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EN
In this paper, I question the notion that tool-use must be driven by an internal representation which specifies the “motor program” enacted in the behaviour of the tool-user. Rather, it makes more sense to define tool-use in terms of characteristics of the dynamics of this behaviour. As the behaviour needs to be adjusted to suit changes in context, so there is unlikely to be a one-to-one, linear mapping between an action and its effect. Thus, tool-use can best be described using concepts from Nonlinear Dynamics. Such an approach can be used to create a sort of cybernetic model of tool-use. However, there is a danger that such a model can either lead us back to internal representations (in that the comparator used to evaluate feedback during behaviour could be assumed to be pre-defined) or could fail to capture cognitive aspects of behaviour. In particular, the question of how the craftworker’s intent can be enacted in the use of tools to produce a specific object seems to be lost in the cybernetic account. My solution is two-fold. First, the “model” is created on-the-fly and adapted through moment-by-moment interactions in the system of tool-user–tool–material–environment. This means that, rather than assuming a pre-defined internal representation that drives behaviour, I propose that cognition involves the selection of salient parameters that characterize the behaviour and the continued monitoring and management of behaviour in terms of these parameters. Second, intent is only loosely defined a priori but crystallizes through the continued interactions between craftworker and object through a process in which the affordances of the object become apparent to, and responded to by, the craftworker.
Przegląd Statystyczny
|
2012
|
vol. 59
|
issue 4
369-385
PL
Naturalnym uogólnieniem współczynnika korelacji liniowej Pearsona jest współczynnik informacji wzajemnej. Współczynnik ten umożliwia pomiar zależności różnego typu, również o charakterze nieliniowym. Podobnie jak współczynnik korelacji liniowej Pearsona, może być zastosowany do pojedynczego szeregu czasowego w celu identyfikacji autozależności. W niniejszej pracy badaniu poddano logarytmiczne stopy zmian wybranych polskich i zagranicznych indeksów giełdowych. Porównując rezultaty otrzymane dla oryginalnych szeregów stóp zmian i dla reszt z dopasowanych modeli ARMA-GARCH, określono charakter wykrytych zależności. Ponadto, wykorzystując procedurę bootstrap, zweryfikowano istotność współczynników informacji wzajemnej, co umożliwiło zidentyfikowanie opóźnień czasowych w analizowanych szeregach. W większości z przebadanych indeksów wykryto zależności nieliniowe innego typu niż GARCH a dominującym poziomem opóźnień czasowych w tych szeregach okazał się lag=1.
EN
The mutual information coefficient is one of the most important generalizations of the Pearson correlation coefficient. Its advantage is that it is able to measure all kinds of dependencies, also nonlinear ones. Like the Pearson correlation coefficient, the mutual information coefficient may be applied to a single time series in order to identify serial dependencies. In this paper the log-returns of the selected Polish and foreign stock indices have been analyzed. By comparing the results obtained for the raw returns and the residuals of the fitted ARMA-GARCH models, the nature of the identified dependencies has been determined. Moreover, the bootstrap procedure has been applied to verify significance of the mutual information coefficients and, in consequence, to determine the number of lags in the analyzed series. In the most investigated indices, nonlinear dependencies different from the ARCH effect have been detected and lag=1 was the dominant time delay in such situations.
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