Philosophy of cognitive neuroscience

The historical debate on representation in cognitive science and neuroscience construes representations as theoretical posits and discusses the degree to which we have reason to posit them. We reject the premise of that debate—that representations are mere theoretical posits. We argue that experimental neuroscientists routinely observe and manipulate neural representations in their laboratory. If a type of entity is observable or manipulable, then it exists. Therefore, neural representations are as real as neurons, action potentials, or any other experimentally established entities in our ontology.

A theory of the structure and cognitive function of the human imagination that attempts to do justice to traditional intuitions about its psychological centrality is developed, largely through a detailed critique of the theory propounded by Colin McGinn. Like McGinn, I eschew the highly deflationary views of imagination, common amongst analytical philosophers, that treat it either as a conceptually incoherent notion, or as psychologically trivial. However, McGinn fails to develop his alternative account satisfactorily because (following Reid, Wittgenstein and Sartre) he draws an excessively sharp, qualitative distinction between imagination and perception, and because of his flawed, empirically ungrounded conception of hallucination. His arguments in defense of these views are rebutted in detail, and the traditional, passive, Cartesian view of visual perception, upon which several of them implicitly rely, is criticized in the light of findings from recent cognitive science and neuroscience. It is also argued that the apparent intuitiveness of the passive view of visual perception is a result of mere historical contingency. An understanding of perception (informed by modern visual science) as an inherently active process enables us to unify our accounts of perception, mental imagery, dreaming, hallucination, creativity, and other aspects of imagination within a single coherent theoretical framework.

Keywords: imagination; mental imagery; mental image; perception; eye movements; active vision; enactive perception; seeing as; dreams; hallucination

Abstract We argue that neural processes are neither analog nor digital computations; they constitute a third kind of computation. Analog computation is the processing of continuous signals; digital computation is the processing of strings of digits. But current neuroscientific evidence indicates that typical neural signals, such as spike rates, are graded like continuous signals but are constituted by discrete elements (spikes); thus typical neural signals are neither continuous signals nor strings of digits.

This paper offers an unorthodox appraisal of empirical research bearing on the question of the low representation of women in philosophy.  It contends that fashionable views in the profession concerning implicit bias and stereotype threat are weakly supported, that philosophers often fail to report the empirical work responsibly, and that the standards for evidence are set very low—so long as you take a certain viewpoint.

Mirror neuron research has come a long way since the early 90’s, and many theorists are now stressing the heterogeneity and complexity of the sensorimotor properties of fronto-parietal circuits. However, core aspects of the initial ‘mirror mechanism’ theory, i.e. the idea of a symmetric encapsulated mirroring function translating sensory action perceptions into motor formats, still appears to be shaping much of the debate. This article challenges the empirical plausibility of the sensorimotor segregation implicit in the original mirror metaphor. It is proposed instead that the teleological organization found in the broader fronto-parietal circuits might be inherently sensorimotor. Thus the idea of an independent ‘purely perceptual’ goal understanding process is questioned. Further, it is hypothesized that the often asymmetric, heterogeneous and contextually modulated mirror and canonical neurons support a function of multisensory mapping and tracking of the perceiving agents affordance space. Such a shift in the interpretative framework offers a different theoretical handle on how sensorimotor processes might ground various aspects of intentional action choice and social cognition. Mirror neurons would under the proposed “social affordance model” be seen as dynamic parts of larger circuits, which support tracking of currently shared and competing action possibilities. These circuits support action selection processes—but also our understanding of the options and action potentials that we and perhaps others have in the affordance space. In terms of social cognition ‘mirror’ circuits might thus help us understand not only the intentional actions others are actually performing—but also what they could have done, did not do and might do shortly.

ABSTRACT: This paper employs a case study from the history of neuroscience—brain reward function—to scrutinize the inductive argument for the so-called 'Heuristic Identity Theory' (HIT). The case fails to support HIT, illustrating why other case studies previously thought to provide empirical support for HIT also fold under scrutiny. After distinguishing two different ways of understanding the types of identity claims presupposed by HIT and considering other conceptual problems, we conclude that HIT is not an alternative to the traditional identity theory so much as a relabeling of previously discussed strategies for mechanistic discovery.

