The roots of scientific reasoning: infancy, modularity and the art of tracking--论文代写范文精选

它不需要强调有惊人的科学知识,以及相关的技术能力,在过去五个世纪。但这在多大程度上依赖外在文化经济因素,以及在多大程度上内在认知，毫无疑问发生了变化,起到了重要的作用。下面的paper代写范文讲述了这一问题。

Abstract
This chapter examines the extent to which there are continuities between the cognitive processes and epistemic practices engaged in by human huntergatherers, on the one hand, and those which are distinctive of science, on the other. It deploys anthropological evidence against any form of no-continuity view, drawing especially on the cognitive skills involved in the art of tracking. It also argues against the child-as-scientist accounts put forward by some developmental psychologists, which imply that scientific thinking is present in early infancy and universal amongst humans who have sufficient time and resources to devote to it. In contrast, a modularist kind of continuity account is proposed, according to which the innately channelled architecture of human cognition provides all the materials necessary for basic forms of scientific reasoning in older children and adults, needing only the appropriate sorts of external support, social context, and background beliefs and skills in order for science to begin its advance.

Introduction
It needs no emphasis that there has been a staggering and explosive increase in scientific knowledge, together with associated technological ability, over the last five centuries. But to what extent has this depended upon extrinsic cultural economic factors, and to what extent upon intrinsic cognitive ones? Undoubtedly changes of both kinds have taken place, and have played a significant role. The invention of the printing press, and the existence of a class of moneyed gentlemen with time to devote to systematic scholarship and scientific enquiry were surely important; as were new inferential practices both mathematical, and those distinctive of the experimental method. And without doubt changes of both kinds have continued to be important, too had it not been for the development of new scientific instruments, and without the economic growth necessary for significant resources to be committed to scientific research, we would certainly not be in the epistemic position we are in today; but the development of statistical methods of reasoning, for example, have also been crucially significant.

The four options
On one view, the innate basis of the mind is mostly domain-general in nature, having to do with general capacities for learning and/or reasoning, though perhaps containing some initial domain-specific information and/or attention-biases (Elman et al., 1996; Gopnik and Melzoff, 1997). On a contrasting view, much of the innate structure of the mind is domain-specific, embodying information about evolutionarily-significant domains, and/or containing learning-principles specific to particular domains (Barkow et al., 1992; Pinker, 1997).
The domain-general account of the innate basis of cognition is one or another version of the general-purpose computer model of the mind. In some versions (e.g. Dennett, 1991, 1995) what is given are a suite of massively parallel and distributed processors which nevertheless have the power to support a serial, language-involving, digital processor running on linguistic structures. (Dennett dubs this the Joycean machine after the stream-of-consciousness writing of James Joyces Ulysses.) This latter system is almost entirely programmed by enculturated language-use, acquiring both its contents and general patterns of processing through the acquisition of both information and habits of thought from other people, via linguistic communication and language-based instruction and imitation. On this view, the basic cognitive differences between ourselves and hunter gatherers will be very large; and there will be a great deal of cognitive linguistic programming required before the human mind becomes capable of anything remotely resembling scientific reasoning.
Quite a different sort of domain-general view is entailed by theorising theory accounts of the nature of human development through infancy and childhood (e.g. Gopnik and Melzoff, 1997; Gopnik and Glymour, this volume).[2] On this view, all human children are already little scientists, in advance of any exposure to scientific cultures  gathering data, framing hypotheses, and altering their theories in the light of recalcitrant data in essentially the same sort of way that scientists do. So on this view, the cognitive continuities between scientific and pre-scientific cultures will be very great, and almost all the emphasis in an explanation of the rise of science over the last five hundred years will have to be on extrinsic factors. On Gopnik and Melzoffs account, most adult humans (including hunter gatherers) are scientists who have ceased to exercise their capacity for science, largely through lack of time and attention. But this account can at the same time emphasise the need for extrinsic support for scientific cognition (particularly that provided by written language, especially after the invention of the printing press) once theories achieve a certain level of complexity in relation to the data.
Domain-specific, more-or-less modular, views of cognition also admit of a similar divide between no-continuity and continuity accounts of science. On one approach, the modular structure of our cognition which those of us in scientific societies share with hunter gatherers is by no means sufficient to underpin science, even when supported by the appropriate extrinsic factors. Rather, that structure needs to be heavily supplemented by culturally-developed and culturally-transmitted beliefs and reasoning practices. In effect, this account can share with Dennett the view that a great deal of the organisation inherent in the scientific mind is culturally acquired differing only in the amount of innate underlying modular structure which is postulated, and in its answer to the question whether intra-modular processing is connectionist in nature (as Dennett, 1991, seems to believe), or whether it rather involves classical transformations of sentence-like structures (as most modularists and evolutionary psychologists think: see, e.g., Fodor, 1983, 2000; Tooby and Cosmides, 1992; Pinker, 1997).

Scientific reasoning
On one view, the goal of science is to discover the causal laws which govern the natural world; and the essential activity of scientists consists in the postulation and testing of theories, and then applying those theories to the phenomena in question (Nagel, 1961; Hempel, 1966). On a contrasting view, science constructs and elaborates a set of models of a range of phenomena in the natural world, and then attempts the develop and apply those models with increasing accuracy (Cartright, 1983; Giere, 1992). But either way science generates principles which are nomic, in the sense of characterising how things have to happen, and in supporting subjunctives and counterfactuals about what would happen, or would not have happened, if certain other things were to happen, or hadnt happened.
Crucial to the activity of science, then, is the provision of theories and/or models to explain the events, processes, and regularities observed in nature. Often these explanations are couched in terms of underlying mechanisms which have not been observed and may be difficult to observe; and sometimes they are given in terms of mechanisms which are unobservable. More generally, a scientific explanation will usually postulate entities and/or properties which are not manifest in the data being explained, and which may be unfamiliar  where perhaps the only reason for believing in those things is that if they did exist, then they would explain what needs explaining.
Science also employs a set of tacit principles for choosing between competing theories or models that is, for making an inference to the best explanation of the data to be explained. The most plausible way of picturing this, is that contained within the principles employed for good explanation are enough constraints to allow one to rank more than one explanation in terms of goodness. While no one any longer thinks that it is possible to codify these principles, it is generally agreed that the good-making features of a theory include such features as; accuracy (predicting all or most of the data to be explained, and explaining away the rest); simplicity (being expressible as economically as possible, with the fewest commitments to distinct kinds of fact and process); consistency (internal to the theory or model); coherence (with surrounding beliefs and theories, meshing together with those surroundings, or at least being consistent with them); fruitfulness (making new predictions and suggesting new lines of enquiry); and explanatory scope (unifying together a diverse range of data).(paper代写)

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