Biological realism (Revonsuo, 2001, 2006) states that dreaming is a biological phenomenon and therefore explainable in naturalistic terms, similar to the explanation of other biological phenomena. In the biological sciences, the structure of explanations can be described with the help of a framework called 'multilevel explanation'. The multilevel model provides a context that assists to clarify what needs to be explained and how, and how to place different theories into the same model. Here, I will argue that the multilevel framework (...) would be useful when we try to construct scientific explanations of dreaming. (shrink)

“Altered State of Consciousness” (ASC) has been defined as a changed overall pattern of conscious experience, or as the subjective feeling and explicit recognition that one's own subjective experience has changed. We argue that these traditional definitions fail to draw a clear line between altered and normal states of consciousness (NSC). We outline a new definition of ASC and argue that the proper way to understand the concept of ASC is to regard it as a representational notion: the alteration that (...) has happened is not an alteration of consciousness (or subjective experience) per se, but an alteration in the informational or representational relationships between consciousness and the world. An altered state of consciousness is defined as a state in which the neurocognitive background mechanisms of consciousness have an increased tendency to produce misrepresentations such as hallucinations, delusions, and memory distortions. Paradigm examples of such generally misrepresentational, temporary, and reversible states are dreaming, psychotic episodes, psychedelic drug experiences, some epileptic seizures, and hypnosis in highly hypnotizable subjects. The representational definition of ASC should be applied in the theoretical and empirical studies of ASCs to unify and clarify the conceptual basis of ASC research. (shrink)

This article addresses the following problems: What is a mechanism, how can it be discovered, and what is the role of the knowledge of mechanisms in scientific explanation and technological control? The proposed answers are these. A mechanism is one of the processes in a concrete system that makes it what it is — for example, metabolism in cells, interneuronal connections in brains, work in factories and offices, research in laboratories, and litigation in courts of law. Because mechanisms are largely (...) or totally imperceptible, they must be conjectured. Once hypothesized they help explain, because a deep scientific explanation is an answer to a question of the form, "How does it work, that is, what makes it tick—what are its mechanisms?" Thus, by contrast with the subsumption of particulars under a generalization, an explanation proper consists in unveiling some lawful mechanism, as when political stability is explained by either coercion, public opinion manipulation, or democratic participation. Finding mechanisms satisfies not only the yearning for understanding, but also the need for control. Key Words: explanation • function • mechanism • process • system • systemism. (shrink)

I propose a cautionary assessment of the recent debate concerning the impact of the dynamical approach on philosophical accounts of scientific explanation in the cognitive sciences and, particularly, the cognitive neurosciences. I criticize the dominant mechanistic philosophy of explanation, pointing out a number of its negative consequences: In particular, that it doesn’t do justice to the field’s diversity and stage of development, and that it fosters misguided interpretations of dynamical models’ contribution. In order to support these arguments, I analyze a (...) case study in computational neuroethology and show why it should not be understood through a mechanistic lens; I specially focus on Zednik’s mechanistic interpretation of the case study. In addition, I argue for a greater appreciation of the relation between explanation and other epistemic goals in the field, as well as an increased sensitivity towards the associated contextual factors. (shrink)

Given that visualisations via medical imaging have tremendously increased over the last decades, the overall presence of colour-coded brain slices generated on the basis of functional imaging, i.e. neuroimaging techniques, have led to the assumption of so-called kinds of brains or cognitive profiles that might be especially related to non-healthy humans affected by neurological, neuropsychological or psychiatric syndromes or disorders. In clinical contexts especially, one must consider that visualisations through medical imaging are suggestive in a twofold way. Imaging data not (...) only visually render pathological entities, but also tend to represent objective and concrete evidence for these psychophysical states in question. This article aims to identify key issues in visually rendering psychiatric disorders via functional approaches of imaging within the neurosciences from an epistemological point of view. (shrink)

According to some philosophers, computational explanation is proprietary
to psychology—it does not belong in neuroscience. But neuroscientists routinely offer computational explanations of cognitive phenomena. In fact, computational explanation was initially imported from computability theory into the science of mind by neuroscientists, who justified this move on neurophysiological grounds. Establishing the legitimacy and importance of computational explanation in neuroscience is one thing; shedding light on it is another. I raise some philosophical questions pertaining to computational explanation and outline some promising answers that (...) are being developed by a number of authors. (shrink)

Mechanisms are now taken widely in philosophy of science to provide one of modern science’s basic explanatory devices. This has raised lively debate concerning the relationship between mechanisms, laws and explanation. This paper focuses on cases where a mechanism gives rise to a ceteris paribus law, addressing two inter-related questions: What kind of explanation is involved? and What is going on in the world when mechanism M affords behavior B described in a ceteris paribus law? We explore various answers offered (...) by ‘new mechanists’ and others before setting out and explaining our own answers: mechanistic explanations are a species of oldfashioned covering-law explanation and this often accounts in part for their explanatory power; and B is what it takes for some set of principles that govern the features of M’s parts in their arrangement in M all to be instanced together. (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)