Levels of Analysis in Cognitive Science

Como tantas veces en filosofía, el título no sólo establece la línea de batalla, sino que expone los prejuicios y errores del autor, ya que si podemos o no dar sentido al juego de idiomas 'Ver las cosas como son' y si es posible tener una 'teoría filosófica' de percepción' (que sólo puede ser sobre cómo funciona el lenguaje de la percepción), a diferencia de uno científico, que es una teoría sobre cómo funciona el cerebro, son exactamente los problemas. Este (...) es el Clásico Searle, excelente y probablemente al menos tan bueno como cualquier otro puede producir, pero carente de una comprensión completa de las ideas fundamentales del Wittgenstein posterior y sin comprender los dos sistemas de marco de pensamiento, lo que podría haberlo hecho brillante. Al igual que en su trabajo anterior, Searle evita en gran medida la ciencia, pero hay lapsos frecuentes y no entiende que los problemas son siempre sobre los juegos de idiomas, un fracaso que comparte con casi todo el mundo. Después de proporcionar un marco que consiste en una Tabla de Intencionalidad basada en los dos sistemas de pensamiento y pensamiento y investigación de decisiones, doy un análisis detallado del libro. Aquellos que deseen un marco completo hasta la fecha para el comportamiento humano de la moderna dos sistemas punto de vista puede consultar mi libros Talking Monkeys 3ª ed (2019), Estructura Logica de Filosofia, Psicología, Mente y Lenguaje en Ludwig Wittgenstein y John Searle 2a ed (2019), Suicidio pela Democracia 4ª ed (2019), La Estructura Logica del Comportamiento Humano (2019), The Logical Structure de la Conciencia (2019), Comprender las Conexiones entre Ciencia, Filosofía, Psicología, Religión, Política , Economía, Hitoria y Literatura (2020) y Delirios Utópicos Suicidas en el siglo 21 5ª ed (2019), Observaciones sobre Imposibilidad, Incompletitud, Paraconsistencia, Indecidibilidad, Aleatoriedad, Computabilidad, Paradoja e Incertidumbre en Chaitin, Wittgenstein, Hofstadter, Wolpert, Doria, da Costa, Godel, Searle, Rodych Berto, Floyd, Moyal-Sharrock y Yanofsky (2019) y otras. Al igual que con Wittgenstein (en adelante W), todo lo que Searle (en adelante S) escribe es un tesoro y es maravilloso que permanezca afilado a medida que se acerca a los 80. A diferencia de la mayoría, incluso sus primeros trabajos siguen siendo relevantes y está trabajando en varios otros libros. También sugiero sus más de 100 conferencias y entrevistas en youtube, vimeo, etc., que, aunque inevitablemente un poco repetitivas, contienen muchas declaraciones que no están en sus escritos. He leído casi toda su obra, y he escuchado todas las conferencias, la mayoría de ellas 2 o 3 veces. Estos son de especial interés como (como Wittgenstein) que no lee de las notas, por lo que cada uno es único y no una réplica de un papel, y es un excelente orador extemporáneo que en su mayoría utiliza un lenguaje sin pretensiones (ambos tan diferentes de la mayoría de los demás). Las conferencias recientes dadas en las Universidades Europeas son magníficas, pero no te pierdas las antiguas como la conferencia de la BBC "Una realidad cambiante, la ciencia del comportamiento humano", que da un excelente relato de por qué la causalidad legítima y repetitiva del rápido cerebro sistema automático, no linguístico 1 (S1) es fundamentalmente diferente y no destacable de la misma manera que la complejidad ilimitada de razones que caracterizan el lento sistema deliberativo, consciente de la lengua 2 (S2), que genera una explosión combinatoria no generalmente representable de una manera útil por las leyes científicas. El sistema dual (S1, S2) método de describir el pensamiento utilizado en esta revisión, común a la investigación de razonamiento desde hace unos 20 años, es mío y no de Searle. Dado que recientemente he escrito un artículo de 75p analizando el trabajo de Searle en comparación con el de Wittgenstein (La estructura lógica de la filosofía, la psicología, la mente y el lenguaje como revelado por Ludwig Wittgenstein y John Searle) no lo repetiré y lo repetiré concentrarse en este libro solamente. (shrink)

