This paper examines conceptual issues that arise in applications of Darwinian natural selection to cultural systems. I argue that many criticisms of cultural selectionist models have been based on an over-detailed reading of the analogy between biological and cultural units of selection. I identify five of the most powerful objections to cultural selection theory and argue that none cuts to its heart. Some objections are based on mistaken assumptions about the simplicity of the mechanisms of biological heredity. Other objections are (...) attributable, rather, to mistaken inferences from observations of biological subject matter to what is essential in natural selection. I argue that such features are idiosyncratic of biological systems, but not essential for natural selection. My arguments throughout are illustrated by examples from biological and cultural evolution, and counter-factual illustrations from the history of theoretical biology. Introduction Cultural Selection Theory First Objection: Lamarckianism Second Objection: Genotype–Phenotype Distinction Third Objection: Common Hereditary Architecture Fourth Objection: Biological Analogue for Cultural Units 6.1 Regarding strict analogues 6.2 Regarding the trait analogue 6.3 Regarding the virus analogue Fifth Objection: Environmental Interaction Conclusion CiteULike Connotea Del.icio.us What's this? (shrink)

Error is protean, ubiquitous and crucial in scientific process. In this paper it is argued that understanding scientific process requires what is currently absent: an adaptable, context-sensitive functional role for error in science that naturally harnesses error identification and avoidance to positive, success-driven, science. This paper develops a new account of scientific process of this sort, error and success driving Self-Directed Anticipative Learning (SDAL) cycling, using a recent re-analysis of ape-language research as test example. The example shows the limitations of (...) other accounts of error, in particular Mayo’s (Error and the growth of experimental knowledge, 1996) error-statistical approach, and SDAL cycling shows how they can be fruitfully contextualised. (shrink)

: The purpose of this paper and its sister paper (Farrell and Hooker, b) is to present, evaluate and elaborate a proposed new model for the process of scientific development: self-directed anticipative learning (SDAL). The vehicle for its evaluation is a new analysis of a well-known historical episode: the development of ape-language research. In this first paper we outline five prominent features of SDAL that will need to be realized in applying SDAL to science: 1) interactive exploration of possibility space; (...) 2) self-directedness; 3) localization of success and error; 4) Synergistic increase in learning capacity; and 5) continuity of SDAL process across scientific change. In this paper we examine the first three features of SDAL in relation to the early history of ape-language research. We show that this history is readily explicated as a self-directed, ever-finer, delineation of possibility space that enables the localization of both success and error. Paper II examines the last two features against this history. (shrink)

In 1895 sociologist and philosopher Georg Simmel published a paper: ‘On a connection of selection theory to epistemology’. It was focussed on the question of how behavioural success and the evolution of the cognitive capacities that underlie it are to be related to knowing and truth. Subsequently, Simmel’s ideas were largely lost, but recently an English translation was published by Coleman in this journal. While Coleman’s contextual remarks are solely concerned with a preceding evolutionary epistemology, it will be argued here (...) that Simmel pursues a more unorthodox, more radically biologically based and pragmatist, approach to epistemology in which the presumption of a wholly interests-independent truth is abandoned, concepts are accepted as species-specific and truth tied intimately to practical success. Moreover, Simmel’s position, shorn of one too-radical commitment, shares its key commitments with the recently developed interactivist–constructivist framework for understanding biological cognition and naturalistic epistemology. There Simmel’s position can be given a natural, integrated, three-fold elaboration in interactivist re-analysis, unified evolutionary epistemology and learnable normativity. (shrink)

This paper outlines an original interactivist-constructivist approach to modelling intelligence and learning as a dynamical embodied form of adaptiveness and explores some applications of I-C to understanding the way cognitive learning is realized in the brain. Two key ideas for conceptualizing intelligence within this framework are developed. These are: intelligence is centrally concerned with the capacity for coherent, context-sensitive, self-directed management of interaction; and the primary model for cognitive learning is anticipative skill construction. Self-directedness is a capacity for integrative process (...) modulation which allows a system to "steer" itself through its world by anticipatively matching its own viability requirements to interaction with its environment. Because the adaptive interaction processes required of intelligent systems are too complex for effective action to be prespecified learning is an important component of intelligence. A model of self-directed anticipative learning is formulated based on interactive skill construction, and argued to constitute a central constructivist process involved in cognitive development. SDAL illuminates the capacity of intelligent learners to start with the vague, poorly defined problems typically posed in realistic learning situations and progressively refine them, transforming them into problems with sufficient structure to guide the construction of a solution. Finally, some of the implications of I-C for modelling of the neuronal basis of intelligence and learning are explored; in particular, Quartz and Sejnowski's recent neural constructivism paradigm, enriched by Montague and Sejnowski's dopaminergic model of anticipative-predictive neural learning, is assessed as a promising, but incomplete, contribution to this approach. The paper concludes with a fourfold reflection on the divergence in cognitive modelling philosophy between the I-C and the traditional computational information processing approaches. (shrink)