The Evolutionary Genetics of Canalization
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Modularity As a Concept Modern Ideas About Mental Modularity Typically Use Fodor (1983) As a Key Touchstone
COGNITIVE PROCESSING International Quarterly of Cognitive Science Mental modularity, metaphors, and the marriage of evolutionary and cognitive sciences GARY L. BRASE University of Missouri – Columbia Abstract - As evolutionary approaches in the behavioral sciences become increasingly prominent, issues arising from the proposition that the mind is a collection of modular adaptations (the multi-modular mind thesis) become even more pressing. One purpose of this paper is to help clarify some valid issues raised by this thesis and clarify why other issues are not as critical. An aspect of the cognitive sciences that appears to both promote and impair progress on this issue (in different ways) is the use of metaphors for understand- ing the mind. Utilizing different metaphors can yield different perspectives and advancement in our understanding of the nature of the human mind. A second purpose of this paper is to outline the kindred natures of cognitive science and evolutionary psychology, both of which cut across traditional academic divisions and engage in functional analyses of problems. Key words: Evolutionary Theory, Cognitive Science, Modularity, Metaphors Evolutionary approaches in the behavioral sciences have begun to influence a wide range of fields, from cognitive neuroscience (e.g., Gazzaniga, 1998), to clini- cal psychology (e.g., Baron-Cohen, 1997; McGuire and Troisi, 1998), to literary theory (e.g., Carroll, 1999). At the same time, however, there are ongoing debates about the details of what exactly an evolutionary approach – often called evolu- tionary psychology— entails (Holcomb, 2001). Some of these debates are based on confusions of terminology, implicit arguments, or misunderstandings – things that can in principle be resolved by clarifying current ideas. -
Transformations of Lamarckism Vienna Series in Theoretical Biology Gerd B
Transformations of Lamarckism Vienna Series in Theoretical Biology Gerd B. M ü ller, G ü nter P. Wagner, and Werner Callebaut, editors The Evolution of Cognition , edited by Cecilia Heyes and Ludwig Huber, 2000 Origination of Organismal Form: Beyond the Gene in Development and Evolutionary Biology , edited by Gerd B. M ü ller and Stuart A. Newman, 2003 Environment, Development, and Evolution: Toward a Synthesis , edited by Brian K. Hall, Roy D. Pearson, and Gerd B. M ü ller, 2004 Evolution of Communication Systems: A Comparative Approach , edited by D. Kimbrough Oller and Ulrike Griebel, 2004 Modularity: Understanding the Development and Evolution of Natural Complex Systems , edited by Werner Callebaut and Diego Rasskin-Gutman, 2005 Compositional Evolution: The Impact of Sex, Symbiosis, and Modularity on the Gradualist Framework of Evolution , by Richard A. Watson, 2006 Biological Emergences: Evolution by Natural Experiment , by Robert G. B. Reid, 2007 Modeling Biology: Structure, Behaviors, Evolution , edited by Manfred D. Laubichler and Gerd B. M ü ller, 2007 Evolution of Communicative Flexibility: Complexity, Creativity, and Adaptability in Human and Animal Communication , edited by Kimbrough D. Oller and Ulrike Griebel, 2008 Functions in Biological and Artifi cial Worlds: Comparative Philosophical Perspectives , edited by Ulrich Krohs and Peter Kroes, 2009 Cognitive Biology: Evolutionary and Developmental Perspectives on Mind, Brain, and Behavior , edited by Luca Tommasi, Mary A. Peterson, and Lynn Nadel, 2009 Innovation in Cultural Systems: Contributions from Evolutionary Anthropology , edited by Michael J. O ’ Brien and Stephen J. Shennan, 2010 The Major Transitions in Evolution Revisited , edited by Brett Calcott and Kim Sterelny, 2011 Transformations of Lamarckism: From Subtle Fluids to Molecular Biology , edited by Snait B. -
Program Nr: 1 from the 2004 ASHG Annual Meeting Mutations in A