This chapter provides a brief overview of the history of behavioral neurology, dividing it roughly into six eras. In the ancient and classical eras, emphasis is placed on two transitions: firstly, from descriptions of head trauma and attempted neurosurgical treatments to the exploratory dissections during the Hellenistic period and the replacement of cardiocentrism; and secondly, to the more systematic investigations of Galenus and the rise of pneumatic ventricular theory. In the medieval through post-Renaissance eras, the scholastic consolidation of knowledge and the role of compendia are emphasized, along with the use of new methods from within a mechanistic framework. With the discovery of electrical conductance and the rise of experimentalism, we frame the modern era as period of intense debate over localization, decomposition, and other mechanistic principles, and marked by rapid discovery about the brain. The chapter ends with a discussion of the contemporary era, focusing on the establishment of behavioral neurology research on aphasia, apraxia, and neuropsychiatric conditions.

According to the computational theory of cognition (CTC), cognitive capacities are explained by inner computations, which in biological organisms are realized in the brain. Computational explanation is so popular and entrenched that it's common for scientists and philosophers to assume CTC without argument. But if we presuppose that neural processes are computations before investigating, we turn CTC into dogma. If, instead, our theory is to be genuinely empirical and explanatory, it needs to be empirically testable. To bring empirical evidence to bear on CTC, we need an appropriate notion of computation. In order to ground an empirical theory of cognition, as CTC was designed to be, a satisfactory notion of computation should satisfy at least two requirements: it should employ a robust notion of computation, such that there is a fact of the matter as to which computations are performed by which systems, and it should not be empirically vacuous, as it would be if CTC could be established a priori. In order to satisfy these requirements, the computational theory of cognition should be grounded in a mechanistic account of computation. Once that is done, I evaluate the computational theory of cognition on empirical grounds in light of our best neuroscience. I reach two main conclusions: cognitive capacities are explained by the processing of spike trains by neuronal populations, and the processing of spike trains is a kind of computation that is interestingly different from both digital computation and analog computation.

Many empirically-minded philosophers have used neuroscientific data to argue against the multiple realization of cognitive functions in existing biological organisms. I argue that neuroscientists themselves have proposed a biologically-based concept of multiple realization as an alternative explanation of empirical findings than one-to-one structure-function mappings. I introduce this concept and its associated research framework, and also how some of the main neuroscience-based arguments against multiple realization go wrong.

To enable the impact of neuroscientific insights on our daily lives, careful translation of research findings is required. However, neuroscientific terminology and common-sense concepts are often hard to square. For example , when neuroscientists study lying to allow the use of brain scans for lie-detection purposes, the concept of lying in the scientific case differs considerably from the concept in court. Furthermore, lying and other cognitive concepts are used unsystematically and have an indirect and divergent mapping onto brain activity. Therefore, scientific findings cannot inform our practical concerns in a straightforward way. How then can neuroscience ultimately help determine if a defendant is legally responsible, or help someone understand their addiction better? Since the above-mentioned problems provide serious obstacles to move from science to common-sense, we call this the 'translation problem'. Here, we describe three promising approaches for neuroscience to face this translation problem. First, neuroscience could propose new 'folk-neuroscience' concepts, beyond the traditional folk-psychological array, which might inform and alter our phenomenology. Second, neuroscience can modify our current array of common-sense concepts by refining and validating scientific concepts. Third, neuroscience can change our views on the application criteria of concepts such as responsibility and consciousness. We believe that these strategies to deal with the translation problem should guide the practice of neuroscientific research to be able to contribute to our day-today life more effectively.

Autonomist accounts of cognitive science suggest that cognitive model building and theory construction (can or should) proceed independently of findings in neuroscience. Common functionalist justifications of autonomy rely on there being relatively few constraints between neural structure and cognitive function (e.g., Weiskopf, 2011). In contrast, an integrative mechanistic perspective stresses the mutual constraining of structure and function (e.g., Piccinini & Craver, 2011; Povich, 2015). In this paper, I show how model-based cognitive neuroscience (MBCN) epitomizes the integrative mechanistic perspective and concentrates the most revolutionary elements of the cognitive neuroscience revolution (Boone & Piccinini, 2016). I also show how the prominent subset account of functional realization supports the integrative mechanistic perspective I take on MBCN and use it to clarify the intralevel and interlevel components of integration.

