What is the relation between perceptual experience and the suite of sensorimotor skills that enable us to act in the very world we perceive? The relation, according to ‘sensorimotor models’ is tight indeed. Perceptual experience, on these accounts, is enacted via skilled sensorimotor activity, and gains its content and character courtesy of our knowledge of the relations between movement and sensory stimulation. I shall argue that this formulation is too extreme, and that it fails to accommodate the substantial firewalls, dis-integrations, (...) and specialpurpose streamings that form the massed strata of human cognition. In particular, such strong sensorimotor models threaten to obscure the computationally potent insensitivity of key information-processing events to the full subtleties of embodied cycles of sensing and moving. (shrink)

An application of the Method of Analysis of Relational Complexity (MARC) to suppositional reasoning in the knight-knave task is outlined. The task requires testing suppositions derived from statements made by individuals who either always tell the truth or always lie. Relational complexity (RC) is defined as the number of unique entities that need to be processed in parallel to arrive at a solution. A selection of five ternary and five quaternary items were presented to 53 psychology students using (...) a pencil and paper format. A computer-administered version was presented to 50 students. As predicted, quaternary problems were associated with higher error rates and longer response times than ternary problems. The computer-administered form was more difficult than the pencil and paper version of the test. These differences are discussed in terms of RC theory and alternative processing accounts. Together, they indicate that the relational complexity metric is a useful and parsimonious way to quantify complexity of reasoning tasks. (shrink)

Researchers of advanced technologies are constantly seeking new ways of measuring and adapting to user performance. Appropriately adapting system feedback requires accurate assessments of user performance. Unfortunately, many assessment algorithms must be trained on and use pre‐prepared data sets or corpora to provide a sufficiently accurate portrayal of user knowledge and behavior. However, if the targeted content of the tutoring system changes depending on the situation, the assessment algorithms must be sufficiently independent to apply to untrained content. Such is the (...) case for Interactive Strategy Training for Active Reading and Thinking (iSTART), an intelligent tutoring system that assesses the cognitive complexity of strategy use while a reader self‐explains a text. iSTART is designed so that teachers and researchers may add their own (new) texts into the system. The current paper explores student self‐explanations from newly added texts (which iSTART had not been trained on) and focuses on evaluating the iSTART assessment algorithm by comparing it to human ratings of the students’ self‐explanations. (shrink)

The confusion/non-consequential thinking explanation proposed by Newstead, Girotto, and Legrenzi (1995) for poor performance on Wason's THOG problem (a hypothetico-deductive reasoning task) was examined in three experiments with 300 participants. In general, as the cognitive complexity of the problem and the possibility of non-consequential thinking were reduced, correct performance increased. Significant but weak facilitation (33-40% correct) was found in Experiment 1 for THOG classification instructions that did not include the indeterminate response option. Substantial facilitation (up to 75% correct) (...) was obtained in Experiment 2 with O'Brien et al.'s (1990) one-other-THOG classification instruction. In Experiment 3, a revised version of O'Brien et al.'s pre-test problem format also led to substantial facilitation, even with the use of the standard three-choice THOG classification instruction. These findings are discussed in terms of Newstead et al.'s theoretical proposal and possible attentional factors. (shrink)

The arbitrariness of a signal has long been seen as a theoretically important but difficult to pin down notion. In this article, we suggest there are at least two different notions of arbitrariness at play in philosophical and scientific debates concerning the use of arbitrary signals, and work towards improved analyses of both. We then consider how these different types of arbitrariness can co-occur and come apart. Finally, we examine the connections between these two types of arbitrariness and the (...) class='Hi'>cognitive complexity of signal users with an eye towards better evaluating one possible form of human-nonhuman communicative continuity. We show that each type of arbitrariness bears its own nuanced relationship to cognitive complexity, demonstrating the theoretical importance of keeping these two notions separate. (shrink)