This chapter argues that the only tenable unconscious inferences theories of cognitive achievement are ones that employ a theory internal technical notion of representation, but that once we give cash-value definitions of the relevant notions of representation and inference, there is little left of the ordinary notion of representation. We suggest that the real value of talk of unconscious inferences lies in (a) their heuristic utility in helping us to make fruitful predictions, such as about illusions, and (b) their providing (...) a high-level description of the functional organization of subpersonal faculties that makes clear how they equip an agent to navigate its environment and pursue its goals. (shrink)

What follows is a first step towards an ontology of conscious mental processes. We provide a theoretical foundation and characterization of conscious mental processes based on a realist theory of intentionality and using BFO as our top-level ontology. We distinguish three components of intentional mental process: character, directedness, and objective referent, and describe several features of the process character and directedness significant to defining and classifying mental processes. We arrive at the definition of representational mental process as a process that (...) is the bringing into being, sustaining, modifying, or terminating of a mental representation. We conclude by outlining some benefits and applications of this approach. (shrink)

Some naturalistic philosophers of mind subscribing to the predictive processing theory of mind have adopted a realist attitude towards the results of Bayesian cognitive science. In this paper, we argue that this realist attitude is unwarranted. The Bayesian research program in cognitive science does not possess special epistemic virtues over alternative approaches for explaining mental phenomena involving uncertainty. In particular, the Bayesian approach is not simpler, more unifying, or more rational than alternatives. It is also contentious that the Bayesian approach (...) is overall better supported by the empirical evidence. So, to develop philosophical theories of mind on the basis of a realist interpretation of results from Bayesian cognitive science is unwarranted. Naturalistic philosophers of mind should instead adopt an anti-realist attitude towards these results and remain agnostic as to whether Bayesian models are true. For continuing on with an exclusive focus and praise of Bayes within debates about the predictive processing theory will impede progress in philosophical understanding of scientific practice in computational cognitive science as well as of the architecture of the mind. (shrink)

People often answer why-questions with what we call experiential explanations: narratives or stories with temporal structure and concrete details. In contrast, on most theories of the epistemic function of explanation, explanations should be abstractive: structured by general relationships and lacking extraneous details. We suggest that abstractive and experiential explanations differ not only in level of abstraction, but also in structure, and that each form of explanation contributes to the epistemic goals of individual learners and of science. In particular, experiential explanations (...) support mental simulation and survive transitions across background theories; as a result, they support learning and help us translate between competing frameworks. Experiential explanations play an irreducible role in human cognition – and perhaps in science. (shrink)

Boelen's recent book provides a systematic investigation and tightly woven discussion of the question, "What does it mean to be mature?" The reader soon learns that the author's interest in this question is motivated by concerns other than those found in the standard textbooks of developmental psychology. Indeed the ensuing discussion unfolds as a continuing critique of the psychobiological concept of maturity. This is the case, the author informs us, not because the traditional scientific treatments of the issue are research-oriented, (...) replete with alleged empirical confirmations, but because the research is not done with the "proper perspective.". (shrink)

In discussions regarding models of cognition, the very mention of “computationalism” often incites reactions against the insufficiency of the Turing machine model, its abstractness, determinism, the lack of naturalist foundations, triviality and the absence of clarity. None of those objections, however, concerns models based on natural computation or computing nature, where the model of computation is broader than symbol manipulation or conventional models of computation. Computing nature consists of physical structures that form layered computational architecture, with computation processes ranging from (...) quantum to chemical, biological/cognitive and social-level computation. It is argued that, on the lower levels of information processing in the brain, finite automata or Turing machines may still be adequate models, while, on the higher levels of whole-brain information processing, natural computing models are necessary. A layered computational architecture of the mind based on the intrinsic computing of physical systems avoids objections against early versions of computationalism in the form of abstract symbols manipulation. (shrink)

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. (shrink)

Rational analysis is an influential but contested account of how probabilistic modeling can be used to construct non-mechanistic but self-standing explanatory models of the mind. In this paper, I disentangle and assess several possible explanatory contributions which could be attributed to rational analysis. Although existing models suffer from evidential problems that question their explanatory power, I argue that rational analysis modeling can complement mechanistic theorizing by providing models of environmental affordances.

