The Brain and the Meaning of Life draws on research in philosophy, psychology, and neuroscience to answer some of the most pressing questions about life's nature and value. Paul Thagard argues that evidence requires the abandonment of many traditional ideas about the soul, free will, and immortality, and shows how brain science matters for fundamental issues about reality, morality, and the meaning of life. The ongoing Brain Revolution reveals how love, work, and play provide good reasons for living.
Defending the superiority of evidence-based reasoning over religious faith and philosophical thought experiments, Thagard argues that minds are brains and that reality is what science can discover. Brains come to know reality through a combination of perception and reasoning. Just as important, our brains evaluate aspects of reality through emotions that can produce both good and bad decisions.
Our cognitive and emotional abilities allow us to understand reality, decide effectively, act morally, and pursue the vital needs of love, work, and play. Wisdom consists of knowing what matters, why it matters, and how to achieve it. The Brain and the Meaning of Life shows how brain science helps to answer questions about the nature of mind and reality, while alleviating anxiety about the difficulty of life in a vast universe.
The book integrates decades of multidisciplinary research, but its clear explanations and humor make it accessible to the general reader. Paul Thagard is professor of philosophy and director of the cognitive science program at the University of Waterloo, Canada. Home The Brain and the Meaning of Life. Add to Cart. Just as important, our brains evaluate aspects of reality through emotions that can produce both good and bad decisions. Our cognitive and emotional abilities allow us to understand reality, decide effectively, act morally, and pursue the vital needs of love, work, and play.
Wisdom consists of knowing what matters, why it matters, and how to achieve it.
The Brain and the Meaning of Life shows how brain science helps to answer questions about the nature of mind and reality, while alleviating anxiety about the difficulty of life in a vast universe. The book integrates decades of multidisciplinary research, but its clear explanations and humor make it accessible to the general reader. Paul Thagard is professor of philosophy and director of the cognitive science program at the University of Waterloo, Canada.
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Add to Cart. More about this book. One of Choice's Outstanding Academic Titles for Paul Thagard's Home Page. Chapter 1 [PDF]. Are mind and brain one or two?
They are not poised to be complete and exclusive accounts of cognition. They are not theories in the sense of providing complete predictions or explanations of phenomena in question. For this, they are too abstract. There is no particular novel prediction that the embodied perspective may offer when applied, for example, to group decision making in a faculty meeting. In this, these perspectives are not different from grand research traditions of cognitive science. Wide approaches are research traditions, and research traditions are not to be conflated with complete theories. As such, however, computationalism does not offer any predictions for group decision making either.
What they do instead is provide certain guiding heuristics. A proponent of traditional computational modeling would ask what the overall task is and why solving it is appropriate; what the algorithms and representations involved are; and how they are physically implemented. The embodied cognition paradigm asks how this task is related to bodily, in particular to sensorimotor, features of decision makers.
The embedded approach points out that there may be important environmental factors, and the extended mind may alert us to the fact that there might be important cognitive artifacts in operation. Finally, the enactive perspective at least in its non-classical version points to participatory negotiation of how the activity is perceived by various agents involved, and the distributed perspective hints that the phenomenon may involve not only human agents but also external representations and instruments.
Note that even if the phenomenon is only slightly influenced by bodily features, for example, it need not mean that embodied cognition is thereby falsified. Heuristics are fallible, after all.
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This kind of explanatory practice goes beyond mere binary oppositions that state that the role of the environment is crucial or not, or that the environment has been appropriated by the cognitive system as its proper part. Instead, these researchers treat—or at least should treat—cognitive systems as organized spatiotemporal systems, comprised of entities and activities that are jointly responsible for their phenomena of interest, which is evidenced by attempts to integrate, for example, embodied and extended approaches Clark, b ; Borghi and Cimatti, ; Borghi et al.
Differences between approaches matter only for expository purposes but not really for their practice, which involves mechanistic modeling of cognition. As proponents of so-called new mechanism stress, many fields of science already appeal to mechanisms to explain their phenomena of interest Machamer et al. While the philosophical analyses of the notion of mechanism differ Glennan and Illari, ; for a review, see Illari and Williamson, , in a nutshell, the idea is the following: A mechanism is an organized spatiotemporal structure responsible for the occurrence of at least one phenomenon to be explained.
