The target article does an excellent job of exploring the behavioral, neural, and theoretical evidence supporting the idea that brain regions are reused in the service of many different cognitive functions and that traditional, modular approaches to neural architecture may be misguided. This viewpoint echoes other recent critics of contemporary cognitive neuroscience (e.g., Uttal Reference Uttal2001) and fits well alongside related distributed, emergent approaches to cognitive functioning (Rumelhart & McClelland Reference Rumelhart and McClelland1986; Thelen & Smith Reference Thelen and Smith1994; Varela et al. Reference Varela, Thompson and Rosch1991). A distinguishing feature of Anderson's neural reuse framework is that it highlights how local neural circuits with fixed “workings” may be combined in evolutionary (or developmental) time to support new cognitive “uses.” However, we are concerned that some of the same criticisms that have been leveled at modular approaches to the mind may also pose problems for the current formulation of the neural reuse theory.
First, much like classical modular views of mind, Anderson's theory of neural reuse de-emphasizes the role that the immediate environment plays in the development of the functional properties of a particular neural circuit (Fodor Reference Fodor1983; Pinker Reference Pinker1997). In fact, the target article explicitly claims that the working of any given anatomical brain site is fixed, in stark contrast to classical PDP (parallel distributed processing) models. However, there is evidence that the function of a given neural circuit may be largely shaped by the structure of its input. For example, Sur and colleagues (Sharma et al. Reference Sharma, Angelucci and Sur2000; von Melchner et al. Reference von Melchner, Pallas and Sur2000) surgically rewired the optic tract of a ferret so that primary auditory cortex received visual input from the eyes of the animal. Not only did the ferret seem to develop normal visual (and auditory) behavior, but also the circuitry in auditory cortex exhibited many of the properties traditionally associated with visual cortex, such as orientation selective cortical columns. This suggests that the working of circuits even in the most evolutionarily ancient cortical regions is not restricted to any particular modality, let alone any specific function. Such flexibility provides evidence in favor of computational mechanisms that derive their function based in part on the statistical structure of the input (Rumelhart & McClelland Reference Rumelhart and McClelland1986).
Second, while Anderson's theory of neural reuse rejects the idea that high-level cognitive functions (e.g., “language comprehension”) can ultimately be mapped onto any single brain module, the approach still calls for the one-to-one mapping between these high-level functions and a specific, distributed set of neural circuits. However, it may be the case that distinct instances of what we would label as the same cognitive behavior might actually emerge from the distributed activation of different, contextually variable sets of neural circuits. For example, although visual object recognition has been shown to automatically activate motor brain regions (Chao & Martin Reference Chao and Martin2000; Tucker & Ellis Reference Tucker and Ellis1998), very different motor circuitry might be recruited to recognize a chair when you are tired and want to sit down than when you need to reach something on a high shelf. There may also be individual differences across a population in what neural resources are recruited for a particular cognitive task. For example, some people seem to readily recruit direction-selective neurons when listening to stories describing both literal and metaphorical motion, whereas others do not, even though both groups comprehend the story (Toskos Dils & Boroditsky, forthcoming). Thus very different neural representations might subserve the very same high-level cognitive behavior (i.e., “object perception” and “language comprehension”) both within and across individuals. This suggests that it may be a category mistake to try to reduce complex, person-level cognitive phenomena to a unique set of neural circuits (Ryle Reference Ryle1949). Rather, these mental operations are always a contextually bound, emergent function of the history of the organism, the immediate environment, and the bodily state of the organism (Thelen & Smith Reference Thelen and Smith1994).
In sum, while Anderson's theories of neural reuse offer a much-needed counterpoint to traditional, modular views of neural architecture, they still suffer from some of the same difficulties these modular views have in accounting for complex cognitive behaviors that develop over the course of learning and experience. Dynamic models of cognitive function preserve many features of the neural reuse framework that account for data unexplained by massive modularity models. They should be preferred because, unlike neural reuse models, they also predict that the function of a given circuit should change as the structure of its input changes, and they do not require that high-level cognitive functions cleanly map onto specific cortical circuits. These approaches currently provide the additional benefit of computational models that can be used to make precise predictions about the development of cognition function. Proponents of neural reuse should point to specific ways in which they can accommodate the limitations of the current formulation of neural reuse theory.
