In his target article, Anderson points to the fact that currently available fMRI neuroimaging data clearly show that “neural reuse” or, more precisely, anatomical polyfunctionality, is a pervasive feature of brain organization. He further argues that this polyfunctionality makes it impossible to distinguish which of the various versions of cognitive embodiment proposed so far is more plausible. His main point is that the evidence just shows that multiple cortical regions are involved in multiple tasks, whereas the different theories of embodied cognition conceive in different ways the functional import of such “reuse” or polyfunctionality: as semantic grounding, as simulation of experience, or as anticipation of feedback. However, Anderson does not develop a sustained criticism of such approaches; rather, he insists in their shortcomings as general approaches to brain function. In this regard, much more could be said, precisely on the grounds of the neurophysiological evidence he discusses. Thus, for instance, “simulationist” accounts that appeal to internal models in the brain as grounding for higher cognitive functions, ought to consider the evidence that the “efference copies” are fed to a distinct brain region, as in the case of motor control, where modelling appears to take place in the cerebellum (Kawato et al. Reference Kawato, Kuroda, Imamizu, Nakano, Miyauchi and Yoshioka2003), not in the motor cortex. Conversely, both simulationist and conceptual metaphor theories should explain how it is possible for the activation of the same circuits to correspond to different tasks and levels of cognitive abstraction (Gomila Reference Gomila, Glenberg, de Vega and Glaesser2008).
Second, Anderson's approach has the potential to avoid the Scylla of full holism and the Charybdis of structural-functional modularism, but maybe not as it stands. In the article, full holism is represented by connectionist neural networks, although it refers to the more general idea that function emerges out of the interaction of basic equipotent units. Modularism, by contrast, views the brain as an aggregate of independent, decomposable, functional units with their own proprietary anatomic (maybe even genetic) structure. Anderson's proposal requires that basic units of brain circuitry be identifiable, both structurally (say, in terms of cell assemblies) and functionally, in order to look for the different “higher-level” circuits in which they can be “used,” again both structurally (how this basic functional units can be multiply connected with many others) and functionally (what they do depending on which connectivity gets activated). The problem here is whether the requirement of a basic, independent “functionality” –in Anderson's terminology, the “work” of the circuit, distinguishable from the “uses” to which it is put through its “redeployment” – makes any neuronal sense. In principle, it could even happen that it is not the whole basic unit that gets reused, but rather that different components are differentially involved across functions. In other words, the challenge resides in the very possibility of specifying such elementary components in the brain, given that the individuation of circuits cannot be made in the abstract, but always within a functional setting.
Moreover, third, although Anderson widely uses the expression “brain circuit,” standard fMRI-based methodologies simply uncover differential, regional, metabolic activity, and are therefore inadequate to unearth brain connectivity as such. Towards the end of the target article, Anderson calls for new methodological approaches, such as multiple- or cross-domain studies; but these should avoid the limitations of subtractive methodologies. An alternative methodology in this regard is to look for common patterns of activity through different tasks. Inspired by a complex systems approach to the brain, this approach applies the analytical techniques of network analysis to find out which nodes are shared by multiple tasks (Eguiluz et al. Reference Eguiluz, Chialvo, Cecchi, Baliki and Apkarian2005; Sporns et al. Reference Sporns, Chialvo, Kaiser and Hilgetag2004). This approach initially confirms a hierarchy of levels of structural organization, suggesting that neural reuse does not characterize equally all network nodes: brain connectivity takes the structure of scale free networks. Another interesting option is tensor diffusion, a method based on structural magnetic resonance, which uncovers white matter interregional connectivity, and whose functional import has already been shown (Behrens & Johansen-Berg Reference Behrens and Johansen-Berg2005; Fuentemilla et al. Reference Fuentemilla, Cámara, Münte, Krämer, Cunillera, Marco-Pallarés, Tempelmann and Rodríguez-Fornells2009).
