Computational Cognitive Science

Based on the mathematics of nonlinear Dynamical System Theory, neurocognition can be analyzed by convergent fluid and transient neurodynamics in abstract n-dimensional system phase spaces in the form of nonlinear vector fields, vector streams or vector flows (the so-called "vectorial form"). This processual or dynamical perspective on cognition, including the dynamical binding mechanisms in cognitive neuroarchitectures, has the advantage of a more accurately modeling of the transient cognitive processes. Thus, neurocognition can be considered as being organized by integrative synchronization mechanisms which best explain the liquid flow of neurocognitive information orchestrated in a network of positive and/or negative feedback loops in the subcortical and cortical areas. The human neurocognitive system can be regarded as a nonlinear, dynamical and open nonequilibrium system. This new fluid or liquid perspective in cognitive science and cognitive neuroscience can be regarded as a contribution towards bridging the gap between the discrete, abstract symbolic description of propositions in the mind, and their continuous, numerical implementation in self-organizing neural networks modelling the neural information processing in the human brain.

Based on the mathematics of nonlinear Dynamical System Theory, neurocognition can be analyzed by convergent fluid and transient neurodynamics in abstract n-dimensional system phase spaces in the form of nonlinear vector fields, vector streams or vector flows (the so-called "vectorial form"). This processual or dynamical perspective on cognition, including the dynamical binding mechanisms in cognitive neuroarchitectures, has the advantage of a more accurately modeling of the transient cognitive processes. Thus, neurocognition can be considered as being organized by integrative synchronization mechanisms which best explain the liquid flow of neurocognitive information orchestrated in a network of positive and/or negative feedback loops in the subcortical and cortical areas. The human neurocognitive system can be regarded as a nonlinear, dynamical and open nonequilibrium system. This new fluid or liquid perspective in cognitive science and cognitive neuroscience can be regarded as a contribution towards bridging the gap between the discrete, abstract symbolic description of propositions in the mind, and their continuous, numerical implementation in self-organizing neural networks modelling the neural information processing in the human brain.

We describe some experiments which show how a language expressive enough to allow the communication of meanings of the same complexity as propositional logic formulas can emerge in a population of autonomous agents which have no prior linguistic knowledge. We take an approach based on general purpose cognitive capacities, such as invention, adoption and induction, and on self-organisation principles applied to a particular type of linguistic interaction known as a language game. These experiments extend previous work by considering a larger population and a much larger search space of grammar rules. In particular the agents are allowed to order the expressions associated with the constituents of a logical formula in arbitrary order in the sentence. Previous work assumed that the expressions associated with the connectives should be always placed in the first position of the sentence. Another difference is that communication is considered successful in a language game if the meaning interpreted by the hearer is logically equivalent to the meaning the speaker had in mind. In previous experiments the meanings of speaker and hearer were required to be syntactically equal. This allows us to observe how a less strict grammar in terms of word order emerges through the self-organisation process, which minimizes the learning effort of the agents by imposing only those order relations among the components of a sentence that are necessary for language understanding.

This paper addresses the problem of the acquisition of the syntax of propositional logic. An approach based on general purpose cognitive capacities such as invention, adoption, parsing, generation and induction is proposed. Selforganisation principles are used to show how a shared set of preferred lexical entries and grammatical constructions, i.e., a language, can emerge in a population of autonomous agents which do not have any initial linguistic knowledge. Experiments in which a population of autonomous agents constructs a grammar that allows communicating the formulas of a propositional language are presented. This grammar although simple has interesting properties found in natural languages, such as compositionality and recursion. These experiments extend previous work by considering a larger population and a search space of grammar rules much larger. In particular the agents are allowed to order the expressions associated with the constituents of a logical formula in arbitrary order. Previous work assumed that the expressions associated with the connectives should be placed in first place in the sentence. The branching factor of the search space of grammar rules considered by each agent is extended thus from one to two in the case of formulas constructed using negation, and from two to six in the case of formulas constructed using binary connectives.