Program Nr: 1 from the 2004 ASHG Annual Meeting Mutations in a novel member of the chromodomain gene family cause CHARGE syndrome. L.E.L.M. Vissers1, C.M.A. van Ravenswaaij1, R. Admiraal2, J.A. Hurst3, B.B.A. de Vries1, I.M. Janssen1, W.A. van der Vliet1, E.H.L.P.G. Huys1, P.J. de Jong4, B.C.J. Hamel1, E.F.P.M. Schoenmakers1, H.G. Brunner1, A. Geurts van Kessel1, J.A. Veltman1. 1) Dept Human Genetics, UMC Nijmegen, Nijmegen, Netherlands; 2) Dept Otorhinolaryngology, UMC Nijmegen, Nijmegen, Netherlands; 3) Dept Clinical Genetics, The Churchill Hospital, Oxford, United Kingdom; 4) Children's Hospital Oakland Research Institute, BACPAC Resources, Oakland, CA. CHARGE association denotes the non-random occurrence of ocular coloboma, heart defects, choanal atresia, retarded growth and development, genital hypoplasia, ear anomalies and deafness (OMIM #214800). Almost all patients with CHARGE association are sporadic and its cause was unknown. We and others hypothesized that CHARGE association is due to a genomic microdeletion or to a mutation in a gene affecting early embryonic development. In this study array- based comparative genomic hybridization (array CGH) was used to screen patients with CHARGE association for submicroscopic DNA copy number alterations. De novo overlapping microdeletions in 8q12 were identified in two patients on a genome-wide 1 Mb resolution BAC array. A 2.3 Mb region of deletion overlap was defined using a tiling resolution chromosome 8 microarray. Sequence analysis of genes residing within this critical region revealed mutations in the CHD7 gene in 10 of the 17 CHARGE patients without microdeletions, including 7 heterozygous stop-codon mutations. -
The Control of Shoot Branching: an Example of Plant Information Processing
Plant, Cell and Environment (2009) 32, 694–703 doi: 10.1111/j.1365-3040.2009.01930.x The control of shoot branching: an example of plant information processing OTTOLINE LEYSER Department of Biology, Area 11, University of York, York YO10 5YW, UK ABSTRACT the apical–basal axis defined by the establishment of the shoot apical meristem at one end, and the root apical mer- Throughout their life cycle, plants adjust their body plan istem at the other. Post-embryonically, the meristems give to suit the environmental conditions in which they are rise to the entire shoot and root systems, respectively. Fur- growing. A good example of this is in the regulation of thermore, the tissues they establish can produce secondary shoot branching. Axillary meristems laid down in each leaf meristems, which if activated can produce entirely new formed from the primary shoot apical meristem can remain axes of growth with the same developmental potential as dormant, or activate to produce a branch. The decision the primary root or shoot from which they were derived. whether to activate an axillary meristem involves the assess- Thus, the body plan of a plant is determined continuously ment of a wide range of external environmental, internal throughout its life cycle, allowing it to be exquisitely envi- physiological and developmental factors. Much of this infor- ronmentally responsive. Plants can alter their body plan to mation is conveyed to the axillary meristem via a network suit their environment, and thus the environmentally regu- of interacting hormonal signals that can integrate inputs lated development of new growth axes is functionally from diverse sources, combining multiple local signals to equivalent to environmentally regulated animal behav- generate a rich source of systemically transmitted informa- iours. -
Evolving Neural Networks That Are Both Modular and Regular: Hyperneat Plus the Connection Cost Technique Joost Huizinga, Jean-Baptiste Mouret, Jeff Clune
Evolving Neural Networks That Are Both Modular and Regular: HyperNeat Plus the Connection Cost Technique Joost Huizinga, Jean-Baptiste Mouret, Jeff Clune To cite this version: Joost Huizinga, Jean-Baptiste Mouret, Jeff Clune. Evolving Neural Networks That Are Both Modular and Regular: HyperNeat Plus the Connection Cost Technique. GECCO ’14: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, ACM, 2014, Vancouver, Canada. pp.697-704. hal-01300699 HAL Id: hal-01300699 https://hal.archives-ouvertes.fr/hal-01300699 Submitted on 11 Apr 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. To appear in: Proceedings of the Genetic and Evolutionary Computation Conference. 2014 Evolving Neural Networks That Are Both Modular and Regular: HyperNeat Plus the Connection Cost Technique Joost Huizinga Jean-Baptiste Mouret Jeff Clune Evolving AI Lab ISIR, Université Pierre et Evolving AI Lab Department of Computer Marie Curie-Paris 6 Department of Computer Science CNRS UMR 7222 Science University of Wyoming Paris, France University of Wyoming [email protected] [email protected] [email protected] ABSTRACT 1. INTRODUCTION One of humanity’s grand scientific challenges is to create ar- An open and ambitious question in the field of evolution- tificially intelligent robots that rival natural animals in intel- ary robotics is how to produce robots that possess the intel- ligence and agility. -