[Andy Clark] What is the relation between perceptual experience and the suite of sensorimotor skills that enable us to act in the very world we perceive? The relation, according to 'sensorimotor models' (O'Regan and Noë 2001, Noë 2004) is tight indeed. Perceptual experience, on these accounts, is enacted via skilled sensorimotor activity, and gains its content and character courtesy of our knowledge of the relations between (typically) movement and sensory stimulation. I shall argue that this formulation is too extreme, and (...) that it fails to accommodate the substantial firewalls, dis-integrations, and special-purpose streamings that form the massed strata of human cognition. In particular, such strong sensorimotor models threaten to obscure the computationally potent insensitivity of key information-processing events to the full subtleties of embodied cycles of sensing and moving. /// [Naomi Eilan] The strong sensorimotor account of perception gives self-induced movements two constitutive roles in explaining visual consciousness. The first says that self-induced movements are vehicles of visual awareness, and for this reason consciousness 'does not happen in the brain only'. The second says that the phenomenal nature of visual experiences is consists in the action-directing content of vision. In response I suggest, first, that the sense in which visual awareness is active should be explained by appeal to the role of attention in visual consciousness, rather than self-induced movements; and second, that the sense in which perceptual consciousness does not happen in the brain only should be explained by appeal to the relational nature of perceptual consciousness, appeal to which also shows why links with action cannot exhaust phenomenal content. (shrink)

(2013). Why humans are (sometimes) less rational than other animals: Cognitive complexity and the axioms of rational choice. Thinking & Reasoning: Vol. 19, No. 1, pp. 1-26. doi: 10.1080/13546783.2012.713178.

Behavioural flexibility is often treated as the gold standard of evidence for more sophisticated or complex forms of animal cognition, such as planning, metacognition and mindreading. However, the evidential link between behavioural flexibility and complex cognition has not been explicitly or systematically defended. Such a defence is particularly pressing because observed flexible behaviours can frequently be explained by putatively simpler cognitive mechanisms. This leaves complex cognition hypotheses open to ‘deflationary’ challenges that are accorded greater evidential weight precisely because they (...) offer putatively simpler explanations of equal explanatory power. This paper challenges the blanket preference for simpler explanations, and shows that once this preference is dispensed with, and the full spectrum of evidence—including evolutionary, ecological and phylogenetic data—is accorded its proper weight, an argument in support of the prevailing assumption that behavioural flexibility can serve as evidence for complex cognitive mechanisms may begin to take shape. An adaptive model of cognitive-behavioural evolution is proposed, according to which the existence of convergent trait–environment clusters in phylogenetically disparate lineages may serve as evidence for the same trait–environment clusters in other lineages. This, in turn, could permit inferences of cognitive complexity in cases of experimental underdetermination, thereby placing the common view that behavioural flexibility can serve as evidence for complex cognition on firmer grounds. (shrink)

In this longitudinal study, we explore the co-evolution of the cognitive complexity of the CEO of Puma, Jochen Zeitz, and his view and initiatives on sustainability. Our purpose was to explore how the changes in a leader’s mindset relate to his/her views and actions on sustainability. In contrast to previous studies, we adopt an in-depth longitudinal case study approach to capture the role of leaders’ cognitive complexity in the context of corporate sustainability. By understanding the (...) class='Hi'>cognitive development of Zeitz as leader of Puma, we provide an important step toward understanding the co-evolution of leaders’ cognitive complexity and proactive corporate sustainability initiatives over time. Our findings show that as he developed a more complex mindset that also included non-business lenses, Zeitz developed a more inclusive understanding of sustainability and adopted proactive initiatives that went beyond business-as-usual. Our study also demonstrates that a longitudinal perspective can offer valuable insights for a better understanding of how individuals and their interactions affect and are affected by an organization’s strategies and performance, in corporate sustainability and beyond. (shrink)

