as concrete hypotheticals raises difficult questions about model-world relations. The view I argue for treats models as direct, albeit simplified, representations of targets in the world. I close by suggesting a treatment of model-world relations that draws on a recent work by Stephen Yablo concerning the notion of partial truth. (shrink)

Experimental modeling in biology involves the use of living organisms (not necessarily so-called "model organisms") in order to model or simulate biological processes. I argue here that experimental modeling is a bona fide form of scientific modeling that plays an epistemic role that is distinct from that of ordinary biological experiments. What distinguishes them from ordinary experiments is that they use what I call "in vivo representations" where one kind of causal process is used to stand in (...) for a physically different kind of process. I discuss the advantages of this approach in the context of evolutionary biology. (shrink)

Computational modeling has long been one of the traditional pillars of cognitive science. Unfortunately, the computer models of cognition being developed today have not kept up with the enormous changes that have taken place in computer technology and, especially, in human-computer interfaces. For all intents and purposes, modeling is still done today as it was 25, or even 35, years ago. Everyone still programs in his or her own favorite programming language, source code is rarely made available, accessibility (...) of models to non-programming researchers is essentially non-existent, and even for other modelers, the profusion of source code in a multitude of programming languages, written without programming guidelines, makes it almost impossible to access, check, explore, re-use, or continue to develop. It is high time to change this situation, especially since the tools are now readily available to do so. We propose that the modeling community adopt three simple guidelines that would ensure that computational models would be accessible to the broad range of researchers in cognitive science. We further emphasize the pivotal role that journal editors must play in making computational models accessible to readers of their journals. (shrink)

What is the relationship between imitation and culture? This article charts how definitions of imitation have changed in the last century, distinguishes three senses of “culture” used by contemporary evolutionists (Culture1–Culture3), and summarises current disagreement about the relationship between imitation and culture. The disagreement arises from ambiguities in the distinction between imitation and emulation, and confusion between two explanatory projects—the anthropocentric project and the cultural selection project. I argue that imitation gives cultural evolution an inheritance (...) mechanism for communicative and gestural skills (but not technological skills), and cultural selection yields the cognitive mechanisms that make imitation possible. (shrink)

Imitation, the replication of observed behaviors, has been proposed as the crucial social learning mechanism for the generation of humanlike cultural complexity. To date, the single published experimental microsociety study that tested this hypothesis found no advantage for imitation. In contrast, the current paper reports data in support of the imitation hypothesis. Participants in “microsociety” groups built weight-bearing devices from reed and clay. Each group was assigned to one of four conditions: three social learning conditions and one (...) asocial learning control condition. Groups able to observe other participants building their devices, in contrast to groups that saw only completed devices, show evidence of successive improvement. These results are consistent with the hypothesis that imitation is required for cumulative cultural evolution. This study adds crucial data for understanding why imitation is needed for cultural accumulation, a central defining feature of our species. (shrink)

The Lotka–Volterra predator-prey-model is a widely known example of model-based science. Here we reexamine Vito Volterra’s and Umberto D’Ancona’s original publications on the model, and in particular their methodological reflections. On this basis we develop several ideas pertaining to the philosophical debate on the scientific practice of modeling. First, we show that Volterra and D’Ancona chose modeling because the problem in hand could not be approached by more direct methods such as causal inference. This suggests a philosophically insightful (...) motivation for choosing the strategy of modeling. Second, we show that the development of the model follows a trajectory from a “how possibly” to a “how actually” model. We discuss how and to what extent Volterra and D’Ancona were able to advance their model along that trajectory. It turns out they were unable to establish that their model was fully applicable to any system. Third, we consider another instance of model-based science: Darwin’s model of the origin and distribution of coral atolls in the Pacific Ocean. Darwin argued more successfully that his model faithfully represents the causal structure of the target system, and hence that it is a “how actually” model. (shrink)

Predictive modeling in education draws on data from past courses to forecast the effectiveness of future courses. The present effort sought to identify such a model of instructional effectiveness in scientific ethics. Drawing on data from 235 courses in the responsible conduct of research, structural equation modeling techniques were used to test a predictive model of RCR course effectiveness. Fit statistics indicated the model fit the data well, with the instructional characteristics included in the model explaining approximately 85% (...) of the variance in RCR instructional effectiveness. Implications for using the model to develop and improve future RCR courses are discussed. (shrink)