Lastly, fourth, one may wonder how we can discover whether neural reuse does constitute an “evolutionary […] strategy for realizing cognitive functions” (sect. 1, para. 3), when the data reported in support of Anderson's framework is not ecological after all. It is noteworthy that in order to enhance neural specificity, experimental designs require a high degree of sophistication; a form of manipulation that, although needed, prevents us from knowing whether the results thus obtained still hold true under naturalistic settings. For example, we do not know if, say, the Fusiform Face Area responds to “ecological” faces in the wild (Spiers & Maguire Reference Spiers and Maguire2007). Hence, in our view, beyond the exploitation of methodologies other than fMRI in order to be able to properly speak of “brain circuits,” the explanation of how structure relates to function requires paying closer attention to the way the environment and the body constrain the sensory and cognitive structure and function in naturalistic, non-task-evoked, settings. In fact, task-evoked responses promote a static interpretation of brain function, which is orthogonal to the spirit of the version of embodiment that underlies Anderson's MRH.
Anderson presents his MRH as a general account of how structure relates to function in the brain. His view somehow reminds us of Simon's (Reference Simon and Simon1962/1982) monograph, “The architecture of complexity.” Even if Anderson does not mention the term “hierarchy” in his proposal, it seems to be implicit in his conception of a set of basic anatomical circuits, with distinctive functionalities, that constitute several “second-order” circuits by re-wiring, thus giving rise to new functionalities, and so on and so forth. Each new level of organization inherits the capabilities of the circuits involved. In addition, the same basic circuitry can become part of multiple higher-level circuits/functions. In Anderson's proposal, this process of amplifying capabilities by re-wiring of circuits is thought to be characteristic of evolution. However, it doesn't need to be so restricted. It could also account for the possibility of new circuits appearing in phylogenesis (that is, new circuits, not just reuse of the ones available, as it was the case in human brain evolution), as well as of functional reorganization in ontogenetic development (Casey et al. Reference Casey, Tottenham, Liston and Durston2005), in learning, and in cases of brain plasticity after stroke, for instance. But, if neuroimaging data are to help us choose among competing views of cognition, the set of issues raised in this commentary must be addressed with an eye to furthering Anderson's project.
In his target article, Anderson points to the fact that currently available fMRI neuroimaging data clearly show that “neural reuse” or, more precisely, anatomical polyfunctionality, is a pervasive feature of brain organization. He further argues that this polyfunctionality makes it impossible to distinguish which of the various versions of cognitive embodiment proposed so far is more plausible. His main point is that the evidence just shows that multiple cortical regions are involved in multiple tasks, whereas the different theories of embodied cognition conceive in different ways the functional import of such “reuse” or polyfunctionality: as semantic grounding, as simulation of experience, or as anticipation of feedback. However, Anderson does not develop a sustained criticism of such approaches; rather, he insists in their shortcomings as general approaches to brain function. In this regard, much more could be said, precisely on the grounds of the neurophysiological evidence he discusses. Thus, for instance, “simulationist” accounts that appeal to internal models in the brain as grounding for higher cognitive functions, ought to consider the evidence that the “efference copies” are fed to a distinct brain region, as in the case of motor control, where modelling appears to take place in the cerebellum (Kawato et al. Reference Kawato, Kuroda, Imamizu, Nakano, Miyauchi and Yoshioka2003), not in the motor cortex. Conversely, both simulationist and conceptual metaphor theories should explain how it is possible for the activation of the same circuits to correspond to different tasks and levels of cognitive abstraction (Gomila Reference Gomila, Glenberg, de Vega and Glaesser2008).
Second, Anderson's approach has the potential to avoid the Scylla of full holism and the Charybdis of structural-functional modularism, but maybe not as it stands. In the article, full holism is represented by connectionist neural networks, although it refers to the more general idea that function emerges out of the interaction of basic equipotent units. Modularism, by contrast, views the brain as an aggregate of independent, decomposable, functional units with their own proprietary anatomic (maybe even genetic) structure. Anderson's proposal requires that basic units of brain circuitry be identifiable, both structurally (say, in terms of cell assemblies) and functionally, in order to look for the different “higher-level” circuits in which they can be “used,” again both structurally (how this basic functional units can be multiply connected with many others) and functionally (what they do depending on which connectivity gets activated). The problem here is whether the requirement of a basic, independent “functionality” –in Anderson's terminology, the “work” of the circuit, distinguishable from the “uses” to which it is put through its “redeployment” – makes any neuronal sense. In principle, it could even happen that it is not the whole basic unit that gets reused, but rather that different components are differentially involved across functions. In other words, the challenge resides in the very possibility of specifying such elementary components in the brain, given that the individuation of circuits cannot be made in the abstract, but always within a functional setting.