Given that knowledge consists of finite models of an infinitely complex reality, how can we explain that it is still most of the time reliable? Survival in a variable environment requires an internal model whose complexity (variety) matches the complexity of the environment that is to be controlled. The reduction of the infinite complexity of the sensed environment to a finite map requires a strong mechanism of categorization. A measure of cognitive complexity (C) is defined, (...) which quantifies the average amount of trial-and-error needed to find the adequate category. C can be minimized by "probability ordering" of the possible categories, where the most probable alternatives ("defaults") are explored first. The reduction of complexity by such ordering requires a low statistical entropy for the cognized environment. This entropy is automatically kept down by the natural selection of "fit" configurations. The high probability, "default" cognitive categorizations are then merely mappings of environmentally "fit" configurations. (shrink)

Throughout most of the 20th century, analytical and reductionist approaches have dominated in biological, social, and humanistic sciences, including linguistics and communication. We generally believed we could account for fundamental phenomena in invoking basic elemental units. Although the amount of knowledge generated was certainly impressive, we have also seen limitations of this approach. Discovering the sound formants of human languages, for example, has allowed us to know vital aspects of the ‘material’ plane of verbal codes, but it tells us little (...) about significant aspects of their social functions. I firmly believe, therefore, that alongside a linguistics that looks ‘inward’ there should also be a linguistics that looks ‘outward’, or one even that is constructed ‘from the outside’, a linguistics that I refer to elsewhere as ‘holistic’ though it could be identified by a different name. My current vision is to promote simultaneously the perspective that goes from the part to the whole and that which goes from the whole to the parts, i.e., both from the top down and from the bottom up. This goal is shared with other disciplines which recognize that many phenomena related to life are interwoven, self-organising, emergent and processual. Thus, we need to re-examine how we have conceived of reality, both the way we have looked at it and the images we have used to talk about it. Several approaches now grouped under the label of complexity have been elaborated towards this objective of finding new concepts and ways of thinking that better fit the complex organisation of facts and events. (shrink)

The complex systems approach to cognitive science invites a new understanding of extended cognitive systems. According to this understanding, extended cognitive systems are heterogenous, composed of brain, body, and niche, non-linearly coupled to one another. This view of cognitive systems, as non-linearly coupled brain–body–niche systems, promises conceptual and methodological advances. In this article we focus on two of these. First, the fundamental interdependence among brain, body, and niche makes it possible to explain extended cognition without invoking (...) representations or computation. Second, cognition and conscious experience can be understood as a single phenomenon, eliminating fruitless philosophical discussion of qualia and the so-called hard problem of consciousness. What we call “extended phenomenological-cognitive systems” are relational and dynamical entities, with interactions among heterogeneous parts at multiple spatial and temporal scales. (shrink)

Following Marr’s famous three-level distinction between explanations in cognitive science, it is often accepted that focus on modeling cognitive tasks should be on the computational level rather than the algorithmic level. When it comes to mathematical problem solving, this approach suggests that the complexity of the task of solving a problem can be characterized by the computational complexity of that problem. In this paper, I argue that human cognizers use heuristic and didactic tools and thus engage (...) in cognitive processes that make their problem solving algorithms computationally suboptimal, in contrast with the optimal algorithms studied in the computational approach. Therefore, in order to accurately model the human cognitive tasks involved in mathematical problem solving, we need to expand our methodology to also include aspects relevant to the algorithmic level. This allows us to study algorithms that are cognitively optimal for human problem solvers. Since problem solving methods are not universal, I propose that they should be studied in the framework of enculturation, which can explain the expected cultural variance in the humanly optimal algorithms. While mathematical problem solving is used as the case study, the considerations in this paper concern modeling of cognitive tasks in general. (shrink)

Complex problem solving (CPS) emerged in the last 30 years in Europe as a new part of the psychology of thinking and problem solving. This paper introduces into the field and provides a personal view. Also, related concepts like macrocognition or operative intelligence will be explained in this context. Two examples for the assessment of CPS, Tailorshop and MicroDYN, are presented to illustrate the concept by means of their measurement devices. Also, the relation of complex cognition and emotion in the (...) CPS context is discussed. The question if CPS requires complex cognition is answered with a tentative “yes.”. (shrink)

