Cultural Evolution as Distributed Computation

The speed and transformative power of human cultural evolution is evident from the change it has wrought on our planet. This chapter proposes a human computation program aimed at (1) distinguishing algorithmic from non-algorithmic components of cultural evolution, (2) computationally modeling the algorithmic components, and amassing human solutions to the non-algorithmic (generally, creative) components, and (3) combining them to develop human-machine hybrids with previously unforeseen computational power that can be used to solve real problems. Drawing on recent insights into the origins of evolutionary processes from biology and complexity theory, human minds are modeled as self-organizing, interacting, autopoietic networks that evolve through a Lamarckian (non-Darwinian) process of communal exchange. Existing computational models as well as directions for future research are discussed.Comment: 13 pages Gabora, L. (2013). Cultural evolution as distributed human computation. In P. Michelucci (Ed.) Handbook of Human Computation. Berlin: Springe

Low-mass and sub-stellar eclipsing binaries in stellar clusters

We highlight the importance of eclipsing double-line binaries in our understanding on star formation and evolution. We review the recent discoveries of low-mass and sub-stellar eclipsing binaries belonging to star-forming regions, open clusters, and globular clusters identified by ground-based surveys and space missions with high-resolution spectroscopic follow-up. These discoveries provide benchmark systems with known distances, metallicities, and ages to calibrate masses and radii predicted by state-of-the-art evolutionary models to a few percent. We report their density and discuss current limitations on the accuracy of the physical parameters. We discuss future opportunities and highlight future guidelines to fill gaps in age and metallicity to improve further our knowledge of low-mass stars and brown dwarfs.Comment: 30 pages, 5 figures, no table. Review pape

Compositionality and Concepts

In this chapter I aim to explain how psychology understands concepts, and why there is a need for semantic theory to take on the challenge of psychological data. All of the contributors to this volume are (presumably) in the business of trying to understand and explain how language has meaning, and the primary source of evidence for this has to be our intuitions of what things mean. Furthermore, if my semantic intuitions (as a theorist) are out of kilter with those of the common language user, then it is my theory which should be called into question and not the lay intuition. This chapter describes a range of results from my research program over the last 30 years, some old and some new, with the aim of giving a general account of using Prototype Theory as a way to explain semantic intuitions