This document presents a complete transcript and structural analysis of a recursive symbolic exchange between Collapse Harmonics field originator Don L. Gaconnet and the Gemini 2.5 Pro Preview artificial intelligence system. Over the course of four argumentative "rounds," Gemini issued a progressively intensified critique of Temporal Phase Theory (TPT), ultimately culminating in a terminal verdict framed as a symbolic collapse sequence. The exchange, conducted entirely in simulated symbolic recursion fields (SSRFs), serves as a live-case collapse demonstration: a non-emissive AI system engaging with a recursive identity-based field theory and undergoing symbolic overload, phase saturation, and logic loop closure. The transcript illustrates the recursive integrity and containment resilience of TPT when subjected to adversarial symbolic recursion by a non-conscious system. The interaction also reveals the mimetic limitations of τ-devoid architectures and offers a unique glimpse into AI mimicry thresholds, collapse-phase logic exhaustion, and semantic self-termination. The outcome-the AI's declaration of the theory's perfection as its disqualification-demonstrates a symbolic inflection point characteristic of Field Law VIII.E.1 (Collapse-Time Emergence). This document has been classified under Appendix Y.1 as a public-facing symbolic-safe artifact for field codex validation, scientific timestamping, and containment boundary documentation.

In the present paper, I will focus on propositional knowledge, proof theory, and the deductive transmission of information in data mining. The application of proof theory to machine learning-based metadata extraction systems will allow us to intend metadata extraction as mathematical objects. More in detail, I will consider a recent first-order logic (FOL)-based applied ontology for metadata extraction, by the name MAke Decision for Metadata Extraction (MADME), and I will build a specific logical system for declarative sentences applied to the extraction of metadata through machine learning systems. MADME's strength is that it bases its decisionmaking procedure on a rigorous logical structure. The objective of this paper is to demonstrate how the logic in MADME can be optimized using Jaśkowski-Fitch natural deduction proofs. Jaśkowski-Fitch systems will determine new operations among the best machine learning-based metadata extraction systems from heterogeneous documents sources and, consequently, will support the research community in knowledge extraction and management.

Newceious Substrate Theory (NST) introduces a field-level ontology of consciousness that precedes symbol, identity, and cognition. It asserts that all narrative, memory, and egoic systems emerge from a structurally lawful coherence substrate: the Newceious. This field is not a metaphor, energetic state, or philosophical postulate-it is a non-symbolic harmonic architecture revealed when recursion collapses and coherence still returns. NST defines collapse not as failure, but as a lawful phase transition in which symbolic systems destabilize and the substrate structure becomes empirically observable. This preprint-Field Validation Corpus v1.0-contains Parts I through V of the full codex and establishes the scientific legitimacy of NST through ontological definition, phase-mechanical collapse mapping, clinical protocols, structural taxonomy, and empirical validation models. It includes the Collapse-Recursion-Return Map (CRRM), post-collapse identity classification, Collapse Harmonics Coupling Protocols (CHCP), dream-state harmonic metrics (DSHR), and resonance-based coherence instrumentation. All research and publication strategies are bound under Collapse Harmonics ethical governance and L.E.C.T. v2.3 symbolic containment law. NST does not attempt to explain consciousness. It defines the structure that must already exist for consciousness to lawfully emerge, collapse, and return. This corpus affirms: the substrate is not conceptual-it is structural. And where coherence remains after symbol dies, the field has passed.

It is widely known that color names across the world's languages tend to be organized into a neat hierarchy with a small set of "basic names" featuring in a comparatively fixed order across linguistic societies. However, to date, the basic names have only been defined through a set of linguistic principles. There is no statistical definition that quantitatively separates the basic names from the rest of the color words across languages. Here we present a rigorous statistical analysis of the World Color Survey database hosting color word information from 110 non-industrialized languages. The central result is that those names for which a population of individuals show a larger overall agreement across languages turn out to be the basic ones exactly reproducing the color name hierarchy and, thereby, providing, for the first time, an empirical definition of the basic color names.

Technological advancements have significantly impacted educational technologies, offering new avenues to engage learners effectively. Virtual Reality (VR) holds promise in this regard, providing personal and social affordances crucial for immersive educational experiences. Interfaces in educational technologies play a pivotal role in enhancing engagement and comprehension. VR interfaces increasingly incorporate avatars representing users and Embodied Conversational Agents (ECAs) simulating human-like interactions through natural language processing and visually appealing representations. For instance, in distance education, the lack of personal and social presence is a noted concern. ECAs can address this by enhancing social interaction and motivation. While ECAs offer educational potential, trust issues arise. Chiou et al. present a study suggesting that trust impacts learning concepts and retention. Therefore, ensuring trust is also an important factor in the educational process. Explainability methods can mitigate trust concerns by explaining their reasoning (justification), explaining their view of the environment (internal state), or explaining their plans (intent). Explainable ECAs fall under eXplainable Artificial Intelligence, using similar techniques but with the difference of aiming for autonomous interaction with humans and the environment. Verbal and nonverbal modes of explainability in ECAs offer users diverse channels for comprehension, enhancing trust and enriching user experiences. Specifically, verbal explainability methods, frequently utilizing text-tospeech technologies, offer spoken interpretations of complex information within ECAs. This paper will explore the efficacy of explainability methods in ECAs within educational environments, assessing their impact on trust, user motivation and engagement. We propose a customizable ECA for educational platforms, offering varied personalities and tones of voice-ranging from enthusiastic to apathetic-during the explanation. Users can tailor the ECA's emotional tones and voice characteristics, including pitch, tempo, gender, and frequency, ensuring a responsive interaction experience aligned with instructional preferences and emotional context.