Modern integrative systems biology defines itself by the complexity of the problems it takes on through computational modeling and simulation. However in integrative systems biology computers do not solve problems alone. Problem solving depends as ever on human cognitive resources. Current philosophical accounts hint at their importance, but it remains to be understood what roles human cognition plays in computational modeling. In this paper we focus on practices through which modelers in systems biology use computational simulation and other (...) tools to handle the cognitive complexity of their modeling problems so as to be able to make significant contributions to understanding, intervening in, and controlling complex biological systems. We thus show how cognition, especially processes of simulative mental modeling, is implicated centrally in processes of model-building. At the same time we suggest how the representational choices of what to model in systems biology are limited or constrained as a result. Such constraints help us both understand and rationalize the restricted form that problem solving takes in the field and why its results do not always measure up to expectations. (shrink)

The essays gathered here demonstrate and justify the excitement and promise of cognitive historicism, providing a lively introduction to this new and quickly growing area of literary studies. Written by eight leading critics whose work has done much to establish the new field, they display the significant results of a largely unprecedented combination of cultural and cognitive analysis. The authors explore both narrative and dramatic genres, uncovering the tensions among presumably universal cognitive processes, and the local contexts (...) within which complex literary texts are produced. Alan Richardson's opening essay evaluates current approaches to the study of literature and cognition, locating them on the map of recent literary studies, indicating their most compelling developments to date, and suggesting the most promising future directions. The seven essays that follow provide innovative readings of topics ranging from Shakespeare (Othello, Macbeth, Cymbeline, The Rape of Lucrece) through Samuel Richardson's Clarissa, to contemporary authors Ian McEwan and Gilbert Sorrentino. They underscore some of the limitations of new historicist and post-structuralist approaches to literary cultural studies while affirming the value of supplementing rather than supplanting them with insights and methods drawn from cognitive and evolutionary theory. Together, they demonstrate the analytical power of considering these texts in the context of recent studies of cultural universals, 'theory of mind, ' cognitive categorization and genre, and neural-materialist theories of language and consciousness. This groundbreaking collection holds appeal for a broad audience, including students and teachers of literary theory, literary history, cultural studies, and literature and science studies. (shrink)

In computational complexity theory, decision problems are divided into complexity classes based on the amount of computational resources it takes for algorithms to solve them. In theoretical computer science, it is commonly accepted that only functions for solving problems in the complexity class P, solvable by a deterministic Turing machine in polynomial time, are considered to be tractable. In cognitive science and philosophy, this tractability result has been used to argue that only functions in P can (...) feasibly work as computational models of human cognitive capacities. One interesting area of computational complexity theory is descriptive complexity, which connects the expressive strength of systems of logic with the computational complexity classes. In descriptive complexity theory, it is established that only first-order systems are connected to P, or one of its subclasses. Consequently, second-order systems of logic are considered to be computationally intractable, and may therefore seem to be unfit to model human cognitive capacities. This would be problematic when we think of the role of logic as the foundations of mathematics. In order to express many important mathematical concepts and systematically prove theorems involving them, we need to have a system of logic stronger than classical first-order logic. But if such a system is considered to be intractable, it means that the logical foundation of mathematics can be prohibitively complex for human cognition. In this paper I will argue, however, that this problem is the result of an unjustified direct use of computational complexity classes in cognitive modelling. Placing my account in the recent literature on the topic, I argue that the problem can be solved by considering computational complexity for humanly relevant problem solving algorithms and input sizes. (shrink)

