Knowledge Measurement Approaches

Since the mid-2000s, cognitive science approaches have been used in biblical studies. Cognitive science came into existence in the 1950s as a reaction to the psychological behaviorism of the time, and it studies human thought processes, such as memory, learning, decision making, and social cognition, including the role of emotions. Cognitive science is multidisciplinary in nature, with contributions from linguistics, philosophy, neuroscience, experimental psychology, ethology, computer modeling, and other disciplines. It has influenced a variety of academic fields, ranging from anthropology and education to musicology and medicine. Its appearance and impact is often referred to as the “cognitive turn.” Cognitive approaches to religion examine cross-culturally recurrent religious thought and behavior from the perspective of cognitive science. Cognitive approaches to religion, in turn, offer important insights and theories for the cognitive scientific study of biblical texts and traditions. Three research areas can be identified that especially influenced the emergence of cognitive science approaches to biblical studies: (1) A considerable part of cognitive research on religion can be linked to the program called cognitive science of religion (CSR), which started in the 1990s. In the beginning, CSR focused mainly on god concepts and rituals, with a strong emphasis on the evolved cognitive structure of the human mind. New research areas of CSR include the role of religion in social cooperation, experimental studies in ethnography, discussions and applications of the theory of cultural evolution, quantitative studies of large-scale historical processes, and computational models of religious thought and behavior. While some scholars have argued that religion is a by-product of cognitive mechanisms (such as traits underlying social cooperation) that evolved previously for other purposes (the so-called by-product hypothesis), others see religion as an evolutionary adaptation. (2) The neuroscientific study of religious experience started in the 1980s and gained momentum with the rapid development of new neuroimaging technologies in the 1990s. Work on religious experience in biblical studies draws on insights from this area. (3) Finally, the field of cognitive linguistics also started in the 1980s and examines the role of the human body in shaping human thought and language. Biblical scholars have drawn especially on cognitive poetics and conceptual metaphor theory. In this article we will first discuss overviews of cognitive science approaches in biblical studies. Second, major contributions to the cognitive science of religion and conceptual metaphor theory will be presented, with an emphasis on publications that have exerted an influence on biblical scholars. This part will also include cognitive studies of ancient religions outside biblical studies. Third, cognitive science approaches to various research questions and topics in biblical studies will be discussed, including Jewish and early Christian history, memory and transmission, ritual, miracle and magic, ethics and morality, religious experience, theological and Christological concepts, applications of conceptual metaphor theory, the use of computational modeling, and other exegetical topics.