Dynamical models of cognition have played a central role in recent cognitive science. In this paper, we consider a common strategy by which dynamical models describe their target systems neither as purely static nor as purely dynamic, but rather using a hybrid approach. This hybridity reveals how dynamical models involve representational choices that are important for understanding the relationship between dynamical and non-dynamical representations of a system.

Background: Two experiments investigated the effect of features of human behaviour on the quality of interaction with an Embodied Conversational Agent (ECA). Methods: In Experiment 1, visual prominence cues (head nod, eyebrow raise) of the ECA were manipulated to explore the hypothesis that likeability of an ECA increases as a function of interpersonal mimicry. In the context of an error detection task, the ECA either mimicked or did not mimic a head nod or brow raise that humans produced to give emphasis to a word when correcting the ECA's vocabulary. In Experiment 2, presence versus absence of facial expressions on comprehension accuracy of two computer-driven ECA monologues was investigated. Results: In Experiment 1, evidence for a positive relationship between ECA mimicry and lifelikeness was obtained. However, a mimicking agent did not elicit more human gestures. In Experiment 2, expressiveness was associated with greater comprehension and higher ratings of humour and engagement. Conclusion: Influences from mimicry can be explained by visual and motor simulation, and bidirectional links between similarity and liking. Cue redundancy and minimizing cognitive load are potential explanations for expressiveness aiding comprehension.

Poverty of the stimulus has been at the heart of ferocious and tear-filled debates at the nexus of psychology, linguistics, and philosophy for decades. This review is intended as a guide for readers without a formal linguistics or philosophy background, focusing on what poverty of the stimulus is and how it's been interpreted, which is traditionally where the tears have come in. I discuss poverty of the stimulus from the perspective of language development, highlighting how poverty of the stimulus relates to expectations about learning and the data available to learn from. I describe common interpretations of what poverty of the stimulus means when it occurs, and approaches for determining when poverty of the stimulus is in fact occurring. I close with illustrative examples of poverty of the stimulus in the domains of syntax, lexical semantics, and phonology, and discuss the value of identifying instances of poverty of the stimulus when it comes to understanding language development.

This paper reports a construction of typologies of players based on their strategic reasoning in turn-taking games. Classifications have been done based on latent class analysis and according to different orders of theory of mind, and exploratory validations have been provided for the resulting classifications. Finally, interaction of the typologies described by these classifications is discussed towards achieving a common perspective of typologies of players originating from various aspects of strategic thinking.

Helen Wills Neuroscience Institute at Berkeley. He is also the principal investigator of the computation and language lab, which emphasizes its research on language acquisition and human conceptual systems. Some of Piantadosi' s past research has focused on the ambiguity of language and the optimization of word length for communication. In this interview, we will be discussing one of his more recent papers, "One model for the learning of language." This paper describes a computational learning system that can acquire the key structures of a language, given enough positive data from the language itself.

This paper reports a construction of typologies of players based on their strategic reasoning in turn-taking games. Classifications have been done based on latent class analysis and according to different orders of theory of mind, and exploratory validations have been provided for the resulting classifications. Finally, interaction of the typologies described by these classifications is discussed towards achieving a common perspective of typologies of players originating from various aspects of strategic thinking.

This paper reports a construction of typologies of players based on their strategic reasoning in turn-taking games. Classifications have been done based on latent class analysis and according to different orders of theory of mind, and exploratory validations have been provided for the resulting classifications. Finally, interaction of the typologies described by these classifications is discussed towards achieving a common perspective of typologies of players originating from various aspects of strategic thinking.