High Heritability of Telomere Length, but Low Evolvability, and No Significant
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.16.423128; this version posted December 16, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1 High heritability of telomere length, but low evolvability, and no significant 2 heritability of telomere shortening in wild jackdaws 3 4 Christina Bauch1*, Jelle J. Boonekamp1,2, Peter Korsten3, Ellis Mulder1, Simon Verhulst1 5 6 Affiliations: 7 1 Groningen Institute for Evolutionary Life Sciences, University of Groningen, Nijenborgh 7, 8 9747AG Groningen, The Netherlands 9 2 present address: Institute of Biodiversity Animal Health & Comparative Medicine, College 10 of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QQ, United 11 Kingdom 12 3 Department of Animal Behaviour, Bielefeld University, Morgenbreede 45, 33615 Bielefeld, 13 Germany 14 15 16 * Correspondence to: 17 [email protected] 18 19 Running head: High heritability of telomere length bioRxiv preprint doi: https://doi.org/10.1101/2020.12.16.423128; this version posted December 16, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 20 Abstract 21 Telomere length (TL) and shortening rate predict survival in many organisms. Evolutionary 22 dynamics of TL in response to survival selection depend on the presence of genetic variation 23 that selection can act upon. However, the amount of standing genetic variation is poorly known 24 for both TL and TL shortening rate, and has not been studied for both traits in combination in 25 a wild vertebrate. -
12 Evolvability of Real Functions
i i i i Evolvability of Real Functions PAUL VALIANT , Brown University We formulate a notion of evolvability for functions with domain and range that are real-valued vectors, a compelling way of expressing many natural biological processes. We show that linear and fixed-degree polynomial functions are evolvable in the following dually-robust sense: There is a single evolution algorithm that, for all convex loss functions, converges for all distributions. It is possible that such dually-robust results can be achieved by simpler and more-natural evolution algorithms. Towards this end, we introduce a simple and natural algorithm that we call wide-scale random noise and prove a corresponding result for the L2 metric. We conjecture that the algorithm works for a more general class of metrics. 12 Categories and Subject Descriptors: F.1.1 [Computation by Abstract Devices]: Models of Computa- tion—Self-modifying machines; G.1.6 [Numerical Analysis]: Optimization—Convex programming; I.2.6 [Artificial Intelligence]: Learning—Concept learning;J.3[Computer Applications]: Life and Medical Sciences—Biology and genetics General Terms: Algorithms, Theory Additional Key Words and Phrases: Evolvability, optimization ACM Reference Format: Valiant, P. 2014. Evolvability of real functions. ACM Trans. Comput. Theory 6, 3, Article 12 (July 2014), 19 pages. DOI:http://dx.doi.org/10.1145/2633598 1. INTRODUCTION Since Darwin first assembled the empirical facts that pointed to evolution as the source of the hugely diverse and yet finely honed mechanisms of life, there has been the mys- tery of how the biochemical mechanisms of evolution (indeed, how any mechanism) can apparently consistently succeed at finding innovative solutions to the unexpected problems of life. -
The Impact of Component Modularity on Design Evolution: Evidence from the Software Industry