Identification of design solutions for a built environment that caters to the human needs at all levels, and more specifically, to the needs of the clients and the society, is the main task addressed by architectural design. Architectural design is a prime example of a design task that is characterized by a high degree of complexity. Architectural design problems by definition entail relationships between decisions and objectives that are all but transparent. For the decision-maker to be able to guide design towards fulfilling objectives, a ‘closed-loop’ approach where variations in design solutions are generated and evaluated in an iterative process is employed. Due to the sheer number of alternative solutions to problems of even a moderate scale (due to combinatorial explosion), it is only feasible to iterate over a minuscule fraction of possible solutions. Design intuition of the professionals involved in design is a strong driving force behind the identification of design direction, in which alternatives are explored as part of the preliminary design process. This is an approach that depends on the human cognitive capabilities to navigate the design space and identify potentially promising solutions. Regardless, the complexity associated with architectural design often poses significant challenges to human cognition. Human cognition, while formidable in its ability to flexibly and efficiently navigate challenging environments, is faced with difficulties in addressing the complexity factors outlined previously, namely: the excessive (combinatorially explosive) number of potential solutions to architectural problems, the complex and non-linear relations between objects and their properties and the conflicting nature of design goals that architectural design entails. Thus, design professionals are often faced with the real threat that their decisions may be biased due to the natural limitations of human cognition acting in complex environments.<br/><br/>Due to the reasons highlighted above, a systematic approach to design space exploration must be undertaken, to maximize the potential for discovering optimal solutions to design problems. Due to the nature of such problems that entail multiple conflicting objectives, a single best solution is generally not attainable. Nonetheless, best-tradeoff solutions are distinguished and highly desirable for such multi-objective design problems. The field of Computational Intelligence, and within that in particular Evolutionary Computation-based (EC) intelligent approaches, offer a lucrative option as decision-support tools in design, as they are able to efficiently address the aforementioned proponents of design complexity. EC approaches are able to navigate the design space efficiently and systematically, considering multiple conflicting objectives and hard constraints, and being able to deal with arbitrary relations between design decision variables and design objectives. <br/><br/>In today's setting, products of architecture must lead the way to a sustainable and environmentally friendlier society. As such, the performance of buildings has become the main driving force behind the design process, being referred to as ``performance-driven design''. This initiative emphasizes the quantitative evaluation of a design's function in accordance with established design objectives, related to aspects such as energy performance, visual and thermal comfort, cost and environmental footprint, etc. Simulation-based tools that enable accurate design evaluation are gaining ground and offering valuable insight into the performance of buildings. Nonetheless, making decisions in this multi-objective environment is not trivial, and, as stipulated above, may be challenging to human cognition. Thus, in today’s setting where the quantitative performance of buildings keeps gaining ground, the research on the application of EC in architectural design is high on the scientific agenda.<br/><br/>Recognizing the impact design complexity has on architectural design and the potential that EC-based approaches offer in addressing it, this thesis proposes a comprehensive computational intelligence decision support system that combines components based on intelligence with ones based on cognition, with the ultimate aim of enabling decision-makers manage design complexity and improve decision making. In particular, this thesis adopts the theoretical standpoint that efficient navigation of an unknown environment assumes a fusion of intelligence and cognition. In this sense, and given the already widespread adoption of intelligent approaches (such as EC mentioned above), the main contribution of this thesis is to endow the intelligent approach with cognitive facilities, so as to improve its efficiency to the point that it is readily applicable to the early stages of the architectural design process.<br/><br/>Fusion of intelligent with cognitive approaches, as outlined in the approach proposed by this thesis, offers the unique advantage of a decision support approach that is both powerful, owing to the extensive capabilities of intelligent search algorithms, and flexible, owing to the extensive knowledge modeling capabilities of cognitive approaches. As such, it is uniquely suited to the early conceptual design stage where the need to explore large design spaces, flexibly redefine the design problem, and satisfy preferences that are not included in the primary design goals, are all paramount.<br/><br/>Thus, the word ``comprehensive'' as it appears on this thesis' title obtains a twofold meaning: On one hand comprehension as in the combination of computational intelligence and cognition in a single approach; on the other hand, as in \\textit{comprehension} of the environment, the result of an intelligent and cognitive approach to understanding.<br/><br/>Firstly, it seeks to address the excessive computational burden associated with the use of modern high-fidelity simulation software in architecture, to render computational optimization more approachable. There is a clear trend in modern design practice to employ accurate simulation-based performance assessment tools from the very early stages of design. The use of such tools provides a valuable advantage to the decision-maker, in endowing objective awareness regarding the performance of a design solution. On the other hand, such tools are associated with a heavy computational burden, which may limit their application to the conceptual design stage. There exist methods to alleviate the computational burden through the use of computational cognitive machine learning tools, also known as surrogate modeling. However, training of surrogate models can be time-consuming itself, thus limiting the application. This thesis proposes a surrogate model that is modular in that it considers each space of the building in question as a separate entity, encoded through generic variables, and as such promotes model reuse in different design cases.<br/><br/>Secondly, it seeks to advance the state of the art on post-Pareto decision support by proposing a cognitive machine-learning based approach that enables the decision-maker to combine near-optimality with preferences regarding concrete features of the design solution. Post-Pareto decision making is an important step of the decision-making process, that seeks to identify a best-tradeoff solution among the possible ones that best matches the decision-maker's preferences in terms of performance. Such preferences are termed second-order because they follow design objectives in terms of importance. Nonetheless, it is often in architectural design that preferences are expressed in terms of design properties and not performance. Due to the non-linearity between the objective function space and the decision variable space that dictates object properties, it is challenging to exercise decision making using second-order preferences. Here the contribution of this thesis is a machine cognitive approach that learns the underlying relationships between object properties, distinguishing those that are relevant when the object is optimal with respect to design objectives. In other words, only imposing relations that are relevant to achieve optimality, it enables the expression of preferences by the decision-maker that are minimally constrained.<br/><br/>The main output of this thesis is a comprehensive decision support framework; it is a framework, in the sense that it comprises a set of methods and implemented tools that seek to augment decision making in architectural design; it is termed comprehensive in that it employs computational cognition and machine learning to augment the intelligent decision support capabilities throughout the design decision support process. It is also generic and applicable as-is to a wide spectrum of architectural design problems. In the context of this thesis, validation of the proposed approach is performed mainly in case studies relevant to facade design, recognizing this design topic as a complexity-exhibiting exemplar in architectural design practice.