The past decade has witnessed a growing awareness of conceptual and methodological hurdles within psychology and neuroscience that must be addressed for taxonomic and explanatory progress in understanding psychological functions to be possible. In this paper, I evaluate several recent knowledge-building initiatives aimed at overcoming these obstacles. I argue that while each initiative offers important insights about how to facilitate taxonomic and explanatory progress in psychology and neuroscience, only a “coordinated pluralism” that incorporates positive aspects of each initiative will have the potential for success.

How do we perceive the agency of others? Do the same rules apply when interacting with others who are radically different from ourselves, like other species or robots? We typically perceive other people and animals through their embodied behavior, as they dynamically engage various aspects of their affordance field. In second personal perception we also perceive social or interactional affordances of others. I discuss various aspects of perceptible agency, which might begin to give us some tools to understand interactions also with agents truly other than ourselves or ‘‘perhaps agents’’ like present and future social robots. Robots have various kinds of physical and behavioral presence and thus make their agency—if we want to call it that—perceptible in ways that computers and other forms of AI do not. The largely dualist assumptions pertaining to the hidden bodies of traditional Turing tests are discussed, as well as the social affordance effects of such indirect interactions as opposed to interactions in physically shared space. The question is what role various abilities to reveal, hide and dynamically control the body and broader behavior plays in heterogeneous tech or machine mediated interactions. I argue that the specifics and richness of perceptible agency matters to the kind of reciprocity we can obtain.

Most computational neuroscientists assume that nervous systems compute and process information. We discuss foundational issues such as what we mean by ‘computation’ and ‘information processing’ in nervous systems; whether computation and information processing are matters of objective fact or of conventional, observer-dependent description; and how computational descriptions and explanations are related to other levels of analysis and organization.

This paper engages critically with anti-representationalist arguments pressed by prominent enactivists and their allies. The arguments in question are meant to show that the “as-such” and “job-description” problems constitute insurmountable challenges to causal-informational theories of mental content. In response to these challenges, a positive account of what makes a physical or computational structure a mental representation is proposed; the positive account is inspired partly by Dretske’s views about content and partly by the role of mental representations in contemporary cognitive scientific modeling.

Please cite this article in press as: Seke Etet PF, et al. Evaluation of the safety of conventional lighting replacement by artificial daylight. J Microsc Ultrastruct (2017), http://dx. a b s t r a c t Background: Short morning exposure to high illuminance visible electromagnetic radiations termed as artificial daylight is beneficial for the mental health of people living in geographical areas with important seasonal changes in daylight illuminance. However, the commercial success of high illuminance light sources has raised the question of the safety of long hour exposure. Methods: We have investigated the effect of the replacement of natural daylight by artificial daylight in Swiss mice raised under natural lighting conditions. Mice were monitored for neurotoxicity and general health changes. They were submitted to a battery of conventional tests for mood, motor and cognitive functions' assessment on exposure day (ED) 14 and ED20. Following sacrifice on ED21 due to marked signs of neurotoxicity, the expression of markers of inflammation and apoptosis was assessed in the entorhinal cortex and neurons were estimated in the hippocampal formation. Results: Signs of severe cognitive and motor impairments, mood disorders, and hepatotoxicity were observed in animals exposed to artificial daylight on ED20, unlike on ED14 and unlike groups exposed to natural daylight or conventional lighting. Activated microglia and astrocytes were observed in the entorhinal cortex, as well as dead and dying neurons. Neuronal counts revealed massive neuronal loss in the hippocampal formation. Conclusions: These results suggest that long hour exposure to high illuminance visible electromagnetic radiations induced severe alterations in brain function and general health in mice partly mediated by damages to the neocortex-entorhinal cortex-hippocampus axis. These findings raise caution over long hour use of high illuminance artificial light.

In their response to our article Foundationalism and neuroscience (Keestra & Cowley, 2009), Hacker and Bennett charge us with failing to understand the project of their book Philosophical Foundations of Neuroscience (PFN; Bennett and Hacker, 2003) and do this by discussing foundationalism, linguistic conservatism and the passivity of perception. In this rebuttal we explore disagreements that explain the alleged errors. First, we reiterate our substantial disagreement with Bennett and Hacker (B&H) regarding their assumption that, even regarding much debated concepts like ‘consciousness’, we can assume conceptual consensus within a community of competent speakers. Instead, we emphasize variability and divergence between individuals and groups in such contexts. Second, we plead for modesty in conceptual analysis, including the use of conceptual ambiguities as heuristics for the investigation of explanatory mechanisms. Third, we elucidate our proposal by discussing the interdependence of perception and action, which in some cases appear to be problematic for PFN. Fourth, we discuss why our view of conceptual innovation is different from B&H’s, as we plead for linking explanatory ingredients with conceptual analysis. We end by repeating our particular agreement with their mereological principle, even though we present different reasons: psychological concepts should not be applied to mere components or operations of explanatory mechanisms, for which another vocabulary should be developed.