In her re-analysis of the evidence presented in Klein and Nichols (2012) to support their argument that patient R.B. temporarily lost possessory custody of consciously apprehended objects (in this case, objects that normally would be non-inferentially taken as episodic memory), Professor Roache concludes Klein and Nichols's claims are untenable. I argue that Professor Roache is incorrect in her re-interpretation, and that this is due, in part, to lack of sufficient familiarity with psychological theory on memory as well as clinical literature (...) on felt loss of ownership of one’s intentional objects. (shrink)

This chapter provides an overview of the basic research strategies and analytic techniques deployed in computational cognitive neuroscience. On the one hand, “top-down” strategies are used to infer, from formal characterizations of behavior and cognition, the computational properties of underlying neural mechanisms. On the other hand, “bottom-up” research strategies are used to identify neural mechanisms and to reconstruct their computational capacities. Both of these strategies rely on experimental techniques familiar from other branches of neuroscience, including functional magnetic resonance imaging, single-cell (...) recording, and electroencephalography. What sets computational cognitive neuroscience apart, however, is the explanatory role of analytic techniques from disciplines as varied as computer science, statistics, machine learning, and mathematical physics. These techniques serve to describe neural mechanisms computationally, but also to drive the process of scientific discovery by influencing which kinds of mechanisms are most likely to be identified. For this reason, understanding the nature and unique appeal of computational cognitive neuroscience requires not just an understanding of the basic research strategies that are involved, but also of the formal methods and tools that are being deployed, including those of probability theory, dynamical systems theory, and graph theory. (shrink)

Philosophers of mind commonly draw a distinction between the personal level – the distinctive realm of conscious experience and reasoned deliberation – and the subpersonal level, the domain of mindless mechanism and brute cause and effect. Moreover, they tend to view cognitive science through the lens of this distinction. Facts about the personal level are given a priori, by introspection, or by common sense; the job of cognitive science is merely to investigate the mechanistic basis of these facts. I argue (...) that this view misrepresents the structure of cognitive-scientific enquiry. Taken at face value, cognitive science makes no commitment to the existence of a distinctive level at which persons or selves appear. Thus, in the age of cognitive science, we should not expect to find the self in an ontologically distinct realm. Instead, we should expect to locate it in cognitive-scientific models themselves. In closing, I indicate likely results of this approach. (shrink)

We discuss recent progress in the development of cognitive ontologies and summarize three challenges in the coordinated development and application of these resources. Challenge 1 is to adopt a standardized definition for cognitive processes. We describe three possibilities and recommend one that is consistent with the standard view in cognitive and biomedical sciences. Challenge 2 is harmonization. Gaps and conflicts in representation must be resolved so that these resources can be combined for mark-up and interpretation of multi-modal data. Finally, Challenge (...) 3 is to test the utility of these resources for large-scale annotation of data, search and query, and knowledge discovery and integration. As term definitions are tested and revised, harmonization should enable coordinated updates across ontologies. However, the true test of these definitions will be in their community-wide adoption which will test whether they support valid inferences about psychological and neuroscientific data. (shrink)