In this paper we discuss the new Tweety puzzle. The original Tweety puzzle was addressed by approaches in non-monotonic logic, which aim to adequately represent the Tweety case, namely that Tweety is a penguin and, thus, an exceptional bird, which cannot fly, although in general birds can fly. The new Tweety puzzle is intended as a challenge for probabilistic theories of epistemic states. In the first part of the paper we argue against monistic Bayesians, who assume that epistemic states can at any given time be adequately described by a single subjective probability function. We show that monistic Bayesians cannot provide an adequate solution to the new Tweety puzzle, because this requires one to refer to a frequency-based probability function. We conclude that monistic Bayesianism cannot be a fully adequate theory of epistemic states. In the second part we describe an empirical study, which provides support for the thesis that monistic Bayesianism is also inadequate as a descriptive theory of cognitive states. In the final part of the paper we criticize Bayesian approaches in cognitive science, insofar as their monistic tendency cannot adequately address the new Tweety puzzle. We, further, argue against monistic Bayesianism in cognitive science by means of a case study. In this case study we show that Oaksford and Chater’s (2007, 2008) model of conditional inference—contrary to the authors’ theoretical position—has to refer also to a frequency-based probability function.

Both human brain and computer (electronic brain) could process data and do some cognition and computation tasks. Is the cognition of human brain equal to the computation of computer? It is obviously not. In this talk, the relationships of brain cognition models and intelligence computation models are summarized into four different types, that is, human brain cognition inspired intelligence computation (BCIIC), intelligence computation without human brain cognition (ICOBC), intelligence computation assisted human brain cognition (ICABC), and the integration of human brain cognition and intelligence computation (BC&IC). There are three paradigms in traditional artificial intelligence (AI) studies, that is, symbolism AI, connectionism AI, and behaviorism AI. The physical symbol system hypothesis is used in the symbolism AI. Human brain cognition is taken as a kind of symbolic processing, and the processes of human thinking are computed by symbol in the symbolism AI [1,2]. The connectionism AI relies on the bionics to simulate human brain. In the connectionism AI, neuron is taken as the basic unite of human thinking, and the intelligence is taken as the result of interconnected neurons competition and collaboration [3,4]. In the behaviorism AI, intelligence depends on the perception and behavior, “Perception-action” model is used, and intelligence may not require knowledge, knowledge representation and knowledge reasoning [5]. The symbolism AI and connectionism AI are two different types of human brain cognition inspired intelligence computation, while the behaviorism AI is a representative case of intelligence computation without human brain cognition. Usually, AI researchers get some inspiration from human brain cognition in their studies. On the other way, intelligence computation could also assist human brain cognition studies. The bidirectional cognitive computing model (BCC) is such a case. It studies the bidirectional transformations between the intension and extension of a concept. It is used to simulate some human brain cognition tasks such as learning and recognition [6,7]. Cognitive computing is one of the core fields of artificial intelligence [8,9]. Data-driven granular cognitive computing (DGCC) is an example of the integration of human brain cognition and intelligence computation [10,11]. It takes data as a special kind of knowledge expressed in the lowest granularity level of a multiple granularity space. It integrates two contradictory mechanisms, namely, the human’s cognition mechanism of ‘‘global precedence’’ which is a cognition process of ‘‘from coarser to finer’’ and the information processing mechanism of machine learning systems which is ‘‘from finer to coarser’’, in a multiple granularity space. It is data-driven cognitive computing model. The integration of human brain cognition and intelligence computation would be an important research topic of artificial intelligence. Some scientific research issues of the integration of human brain cognition and intelligence computation are discussed based on DGCC.