In 2007, ten world-renowned neuroscientists proposed "A Decade of the Mind Initiative." The contention was that, despite the successes of the Decade of the Brain, "a fundamental understanding of how the brain gives rise to the mind [was] still lacking" (2007, 1321). The primary aims of the decade of the mind were "to build on the progress of the recent Decade of the Brain (1990-99)" by focusing on "four broad but intertwined areas" of research, including: healing and protecting, understanding, enriching, and modeling the mind. These four aims were to be the result of "transdisciplinary and multiagency" research spanning "across disparate fields, such as cognitive science, medicine, neuroscience, psychology,

In Reading in the Brain, Stanislas Dehaene presents a compelling account of how the brain learns to read. Central to this account is his neuronal recycling hypothesis: neural circuitry is capable of being ‘recycled’ or converted to a different function that is cultural in nature. The original function of the circuitry is not entirely lost and
constrains what the brain can learn. It is argued that the neural niche co-evolves with the environmental niche in a way that does not undermine the core ideas of neuronal recycling, but which is quite different from the models of cognitive and cultural evolution
provided by evolutionary psychology and epidemiology. Dehaene contrasts neuronal recycling with a naïve model of the brain as a general learning device that is unconstrained in what it can learn. Consequently a tension develops in Dehaene’s account of the role of plasticity in the acquisition of language. It is argued that the functional and structural changes in the brain that Dehaene documents in great detail are driven by learning and that this learning-driven plasticity does not commit us to a naïve model of the brain.

Cognitive neuroscientists are increasingly required by funding agencies to link their research to specific potential outcomes of public benefit or interest (NIH Office of Portfolio Analysis and Strategic Initiatives 2008; Maienschein et al. 2008). They are also increasingly aware of the need to communicate their research results more effectively to the public (Illes et al. 2010). This paper argues that miscommunication and public misunderstanding of neuroscience results and implications stem to a significant degree from neuroscientists' failure to be sufficiently sensitive to the nature of the mental or cognitive concepts in terms of which they interpret their results and draw implications. In consequence, more effective communication of results and the drawing of justifiable translational implications depends in part on neuroscientists' willingness to assume greater responsibility for these choices. This problem can also affect collaborations with the social sciences and psychology and generates new neuroethical concerns.

This essay examines the prospects and limits of 'reverse inferring' cognitive processes from neural data, a technique commonly used in cognitive neuroscience for discriminating between competing psychological hypotheses. Specifically, we distinguish between two main types of reverse inference. The first kind of inference moves from the locations of neural activation to the underlying cognitive processes. We illustrate this strategy by presenting a well-known example involving mirror neurons and theories of low-level mind-reading, and discuss some general methodological problems. Next we present the second type of reverse inference by discussing an example from recognition memory research. These inferences, based on pattern-decoding techniques, do not presuppose strong assumptions about the functions of particular neural locations. Consequently, while they have been largely ignored in methodological critiques, they overcome important objections plaguing traditional methods.

Artificial intelligence has dramatically changed the world as we know it, but is yet to fully embrace ‘hot’ cognition, i.e., the way an intelligent being's thinking is affected by their emotional state. Artificial intelligence encompassing hot cognition will not only usher in enhanced machine-human interactions, but will also promote a much needed ethical approach. Theory of Mind, the ability of the human mind to attribute mental states to others, is a key component of hot cognition. To endow machines with (limited) Theory of Mind capabilities, computer scientists will need to work closely with psychiatrists, psychologists and neuroscientists. They will need to develop new models, but also to formally define what problems need to be solved and how the results should be assessed.