Radical and autopoietic enactivists disagree concerning how to understand the concept of sense-making in enactivist discourse and the extent of its distribution within the organic domain. I situate this debate within a broader conflict of commitments to naturalism on the part of radical enactivists, and to phenomenology on the part of autopoietic enactivists. I argue that autopoietic enactivists are in part responsible for the obscurity of the notion of sense-making by attributing it univocally to sentient and non-sentient beings and following (...) Hans Jonas in maintaining a phenomenological dimension to life-mind continuity among all living beings, sentient or non-sentient. I propose following Merleau-Ponty instead, who offers a properly phenomenological notion of sense-making for which sentience is a necessary condition. Against radicalist efforts to replace sense-making with a deflationary, naturalist conception of intentionality, I discuss the role of the phenomenological notion of sense-making for understanding animal behavior and experience. (shrink)

Tacit knowledge is both a ubiquitous and puzzling notion, related to the idea of hidden assumptions. The puzzle is partly a result of the conflict between the idea that assumptions are in the mind and the apparent audience-relativity of the "fact" of possessing an assumption or of the tacit knowledge that is articulated. If we think of making the tacit explicit as constructing a certain kind of inference repairing explanation for a particular audience "on the fly" we come closer to (...) an explanation of what happens when we "make our tacit knowledge explicit." We can account for our capacity to construct such statements for particular audiences by reference to our non-conceptual capacities to understand others. This approach avoids problematic assumptions about shared representations that are common in cognitive science, and the equally problematic notion that tacit knowledge is sentence-like content that we retrieve when we articulate something based on our tacit knowledge. (shrink)

We extend Smaldino’s approach to collaboration and social organization in cultural evolution to include cognition. By showing how recent work on emergent group-level cognition can be incorporated within Smaldino’s framework, we extend that framework’s scope to encompass collaborative memory, decision-making, and intelligent action. We argue that beneficial effects arise only in certain forms of cognitive interdependence, in surprisingly fragile conditions.

This chapter subsumes David Marr’s levels of analysis account of explanation in cognitive science under the framework of mechanistic explanation: Answering the questions that define each one of Marr’s three levels is tantamount to describing the component parts and operations of mechanisms, as well as their organization, behavior, and environmental context. By explicating these questions and showing how they are answered in several different cognitive science research programs, this chapter resolves some of the ambiguities that remain in Marr’s account, and (...) shows that many different areas and traditions of cognitive scientific research can be unified under the mechanistic framework. (shrink)

Complex systems are to be seen as typically having multiple levels of organization. For instance, in the behavioural and cognitive sciences, there has been a long lasting trend, promoted by the seminal work of David Marr, putting focus on three distinct levels of analysis: the computational level, accounting for the What and Why issues, the algorithmic and the implementational levels specifying the How problem. However, the tremendous developments in neuroscience knowledge about processes at different scales of organization together with the (...) complexity of today cognitive theories suggest that there will hardly be only three levels of explanation. Instead, there will be many different degrees of commitments corresponding to the different granularities—from high-level (behavioural) models to low-level (neural and molecular) models of the cognitive research program. For instance, Bayesian approaches, that are usually advocated for formalizing Marr’s computational level and rational behaviour, have even been adopted to model synaptic plasticity and axon guidance by molecular gradients. As a result, we can consider the behavioural scientist as dealing with models at a multiplicity of levels. The purpose of this Research Topic in Frontiers in Theoretical and Philosophical Psychology is to promote an approach to the role of the levels and explanation and models which is of interest for cognitive scientists, neuroscientists, psychologists, behavioural scientists, and philosophers of science. (shrink)