The Seventh International Symposium on Neural Networks (ISNN 2010) was held on June 6–9, 2010 in Shanghai, China. The ISNN 2010 was a great success and provided a high-level international forum for scientists and engineers to present the latest research in neural networks, computational intelligence, cognitive systems, and related fields. To highlight the success of this conference, we edited this special issue for Cognitive Computation. We chose nine papers from over 591 papers submitted to ISNN 2010, which reflect the high quality of the presentations at the conference while capturing the spirit of our theme, ‘‘Advances in Computational Intelligence and Applications,’’ for this special issue. Our goal for this special issue is to present the latest research developments in computational intelligence with a focus on cognitive computation and their applications across a wide range of domains. Over the past decades, we have witnessed tremendous interest and developments in all aspects of computational intelligence research, ranging from both theoretical foundations, principles, to practical applications in different domains. To reflect a flavor of recent research activities in the community, we carefully selected these nine papers for this special issue. The selected papers can be organized into the following four coherent sections. The first section is directly related to neural network models and applications. The paper by Deng presents a new control strategy for the chaotic neural network, in which the refractoriness is tuned by using feedback control based on online averaging of network states. Simulation results demonstrate that the proposed model can achieve favorable performance in handling noisy, incomplete, and composite patterns, while also achieving either enhanced or comparable memory capacity compared with the classic Hopfield net and other chaotic neural network models. In the other paper, Gluge et al. discuss recurrent networks for implicit learning of temporal sequences, a very important area to understand high-level cognitive intelligence. The authors propose a recurrent network with backpropagation training algorithm that is adapted to the reinforcement learning scheme. The simulation results fit quantitatively as well as qualitatively to the behavioral results, suggesting the role of temporal context in associative learning scenarios. The second section is related to language processing and understanding, an important topic in cognitive science and computational intelligence. The paper by Murata et al. investigates the maximum entropy (ME) approach for natural language processing (NLP) problems including machine translation and information extraction. Comparative studies of the proposed approach with the existing techniques are illustrated in this paper. In related work to this field, the paper by Kaznatcheev investigates connectionist models on the interplay of nouns and pronouns in personal pronoun acquisition, a very interesting and important problem in psychology. For instance, the author discusses the learning in the shifting reference situation for children. It is reported that learning of two different noun-and-pronoun addressee patterns is consistent with Z. Zeng (&) Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, P. R. China e-mail: zgzeng@gmail.com

Working with leading experts in the field of cognitive neuroscience and computational intelligence, SNL has developed a computational architecture that represents neurocognitive mechanisms associated with how humans remember experiences in their past. The architecture represents how knowledge is organized and updated through information from individual experiences (episodes) via the cortical-hippocampal declarative memory system. We compared the simulated behavioral characteristics with those of humans measured under well established experimental standards, controlling for unmodeled aspects of human processing, such as perception. We used this knowledge to create robust simulations of & human memory behaviors that should help move the scientific community closer to understanding how humans remember information. These behaviors were experimentally validated against actual human subjects, which was published. An important outcome of the validation process will be the joining of specific experimental testing procedures from the field of neuroscience with computational representations from the field of cognitive modeling and simulation.