Imitation research has focused on turn‐taking contexts in which one person acts and one person then copies that action. However, people also imitate when engaging in joint actions, where two or more people coordinate their actions in space and time in order to achieve a shared goal. We discuss how the various constraints imposed by joint action modulate imitation, and the close links between perception and action that form the basis of this phenomenon. We also explore how understanding (...) imitation in the context of joint action yields new insights into cultural phenomena such as teaching and innovation. (shrink)

The fate of optimality modeling is typically linked to that of adaptationism: the two are thought to stand or fall together (Gould and Lewontin, Proc Relig Soc Lond 205:581–598, 1979; Orzack and Sober, Am Nat 143(3):361–380, 1994). I argue here that this is mistaken. The debate over adaptationism has tended to focus on one particular use of optimality models, which I refer to here as their strong use. The strong use of an optimality model involves the claim that selection (...) is the only important influence on the evolutionary outcome in question and is thus linked to adaptationism. However, biologists seldom intend this strong use of optimality models. One common alternative that I term the weak use simply involves the claim that an optimality model accurately represents the role of selection in bringing about the outcome. This and other weaker uses of optimality models insulate the optimality approach from criticisms of adaptationism, and they account for the prominence of optimality modeling (broadly construed) in population biology. The centrality of these uses of optimality models ensures a continuing role for the optimality approach, regardless of the fate of adaptationism. (shrink)

Over 35 years ago, Meltzoff and Moore (1977) published their famous article ‘Imitation of facial and manual gestures by human neonates’. Their central conclusion, that neonates can imitate, was and continues to be controversial. Here we focus on an often neglected aspect of this debate, namely on neonatal spontaneous behaviors themselves. We present a case study of a paradigmatic orofacial ‘gesture’, namely tongue protrusion and retraction (TP/R). Against the background of new research on mammalian aerodigestive development, we ask: How (...) does the human aerodigestive system develop and what role does TP/R play in the neonate’s emerging system of aerodigestion? We show that mammalian aerodigestion develops in two phases: (1) from the onset of isolated orofacial movements in utero to the post-natal mastery of suckling at 4 months after birth, and; (2) thereafter, from preparation to the mastery of mastication and deglutition of solid foods. Like other orofacial stereotypies, TP/R emerges in the first phase and vanishes prior to the second. Based upon recent advances in activity-driven early neural development, we suggest a sequence of three developmental events in which TP/R might participate: the acquisition of tongue control, the integration of the central pattern generator for TP/R with other aerodigestive CPGs, and the formation of connections within the cortical maps of S1 and M1. If correct, orofacial stereotypies are crucial to the maturation of aerodigestion in the neonatal period but also unlikely to co-occur with imitative behavior. (shrink)

Imitation has been understood in different ways: as a cognitive adaptation subtended by genetically specified cognitive mechanisms; as an aspect of domain general human cognition. The second option has been advanced by Cecilia Heyes who treats imitation as an instance of associative learning. Her argument is part of a deflationary treatment of the “mirror neuron” phenomenon. I agree with Heyes about mirror neurons but argue that Kim Sterelny has provided the tools to provide a better account of the (...) nature and role of human imitation. What we call imitative learning is an instance of social learning. It has little to do with empathy, emotional contagion, or mind reading. (shrink)

Computational modeling should play a central role in philosophy. In this introduction to our topical collection, we propose a small topology of computational modeling in philosophy in general, and show how the various contributions to our topical collection fit into this overall picture. On this basis, we describe some of the ways in which computational models from other disciplines have found their way into philosophy, and how the principles one found here still underlie current trends in the field. (...) Moreover, we argue that philosophers contribute to computational modeling not only by building their own models, but also by thinking about the various applications of the method in philosophy and the sciences. In this context, we note that models in philosophy are usually simple, while models in the sciences are often more complex and empirically grounded. Bridging certain methodological gaps that arise from this discrepancy may prove to be challenging and fruitful for the further development of computational modeling in philosophy and beyond. (shrink)