Is any unified theory of brain function possible? Following a line of thought dat- ing back to the early cybernetics (see, e.g., Cordeschi, 2002), Clark (in press) has proposed the action-oriented Hierarchical Predictive Coding (HPC) as the account to be pursued in the effort of gain- ing the “Grand Unified Theory of the Mind”—or “painting the big picture,” as Edelman (2012) put it. Such line of thought is indeed appealing, but to be effectively pursued it should be confronted with experimental (...) findings and explana- tory capabilities (Edelman, 2012). The point we are making in this note is that a brain with predictive capa- bilities is certainly necessary to endow the agent situated in the environment with forethought or foresight, a crucial issue to outline the unified account advocated by Clark. But the capacity for fore- thought is deeply entangled with the capacity for emotions and when emotions are brought into the game, cogni- tive functions become part of a large-scale functional brain network. However, for such complex networks a consistent view of hierarchical organization in large-scale functional networks has yet to emerge (Bressler and Menon, 2010), whilst heterarchical organization is likely to play a strategic role (Berntson et al., 2012). This raises the necessity of a multilevel approach that embraces causal relations across levels of explanation in either direc- tion (bottom–up or top–down), endorsing mutual calibration of constructs across levels (Berntson et al., 2012). Which, in turn, calls for a revised perspective on Marr’s levels of analysis framework (Marr, 1982). In the following we highlight some drawbacks of Clark’s proposal in address- ing the above issues. (shrink)

Researchers in the cognitive sciences often seek neural correlates of psychological constructs. In this paper, I argue that even when these correlates are discovered, they do not always lead to reductive outcomes. To this end, I examine the psychological construct of a critical period and briefly describe research identifying its neural correlates. Although the critical period is correlated with certain neural mechanisms, this does not imply that there is a reductionist relationship between this psychological construct and its neural correlates. Instead, (...) this case study suggests that there may be many-to-many psychological-neural mappings, not just one-to-one or even one-to-many relations between psychological kinds and types of neural mechanisms. (shrink)

We discuss recent progress in the development of cognitive ontologies and summarize three challenges in the coordinated development and application of these resources. Challenge 1 is to adopt a standardized definition for cognitive processes. We describe three possibilities and recommend one that is consistent with the standard view in cognitive and biomedical sciences. Challenge 2 is harmonization. Gaps and conflicts in representation must be resolved so that these resources can be combined for mark-up and interpretation of multi-modal data. Finally, Challenge (...) 3 is to test the utility of these resources for large-scale annotation of data, search and query, and knowledge discovery and integration. As term definitions are tested and revised, harmonization should enable coordinated updates across ontologies. However, the true test of these definitions will be in their community-wide adoption which will test whether they support valid inferences about psychological and neuroscientific data. (shrink)

Recent mechanistic philosophers :1287–1321, 2016) have argued that the cognitive sciences are not autonomous from neuroscience proper. I clarify two senses of autonomy–metaphysical and epistemic—and argue that cognitive science is still autonomous in both senses. Moreover, mechanistic explanation of cognitive phenomena is not therefore an alternative to the view that cognitive science is autonomous of neuroscience. If anything, it’s a way of characterizing just how cognitive processes are implemented by neural mechanisms.

In fMRI research, the goal of correcting for multiple comparisons is to identify areas of activity that reflect true effects, and thus would be expected to replicate in future studies. Finding an appropriate balance between trying to minimize false positives (Type I error) while not being too stringent and omitting true effects (Type II error) can be challenging. Furthermore, the advantages and disadvantages of these types of errors may differ for different areas of study. In many areas of social neuroscience (...) that involve complex processes and considerable individual differences, such as the study of moral judgment, effects are typically smaller and statistical power weaker, leading to the suggestion that less stringent corrections that allow for more sensitivity may be beneficial, but also result in more false positives. Using moral judgment fMRI data, we evaluated four commonly used methods for multiple comparison correction implemented in SPM12 by examining which method produced the most precise overlap with results from a meta-analysis of relevant studies and with results from nonparametric permutation analyses. We found that voxel-wise thresholding with family-wise error correction based on Random Field Theory provides a more precise overlap (i.e., without omitting too few regions or encompassing too many additional regions) than either clusterwise thresholding, Bonferroni correction, or false discovery rate correction methods. (shrink)

A friend of my sent me the address where this paper has been posted by Adam Frank. Reading it, I realized that more than 90% of the main ideas of this paper (about the mind-brain problem, quantum mechanics (microparticles-wave relationship, Schrodinger equation, probabilities, “perceiving subject in physics”, the idea about consciousness and Nagel, etc. etc.) are UNBELIEVABLE similar to my ideas from my paper 2005 or my book 2008!!!