Reviewed by: Cognitive Science, Literature, and the Arts: A Guide for Humanists Claiborne Rice Hogan, Patrick Colm . 2003. Cognitive Science, Literature, and the Arts: A Guide for Humanists. London and New York: Routledge. $85.00 hc. $19.95 sc. 244 pp. The cognitive revolution that has affected so many areas of contemporary thought is currently capturing literary studies as part of its stronghold. In addition to the many popular cognitive scientists who have extended the discipline's reach into the literary mind, like Steven Pinker or George Lakoff, we have begun to see an impact on current literature and also literary criticism. Patrick Colm Hogan's aptly named book, Cognitive Science, Literature, and the Arts: A Guide for Humanists, is a solid introduction to the growing range of artistic forms and functions amenable to explanation in the cognitive sciences. For many years now, Hogan has been a prolific scholar of literature who is fully conversant with what we have come to call "theory." His many books and articles rewardingly engage highly theorized sub-topics within literary studies, such as influence, social and cultural identity, professionalism, law, and colonialism, from a serious philosophical, psychoanalytic, and, now, cognitive scientific perspective. Like all of Hogan's work, the current volume is significantly informed by his easy familiarity with the literature, art, and philosophy of many distinct non-Western traditions. The book argues for the cognitive study of the arts because of the benefits such study offers to both the humanities and the cognitive sciences. Specifically, the issues familiarly addressed by humanists (Hogan's term for [End Page 242] humanities scholars) are rarely treated effectively by neurobiologists or psychologists who see in certain instances of art fodder for their own pet theories. Only practicing humanists comprehend the complexity and range of humanities questions and, as such, can be expected to see how what we are beginning to learn about functions of the mind and body can best be applied to humanistic concerns. Such complexity and range in turn challenges cognitive scientists to refine their models and methods. The book's opening chapter, "My Favorite Things," introduces the procedural model that a cognitivist approach to art suggests by analyzing the form of John Coltrane's famous version of the tune "My Favorite Things." Though much less is known specifically about how humans process music than, say, vision, music is a good place to start because it avoids some of the issues of "meaning" that language processing entails. In the course of introducing readers to certain basic concerns of cognitive science, such as short- and long- term memory structures, segmentation, and structuration, he also offers explanations of basic art appreciation issues like boredom, improvisation, and listener variability. Chapter two, "Is It Cognitive Science Yet?", moves on to a lengthier discussion of elements of cognitive science theory that will play a role in all of the analyses to follow. He introduces an overall framework of cognitive science approaches that helps guide the reader through the labyrinth of technical terminology. Briefly, three stages of analysis pertain to any cognitive science approach to a problem: conceiving of the problem in terms of information processing, specifying the exact steps (an algorithmic sequence) that will lead from input to output, and identifying the cognitive architecture that will be necessary for dealing with the problem. Cognitive architecture, in its turn, is understood as having three separate components: structures, processes, and contents. Structure refers to "the general organizational principles of the mind" (30), such as the distinction between long-term and short-term (working) memory. Content usually refers to representations or symbols that have specific locations in structures. Finally, processes are structurally constrained operations that are performed on contents. For example, our mental storehouse of words, the lexicon, is a substructure of our long-term memory system that provides structural links between individual words; the activation of one word primes, or prepares, many other words for access. The meanings of each word, in this view, are representational contents, and formulating a sentence for pronunciation would be a process that at some point involves accessing the lexicon to retrieve the phonemic profile for the appropriate words. Because of the complexities of mental contents and...

