I would first like to welcome Anderson's target article. Extant cognitive neuroscience and neuroimaging studies, as well as the growing importance of biological analyses in cognitive science, increasingly show the unsuitability of a modular approach to cognition. In this situation, a new framework is required to model the functional architecture of cognitive processes in the nervous system. Anderson's article is a remarkable effort in this direction. I agree with his general approach to the issue. My main concern, though, is twofold. On the one hand, Anderson assumes certain nomological regularities in reuse phenomena that are simply conjectures supported by shaky evidence. On the other hand, a biological theory of reuse by itself is inadequate for the task of evaluating particular models of cognition, such as concept empiricism or conceptual metaphor. We need an independent characterization of cognitive phenomena, a model that we currently lack.
First, extracting biological regularities from evolutionary phenomena is not a straightforward issue. Elsewhere (Vilarroya Reference Vilarroya2001), I have suggested that cognitive systems are constrained by what I called “bounded functionality,” which accounts for the dynamics of the functional paths leading to solutions to adaptive problems. One of the bounded functionality constraints is what I call the “bricoleur constraint,” defined as the fact that natural selection favors the shortest design path. In other words, the solutions to adaptive problems have to take into account the resources that were available to the system before the adaptive problem appeared. The bricoleur constraint is the evolutionary characterization of the reuse approach. However, the bricoleur constraint can be realized in many ways for any evolutionary phenomenon. For instance, Anderson's principle, that “older areas, having been available for reuse for longer, are ceteris paribus more likely to have been integrated into later-developing functions” (sect. 1.1, para. 1), can be a good starting point, but it cannot be taken as an evolutionary law. Evolutionary biology is full of surprises; older areas can serve a small range of functions at the same time that an intermediately incorporated area which proved more useful in later functions results in more pervasive implications. Evolutionary tinkering is, in itself, not susceptible to lawlike regularities (see, e.g., Jacob Reference Jacob1977). Additionally, the evidence by which Anderson tries to sanction the abovementioned principle is based on the hypothesis that “the older the areas, the more back in the brain they are” (see sect. 1.1, para. 3), which is, to say the least, highly contentious. The foundation of his entire argument is therefore a shaky one.
Second, in order to address the evaluation of particular models of cognition, we require, apart from reuse theory, a characterization of the cognitive processes the nervous system actually carries out; and the jury is still out on nearly all the available hypotheses. Indeed, Anderson examines cognitive models while taking for granted some functional attributions, for example, of fMRI studies, to form the basis of his argumentation, but such characterizations are under discussion precisely in part because of reuse theories. For example, in section 4.4, Anderson uses neuroimaging studies to argue against conceptual metaphor. However, the functional interpretation of such studies (e.g., “finger representation”) are prone to criticism, as is any other neuroimaging study, precisely on account of reuse theories, and therefore cannot be used as arguments against conceptual metaphor or any other hypotheses. Neuroimaging studies are task-oriented, and the interpretations are reverse-engineering biased. Previously (Vilarroya Reference Vilarroya2001), I addressed the issue of “functional mesh,” that is, the assumed tight fit between a cognitive trait's design and the adaptive problem it is supposed to solve. It is now widely assumed, even by Anderson, that the “optimality move” that creeps in behind functional mesh is misplaced – namely, that cognitive mechanisms need not be specially designed to solve the adaptive problems for which they were selected. Even if Anderson seems to agree with such an approach, my impression is that he eventually falls into the functional mesh trap, by assuming the functions of certain areas.
I have also defended (Vilarroya Reference Vilarroya2002) a dual reverse-engineering and biological analysis to characterize cognitive functioning. However, biological analyses in cognitive science are of a particular type. Usually, biological explanations are teleonomic explanations that first identify the trait that is likely to be under selection, and then identify the adaptive problem that the trait is supposed to solve. Yet, certain aspects of cognitive science force a change in this methodology. In trying to explain the cognitive mechanisms of a biological organism, the researcher can identify the adaptive problem that the brain is supposed to solve, but in reality it is difficult to identify the actual trait itself, because the trait is not as self-evident as, say, an eye, a liver, or a wing. Moreover, the explanatory strategy of cognitive science cannot simply be an inversion of the first steps of the teleonomic explanation. It is not enough to identify the adaptive problem and then infer the mechanism. Rather, we need to complement an initial assumption about a trait's design with a characterization of how the adaptation might have appeared over evolutionary time – first characterizing the adaptive problem that the organism is supposed to solve, then the fitness-maximization process, as well as showing that the trait is specialized for solving the adaptive problem, unlikely to have arisen by chance alone, and not better explained as the byproduct of mechanisms designed to solve some alternative adaptive problem.
In summary, functional attribution in cognitive science is not a straightforward operation, but rather, requires an independent characterization from the functional mesh assumption; reuse theory alone cannot provide this type of tool. Hence, in my opinion, Anderson lacks the basis to apply his functional characterizations as arguments against specific models of cognition. Once we have the necessary tools to account for functional characterization in cognitive science, of course, reuse theory will prove extremely useful.