[AMAZON LINK BELOW TO BOOK ITSELF -- TOC and book intro in downloadable .pdf] 

"It is a common experience that a problem difficult at night is resolved in the morning after the committee of sleep has worked on it." --John Steinbeck Scientific research confirms what people have always known: answers, ideas, and inspiration do come to us in dreams. Harvard psychologist and world-renowned dream specialist Deirdre Barrett, Ph.D., offers this rich collection of examples of how the world's most creative practitioners in art, music, film, science, literature and other fields have used the revelations of their dream life to inform their work. Dr. Barrett offers insights showing us how to encourage lucid, meaningful dreaming, and how to apply the meanings of our dreams to solving problems--from the everyday to the extraordinary. This is the stuff dreams are made of. In the visual arts, Jasper Johns couldn't find his unique artistic vision until he dreamed it in the form of a large American flag. Salvador Dali and his colleagues built the startling new genre of surrealism out of dreams. Kubla Kahn dreamed the design for his stately pleasure dome; thousands of years later, Lucy Davis, chief architect at a major firm, continues the tradition of dreaming designs into life in her extraordinary buildings. Film is a fertile avenue for dreams: "Twice I have transferred dreams to film exactly as I had dreamed them," confides director Ingmar Bergman, as have Federico Fellini, Orson Welles, Akira Kurosawa, Robert Altman, and John Sayles. From Mary Shelley's terrible nightmare, which became Frankenstein, to Stephen King's haunting dream as a little boy, which led to his first bestseller, countless writers have consulted the Committee. Musicians from Beethoven to Billy Joel and Paul McCartney have whistled the Committee's tunes. In science, physiologist Otto Loewi dreamed the medical experiment that earned him the Nobel Prize. In sports, Marion Jones dreamed she'd broken a world record, then brought the dream to life. Gandhi translated his dream of resistance into a movement that changed the world. Since Freud, we take it for granted that our dreams reflect our past. In The Committee of Sleep, Barrett reveals how dreams can also tell us about our future potential--and how to reach it.

Some recent accounts of constitutive relevance have identified mechanism components with entities that are causal intermediaries between the input and output of a mechanism. I argue that on such accounts there is no distinctive inter-level form of mechanistic explanation and that this highlights an absence in the literature of a compelling argument that there are such explanations. Nevertheless, the entities that these accounts call 'components' do play an explanatory role. Studying causal intermediaries linking variables X and Y provides knowledge of the counterfactual conditions under which X will continue to bring about Y. This explanatory role does not depend on whether intermediate variables count as components. The question of whether there are distinctively mechanistic explanations remains open.

Because biologization of psychiatric constructs does not involve derivation of laws, or reduce the number of entities involved, the traditional term of ‘reduction’ should be replaced. This paper describes biologization in terms of redefinition, which involves changing the definition of terms sharing the same extension. Redefinition obtains through triangulation and calibration, that is, respectively, detection of an object from two different spots, and tweaking parameters of detection in order to optimize the picture. The unity of the different views of the same object does not occur through derivation from one of them, as reduction suggests, nor does it obtain through mechanistic unity or the goal of explaining one mechanism, as the phrase ‘mosaic unity’ suggests. Instead, it depends on finding a specific angle of observation, from which linguistic consistency matches sound localization in the brain, so that all observations make sense together, just as an anamorphic picture makes clear sense only when observed from the right spot.

The "top-down" and "bottom-up" approaches have been thought to exhaust the possibilities for doing cognitive neuroscience. We argue that neither approach is likely to succeed in providing a theory that enables us to understand how cognition is achieved in biological creatures like ourselves. We consider a promising third way of doing cognitive neuroscience, what might be called the "neural dynamic systems" approach, that construes cognitive neuroscience as an autonomous explanatory endeavor, aiming to characterize in its own terms the states and processes responsible for brain-based cognition. We sketch the basic motivation for the approach, describe a particular version of the approach, so-called 'Dynamic Causal Modeling' (DCM), and consider a concrete example of DCM. This third way, we argue, has the potential to avoid the problems that afflict the other two approaches.

This paper offers a critique of an account of explanatory integration that claims that explanations of cognitive capacities by functional analyses and mechanistic explanations can be seamlessly integrated. It is shown that achieving such explanatory integration requires that the terms designating cognitive capacities in the two forms of explanation are stable but that experimental practice in the mind-brain sciences currently is not directed at achieving such stability. A positive proposal for changing experimental practice so as to promote such stability is put forward and its implications for explanatory integration are briefly considered.