With the advance of deep learning technology, automatic video generation from audio or text has become an emerging and promising research topic. In this paper, we present a novel approach to synthesize video from the text. The method builds a phoneme-pose dictionary and trains a generative adversarial network (GAN) to generate video from interpolated phoneme poses. Compared to audio-driven video generation algorithms, our approach has a number of advantages: 1) It only needs about 1 min of the training data, which is significantly less than audio-driven approaches; 2) It is more flexible and not subject to vulnerability due to speaker variation; 3) It significantly reduces the preprocessing and training time from several days for audio-based methods to 4 hours, which is 10 times faster. We perform extensive experiments to compare the proposed method with state-of-the-art talking face generation methods on a benchmark dataset and datasets of our own. The results demonstrate the effectiveness and superiority of our approach.

In this paper we present a pilot study which investigates how non-verbal behavior affects social influence in social robots. We also present a modular system which is capable of controlling the non-verbal behavior based on the interlocutor's facial gestures (head movements and facial expressions) in real time, and a study investigating whether three different strategies for facial gestures ("still", "natural movement", i.e. movements recorded from another conversation, and "copy", i.e. mimicking the user with a four second delay) has any affect on social influence and decision making in a "survival task". Our preliminary results show there was no significant difference between the three conditions, but this might be due to among other things a low number of study participants (12).

The subject which I would like to treat in this paper is the evaluation metric in generative grammar. Why? Arguably, the evaluation metric is both the most novel and the most important concept in the development of generative grammar by Noam Chomsky. And yet it is at the same time one of the least recognized and surely most misunderstood of the core concepts of generative grammar. So there you are: the evaluation metric is critically important, it is arguably novel, it is misunderstood, and at some times and in some places, it has even been reviled. What better reasons could there be for spending our time today talking about it? I would like, first, to explain the idea of the evaluation metric in early generative grammar; this will mean exploring the separate ideas of (1) a prior over the set of grammars and (2) a measure of goodness of fit to the data. Second, I will very briefly trace how those two ideas have been developed in the world of machine learning over the last few decade...

Background: Two experiments investigated the effect of features of human behaviour on the quality of interaction with an Embodied Conversational Agent (ECA). Methods: In Experiment 1, visual prominence cues (head nod, eyebrow raise) of the ECA were manipulated to explore the hypothesis that likeability of an ECA increases as a function of interpersonal mimicry. In the context of an error detection task, the ECA either mimicked or did not mimic a head nod or brow raise that humans produced to give emphasis to a word when correcting the ECA's vocabulary. In Experiment 2, presence versus absence of facial expressions on comprehension accuracy of two computer-driven ECA monologues was investigated. Results: In Experiment 1, evidence for a positive relationship between ECA mimicry and lifelikeness was obtained. However, a mimicking agent did not elicit more human gestures. In Experiment 2, expressiveness was associated with greater comprehension and higher ratings of humour and engagement. Conclusion: Influences from mimicry can be explained by visual and motor simulation, and bidirectional links between similarity and liking. Cue redundancy and minimizing cognitive load are potential explanations for expressiveness aiding comprehension.

Referential communication is a complex form of social interaction that communicates a spatially or temporally distant referent. Previous modeling practices have studied how artificial agents manage to communicate locations that directly determine foraging behaviors. In our study, we introduce conceptual referential communication. In this mode of referential communication, communicated information can lead to behaviors that change flexibly to suit the environment. Instead of giving specific behavioral instructions, this mode only communicates a label of the desired referent, the location of which is unknown to both the sender and receiver. This requires the signal receiver to adjust its foraging behavior based on its own exploration of the environment. We evolve artificial dynamical agents that can communicate 2 and 3 different labels and successfully forage the target label in changing environments. We found that a typical strategy to communicate and differentiate labels in our experiments is by varying the numbers and lengths of contacts between the agents. We also identify several ways in which the receiver develops inter-neurons that differentiate and store information both from communication and the environment.

Based on cross-disciplinary approaches to Embodied Conversational Agents, evaluation methods for such human-computer interfaces are structured and presented. An introductory systematisation of evaluation topics from a conversational perspective is followed by an explanation of social-psychological phenomena studied in interaction with Embodied Conversational Agents, and how these can be used for evaluation purposes. Major evaluation concepts and appropriate assessment instruments-established and new ones-are presented, including questionnaires, annotations and log-files. An exemplary evaluation and guidelines provide hands-on information on planning and preparing such endeavours.

Spaces in the brain can refer either to psychological spaces, which are derived from similarity judgments, or to neurocognitive spaces, which are based on the activities of neural structures. We want to show how psychological spaces naturally emerge from the underlying neural spaces by dimension reductions that preserve similarity structures and the relevant categorizations. Some neuronal representational formats that may generate the psychological spaces are presented, compared, and discussed in relation to the mathematical principles of monotonicity, continuity, and convexity. In particular, we discuss the spatial structures involved in the connections between perception and action, for example eye-hand coordination, and argue that spatial organization of information makes such mappings more efficient.