The interaction of robotics with behavioral and cognitive sciences has always been tight. As often described in the literature, the living has inspired the construction of many robots. Yet, in this article, we focus on the reverse phenomenon: building robots can impact importantly the way we conceptualize behavior and cognition in animals and humans. This article presents a series of paradigmatic examples spanning from the modelling of insect navigation, the experimentation of the role of morphology to control locomotion, the development of foundational representations of the body and of the self/other distinction, the self-organization of language in robot societies, and the use of robots as therapeutic tools for children with developmental disorders. Through these examples, I review the way robots can be used as operational models confronting specific theories to reality, or can be used as proof of concepts, or as conceptual exploration tools generating new hypotheses, or used as experimental set ups to uncover particular behavioral properties in animals or humans, or even used as therapeutic tools. Finally, I discuss the fact that in spite of its role in the formation of many fundamental theories in behavioral and cognitive sciences, the use of robots is far from being accepted as a standard tool and contributions are often forgotten, leading to regular rediscoveries and slowing down cumulative progress. The article concludes by highlighting the high priority of further historical and epistemological work.

Cognitive science is a network of interrelated scientific disciplines engaged in researching human cognition and its brain mechanisms. The birth of cognitive science has been the result of numerous integrated processes. Cognitive science is made up of experimental psychology cognition, philosophy consciousness, neuroscience, cognitive anthropology, linguistics, computer science and artificial intelligence. In recent years, a number of other research areas have been added to the body of cognitive science. Among researchers there have been discussions about whether cognitive science is a separate research area or it consists of a series of specialized areas. In fact, the point at issue is whether cognitive science is still a multidisciplinary project or already an interdisciplinary one. P. Thagard believes that cognitive science has reached the level of interdisciplinarity and explains the advances in this area through the metaphor of “trading zones”. The success elements of cognitive science are: fruitful unification of scientific interests of cognitive science founders; organizational structure of the scientific community – universities, where a special interdisciplinary intellectual environment has been created; a large number of joint research projects supported by governments and business; integrated use of scientific methods and fundamental ideas. D. Sperber and J. Miller prefer to talk not about a unified cognitive science but cognitive sciences, i.e., the commonwealth of sciences working together on the study of a single object - human cognition, however, the extent of their interactive communication is still small. Thus, we should speak about multidisciplinarity rather than genuine interdisciplinarity of the joint research of separate sciences.

This paper reports the results of an international survey on the place and role of psychology in cognitive science (CS). The respondents were psychological societies from 31 countries illustrating a variety of situations, from countries where psychology has been established as a science for a long time to countries where it has been established more recently and is still developing. The paper reviews topics covered by the CS approach, as well as research operations and initiatives taken in this domain. The analysis of responses reflects the belief that psychology, in general, has a central role in CS. This is based on epistemological considerations, as well as on the recognition of the methodological expertise of psychology for collecting and analyzing empirical data. Psychology is reported to have a unique integrative capacity and to take care to maintain the place of the human being at the centre of CS. Limitations and obstacles to the development of pluridisciplinary research on cognition are also reported, and information is provided on the training programmes in CS and the institutions that support research in this domain.

Cognitive and educational sciences share a fundamental interest in learning. While theoretical bases and practical interests seem to match well at first sight, it is well worth to take a closer look. In the different disciplines involved in the Cognitive Sciences there is a large number of approaches to study learning, but closer glance reveals a limited number of ways to conceptualize learning. In its short history, the field has seen some paradigm shifts, which changed the way we look at human cognition and thus at learning. It is worth noting that each paradigm also transports an implicit notion of knowledge. In the field of education we find these paradigms as different learning theories, which in turn determine approaches to teaching and learning. Dealing with knowledge is central to educational practice. Furthermore, educational research indicates that our personal concept of knowledge qualitatively influences the way students learn. Thus there is a deep, but the intricate relationship between our concepts learning and knowledge. If the interaction between the two fields is to go beyond the (often unsatisfying) attempt to find direct-to-classroom recipes, explicit concepts of learning and knowledge must be the starting point for any successful interdisciplinary collaboration. Key words: cognitive sciences, education, knowledge, learning.