Une question controversée est de savoir si, grâce en particulier aux techniques d’imagerie cérébrale fonctionnelle, une nouvelle science du cerveau occupe désormais, sous le nom de “neurosciences cognitives”, le cœur des sciences cognitives. Quels sont les fondements de cette nouvelle spécialité ? Comment s’articuleront, demain, les neurosciences cognitives et la psychologie ? On replacera les techniques d’imagerie dans le contexte théorique des neurosciences fondamentales, et on montrera que les neurosciences cognitives en sont tributaires, comme elles le sont de l’ensemble des sciences cognitives, au sein desquelles elles constituent aujourd’hui un programme de recherche parmi d’autres. S’il venait à constituer, dans un avenir lointain, une science achevée des fonctionnalités du cerveau, il ne remplacerait sans doute pas la psychologie cognitive, appuyée sur les autres sciences de l’esprit, mais entrerait avec elle dans un rapport de complémentarité en un sens fort, très éloigné de toute forme de réduction. It is a matter of considerable controversy whether cognitive neuroscience, thanks in large part to functional neuroimaging techniques, is in the process of becoming a new science of the brain and moving into the heart of cognitive science. What are the foundations of this new field ? How will neuroscience and cognitive science coexist in the future ? The paper will attempt to situate neuroimagery in the theoretical framework of fundamental neuroscience, and will show the extent to which cognitive neuroscience depends on it, as it depends on the rest of cognitive science, within which it stands as one research program among several. Should it lead, in a distant future, to a completed science of the brain’s functionalities, such a science would likely not replace cognitive psychology and allied disciplines. Instead, I envisage a form of strong complementarity between the two branches, exclusive of any form of reduction.

A primary focus of the debates in philosophy of psychiatry addressed in each of the chapters in this volume is whether mental disorders are natural kinds. The question subdivides into several interrelated questions: Are mental disorders real and stable regularities in nature that exist independent of our systems of classifying them? Do the sets of necessary and sufficient conditions that constitute the categories of mental disorders put forward in the Diagnostic and Statistical Manual of Mental Disorders and in the International Classification of Diseases track these regularities? Are those groups of phenomena individuated by the categories suitable for discovering their causes and identifying viable targets for therapeutic intervention?

Functions play an important explanatory role in both psychology and neuroscience. Any effort to integrate psychology and neuroscience must provide an account of functions and how they explain in psychology and neuroscience. Yet the ontological foundations for function attributions and functional explanation remain unsettled. In this chapter, we contribute to an integrated science of cognition and behavior by offering a unified account of the teleological functions of multi-level mechanisms. The account applies to both biological traits and artifacts. Teleological functions are stable causal contributions towards the goals of organisms belonging to a reference class within a biological population. The paradigmatic goals of organisms are survival and inclusive fitness, although organisms may have additional goals. Truthmakers for claims about teleological functions are non-teleological features of the world.

Each living creature exists as a unit, as self. Understanding the self, then should be a major goal of scientific research. This volume takes stock of current understanding of the self and its relation to the brain, and considers future directions for scientific research in a multidisciplinary context.

We compare the computational power of different classes of computational systems and relate it to whether they contain closed loops. Adding closed loops to the architecture of computational systems increases their computational power. Different computational models are apt for capturing the computational power of different classes of neural systems. We argue that while ordinary Turing machines are a poor model for a kind of feedback that the closed-loop approach to neuroscience highlights, suitably modified Turing machines are a better fit.

The controversy between the theory-theory (TT) and simulation-theory (ST) has evolved so that it is often hard to tell exactly what the difference is between a simulation and a theory. I believe that this distinction was originally inspired, and can be freshly reconceived, as the distinction between verbal abstractions and concrete pictures. I argue that the multi-dimensional spaces described by connectionist neuroscience are best understood as pictures of a special sort. These multi-dimensional pictures do not have the limitations of ordinary three-dimensional pictures, and are capable of performing many of the cognitive functions that were traditionally thought to be the exclusive domain of abstract linguistic concepts. Consequently, there is a real possibility that a pure simulation theory could actually explain some sophisticated kinds of social cognition, without having to rely on a hybrid that combines simulations and theories. Paradoxically, such a pure simulation theory would not actually use simulations in the strictest sense of that word, because something can be a simulation only if it is verbally labeled as a copy of something else. Rather this kind of social cognition would establish vector transformations between perception and behavior without requiring any verbal labels at all. This would mean that the emotions caused by perceptions of other people would not be simulations of other people’s emotions, but rather the same emotion transferred by a kind of emotional contagion.