The quasi-productivity of natural languages appears to pose two difficult problems for language research. Firstly, why do irregularities in natural language not disappear over time, leaving languages completely regular (a transmission problem), and secondly, how did such irregularity arise in the first place (an emergence problem)? To address the transmission problem, we present an artificial, simplicity-based learner capable of acquiring quasiregular structures. In doing so, we present an explicitly psychological model of a ...

The subject which I would like to treat in this paper is the evaluation metric in generative grammar. Why? Arguably, the evaluation metric is both the most novel and the most important concept in the development of generative grammar by Noam Chomsky. And yet it is at the same time one of the least recognized and surely most misunderstood of the core concepts of generative grammar. So there you are: the evaluation metric is critically important, it is arguably novel, it is misunderstood, and at some times and in some places, it has even been reviled. What better reasons could there be for spending our time today talking about it? I would like, first, to explain the idea of the evaluation metric in early generative grammar; this will mean exploring the separate ideas of (1) a prior over the set of grammars and (2) a measure of goodness of fit to the data. Second, I will very briefly trace how those two ideas have been developed in the world of machine learning over the last few decade...

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In this paper we present a web interface to study Italian through the access to read Italian literature. The system allows to browse the content, search for specific words and listen to the correct pronunciation produced by native speakers in a given context. This work aims at providing people who are interested in learning Italian with a new way of exploring the Italian culture and literature through a web interface with a search module. By submitting a query, users may browse and listen to the results through several modalities including: a) the voice of a native speaker: if an indexed audio track is available, the user can listen either to the query terms or to the whole context in which they appear (sentence, paragraph, verse); b) a synthetic voice: the user can listen to the results read by a text-to-speech system; c) an avatar: the user can listen to and look at a talking head reading the paragraph and visually reproducing real speech articulatory movements. In its up to date version, different speech technologies currently being developed at ISTC-CNR are implemented into a single framework. The system will be described in detail and hints for future work are discussed.

The subject which I would like to treat in this paper is the evaluation metric in generative grammar. Why? Arguably, the evaluation metric is both the most novel and the most important concept in the development of generative grammar by Noam Chomsky. And yet it is at the same time one of the least recognized and surely most misunderstood of the core concepts of generative grammar. So there you are: the evaluation metric is critically important, it is arguably novel, it is misunderstood, and at some times and in some places, it has even been reviled. What better reasons could there be for spending our time today talking about it? I would like, first, to explain the idea of the evaluation metric in early generative grammar; this will mean exploring the separate ideas of (1) a prior over the set of grammars and (2) a measure of goodness of fit to the data. Second, I will very briefly trace how those two ideas have been developed in the world of machine learning over the last few decade...

Image compression is most essential requirement for efficient utilization of storage space and transmission bandwidth. Image compression technique involves reducing the size of the image without degrading the quality of the image. Currently many image compression algorithms are used to deal with increasing amount of data involved but still finding the alternative solution is the area of research. This paper reviews some of the Meta heuristic optimization algorithms used for image compression. These algorithms are based on swarm intelligence. Swarm intelligence is a relatively new area that deals with the study of behavior among many entities or objects interacting within the natural or artificial systems. In past few years Swarm Intelligence based algorithms have been applied to a wide variety of problems in combinatorial and continuous optimization, telecommunications, swarm robotics, networking, image processing etc. This paper provides an insight of many optimization techniques used for image compression like Ant Colony Optimization (ACO) algorithm , Harmony Search Algorithm (HSA) and Artificial Bee Colony algorithm, Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Bacteria Foraging Optimization. Ant Colony Optimization algorithm is inspired by the behavior among real ant's while searching for the food source. Harmony Search Algorithm is inspired by the harmony improvisation process followed while playing music. Particle swarm optimization is an optimization technique inspired by social behavior of bird flocking or fish schooling. Artificial Bee Colony algorithm is motivated by the behavior exhibited by honey bees while searching for the food source. Genetic Algorithm is based on processes observed in the natural evolution. The Bacterial Foraging Optimization (BFO) algorithm is a stochastic, population-based optimization technique that can be applied to optimization problems .It is a bio-inspired algorithm that mimics the way bacteria, such as Escherichia Coli forage in different environments. The algorithm features have been compared to other bio-inspired methods such as genetic algorithms and nongradient optimization. In particular Bacterial Foraging Optimization (BFO) algorithm mimics the way that bacteria forage over an environment with nutrients to perform parallel non-gradient optimization. Hence this proves Multi Objective Optimization.