Although the personal-subpersonal distinction was first proposed in 1969 by D. Dennett, it has been approximately in the last ten years that it has received in­creasing attention and has became a widely used distinction particularly in the philosophy of mind and cognitive psychology literature. While the distinction is ubiquitous there are a few recent proposals about the relationship between the levels, namely, inter alia, the mixed horizontal explanation, the semantic view of computation, and interaction without reduction. In this paper I (...) will first describe the way of understanding the distinction shared by the proposals aforementioned. Second, I will show some of the difficulties facing each proposal. Finally, I will suggest what I consider are the misleading assumptions that are at the root of the proposed strategies of inter-level relations. (shrink)

Harré brilliantly develops and systematizes insights from contemporary social science and analytic philosophy to establish the primacy of socio-political factors and ethical practices in shaping psychological structures and the beliefs people hold about what it means to be oneself. Psychology must acknowledge the primacy of collective practices, beliefs and talk in the construction of the sense of self, but also the possibility of people genuinely individuating themselves. The accomplishment of truly personal being can change the collective practices giving rise to (...) individuating developments in the first place. (shrink)

Notwithstanding many similarities between Thelen et al.'s and Piaget's accounts of the A-not-B error, we argue that, in contrast to Piaget, they do not explicitly address the issue of objectivity. We suggest that this omission is partly due to the fact that Thelen et al. and Piaget's accounts are pitched at different levels of explanation.

Marr’s celebrated contribution to cognitive science (Marr 1982, chap. 1) was the introduction of (at least) three levels of description/explanation. However, most contemporary research has relegated the distinction between levels to a rather dispensable remark. Ignoring such an important contribution comes at a price, or so we shall argue. In the present paper, first we review Marr’s main points and motivations regarding levels of explanation. Second, we examine two cases in which the distinction between levels has been neglected when considering (...) the structure of mental representations: Cummins et al.’s distinction between structural representation and encodings (Cummins in Journal of Philosophy, 93(12):591–614, 1996; Cummins et al. in Journal of Philosophical Research, 30:405–408, 2001) and Fodor’s account of iconic representation (Fodor 2008). These two cases illustrate the kind of problems in which researchers can find themselves if they overlook distinctions between levels and how easily these problems can be solved when levels are carefully examined. The analysis of these cases allows us to conclude that researchers in the cognitive sciences are well advised to avoid risks of confusion by respecting Marr’s old lesson. (shrink)

I claim that consciousness, just as thought or action, is only to be found at the personal level of explanation. Dennett's account is often taken to be at odds with this view, as it is seen as explicating consciousness in terms of sub‐personal processes. Against this reading, and especially as it is developed by John McDowell, I argue that Dennett's work is best understood as maintaining a sharp personal/sub‐personal distinction. To see this, however, we need to understand better what content (...) ascription at the sub‐personal level actually means. When we do we can see how Dennett presents both a philosophical account of consciousness and informed empirical speculation on the nature of its sub‐personal underpinnings. Consciousness is a product of certain capacities that are intelligible only at the personal level, capacities that are neither present at the sub‐personal level of brain mechanism nor present in ‘sub‐persons’, e.g. some, if not all, non‐human animals. (shrink)

Arguments, claims, and discussions about the “level of description” of a theory are ubiquitous in cognitive science. Such talk is typically expressed more precisely in terms of the granularity of the theory, or in terms of Marr’s three levels. I argue that these ways of understanding levels of description are insufficient to capture the range of different types of theoretical commitments that one can have in cognitive science. When we understand these commitments as points in a multi-dimensional space, we find (...) that we must also reconsider our understanding of intertheoretic relations. In particular, we should understand cognitive theories as constraining one another, rather than reducing to one another. (shrink)