Mechanistic frameworks of investigation and explanation dominate the cognitive, neural, and psychological sciences. In this dissertation, I argue that mechanistic frameworks cannot, in principle, explain some kinds of cognition. In its place, I argue that complexity science has methods and theories more appropriate for investigating and explaining some cognitive phenomena. -/- I begin with an examination of the term 'cognition.' I defend the idea that "cognition" has been a moving target of investigation in the relevant sciences. As (...) such it is not historically true that there has been a thoroughly entrenched and agreed upon conception of "cognition." Next, I take up mechanistic frameworks. Although 'mechanism' is an umbrella term for a set of loosely related characteristics, there are common features: linearity, localization, and component dominance. I then describe complexity science, with emphasis on its utilization of dynamical systems modeling. Next, I discuss two phenomena that typically fall under the purview of complexity science: nonlinearity and interaction dominance. -/- A complexity science framework guided by the theory of self-organized criticality and utilizing the methods of dynamical systems modeling can surmount a number of challenges that face mechanistic frameworks when investigating some kinds of cognition. The first challenge is epistemic and concerns the inadequacy of mechanistic frameworks to facilitate the comprehensibility of massive amounts of data across various scales and areas of inquiry. I argue that complexity science is more appropriate for making big data comprehensible when investigating cognition, particularly across disciplines. I demonstrate this via an approach called nested dynamical modeling (NDM). NDM can facilitate comprehensibility of large amounts of data obtained from various scales of investigation by eliminating irrelevant degrees of freedom of that system as relates to the target of investigation. -/- The second shortcoming concerns ontological blind spots within mechanistic frameworks. Cognitive phenomena like extended cognition often fail to meet most, if not all, of the criteria assumed by many mechanistic approaches, especially component dominance. I argue that research guided by the notion of interaction dominance allows for extended cognition to be a real, empirically supported phenomenon within complex systems frameworks. In this chapter I discuss some of my experimental work on extended cognitive systems. -/- The search for mechanisms can be a reasonable starting position when attempting to explain natural phenomena in the life sciences. However, too strict of an adherence to theoretical and methodological commitments such as linearity, localization, and component dominance can result in intractable epistemic challenges and ontological blind spots. Complexity science has theories and methods to overcome such challenges in the investigation of cognition. (shrink)

The core issue of our target article concerns how relational complexity should be assessed. We propose that assessments must be based on actual cognitive processes used in performing each step of a task. Complexity comparisons are important for the orderly interpretation of research findings. The links between relational complexity theory and several other formulations, as well as its implications for neural functioning, connectionist models, the roles of knowledge, and individual and developmental differences, are considered.

The present article constitutes an attempt at a review of a few selected questions related to the complexity paradigm and its implications for research on cognition, especially within the so-called ecological approach framework. I propose several theses, among others concerning the two contrary tendencies within the dominant methodology (the propensity to search for simplicity and the growing emphasis on recognizing complexity), as well as the ontological consequences of the phenomenon under discussion (ontological emergence and processual emergentism).

Intractability is a growing concern across the cognitive sciences: while many models of cognition can describe and predict human behavior in the lab, it remains unclear how these models can scale to situations of real-world complexity. Cognition and Intractability is the first book to provide an accessible introduction to computational complexity analysis and its application to questions of intractability in cognitive science. Covering both classical and parameterized complexity analysis, it introduces the mathematical concepts and proof (...) techniques that can be used to test one's intuition of tractability. It also describes how these tools can be applied to cognitive modeling to deal with intractability, and its ramifications, in a systematic way. Aimed at students and researchers in philosophy, cognitive neuroscience, psychology, artificial intelligence, and linguistics who want to build a firm understanding of intractability and its implications in their modeling work, it is an ideal resource for teaching or self-study. (shrink)

This paper explores a line of argument against the classical paradigm in cognitive science that is based upon properties of non-linear dynamical systems, especially in their chaotic and near-chaotic behavior. Systems of this kind are capable of generating information-rich macro behavior that could be useful to cognition. I argue that a brain operating at the edge of chaos could generate high-complexity cognition in this way. If this hypothesis is correct, then the symbolic processing methodology in cognitive science (...) faces serious obstacles. A symbolic description of the mind will be extremely difficult, and even if it is achieved to some approximation, there will still be reasons for rejecting the hypothesis that the brain is in fact a symbolic processor. (shrink)