In a certain sense, the new mechanistic account is extremely lean, leaving out practically all physical details of what mechanisms as such might be. They are only causally organized spatiotemporal structures. According to the mechanistic account of explanation, there are no mechanisms per se , only mechanisms of some phenomena; mechanisms should not be confused with organized spatiotemporal systems, processes or structures.
Mechanisms are wholes comprised of component parts and operations but they are delineated by the phenomena they are responsible for, and phenomena are not to be conflated with observable features of a given spatiotemporal system; to understand the nature of a phenomenon, extensive theoretical considerations may be required Bogen and Woodward, Obviously, it is physically impossible to observe anyone producing literally an infinite series of utterances because people are mortal. But there are theoretical reasons to specify the phenomenon of linguistic productivity in such an idealizing fashion.
Mechanistic explanations that elucidate how a given phenomenon occurs by referring to component parts and operations of a mechanism are called constitutive explanations. Explanatory texts of this kind cite the internal causal and part-whole organization of the mechanism as responsible for the phenomenon at hand. For example, to explain the phenomenon of how one cuts a piece of rope with scissors, we can describe the scissors as composed of two metal blades with handles connected so that the sharpened edges slide against each other when handles are closed.
In other words, there are components blades with handles, a screw that joints the blades and operations bringing together the blades organized in such a way that cutting occurs. Given the importance of the study of components and operations, it is not surprising that mechanistic explanations are guided by two important heuristics: localization and decomposition Bechtel and Richardson, By localizing where operations happen and breaking down the whole system into its component parts, researchers discover the internal structure of the mechanism.
Importantly, a larger mechanism may comprise a number of individual mechanisms organized together; while the circulatory system is a mechanism responsible for blood circulation, its component mechanism, the heart, is also a mechanism, which is a proper part of the circulatory system. The goal of mechanistic modeling is to be able to conceptually recompose the mechanism from its component parts and operations.
Recomposing is only possible when the explanatory text is complete, that is, when it satisfies the completeness norm. This is not to say that mechanistic explanations are supposed to give every possible detail about the mechanism; no, only explanatorily relevant detail counts Baetu, ; Craver and Kaplan, Localization and decomposition are merely heuristics; they may fail without making mechanistic explanation impossible.
Fully decomposable systems are an extreme case, in which the sum is nothing more than its parts. As Bechtel and Richardson stress, it is much more likely that biological systems are near decomposable Bechtel and Richardson, , p. The notion of near decomposability was introduced by Herbert A. Simon, who stressed that the behavior of near decomposable component systems in the short run is approximately independent from the behaviors of other systems, and in the long run, it depends only in an aggregate way on the behavior of the other components Simon, This is because mechanisms may include a highly complex organization of their internal component parts and operations.
Such mechanisms, however, may be much more difficult to study and could require the use of specific mathematical techniques developed for research on complex systems. In particular, the dynamic approach to cognition may stress systemic interactions in cognitive systems, but they need not exclude the possibility of providing dynamical and mechanical explanations.
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As many have argued Kaplan and Bechtel, ; Zednik, ; Kaplan, ; Lyre, , some phenomena require the use of mathematical methods typical of dynamical systems to build complete mechanistic causal explanations. Part-whole relationships and the causal structure of the mechanism are usually not sufficient to wholly explain the phenomenon at hand: the explanation must include crucial environmental modulation. Only in some very special circumstances, where the whole dynamics of the phenomenon depends merely on the underlying mechanism, can one ignore the environmental modulation as Craver a does.
In the case of scissors, one has to include fingers that are required to move the blades if one is to give a complete explanation of cutting. However, fingers are not parts of scissors because they are not constitutively relevant to cutting.
follow link As Carl Craver has argued, a good criterion of what counts as a component x of a mechanism S is its constitutive relevance in the operation of S for example, cutting a piece of rope is an operation of scissors. A component x of S is constitutively relevant for S if and only if there is a relationship of mutual manipulability between x and S :.
Craver, b , p. In other words, mechanisms are not conceived as exclusively responsible for their phenomena. Mechanistic explanatory strategies have been nicely summarized by Bechtel in his metaphor of looking down, around and up:. Accounts of mechanistic explanation have emphasized the importance of looking down—decomposing a mechanism into its parts and operations. When looking down is combined with looking around and up, mechanistic research results in an integrated, multi-level perspective Bechtel, , p.
In fact, the accounts of explanation defended by new mechanists are already wide.