Many ideas from Georg Nortoff’s works (published one paper in 2010, mainly his book in 2011, other papers in 2012, 2103, 2014, especially those related to Kant’s philosophy and the notion of the “observer”, the mind-brain problem, default mode network, the self, the mental states and their “correspondence” to the brain) are surprisingly very similar to my ideas published in my article from 2002, 2005 and my book from 2008. In two papers from 2002 (also my paper from 2005 and (...) my book 2008), following Kant’s philosophy, I introduced the notion of the “observer” for the mind-brain problem. After 2010 (mainly his book 2011 and other papers after this book), Nortoff also uses Kant’s philosophy (even if his knowledge about Kant’s philosophy is very superficial!) and the notion of the “observer” for the mind-brain problem in a methodology very similar to my methodology. Moreover, instead of EDWs, Nortoff uses a kind of “transdisciplinary” view, quite close to parallelism – the closest approach to my EDWs! In his works until 2014, Northoff’s conclusion within the unicorn world was different than my conclusion. However, in his book 2014 (two volumes) using notions like “correlations” and even “correspondences” many times, his conclusion is very closed to my EDWs! This dramatic change of framework in 3 years is quite unbelievable!!! Incredible many ideas from this book are very similar to my ideas from 2005 and 2008! (shrink)

Introduction: the neuroscientific turn in political science The observation that brains and political orders are interdependent is almost trivial. Obviously, political orders require brain processes in order to emerge and to remain in place, as these processes enable action and cognition. Conversely, every since Aristotle coined man as “by nature a political animal” (Aristotle, Pol.: 1252a 3; cf. Eth. Nic.: 1097b 11), this also suggests that the political engagements of this animal has likely consequences for its natural development, including the (...) development of its psychological functions. Given these mutual interdependencies, it is remarkable that only since the 1960s, the more general domain of ‘biopolitics’ has attracted attention though first particularly in the form of behavioral politics (Alford and Hibbing). Since then biopolitics has gained in interest, so much so that different subdomains can be identified. Indeed, a 2008 review of the field of biopolitics identified five ‘headings’ of it: “(1) the case for a ‘more biologically oriented political science’, (2) ‘biologically related’ public policy issues, (3) physiological measures of political attitudes and behaviour, (4) the influence of physiological factors on actual political behaviour, and (5) the manner in which our species’ evolutionary history has left homo sapiens genetically endowed with certain social and political behavioural tendencies” (Somit and Peterson 43). Striking is how the relation between biology and politics is taken here in a rather unidirectional way, emphasizing particularly the decisive power of biology upon politics. The reverse relation is not mentioned specifically, reflecting the field of biopolitics, perhaps until quite recently. This absence of studies of political influences on our biology may have to do with the difficulty in investigating such influences. Empirical studies in biopolitics have two foci, broadly speaking: genetics and the brain, both of which have turned out to be complex and dynamic phenomena (Alford and Hibbing). Yet the studies of genetics and brain processes have made much progress in the last few decades, thanks to the development of research instruments - like fMRI brain scanners and TMS brain stimulation instruments - and of computational tools for data analysis and the simulation of explanatory models. For the field of biopolitics it is particularly relevant that within cognitive neuroscience the study of social and political issues has witnessed an increasing interest of researchers even more recently. Indeed, aware of the enabling and mediating role of the brain regarding those issues, a truly ‘neuroscientific turn’ can be observed in the social sciences, testified for example by the emergence of the field of ‘neuropolitics’ (Connolly; Vandervalk). Developing a systematic neuropolitics or biopolitics in general is a difficult challenge because of the wealth of causal influences on and interdependencies between biological, brain, cognitive and socio-political factors. Taking a somewhat more abstract perspective, this paper focuses on the process of emerging complexity in adaptive systems, enabling those to conduct ever more complex processes. Yet, parallel to that development can be observed that such systems, or organisms, are also capable in reducing the complexity of the information they are to process. Once they’re capable of developing and adjusting such compressed and complex representations of information, those systems or organisms can handle more information faster and more efficient and adaptive, yielding important benefits to the organism in navigating its environment (Halford, Wilson and Phillips). Before focusing on the role of narrative as a cognitive strategy for such a reduction of informational complexity, I will discuss the development of stable structures and increasing complexity in dynamic systems. Such a more general perspective prepares our discussion of the structures of both narratives and politics and in doing so contributes to the explanation of their interaction. (shrink)