The paper presents evidence in bacteria for the utility of Godfrey-Smith’s environmental complexity thesis, using certain kinds of signal transduction systems as proxies for cognitive/behavioral complexity. Microbiologists already accept that the number of signal transduction proteins in a bacterial genome indicates the level of ecological complexity to which the organism is subject: the more signalling proteins, the greater the complexity. Sheer numbers are not always a reliable indicator of behavioral complexity, however. The paper proposes (...) a new, ECT-based procedure for identifying, from genomic sequence and signalling repertoire, novel bacterial candidates likely to exhibit behavioral complexity in response to a complex ecological niche. (shrink)

Readers of TopiCS are invited to join a debate about the utility of ideas and methods of complexity science. The topics of debate include empirical instances of qualitative change in cognitive activity and whether this empirical work demonstrates sufficiently the empirical flags of complexity. In addition, new phenomena discovered by complexity scientists, and motivated by complexity theory, call into question some basic assumptions of conventional cognitive science such as stable equilibria and homogeneous variance. The (...) articles and commentaries that appear in this issue also illustrate a new debate style format for topiCS. (shrink)

Complexity science is an investigative framework that stems from a number of tried and tested disciplines—including systems theory, nonlinear dynamical systems theory, and synergetics—and extends a common set of concepts, methods, and principles to understand how natural systems operate. By quantitatively employing concepts, such as emergence, nonlinearity, and self‐organization, complexity science offers a way to understand the structures and operations of natural cognitive systems in a manner that is conceptually compelling and mathematically rigorous. Thus, complexity science (...) both transforms understandings of cognition and reframes more traditional approaches. Consequently, if cognitive systems are indeed complex systems, then cognitive science ought to consider complexity science as a centerpiece of the discipline. (shrink)

One problem faced in discussions of the evolution of intelligence is the need to get a precise fix on what is to be explained. Terms like "intelligence," "cognition" and "mind" do not have simple and agreed-upon meanings, and the differences between conceptions of intelligence have consequences for evolutionary explanation. I hope the papers in this volume will enable us to make progress on this problem. The present contribution is mostly focused on these basic and foundational issues, although the last section (...) of the paper will look at some specific models and programs of empirical work. (shrink)

This paper proposes an account of neurocognitive activity without leveraging the notion of neural representation. Neural representation is a concept that results from assuming that the properties of the models used in computational cognitive neuroscience must literally exist the system being modelled. Computational models are important tools to test a theory about how the collected data has been generated. While the usefulness of computational models is unquestionable, it does not follow that neurocognitive activity should literally entail the properties construed (...) in the model. While this is an assumption present in computationalist accounts, it is not held across the board in neuroscience. In the last section, the paper offers a dynamical account of neurocognitive activity with Dynamical Causal Modelling that combines dynamical systems theory mathematical formalisms with the theoretical contextualisation provided by Embodied and Enactive Cognitive Science. (shrink)

Qualitative Complexity offers a critique of the humanist paradigm in contemporary social theory. Drawing from sources in sociology, philosophy, complexity theory, 'fuzzy logic', systems theory, cognitive science and evolutionary biology, the authors present a new series of interdisciplinary perspectives on the sociology of complex, self-organizing structures.

ABSTRACTThis article discusses the complexity in ironic echoic mention from the perspective of Cognitive Linguistics. It builds on the scenario-based approach to irony where ironic meaning is treat...

Impaired neurodevelopmental outcome, in particular cognitive impairment, after neonatal hypoxic-ischemic encephalopathy is a major concern for parents, clinicians, and society. This study aims to investigate the potential benefits of using advanced quantitative electroencephalography analysis for early prediction of cognitive outcomes, assessed here at 2 years of age. EEG data were recorded within the first week after birth from a cohort of twenty infants with neonatal hypoxic-ischemic encephalopathy. A proposed regression framework was based on two different sets of features, (...) namely graph-theoretical features derived from the weighted phase-lag index and entropies metrics represented by sample entropy, permutation entropy, and spectral entropy. Both sets of features were calculated within the noise-assisted multivariate empirical mode decomposition domain. Correlation analysis showed a significant association in the delta band between the proposed features, graph attributes and entropy features from the neonatal EEG data and the cognitive development at age two years. These features were used to train and test the tree ensemble regression models. The highest prediction performance was reached to 14.27 root mean square error, 12.07 mean absolute error, and 0.45 R-squared using the entropy features with a boosted tree regression model. Thus, the results demonstrate that the proposed qEEG features show the state of brain function at an early stage; hence, they could serve as predictive biomarkers of later cognitive impairment, which could facilitate identifying those who might benefit from early targeted intervention. (shrink)