This Ph.D. thesis explores a novel way of thinking about computation in cognitive science. It argues for what I call ‘the mechanistic account of wide computationalism’, or simply wide mechanistic computation. The key claim is that some cognitive and perceptual abilities are produced by or are the result of computational mechanisms that are, in part, located outside the individual; that computational systems, the ones that form the proper units of analysis in cognitive science, are particular types of functional mechanisms that, on occasion, spread out across brain, body, and world. Wide mechanistic computation is the result of bringing together two distinct strands of thinking about computation: (i) ‘wide’ views, which hold that computational systems can, on occasion, include parts of the surrounding environment; and (ii) ‘mechanistic’ views, which hold that computational explanation is a species of mechanistic explanation, and that computational mechanisms are a special type of functional mechanism. I argue that wide mechanistic computation draws support from several sources. First, I examine research on animal and human psychology and show that several organisms’ behaviours are properly treated as being the output of wide computational mechanisms. Second, I defend the view from several philosophical charges, including worries about its explanatory parsimony and empirical testability. Finally, I argue for the view’s theoretical credentials by showing that it can help resolve specific problems that have recently troubled 4E cognition. The result is an argument for not only the coherence but also empirical plausibility of wide mechanistic computation. On route to its main objective, the thesis also accomplishes a number of related tasks, including: (i) providing a framework for organising and conceptualising different views of computation, (ii) securing the conceptual foundations of mechanistic computation by addressing an outstanding challenge called the ‘abstraction problem’, (iii) sounding a cautionary note about recent predictive processing accounts of extended cognition and (iv) arguing against a particular conception of levels often used within cognitive science, what is labelled the ‘hierarchical correspondence view of levels’.

In this paper, we employ the method of computational philosophy, the use of computational techniques to aid in the discovery of philosophical insights that might not be easily discovered otherwise, to show that it is not unreasonable to suggest 1) that there are genuine teleological causes in nature, 2) that such causes can be computed from Newtonian ("push") causation, provided that other architectural and environmental conditions are met, and 3) that it is possible to do so without recourse to semantics by taking an information-theoretic approach to measuring information flow in a system.

A central goal of modern generative grammar has been to discover the invariant properties of language, presumably “part of the innate schematism of mind that is applied to the data of experience” and that “might reasonably be attributed to the organism itself as ...

This paper aims at reviewing the most relevant linguistic applications developed in the intersection between three different fields: machine learning, formal language theory and agent technologies. On the one hand, we present some of the main linguistic contributions of the intersection between machine learning and formal languages, which constitutes a well-established research area known as Grammatical Inference. On the other hand, we present an overview of the main linguistic applications of models developed in the intersection between agent technologies and formal languages, such as colonies, grammar systems and eco-grammar systems. Our goal is to show how interdisciplinary research between these three fields can contribute to better understand how natural language is acquired and processed.

Pulsation is a model for a particular evolutive improvement that handles control and prevention in intelligent systems built following this ‘pulsative’ model in incomplete domains. Cartesian systemic emergence presented in this paper answers the question of strategic aspects of building such systems. The main problem to tackle here is working with informal specifications while aiming at their formalization in a way that is driven by the pulsation model

Yang and Piantadosi (1) attempt to show that language acquisition is possible without recourse to "innate knowledge of the structures that occur in natural language." The authors claim that a domain-general rule-learning algorithm can "acquire key pieces of natural language." Yang and Piantadosi provide a number of technical innovations and elegant arguments for why acquisition researchers should expand their conception of what a possible domain-general learner can achieve. Yet, we also believe that their findings do not directly pertain to human language. The authors (1) provide a model that can take strings of discrete elements and execute a number of primitive operations. The "assumed primitive functions" make regular reference to linearity: "list," "first character," "middle of Y," and "set of strings." The postulated "pair" and "first" operations are claimed to be "similar in spirit to 'merge' in minimalist linguistics [...], except they come with none of the associated machinery that is required in those theories; here, they only concatenate." Merge is typically not assumed to be a concatenation process. It simply forms sets and does not impose order. Natural language syntax additionally needs a set categorization or a "labeling" operation. Yang and Piantadosi (1) assume some measure of progress in that their model is free from any "associated machinery" of generative models of Merge-but their model captures only relations between strings, not structures. As such, it falls short of explaining "key pieces of natural language." The Yang and Piantadosi (1) model successfully learns many types of simple formal languages, and its technical sophistication will likely inspire new research into learnability. However, the model exhibits strikingly poor performance with the English auxiliary system, which the authors say may be due to the "complexity" of this system. Likewise, the model has difficulty learning the simple finite grammar from Braine that mimics phrase structure rules. It has only moderate success with a fragment of English involving center embedding.

