REMARKS ON EVOLUTION AND TIME-SCALES, Graham Cairns-Smith; HODGSON'S BLACK BOX, Thomas Clark; DO HODGSON'S PROPOSITIONS UNIQUELY CHARACTERIZE FREE WILL?, Ravi Gomatam; WHAT SHOULD WE RETAIN FROM A PLAIN PERSON'S CONCEPT OF FREE WILL?, Gilberto Gomes; ISOLATING DISPARATE CHALLENGES TO HODGSON'S ACCOUNT OF FREE WILL, Liberty Jaswal; FREE AGENCY AND LAWS OF NATURE, Robert Kane; SCIENCE VERSUS REALIZATION OF VALUE, NOT DETERMINISM VERSUS CHOICE, Nicholas Maxwell; COMMENTS ON HODGSON, J.J.C. Smart; THE VIEW FROM WITHIN, Sean Spence; COMMENTARY ON HODGSON, Henry (...) Stapp. (shrink)

The paper discusses Dr. Floris Tomasini's paper “What Is Bioethics: Notes toward a New Approach?”. Based on Tomasini's account of methodological and ethical pluralism, the paper explores the demarcation problem of bioethics and suggests a full methodological laissez-faire.

© 2017. The American Astronomical Society. All rights reserved.We present new observations of the low-mass companion to HD 984 taken with the Gemini Planet Imager as a part of the GPI Exoplanet Survey campaign. Images of HD 984 B were obtained in the J and H bands. Combined with archival epochs from 2012 and 2014, we fit the first orbit to the companion to find an 18 au orbit with a 68% confidence interval between 14 and 28 au, an eccentricity (...) of 0.18 with a 68% confidence interval between 0.05 and 0.47, and an inclination of 119°with a 68% confidence interval between 114°and 125°. To address the considerable spectral covariance in both spectra, we present a method of splitting the spectra into low and high frequencies to analyze the spectral structure at different spatial frequencies with the proper spectral noise correlation. Using the split spectra, we compare them to known spectral types using field brown dwarf and low-mass star spectra and find a best-fit match of a field gravity M6.5 ±1.5 spectral type with a corresponding temperature of K. Photometry of the companion yields a luminosity of log=2.88 ± 0.07 dex with DUSTY models. Mass estimates, again from DUSTY models, find an age-dependent mass of 34 ±1 to 95 ±4 M Jup. These results are consistent with previous measurements of the object. (shrink)