Unlike birds and mammals, reptiles are commonly thought to possess only the most rudimentary means of interacting with their environments, reflexively responding to sensory information to the near exclusion of higher cognitive function. However, reptilian brains, though structurally somewhat different from those of mammals and birds, use many of the same cellular and molecular processes to support complex behaviors in homologous brain regions. Here, the neurological mechanisms supporting reptilian cognition are reviewed, focusing specifically on spatial cognition and the hippocampus. (...) These processes are compared to those seen in mammals and birds within an ecologically and evolutionarily relevant context. By viewing reptilian cognition through an integrative framework, a more robust understanding of reptile cognition is gleaned. Doing so yields a broader view of the evolutionarily conserved molecular and cellular mechanisms that underlie cognitive function and a better understanding of the factors that led to the evolution of complex cognition. (shrink)

Over the last decade or so, several researchers have considered the predictive processing framework (PPF) to be a useful perspective from which to shed some much-needed light on the mechanisms behind psychosis. Most approaches to psychosis within PPF come down to the idea of the “atypical” brain generating inaccurate hypotheses that the “typical” brain does not generate, either due to a systematic top-down processing bias or more general precision weighting breakdown. Strong at explaining common individual symptoms of psychosis, such approaches (...) face some issues when we look at a more general clinical picture. In this paper, we propose an update on the current accounts of psychosis based on the realization that a neurotypical brain constantly generates non-actual, de-coupled, counterfactual hypotheses as part of healthy cognition. We suggest that what is going on in psychosis, at least in some cases, is not so much a generation of erroneous hypotheses, but rather an inability to correctly use the counterfactual ones. This updated view casts “accurate” cognition as more fragile and delicate, but also closes the gap between psychosis and typical cognition. (shrink)

In this commentary, we propose that the shifts in symmetry Wynn documents may be explained in terms of simpler mechanisms than he suggests. Furthermore, we argue that it is dangerous to draw definitive conclusions about the cognitive abilities of a species from the level of symmetry observed in the artefacts produced by that species.

The present study evaluates the cognitive representation of a kicking movement performed by a human and a humanoid robot, and how they are represented in experts and novices of soccer and robotics, respectively. To learn about the expertise-dependent development of memory structures, we compared the representation structures of soccer experts and robot experts concerning a human and humanoid robot kicking movement. We found different cognitive representation structures for both expertise groups under two different motor performance conditions . In (...) general, the expertise relies on the perceptual-motor knowledge of the human motor system. Thus, the soccer experts’ cognitive representation of the humanoid robot movement is dominated by their representation of the corresponding human movement. Additionally, our results suggest that robot experts, in contrast to soccer experts, access functional features of the technical system of the humanoid robot in addition to their perceptual-motor knowledge about the human motor system. Thus, their perceptual-motor and neuro-functional machine representation are integrated into a cognitive representation of the humanoid robot movement. (shrink)

When a young boy playing in a wooded area, I tripped over exposed roots extending from the trunk of a tree. I threw my arms out in front of me to break my fall and disturbed a nest of bees. As I lay on the ground, I was repeatedly stung by bees until I could regain my feet and run away. Frightened and in a great deal of pain - that is what I remember most vividly - I walked home. (...) My mother took me to the doctor, who undoubtedly gave me some sort of treatment and medication, but this has been lost to memory. The part of the visit to the doctor's office that I remember is his removing any stingers remaining in me; this too I remember for its pain. The doctor counted more than seventy stings. Although the exact number escapes memory, I believe it was seventy-one. In brief, the descriptive details of the day's experiences elude memory, but the affective dimension - the pain and fear - does not. (shrink)