2022 UPDATE: The approach of this monograph has been updated and developed further in Appendix II, "Epistemological Intelligence," of the author’s 2021 book _Critique of Impure Reason: Horizons of Possibility and Meaning_. The book is available both in a printed edition (under ISBN 978-0-578-88646-6 from Barnes & Noble, Amazon, and other booksellers) and an Open Access eBook edition (available through Philpapers under the book’s title and other philosophy online archives). ●●●●● -/- The monograph’s twofold purpose is to recognize epistemological intelligence (...) as a distinguishable variety of human intelligence, one that is especially important to philosophers, and to understand the challenges posed by the psychological profile of philosophers that can impede the development and cultivation of the skills associated with epistemological intelligence. (shrink)

According to the phenomenal intentionality research program, a state’s intentional content is fixed by its phenomenal character. Defenders of this view have little to say about just how this grounding is accomplished. I argue that without a robust account of representation, the research program promises too little. Unfortunately, most of the well-developed accounts of representation – asymmetric dependence, teleosemantics, and the like – ground representation in external relations such as causation. Such accounts are inconsistent with the core of the phenomenal (...) intentionality program. I argue that, however counter-intuitive it may seem, the best prospect for explaining how phenomenal character represents appeals to resemblance. (shrink)

For more than fifty years, taxonomists have proposed numerous alternative definitions of species while they searched for a unique, comprehensive, and persuasive definition. This monograph shows that these efforts have been unnecessary, and indeed have provably been a pursuit of a will o’ the wisp because they have failed to recognize the theoretical impossibility of what they seek to accomplish. A clear and rigorous understanding of the logic underlying species definition leads both to a recognition of the inescapable ambiguity that (...) affects the definition of species, and to a framework-relative approach to species definition that is logically compelling, i.e., cannot not be accepted without inconsistency. An appendix reflects upon the conclusions reached, applying them in an intellectually whimsical taxonomic thought experiment that conjectures the possibility of an emerging new human species. (shrink)

Probability ratio and likelihood ratio measures of inductive support and related notions have appeared as theoretical tools for probabilistic approaches in the philosophy of science, the psychology of reasoning, and artificial intelligence. In an effort of conceptual clarification, several authors have pursued axiomatic foundations for these two families of measures. Such results have been criticized, however, as relying on unduly demanding or poorly motivated mathematical assumptions. We provide two novel theorems showing that probability ratio and likelihood ratio measures can be (...) axiomatized in a way that overcomes these difficulties. (shrink)

The claim that similarity plays a role in representation has been philosophically discredited. Psychologists, however, routinely analyse the success of mental representations for guiding behaviour in terms of a similarity between representation and the world. I provide a foundation for this practice by developing a philosophically responsible account of the relationship between similarity and representation in natural systems. I analyse similarity in terms of the existence of a suitable homomorphism between two structures. The key insight is that by restricting attention (...) to only those homomorphisms induced by causal processes, we can solve two philosophical problems with a single assumption. First, causal structure provides an adequate source for the bias required to ensure the similarity relation is non-trivial; second, it provides an adequate source for the directionality required to move from similarity to representation. I defend this account against objections by Goodman and van Fraassen and demonstrate that it is indeed the account of similarity’s role in representation assumed by psychological practice. (shrink)

Quantum systems have a holistic structure, which implies that they cannot be divided into parts. In order tocreate (sub)objects like individual substances, molecules, nuclei, etc., in a universal whole, the Einstein-Podolsky-Rosen correlations between all the subentities, e.g. all the molecules in a substance, must be suppressed by perceptual and mental processes.Here the particular problems ofGestalt (shape)perception are compared with the attempts toattribute a shape to a quantum mechanical system like a molecule. Gestalt perception and quantum mechanics turn out (on an (...) informal level) to show similar features and problems: holistic aspects, creation of objects, dressing procedures, influence of the observer, classical quantities and structures. The attribute classical of a property or structure means thatholistic correlations to any other quantity do not exist or that these correlations are considered as irrelevant and therefore eliminated (either deliberately and by declaration or in a mental process that is not under rational control). An example of animposed classical structure is the nuclear frame of a molecule. Candidates for classical properties that arenot imposed by the observer could be the charge of a particle or the handedness of a molecule. It is argued here that at least part of a molecule's shape can begenerated automatically by the environment. A molecular shape of this sort arises in addition to Lamb shift-type energy corrections. (shrink)