Part I: The Life of Cognitive Science:. William Bechtel, Adele Abrahamsen, and George Graham. Part II: Areas of Study in Cognitive Science:. 1. Analogy: Dedre Gentner. 2. Animal Cognition: Herbert L. Roitblat. 3. Attention: A.H.C. Van Der Heijden. 4. Brain Mapping: Jennifer Mundale. 5. Cognitive Anthropology: Charles W. Nuckolls. 6. Cognitive and Linguistic Development: Adele Abrahamsen. 7. Conceptual Change: Nancy J. Nersessian. 8. Conceptual Organization: Douglas Medin and Sandra R. Waxman. 9. Consciousness: Owen Flanagan. 10. Decision Making: J. Frank (...) Yates and Paul A. Estin. 11. Emotions: Paul E. Griffiths. 12. Imagery and Spatial Representation: Rita E. Anderson. 13. Language Evolution and Neuromechanisms: Terrence W. Deacon. 14. Language Processing: Kathryn Bock and Susan M. Garnsey. 15. Linguistics Theory: D. Terence Langendoen. 16. Machine Learning: Paul Thagard. 17. Memory: Henry L. Roediger III and Lyn M. Goff. 18. Perception: Cees Van Leeuwen. 19. Perception: Color: Austen Clark. 20. Problem Solving: Kevin Dunbar. 21. Reasoning: Lance J. Rips. 22. Social Cognition: Alan J. Lambert and Alison L. Chasteen. 23. Unconscious Intelligence: Rhianon Allen and Arthur S. Reber. 24. Understanding Texts: Art Graesser and Pam Tipping. 25. Word Meaning: Barbara C. Malt. Part III: Methodologies of Cognitive Science:. 26. Artificial Intelligence: Ron Sun. 27. Behavioral Experimentation: Alexander Pollatsek and Keith Rayner. 28. Cognitive Ethology: Marc Bekoff. 29. Deficits and Pathologies: Christopher D. Frith. 30. Ethnomethodology: Barry Saferstein. 31. Functional Analysis: Brian Macwhinney. 32. Neuroimaging: Randy L. Buckner and Steven E. Petersen. 33. Protocal Analysis: K. Anders Ericsson. 34. Single Neuron Electrophysiology: B. E. Stein, M.T. Wallace, and T.R. Stanford. 35. Structural Analysis: Robert Frank. Part IV: Stances in Cognitive Science:. 36. Case-based Reasoning: David B. Leake. 37. Cognitive Linguistics: Michael Tomasello. 38. Connectionism, Artificial Life, and Dynamical Systems: Jeffrey L. Elman. 39. Embodied, Situated, and Distributed Cognition: Andy Clark. 40. Mediated Action: James V. Wertsch. 41. Neurobiological Modeling: P. Read Montague and Peter Dayan. 42. Production Systems: Christian D. Schunn and David Klahr. Part V: Controversies in Cognitive Science:. 43. The Binding Problem: Valerie Gray Hardcastle. 44. Heuristics and Satisficing: Robert C. Richardson. 45. Innate Knowledge: Barbara Landau. 46. Innateness and Emergentism: Elizabeth Bates, Jeffrey L. Elman, Mark H. Johnson, Annette Karmiloff-Smith, Domenico Parisi, and Kim Plunkett. 47. Intentionality: Gilbert Harman. 48. Levels of Explanation and Cognition Architectures: Robert N. McCauley. 49. Modularity: Irene Appelbaum. 50. Representation and Computation: Robert S. Stufflebeam. 51. Representations: Dorrit Billman. 52. Rules: Terence Horgan and John Tienson. 53. Stage Theories Refuted: Donald G. Mackay. Part VI: Cognitive Science in the Real World:. 54. Education: John T. Bruer. 55. Ethics: Mark L. Johnson. 56. Everyday Life Environments: Alex Kirlik. 57. Institutions and Economics: Douglass C. North. 58. Legal Reasoning: Edwina L. Rissland. 59. Mental Retardation: Norman W. Bray, Kevin D. Reilly, Lisa F. Huffman, Lisa A. Grupe, Mark F. Villa, Kathryn L. Fletcher, and Vivek Anumolu. 60. Science: William F. Brewer and Punyashloke Mishra. Selective Biographies of Major Contributors to Cognitive Science: William Bechtel and Tadeusz Zawidzki. (shrink)

Both Carnap and Quine made significant contributions to the philosophy of mathematics despite their diversedviews. Carnap endorsed the dichotomy between analytic and synthetic knowledge and classified certainmathematical questions as internal questions appealing to logic and convention. On the contrary, Quine wasopposed to the analytic-synthetic distinction and promoted a holistic view of scientific inquiry. The purpose of thispaper is to argue that in light of the recent advancement of experimental mathematics such as Monte Carlosimulations, limiting mathematical inquiry to the domain of (...) logic is unjustified. Robustness studies implemented inMonte Carlo Studies demonstrate that mathematics is on par with other experimental-based sciences. (shrink)

It is traditionally believed that cerebral activation (the presence of low voltage fast electrical activity in the neocortex and rhythmical slow activity in the hippocampus) is correlated with arousal, while deactivation (the presence of large amplitude irregular slow waves or spindles in both the neocortex and the hippocampus) is correlated with sleep or coma. However, since there are many exceptions, these generalizations have only limited validity. Activated patterns occur in normal sleep (active or paradoxical sleep) and during states of anesthesia (...) and coma. Deactivated patterns occur, at times, during normal waking, or during behavior in awake animals treated with atropinic drugs. Also, the fact that patterns characteristic of sleep, arousal, and waking behavior continue in decorticate animals indicates that reticulo-cortical mechanisms are not essential for these aspects of behavior. (shrink)

Many of the features that distinguish the vertebrates from other chordates are derived from the neural crest, and it has long been argued that the emergence of this multipotent embryonic population was a key innovation underpinning vertebrate evolution. More recently, however, a number of studies have suggested that the evolution of the neural crest was less sudden than previously believed. This has exposed the fact that neural crest, as evidenced by its repertoire of derivative cell types, has evolved through vertebrate (...) evolution. In this light, attempts to derive a typological definition of neural crest, in terms of molecular signatures or networks, are unfounded. We propose a less restrictive, embryological definition of this cell type that facilitates, rather than precludes, investigating the evolution of neural crest. While the evolutionary origin of neural crest has attracted much attention, its subsequent evolution has received almost no attention and yet it is more readily open to experimental investigation and has greater relevance to understanding vertebrate evolution. Finally, we provide a brief outline of how the evolutionary emergence of neural crest potentiality may have proceeded, and how it may be investigated. BioEssays 30:530–541, 2008. © 2008 Wiley Periodicals, Inc. (shrink)