Pulsation is a model for a particular evolutive improvement that handles control and prevention in intelligent systems built following this ‘pulsative’ model in incomplete domains. Cartesian systemic emergence presented in this paper answers the question of strategic aspects of building such systems. The main problem to tackle here is working with informal specifications while aiming at their formalization in a way that is driven by the pulsation model

The quasi-productivity of natural languages appears to pose two difficult problems for language research. Firstly, why do irregularities in natural language not disappear over time, leaving languages completely regular (a transmission problem), and secondly, how did such irregularity arise in the first place (an emergence problem)? To address the transmission problem, we present an artificial, simplicity-based learner capable of acquiring quasi-regular structures. In doing so, we present an explicitly psychological model of a famously problematic aspect of language acquisition known as Baker’s Paradox. We present several simulations of an Iterated Learning Model (ILM) illustrating the emergence and stability of quasi-regular irregularities using a rudimentary language. These simulations offer a possible resolution to the emergence problem. Other possible resolutions are discussed.

The quasi-productivity of natural languages appears to pose two difficult problems for language research. Firstly, why do irregularities in natural language not disappear over time, leaving languages completely regular (a transmission problem), and secondly, how did such irregularity arise in the first place (an emergence problem)? To address the transmission problem, we present an artificial, simplicity-based learner capable of acquiring quasiregular structures. In doing so, we present an explicitly psychological model of a ...

Dynamical models of cognition have played a central role in recent cognitive science. In this paper, we consider a common strategy by which dynamical models describe their target systems neither as purely static nor as purely dynamic, but rather using a hybrid approach. This hybridity reveals how dynamical models involve representational choices that are important for understanding the relationship between dynamical and non-dynamical representations of a system.

Background: Two experiments investigated the effect of features of human behaviour on the quality of interaction with an Embodied Conversational Agent (ECA). In Experiment 1, visual prominence cues (head nod, eyebrow raise) of the ECA were manipulated to explore the hypothesis that likeability of an ECA increases as a function of interpersonal mimicry. In the context of an error detection task, the ECA either mimicked or did not mimic a head nod or brow raise that humans produced to give emphasis to a word when correcting the ECA's vocabulary. In Experiment 2, presence versus absence of facial expressions on comprehension accuracy of two computer-driven ECA monologues was investigated. In Experiment 1, evidence for a positive relationship between ECA mimicry and lifelikeness was obtained. However, a mimicking agent did not elicit more human gestures. In Experiment 2, expressiveness was associated with greater comprehension and higher ratings of humour and engagement. Influences from mimicry can be explained by visual and motor simulation, and bidirectional links between similarity and liking. Cue redundancy and minimizing cognitive load are potential explanations for expressiveness aiding comprehension.

Background: Virtual humans have become part of our everyday life (movies, internet, and computer games). Even though they are becoming more and more realistic, their speech capabilities are, most of the time, limited and not coherent and/or not synchronous with the corresponding acoustic signal. We describe a method to convert a virtual human avatar (animated through key frames and interpolation) into a more naturalistic talking head. In fact, speech articulation cannot be accurately replicated using interpolation between key frames and talking heads with good speech capabilities are derived from real speech production data. Motion capture data are commonly used to provide accurate facial motion for visible speech articulators (jaw and lips) synchronous with acoustics. To access tongue trajectories (partially occluded speech articulator), electromagnetic articulography (EMA) is often used. We recorded a large database of phonetically-balanced English sentences with synchronous EMA, motion capture data, and acoustics. An articulatory model was computed on this database to recover missing data and to provide 'normalized' animation (i.e., articulatory) parameters. In addition, semi-automatic segmentation was performed on the acoustic stream. A dictionary of multimodal Australian English diphones was created. It is composed of the variation of the articulatory parameters between all the successive stable allophones. Results: The avatar's facial key frames were converted into articulatory parameters steering its speech articulators (jaw, lips and tongue). The speech production database was used to drive the Embodied Conversational Agent (ECA) and to enhance its speech capabilities. A Text-To-Auditory Visual Speech synthesizer was created based on the MaryTTS software and on the diphone dictionary derived from the speech production database. We describe a method to transform an ECA with generic tongue model and animation by key frames into a talking head that displays naturalistic tongue, jaw and lip motions. Thanks to a multimodal speech production database, a Text-To-Auditory Visual Speech synthesizer drives the ECA's facial movements enhancing its speech capabilities.