When rejecting and accepting hypotheses, it is important to determine the risk of false positive conclusions. Hierarchical Bayesian models are frequently applied in cognitive science, psychology, cognitive neuroscience, and psychiatry to reduce the variability of low-level parameters through partial pooling, and to obtain more robust parameter estimates for complex models using limited data. This may lower the risk of false positive inferences from lower-level estimates. In contrast, higher-level (e.g. group-level) estimates may be more affected by modeling choices (e.g. prior distributions), population-level variances in effects, and inference methods, which may in turn affect false positive rates for group-level effects. Here we used simulation and computational modeling to systematically examine false positive rates and effects of specific modeling choices on inferences in a simple hierarchical Bayesian temporal discounting model. To this end, 600 data sets of a temporal discounting task with two conditions were simulated, drawing individual condition effect parameters from a null distribution with varying standard deviations. For each simulated data set, the group-level mean change in the discount rate was modeled with eight different prior variances of the condition effect. Results were subsequently evaluated using several commonly applied Bayesian inference methods: the Savage-Dickey Bayes factor (BF), the directional Bayes factor (dBF), P(effect > 0), and the highest density interval (HDI) against zero decision rule. Results confirm that inference methods that directly depend on the posterior distribution can be used regardless of prior variances, whereas the Savage-Dickey BF should only be used with sufficiently informative priors. Based on the simulation work, decision thresholds that ensure false positive rates below 5% are provided for each inference method. Results are discussed in the context of computational modeling approaches in cognitive (neuro-)science and psychology.

GENERAL COMMENTARY article Front. Psychol., 21 December 2015Sec. Comparative Psychology Volume 6 - 2015 | https://doi.org/10.3389/fpsyg.2015.01928

The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the field of Computational Intelligence and especially on a correct comparison between the different results provided for different methods, as those techniques are complex systems that require further study to be fully understood. A methodology commonly accepted in Computational intelligence is implemented in an R package called RRegrs. This package includes ten simple and complex regression models to carry out predictive modeling using Machine Learning and well-known regression algorithms. The framework for experimental design presented herein is evaluated and validated against RRegrs. Our results are different for three out of five state-of-the-art simple datasets and it can be stated that the selection of the best model according to our proposal is statistically significant and relevant. It is of relevance to use a statistical approach to indicate whether the differences are statistically significant using this kind of algorithms. Furthermore, our results with three real complex datasets report different best models than with the previously published methodology. Our final goal is to provide a complete methodology for the use of different steps in order to compare the results obtained in Computational Intelligence problems, as well as from other fields, such as for bioinformatics, cheminformatics, etc., given that our proposal is open and modifiable.

Despite the increase of both dynamic and embodied/situated approaches in cognitive science, there is still little research on how coordination dynamics under a closed sensorimotor loop might induce qualitatively different patterns of neural oscillations compared to those found in isolated systems. We take as a departure point the Haken-Kelso-Bunz (HKB) model, a generic model for dynamic coordination between two oscillatory components, which has proven useful for a vast range of applications in cognitive science and whose dynamical properties are well understood. In order to explore the properties of this model under closed sensorimotor conditions we present what we call the situated HKB model: a robotic model that performs a gradient climbing task and whose “brain” is modeled by the HKB equation. We solve the differential equations that define the agent-environment coupling for increasing values of the agent's sensitivity (sensor gain), finding different behavioral strategies. These results are compared with two different models: a decoupled HKB with no sensory input and a passively-coupled HKB that is also decoupled but receives a structured input generated by a situated agent. We can precisely quantify and qualitatively describe how the properties of the system, when studied in coupled conditions, radically change in a manner that cannot be deduced from the decoupled HKB models alone. We also present the notion of neurodynamic signature as the dynamic pattern that correlates with a specific behavior and we show how only a situated agent can display this signature compared to an agent that simply receives the exact same sensory input. To our knowledge, this is the first analytical solution of the HKB equation in a sensorimotor loop and qualitative and quantitative analytic comparison of spatially coupled vs. decoupled oscillatory controllers. Finally, we discuss the limitations and possible generalization of our model to contemporary neuroscience and philosophy of mind.