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I would first like to welcome Anderson's target article. Extant cognitive neuroscience and neuroimaging studies, as well as the growing importance of biological analyses in cognitive science, increasingly show the unsuitability of a modular approach to cognition. In this situation, a new framework is required to model the functional architecture of cognitive processes in the nervous system. Anderson's article is a remarkable effort in this direction. I agree with his general approach to the issue. My main concern, though, is twofold. On the one hand, Anderson assumes certain nomological regularities in reuse phenomena that are simply conjectures supported by shaky evidence. On the other hand, a biological theory of reuse by itself is inadequate for the task of evaluating particular models of cognition, such as concept empiricism or conceptual metaphor. We need an independent characterization of cognitive phenomena, a model that we currently lack.
First, extracting biological regularities from evolutionary phenomena is not a straightforward issue. Elsewhere (Vilarroya Reference Vilarroya2001), I have suggested that cognitive systems are constrained by what I called “bounded functionality,” which accounts for the dynamics of the functional paths leading to solutions to adaptive problems. One of the bounded functionality constraints is what I call the “bricoleur constraint,” defined as the fact that natural selection favors the shortest design path. In other words, the solutions to adaptive problems have to take into account the resources that were available to the system before the adaptive problem appeared. The bricoleur constraint is the evolutionary characterization of the reuse approach. However, the bricoleur constraint can be realized in many ways for any evolutionary phenomenon. For instance, Anderson's principle, that “older areas, having been available for reuse for longer, are ceteris paribus more likely to have been integrated into later-developing functions” (sect. 1.1, para. 1), can be a good starting point, but it cannot be taken as an evolutionary law. Evolutionary biology is full of surprises; older areas can serve a small range of functions at the same time that an intermediately incorporated area which proved more useful in later functions results in more pervasive implications. Evolutionary tinkering is, in itself, not susceptible to lawlike regularities (see, e.g., Jacob Reference Jacob1977). Additionally, the evidence by which Anderson tries to sanction the abovementioned principle is based on the hypothesis that “the older the areas, the more back in the brain they are” (see sect. 1.1, para. 3), which is, to say the least, highly contentious. The foundation of his entire argument is therefore a shaky one.
Second, in order to address the evaluation of particular models of cognition, we require, apart from reuse theory, a characterization of the cognitive processes the nervous system actually carries out; and the jury is still out on nearly all the available hypotheses. Indeed, Anderson examines cognitive models while taking for granted some functional attributions, for example, of fMRI studies, to form the basis of his argumentation, but such characterizations are under discussion precisely in part because of reuse theories. For example, in section 4.4, Anderson uses neuroimaging studies to argue against conceptual metaphor. However, the functional interpretation of such studies (e.g., “finger representation”) are prone to criticism, as is any other neuroimaging study, precisely on account of reuse theories, and therefore cannot be used as arguments against conceptual metaphor or any other hypotheses. Neuroimaging studies are task-oriented, and the interpretations are reverse-engineering biased. Previously (Vilarroya Reference Vilarroya2001), I addressed the issue of “functional mesh,” that is, the assumed tight fit between a cognitive trait's design and the adaptive problem it is supposed to solve. It is now widely assumed, even by Anderson, that the “optimality move” that creeps in behind functional mesh is misplaced – namely, that cognitive mechanisms need not be specially designed to solve the adaptive problems for which they were selected. Even if Anderson seems to agree with such an approach, my impression is that he eventually falls into the functional mesh trap, by assuming the functions of certain areas.
I have also defended (Vilarroya Reference Vilarroya2002) a dual reverse-engineering and biological analysis to characterize cognitive functioning. However, biological analyses in cognitive science are of a particular type. Usually, biological explanations are teleonomic explanations that first identify the trait that is likely to be under selection, and then identify the adaptive problem that the trait is supposed to solve. Yet, certain aspects of cognitive science force a change in this methodology. In trying to explain the cognitive mechanisms of a biological organism, the researcher can identify the adaptive problem that the brain is supposed to solve, but in reality it is difficult to identify the actual trait itself, because the trait is not as self-evident as, say, an eye, a liver, or a wing. Moreover, the explanatory strategy of cognitive science cannot simply be an inversion of the first steps of the teleonomic explanation. It is not enough to identify the adaptive problem and then infer the mechanism. Rather, we need to complement an initial assumption about a trait's design with a characterization of how the adaptation might have appeared over evolutionary time – first characterizing the adaptive problem that the organism is supposed to solve, then the fitness-maximization process, as well as showing that the trait is specialized for solving the adaptive problem, unlikely to have arisen by chance alone, and not better explained as the byproduct of mechanisms designed to solve some alternative adaptive problem.
In summary, functional attribution in cognitive science is not a straightforward operation, but rather, requires an independent characterization from the functional mesh assumption; reuse theory alone cannot provide this type of tool. Hence, in my opinion, Anderson lacks the basis to apply his functional characterizations as arguments against specific models of cognition. Once we have the necessary tools to account for functional characterization in cognitive science, of course, reuse theory will prove extremely useful.