ABSTRACT: 'New wave' reductionism aims at advancing a kind of reduction that is stronger than unilateral dependency of the mental on the physical. It revolves around the idea that reduction between theoretical levels is a matter of degree, and can be laid out on a continuum between a 'smooth' pole (theoretical identity) and a 'bumpy' pole (extremely revisionary). It also entails that both higher and lower levels of the reductive relationship sustain some degree of explanatory autonomy. The new wave predicts that reductions of folk psychology to neuroscience will be located in the middle of this continuum; as neuroscientific evidence about mental states checks in, theoretical folk psychology will therefore be moderately revised. However, the model has conceptual problems which preclude its success in reviving reductionism, and its commitment to a syntactic approach wrecks its attempt to rescue folk psychology. Moreover, the architecture of the continuum operates on a category mistake that sneaks in an eliminativist conclusion. I argue that new wave reductionism therefore tends to be eliminativism in disguise.

This article clarifies three principles that should guide the development of any cognitive ontology. First, that an adequate cognitive ontology depends essentially on an adequate task ontology; second, that the task of developing a cognitive ontology is independent of the task of finding neural implementations of the processes referred to in the ontology; and third, that cognitive ontologies are neutral regarding the metaphysical relationship between cognitive and neural processes. In a recent paper, Lenartowicz et al. (2010) seek to demonstrate the potential of data-mining in the development of a cognitive ontology -a searchable vocabulary of clearly defined cognitive terms and their relations -of use in cognitive neuroscience. Their study is prompted by a fundamental background question: given cognitive neuroscience's goal of mapping cognitive functions to brain structures, what are the cognitive functions that should be mapped? This question would be boring if

Regina Fabry has proposed an intriguing marriage of enculturated cognition and predictive processing. I raise some questions for whether this marriage will work and warn against expecting too much from the predictive processing framework. Furthermore I argue that the predictive processes at a sub-personal level cannot be driving the innovations at a social level that lead to enculturated cognitive systems, like those explored in my target paper.

This study was planned to evaluate the protective role of curcumin (Cur) against maternal and fetal oxidative stress and cerebral damage induced by lead (Pb) during pregnancy. In this study, positively pregnant female rats were divided into seven groups: control group, Curgroup (300 mg/kg of Cur/b.wt.), DMSO group (50% DMSO), two Pb-treated groups (exposed to 160 and 320 mg/kg b.wt./day of Pb acetate, respectively), and two groups treated with both Pb and Cur (exposed to Pb as previous groups together with 300 mg/kg b.wt./day of Cur). Treatments started from gestation day 1 (GD1) till day 20 (GD20) through oral gavage once a day, where the mother rats of different experimental groups were sacrificed to obtain the fetuses. Different chemical parameters were assessed. Brain specimens from the mother and fetal groups were processed with examination. The results displayed that Pb administration to pregnant rats resulted in a dose-dependent toxicity for both mothers and fetuses. Also, there was a significant risein lipid peroxidation and decreased antioxidant enzyme activities in the brains of the different Pb-treated groups. The histological examination of the brain of treated dams and fetuses showed marked alterations. Co-treatment of Cur along with Pb caused a significant decrease in Pb levels as compared with those treated with Pb alone, improving the oxidative condition with amelioration of the brain'shistopathological changes. In conclusion, co-administration of Cur could haveameliorative effect against Pb-induced neurotoxicity through the reduction of oxidative stress and reversal of histopathological changes.

I outline and defend a theory of mental representation based on three ideas that I extract from the work of the mid-twentieth century philosopher, psychologist, and cybernetician Kenneth Craik: first, an account of mental representation in terms of idealised models that capitalize on structural similarity to their targets; second, an appreciation of prediction as the core function of such models; and third, a regulatory understanding of brain function. I clarify and elaborate on each of these ideas, relate them to contemporary advances in neuroscience and machine learning, and favourably contrast a predictive model-based theory of mental representation with other prominent accounts of the nature, importance, and functions of mental representations in cognitive science and philosophy.

Many neuroscientists, cognitive scientists and philosophers take it that nervous systems, and even their single cells, perform computations and that these computational operations play a role in producing and explaining cognition. Scientists and philosophers also tend to account for neurocognitive phenomena within one or another framework of levels.