The target article does an excellent job of exploring the behavioral, neural, and theoretical evidence supporting the idea that brain regions are reused in the service of many different cognitive functions and that traditional, modular approaches to neural architecture may be misguided. This viewpoint echoes other recent critics of contemporary cognitive neuroscience (e.g., Uttal Reference Uttal2001) and fits well alongside related distributed, emergent approaches to cognitive functioning (Rumelhart & McClelland Reference Rumelhart and McClelland1986; Thelen & Smith Reference Thelen and Smith1994; Varela et al. Reference Varela, Thompson and Rosch1991). A distinguishing feature of Anderson's neural reuse framework is that it highlights how local neural circuits with fixed “workings” may be combined in evolutionary (or developmental) time to support new cognitive “uses.” However, we are concerned that some of the same criticisms that have been leveled at modular approaches to the mind may also pose problems for the current formulation of the neural reuse theory.
First, much like classical modular views of mind, Anderson's theory of neural reuse de-emphasizes the role that the immediate environment plays in the development of the functional properties of a particular neural circuit (Fodor Reference Fodor1983; Pinker Reference Pinker1997). In fact, the target article explicitly claims that the working of any given anatomical brain site is fixed, in stark contrast to classical PDP (parallel distributed processing) models. However, there is evidence that the function of a given neural circuit may be largely shaped by the structure of its input. For example, Sur and colleagues (Sharma et al. Reference Sharma, Angelucci and Sur2000; von Melchner et al. Reference von Melchner, Pallas and Sur2000) surgically rewired the optic tract of a ferret so that primary auditory cortex received visual input from the eyes of the animal. Not only did the ferret seem to develop normal visual (and auditory) behavior, but also the circuitry in auditory cortex exhibited many of the properties traditionally associated with visual cortex, such as orientation selective cortical columns. This suggests that the working of circuits even in the most evolutionarily ancient cortical regions is not restricted to any particular modality, let alone any specific function. Such flexibility provides evidence in favor of computational mechanisms that derive their function based in part on the statistical structure of the input (Rumelhart & McClelland Reference Rumelhart and McClelland1986).
Second, while Anderson's theory of neural reuse rejects the idea that high-level cognitive functions (e.g., “language comprehension”) can ultimately be mapped onto any single brain module, the approach still calls for the one-to-one mapping between these high-level functions and a specific, distributed set of neural circuits. However, it may be the case that distinct instances of what we would label as the same cognitive behavior might actually emerge from the distributed activation of different, contextually variable sets of neural circuits. For example, although visual object recognition has been shown to automatically activate motor brain regions (Chao & Martin Reference Chao and Martin2000; Tucker & Ellis Reference Tucker and Ellis1998), very different motor circuitry might be recruited to recognize a chair when you are tired and want to sit down than when you need to reach something on a high shelf. There may also be individual differences across a population in what neural resources are recruited for a particular cognitive task. For example, some people seem to readily recruit direction-selective neurons when listening to stories describing both literal and metaphorical motion, whereas others do not, even though both groups comprehend the story (Toskos Dils & Boroditsky, forthcoming). Thus very different neural representations might subserve the very same high-level cognitive behavior (i.e., “object perception” and “language comprehension”) both within and across individuals. This suggests that it may be a category mistake to try to reduce complex, person-level cognitive phenomena to a unique set of neural circuits (Ryle Reference Ryle1949). Rather, these mental operations are always a contextually bound, emergent function of the history of the organism, the immediate environment, and the bodily state of the organism (Thelen & Smith Reference Thelen and Smith1994).
In sum, while Anderson's theories of neural reuse offer a much-needed counterpoint to traditional, modular views of neural architecture, they still suffer from some of the same difficulties these modular views have in accounting for complex cognitive behaviors that develop over the course of learning and experience. Dynamic models of cognitive function preserve many features of the neural reuse framework that account for data unexplained by massive modularity models. They should be preferred because, unlike neural reuse models, they also predict that the function of a given circuit should change as the structure of its input changes, and they do not require that high-level cognitive functions cleanly map onto specific cortical circuits. These approaches currently provide the additional benefit of computational models that can be used to make precise predictions about the development of cognition function. Proponents of neural reuse should point to specific ways in which they can accommodate the limitations of the current formulation of neural reuse theory.