The distinction between personal level explanations and subpersonal ones has been subject to much debate in philosophy. We understand it as one between explanations that focus on an agent’s interaction with its environment, and explanations that focus on the physical or computational enabling conditions of such an interaction. The distinction, understood this way, is necessary for a complete account of any agent, rational or not, biological or artificial. In particular, we review some recent research in Artificial Life that pretends to (...) do completely without the distinction, while using agent-centred concepts all the way. It is argued that the rejection of agent level explanations in favour of mechanistic ones is due to an unmotivated need to choose among representationalism and eliminativism. The dilemma is a false one if the possibility of a radical form of externalism is considered. (shrink)

My topic is the confluence of two recently active philosophical research programs. One research program concerns the metaphysics of realization. The other research program concerns scientific explanation in terms of mechanisms. In this paper I introduce a distinction between descriptive and explanatory approaches to realization. I then use this distinction to argue that a well-known account of realization, due to Carl Gillett, is incompatible with a well-known account of mechanistic explanation, due to Peter Machamer, Lindley Darden, and Carl Craver (MDC, (...) Philos Sci 57: 1-25, 2000). This is surprising, not least of which because Gillett has cited MDC's work as evidence that his account of realization is the right way to think about realization in the sciences. (shrink)

Consider what the brain-state theorist has to do to make good his claims. He has to specify a physical–chemical state such that any organism (not just a mammal) is in pain if and only if (a) it possesses a brain of suitable physical–chemical structure; and (b) its brain is in that physical–chemical state. This means that the physical–chemical state in question must be a possible state of a mammalian brain, a reptilian brain, a mollusc’s brain (octopuses are mollusca, and certainly (...) feel pain), etc. At the same time, it must not be a possible (physically possible) state of the brain of any physically possible creature that cannot feel pain. Even if such a state can be found, it must be nomologically certain that it will also be a state of the brain of any extraterrestrial life that may be found that will be capable of feeling pain before we can even entertain the supposition that it may be pain. It is not altogether impossible that such a state will be found... . But this is certainly an ambitious hypothesis. (Putnam 1967/1975, p. 436) The belief that mental states are multiply realized is now nearly universal among philosophers, as is the belief that this fact decisively refutes the identity theory. I argue that the empirical support for multiple realization does not justify the confidence that has been placed in it. In order for multiple realization of mental states to be an objection to the identity theory, the neurological differences among pains, for example, must be such as to guarantee that they are of distinct neurological kinds. But the phenomena traditionally cited do not provide evidence of that sort of variation. In particular, examples of neural plasticity do not provide such evidence. (shrink)

Recent extensions of mechanistic explanation into psychology suggest that cognitive models are only explanatory insofar as they map neatly onto, and serve as scaffolding for more detailed neural models. Filling in those neural details is what these accounts take the integration of cognitive psychology and neuroscience to mean, and they take this process to be seamless. Critics of this view have given up on cognitive models possibly explaining mechanistically in the course of arguing for cognitive models having explanatory value independent (...) of how well they align with neural mechanisms. We can have things both ways, however. The problem with seamless integration accounts is their seamlessness, not that they take cognitive models to be mechanistic. A non-componential view of mechanisms allows for cognitive and neural models that cross cut one another, and for cognitive models that don’t decompose into parts. I illustrate the inadequacy of seamless accounts of integration by contrasting how “filter” models of attention in psychology and of sodium channels in neuroscience developed; by questioning whether the mappings generated by neuroimaging subtraction studies achieve integration; and by reinterpreting the evidence for cognitive models of memory having been successfully integrated with neural models. I argue that the integrations we can realistically expect are more partial, patchy, and full of loose threads than the mosaic unity Craver describes. (shrink)