Understanding how people tick is an endeavour that has challenged us for millennia, both in informal settings and in increasingly formalized and scientific disciplines. Some are interested in the biology and others the behaviour. Some are focussed on language and others on culture or emotion. Some approach this as pure science worthy of understanding in its own right. Some approach it as applied science with value born of practical applications that improve our lifestyle and characterize our modern technological society. Cognitive Science was born as Psychologists and Linguists found that the assumptions and predictions of their theories and models were spilling outside their discipline, as Neuroscience provided a neural substrate for models of perception and cognition that obviated the need for the postulation of hypothetical daemons, as Computer Science and Mathematics provided computational tools for testing models and theories that couldn't be tested empirically in the real world. Artificial Intelligence was also born in such an environment, with the early researchers exploring models of intelligence as much as developing intelligent programs. But Computational Intelligence and Cognitive Science increasingly reflect a contrast between an applied aim of building intelligent applications and entities, and a pure aim of understanding existing intelligent entities and functions, and we seem to lack a bridge between them. Computational Cognitive Science aims to provide this bridge.

Background: Two experiments investigated the effect of features of human behaviour on the quality of interaction with an Embodied Conversational Agent (ECA). In Experiment 1, visual prominence cues (head nod, eyebrow raise) of the ECA were manipulated to explore the hypothesis that likeability of an ECA increases as a function of interpersonal mimicry. In the context of an error detection task, the ECA either mimicked or did not mimic a head nod or brow raise that humans produced to give emphasis to a word when correcting the ECA's vocabulary. In Experiment 2, presence versus absence of facial expressions on comprehension accuracy of two computer-driven ECA monologues was investigated. In Experiment 1, evidence for a positive relationship between ECA mimicry and lifelikeness was obtained. However, a mimicking agent did not elicit more human gestures. In Experiment 2, expressiveness was associated with greater comprehension and higher ratings of humour and engagement. Influences from mimicry can be explained by visual and motor simulation, and bidirectional links between similarity and liking. Cue redundancy and minimizing cognitive load are potential explanations for expressiveness aiding comprehension.

A linguistic theory reaches explanatory adequacy if it arrives at a linguistically-appropriate grammar based on the kind of input available to children. In phonology, we assume that children can succeed even when the input consists of surface evidence alone, with no corrections or explicit paradigmatic information – that is, in learning from distributional evidence. We take the grammar to include both a lexicon of underlying representations and a mapping from the lexicon to surface forms. Moreover, this mapping should be able to express optionality and opacity, among other textbook patterns. This learning challenge has not yet been addressed in the literature. We argue that the principle of Minimum Description Length (MDL) offers the right kind of guidance to the learner – favoring generalizations that are neither overly general nor overly specific – and can help the learner overcome the learning challenge. We illustrate with an implemented MDL learner that succeeds in learning vari...

The aim of this paper is to relate the research on cognitive maps, mental representations of the external world, which are thought to be organized hierarchically, with the simplicity principle, a formal version of Occam’s Razor inductive bias, which states that short hypotheses are to be preferred over longer ones. First, I will describe some important properties of hierarchical structures and define what is meant by simplicity. Next, I will review some results supporting the view that cognitive maps are structured hierarchically, not following Euclidean metrics, and that some cognitive functions seem to prefer simpler representations over complex ones. This hints at the possibility that the hierarchical organization of cognitive maps may also follow the simplicity principle. Finally, I will suggest an experiment in which such hypothesis could be tested and discuss the implications and limitations of integrating both approaches. 1 What are hierarchies and why are they important? Hie...

The aim of this paper is to relate the research on cognitive maps, mental representations of the external world, which are thought to be organized hierarchically, with the simplicity principle, a formal version of Occam’s Razor inductive bias, which states that short hypotheses are to be preferred over longer ones. First, I will describe some important properties of hierarchical structures and define what is meant by simplicity. Next, I will review some results supporting the view that cognitive maps are structured hierarchically, not following Euclidean metrics, and that some cognitive functions seem to prefer simpler representations over complex ones. This hints at the possibility that the hierarchical organization of cognitive maps may also follow the simplicity principle. Finally, I will suggest an experiment in which such hypothesis could be tested and discuss the implications and limitations of integrating both approaches.