08-038 The Impact of Component Modularity on Design Evolution: Evidence from the Software Industry Alan MacCormack John Rusnak Carliss Baldwin Copyright © 2007 by Alan MacCormack, John Rusnak, and Carliss Baldwin. Working papers are in draft form. This working paper is distributed for purposes of comment and discussion only. It may not be reproduced without permission of the copyright holder. Copies of working papers are available from the author. Abstract Much academic work asserts a relationship between the design of a complex system and the manner in which this system evolves over time. In particular, designs which are modular in nature are argued to be more “evolvable,” in that these designs facilitate making future adaptations, the nature of which do not have to be specified in advance. In essence, modularity creates “option value” with respect to new and improved designs, which is particularly important when a system must meet uncertain future demands. Despite the conceptual appeal of this research, empirical work exploring the relationship between modularity and evolution has had limited success. Three major challenges persist: first, it is difficult to measure modularity in a robust and repeatable fashion; second, modularity is a property of individual components, not systems as a whole, hence we must examine these dynamics at the microstructure level; and third, evolution is a temporal phenomenon, in that the conditions at time t affect the nature of the design at time t+1, hence exploring this phenomenon requires longitudinal data. In this paper, we tackle these challenges by analyzing the evolution of a successful commercial software product over its entire lifetime, comprising six major “releases.” In particular, we develop measures of modularity at the component level, and use these to predict patterns of evolution between successive versions of the design. -
Task-Dependent Evolution of Modularity in Neural Networks1
Task-dependent evolution of modularity in neural networks1 Michael Husk¨ en, Christian Igel, and Marc Toussaint Institut fur¨ Neuroinformatik, Ruhr-Universit¨at Bochum, 44780 Bochum, Germany Telephone: +49 234 32 25558, Fax: +49 234 32 14209 fhuesken,igel,[email protected] Connection Science Vol 14, No 3, 2002, p. 219-229 Abstract. There exist many ideas and assumptions about the development and meaning of modu- larity in biological and technical neural systems. We empirically study the evolution of connectionist models in the context of modular problems. For this purpose, we define quantitative measures for the degree of modularity and monitor them during evolutionary processes under different constraints. It turns out that the modularity of the problem is reflected by the architecture of adapted systems, although learning can counterbalance some imperfection of the architecture. The demand for fast learning systems increases the selective pressure towards modularity. 1 Introduction The performance of biological as well as technical neural systems crucially depends on their ar- chitectures. In case of a feed-forward neural network (NN), architecture may be defined as the underlying graph constituting the number of neurons and the way these neurons are connected. Particularly one property of architectures, modularity, has raised a lot of interest among researchers dealing with biological and technical aspects of neural computation. It appears to be obvious to emphasise modularity in neural systems because the vertebrate brain is highly modular both in an anatomical and in a functional sense. It is important to stress that there are different concepts of modules. `When a neuroscientist uses the word \module", s/he is usually referring to the fact that brains are structured, with cells, columns, layers, and regions which divide up the labour of information processing in various ways' 1This paper is a revised and extended version of the GECCO 2001 Late-Breaking Paper by Husk¨ en, Igel, & Toussaint (2001). -
The Genetic Architecture of Economic and Political Preferences
1 The Genetic Architecture of Economic and Political Preferences Daniel J. Benjamin1*, David Cesarini2, Matthijs J.H.M. van der Loos3, Christopher T. Dawes4, Philipp D. Koellinger3, Patrik K.E. Magnusson5, Christopher F. Chabris6, Dalton Conley7, David Laibson8, Magnus Johannesson9, and Peter M. Visscher10 1Department of Economics, Cornell University, 480 Uris Hall, Ithaca, NY 14853, USA. 2Center for Experimental Social Science, Department of Economics, New York University, 19 W. 4th Street, New York, NY 10012, USA. 3 Department of Applied Economics, Erasmus Universiteit Rotterdam, Post Box 1738, 3000 DR Rotterdam, Netherlands. 4 Department of Politics, New York University, 19 W. 4th Street, New York, NY 10012, USA. 5 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, SE- 171 77 Stockholm, Sweden. 