Moreover, third, although Anderson widely uses the expression “brain circuit,” standard fMRI-based methodologies simply uncover differential, regional, metabolic activity, and are therefore inadequate to unearth brain connectivity as such. Towards the end of the target article, Anderson calls for new methodological approaches, such as multiple- or cross-domain studies; but these should avoid the limitations of subtractive methodologies. An alternative methodology in this regard is to look for common patterns of activity through different tasks. Inspired by a complex systems approach to the brain, this approach applies the analytical techniques of network analysis to find out which nodes are shared by multiple tasks (Eguiluz et al. Reference Eguiluz, Chialvo, Cecchi, Baliki and Apkarian2005; Sporns et al. Reference Sporns, Chialvo, Kaiser and Hilgetag2004). This approach initially confirms a hierarchy of levels of structural organization, suggesting that neural reuse does not characterize equally all network nodes: brain connectivity takes the structure of scale free networks. Another interesting option is tensor diffusion, a method based on structural magnetic resonance, which uncovers white matter interregional connectivity, and whose functional import has already been shown (Behrens & Johansen-Berg Reference Behrens and Johansen-Berg2005; Fuentemilla et al. Reference Fuentemilla, Cámara, Münte, Krämer, Cunillera, Marco-Pallarés, Tempelmann and Rodríguez-Fornells2009).
Lastly, fourth, one may wonder how we can discover whether neural reuse does constitute an “evolutionary […] strategy for realizing cognitive functions” (sect. 1, para. 3), when the data reported in support of Anderson's framework is not ecological after all. It is noteworthy that in order to enhance neural specificity, experimental designs require a high degree of sophistication; a form of manipulation that, although needed, prevents us from knowing whether the results thus obtained still hold true under naturalistic settings. For example, we do not know if, say, the Fusiform Face Area responds to “ecological” faces in the wild (Spiers & Maguire Reference Spiers and Maguire2007). Hence, in our view, beyond the exploitation of methodologies other than fMRI in order to be able to properly speak of “brain circuits,” the explanation of how structure relates to function requires paying closer attention to the way the environment and the body constrain the sensory and cognitive structure and function in naturalistic, non-task-evoked, settings. In fact, task-evoked responses promote a static interpretation of brain function, which is orthogonal to the spirit of the version of embodiment that underlies Anderson's MRH.
Anderson presents his MRH as a general account of how structure relates to function in the brain. His view somehow reminds us of Simon's (Reference Simon and Simon1962/1982) monograph, “The architecture of complexity.” Even if Anderson does not mention the term “hierarchy” in his proposal, it seems to be implicit in his conception of a set of basic anatomical circuits, with distinctive functionalities, that constitute several “second-order” circuits by re-wiring, thus giving rise to new functionalities, and so on and so forth. Each new level of organization inherits the capabilities of the circuits involved. In addition, the same basic circuitry can become part of multiple higher-level circuits/functions. In Anderson's proposal, this process of amplifying capabilities by re-wiring of circuits is thought to be characteristic of evolution. However, it doesn't need to be so restricted. It could also account for the possibility of new circuits appearing in phylogenesis (that is, new circuits, not just reuse of the ones available, as it was the case in human brain evolution), as well as of functional reorganization in ontogenetic development (Casey et al. Reference Casey, Tottenham, Liston and Durston2005), in learning, and in cases of brain plasticity after stroke, for instance. But, if neuroimaging data are to help us choose among competing views of cognition, the set of issues raised in this commentary must be addressed with an eye to furthering Anderson's project.
ACKNOWLEDGMENT
This work was supported by a grant from the Spanish Government through project FFI2009-13416-C02-01 and from Fundación Séneca-Agencia de Ciencia y Tecnología de la Región de Murcia, through project 11944/PHCS/09.