Computational theories of vision typically rely on the analysis of two aspects of human visual function: (1) object and shape recognition (2) co-calibration of sensory measurements. Both these approaches are usually based on an inverse-optics model, where visual perception is viewed as a process of inference from a 2D retinal projection to a 3D percept within a Euclidean space schema. This paradigm has had great success in certain areas of vision science, but has been relatively less successful in understanding perceptual (...) representation, namely, the nature of the perceptual encoding. One of the drawbacks of inverse-optics approaches has been the difficulty in defining the constraints needed to make the inference computationally tractable (e.g. regularity assumptions, Bayesian priors, etc.). These constraints, thought to be learned assumptions about the nature of the physical and optical structures of the external world, have to be incorporated into any workable computational model in the inverse-optics paradigm. But inference models that employ an inverse optics plus structural assumptions approach inevitably result in a na. (shrink)

The received theories of epistemology identify abductive inferences with the cognitive patterns of speculation (hypothesis formation) and insist that they cannot verify or confirm hypotheses. I criticize various descriptions of abduction, offer a structural analysis of abductive inference,, characterize abduction without alluding to its putative role in inquiry, and then demonstrate that some abductions do provide evidence and that not all scientific hypotheses derive from abductive inferences. This result challenges those notions of scientific k knowledge that dismiss some central scientific (...) ideas (for example, evolution) as ‘meta-physical research programs’ or ‘just theories’ when they are instead well substantiated by abductive evidence. (shrink)

In present-day physics the fundamental dynamical laws are taken as a time-translation-invariant and time-reversal-invariant one-parameter groups of automorphisms of the underlying mathematical structure. In this context-independent and empirically inaccessible description there is no past, present or future, hence no distinction between cause and effect. To get the familiar description in terms of causes and effects, the time-reversal symmetry of the fundamental dynamics has to be broken. Thereby one gets two representations, one satisfying the generally accepted rules of retarded causality (“no (...) effect can precede its cause”). The other one describes the strange rules of advanced causality. For entangled (but not necessarily interacting) quantum systems the arrow of time must have the same direction for all subsystems. But for classical systems, or for classical subsystems of quantum systems, this argument does not hold. As a cosequence, classical systems allow the conceptual possibility of advanced causality in addition to retarded causality. Every mathematically formulated dynamics of statistically reproducible events can be extended to a description in terms of a one-parameter group of automorphisms of an enlarged mathematical structure which describes a fictitious hidden determinism. Consequently, randomness in the sense of mathematical probability theory is only a weak generalization of determinism. The popular ideas that in quantum theory there are gaps in the causal chain which allow the accommodation of the freedom of human action are fantasies which have no basis in present-day quantum mechanics. Quantum events are governed by strict statistical laws. Freedom of action is a constitutive necessity of all experimental science which requires a violation of the statistical predictions of physics. We conclude that the presently adopted first principles of theoretical physics can neither explain the autonomy of the psyche nor account for the freedom of action necessary for experimental science. (shrink)

The no-free-lunch theorems promote a skeptical conclusion that all possible machine learning algorithms equally lack justification. But how could this leave room for a learning theory, that shows that some algorithms are better than others? Drawing parallels to the philosophy of induction, we point out that the no-free-lunch results presuppose a conception of learning algorithms as purely data-driven. On this conception, every algorithm must have an inherent inductive bias, that wants justification. We argue that many standard learning algorithms should rather (...) be understood as model-dependent: in each application they also require for input a model, representing a bias. Generic algorithms themselves, they can be given a model-relative justification. (shrink)

Measuring processes of a single spin-1/2 object and of a pair of spin-1/2 objects in the EPR-Bohm state are modeled by systems of differential equations. The latter model is a local model with hidden variables of the EPR-Bohm gedanken experiment. Although there is no dynamical interaction between the pair of spin-1/2 objects, the model reproduces approximately the quantum-mechanical correlations by coincidence counting. Hence the Bell inequality is violated. This result supports the idea that the coincidence counting is the source of (...) the apparent nonlocality in the EPR-Bohm gedanken experiment. (shrink)