6 Department of Psychology, Union College, 807 Union Street, Schenectady, NY 12308, USA. 7 Department of Sociology, New York University, Department of Sociology, New York University, 295 Lafayette Street, 4th floor, New York, NY 10012, USA. 8 Department of Economics, Harvard University, Littauer Center, 1805 Cambridge Street, Cambridge, MA 02138, USA. 9 Department of Economics, Stockholm School of Economics, Box 6501, SE-113 83 Stockholm, Sweden. 10 University of Queensland Diamantina Institute, Level 4, R Wing, Princess Alexandra Hospital, Ipswich Road, Woolloongabba 4102, Australia. Classification: Social Sciences; Economic Sciences; Social Sciences; Political Sciences; Biological Sciences; Genetics. Manuscript information: 12 text pages, 1 Figure, 2 Tables. *To whom correspondence should be addressed: phone: +1 607 255 2355. E-mail: [email protected]. 2 Abstract: Preferences are fundamental constructs in all models of economic and political behavior and important precursors to many lifetime outcomes. -
The University of Chicago Epistasis, Contingency, And
THE UNIVERSITY OF CHICAGO EPISTASIS, CONTINGENCY, AND EVOLVABILITY IN THE SEQUENCE SPACE OF ANCIENT PROTEINS A DISSERTATION SUBMITTED TO THE FACULTY OF THE DIVISION OF THE BIOLOGICAL SCIENCES AND THE PRITZKER SCHOOL OF MEDICINE IN CANDIDACY FOR THE DEGREE OF DOCTOR OF PHILOSOPHY GRADUATE PROGRAM IN BIOCHEMISTRY AND MOLECULAR BIOPHYSICS BY TYLER NELSON STARR CHICAGO, ILLINOIS AUGUST 2018 Table of Contents List of Figures .................................................................................................................... iv List of Tables ..................................................................................................................... vi Acknowledgements ........................................................................................................... vii Abstract .............................................................................................................................. ix Chapter 1 Introduction ......................................................................................................1 1.1 Sequence space and protein evolution .............................................................1 1.2 Deep mutational scanning ................................................................................2 1.3 Epistasis ...........................................................................................................3 1.4 Chance and determinism ..................................................................................4 1.5 Evolvability ......................................................................................................6 -
Genetic Correlations Reveal the Shared Genetic Architecture of Transcription in Human Peripheral Blood
Hemani, G. (2017). Genetic correlations reveal the shared genetic architecture of transcription in human peripheral blood. Nature Communications, 8, [483]. https://doi.org/10.1038/s41467-017-00473- z Publisher's PDF, also known as Version of record License (if available): CC BY Link to published version (if available): 10.1038/s41467-017-00473-z Link to publication record in Explore Bristol Research PDF-document This is the final published version of the article (version of record). It first appeared online via Nature Publishing Group at https://www.nature.com/articles/s41467-017-00473-z . Please refer to any applicable terms of use of the publisher. University of Bristol - Explore Bristol Research General rights This document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/red/research-policy/pure/user-guides/ebr-terms/ ARTICLE DOI: 10.1038/s41467-017-00473-z OPEN Genetic correlations reveal the shared genetic architecture of transcription in human peripheral blood Samuel W. Lukowski 1, Luke R. Lloyd-Jones1,2, Alexander Holloway2, Holger Kirsten3,4, Gibran Hemani 5,6, Jian Yang 1,2, Kerrin Small 7, Jing Zhao8, Andres Metspalu9, Emmanouil T. Dermitzakis10, Greg Gibson 8, Timothy D. Spector7, Joachim Thiery4,11, Markus Scholz 3,4, Grant W. Montgomery1,12, Tonu Esko 9, Peter M. Visscher 1,2 & Joseph E. Powell1,2 Transcript co-expression is regulated by a combination of shared genetic and environmental factors. Here, we estimate the proportion of co-expression that is due to shared genetic variance.