Anderson begins his article by quoting one of Darwin's explanations about how homologous structures can differ in function across species. Such a realization was clear even to Richard Owen who, although not accepting Darwin's theory of evolution, defined homology as “the same organ in different animals under every variety of form and function” (Owen Reference Owen1843). It is therefore surprising that Anderson uses very little comparative data to support his theory of neural reuse through “massive redeployment.” Comparative research examining neural circuitry across species, which has led to important insights into the evolution of neural circuits, needs to be included in any global theory about the evolution of human cognitive abilities. By concentrating solely on humans and extending analogies only to primates, one misses the strength of the comparative approach. Evolutionary principles can be generalized across species; humans are not more special for their cognitive abilities than bats are for their sonar abilities or song birds are for vocal learning abilities. Even the more distantly related invertebrates can provide lessons about how nervous systems evolved.

As a structure, the cortex is very adaptable; similar circuitry can be used for different functions. For example, in the absence of auditory afferents, primary auditory cortex in ferrets can be experimentally induced to process visual information (Sur et al. Reference Sur, Garraghty and Roe1988), and the ferrets respond to visual stimuli as being visual in nature (Von Melchner et al. Reference von Melchner, Pallas and Sur2000). Such a situation may occur naturally in congenitally blind humans; primary visual cortex, which lacks visual input, is instead responsive to somatosensory input and is necessary for reading Braille (Cohen et al. Reference Cohen, Celnik, Pascual-Leone, Corwell, Falz, Dambrosia, Honda, Sadato, Gerloff, Catala and Hallett1997). Therefore, the “function” of cortex is very much determined by the inputs that it receives. It may be better to refer to the algorithm that cortex performs on its inputs than on its innate function.

Because of cortical plasticity, it can be problematic to call one area of cortex “homologous” to a region in other species based on its function (Kaas Reference Kaas2005). Evidence suggests independent evolution of higher-order cortical areas, indicating that there may be innate directions for evolutionary change (Catania Reference Catania2000; Krubitzer Reference Krubitzer2007; Reference Krubitzer2009; Padberg et al. Reference Padberg, Franca, Cooke, Soares, Rosa, Fiorani, Gattass and Krubitzer2007). In discussing the “neuronal recycling hypothesis,” Anderson refers to changes following tool training in an area of the macaque brain that is “roughly homologous to the regions associated with tool-use in the human brain” (sect. 6.3, para. 4). It is difficult to develop any theory about the evolution of a structure without being able to unambiguously identify homologous structures in other species.

Homology of neural structures can be more precisely determined in invertebrates, where individual neurons are uniquely identifiable and can be recognized as homologous across species (Comer & Robertson Reference Comer and Robertson2001; Croll Reference Croll and Ali1987; Meier et al. Reference Meier, Chabaud and Reichert1991). This allows the role of homologous neurons across species exhibiting different behaviors to be assessed. For example, homologous neurons in nudibranch molluscs have different effects and are involved differently in the production of different types of swimming behavior (Newcomb & Katz Reference Newcomb and Katz2007; Reference Newcomb and Katz2008). There is also evidence to suggest that homologous neurons have independently been incorporated into circuits that perform analogous swimming behaviors (Katz & Newcomb Reference Katz, Newcomb and Kaas2007). This is reminiscent of the reuse of cortical areas across mammals for similar tasks (Catania Reference Catania2000; Krubitzer Reference Krubitzer2007; Reference Krubitzer2009; Padberg et al. Reference Padberg, Franca, Cooke, Soares, Rosa, Fiorani, Gattass and Krubitzer2007). Thus, a corollary of neuronal reuse may be that constraints on neuronal structure preclude some potential avenues and allow evolution to proceed in only particular directions, which leads to reuse.

Work on invertebrates has established the existence of multifunctional neural circuits, in which the same set of neurons in a single animal produces different types of behaviors at different times (Briggman & Kristan Reference Briggman and Kristan2008). One mechanism for shifting activity of neurons is neuromodulatory inputs, which alter cellular and synaptic properties (Calabrese Reference Calabrese1998; Katz Reference Katz1999; Katz & Calin-Jageman Reference Katz, Calin-Jageman and Squire2008; Marder & Thirumalai Reference Marder and Thirumalai2002). This has been particularly well studied in circuits that produce rhythmic motor patterns. Cortex has been likened to such a circuit in that it can exhibit different dynamic activity states depending upon its neuromodulatory input (Yuste et al. Reference Yuste, MacLean, Smith and Lansner2005). It has been proposed that phylogenetic differences in neuromodulation could be a mechanism by which neural circuits exhibit different behaviors across species (Arbas et al. Reference Arbas, Meinertzhagen and Shaw1991; Katz & Harris-Warrick Reference Katz and Harris-Warrick1999; Meyrand et al. Reference Meyrand, Faumont, Simmers, Christie and Nusbaum2000; Wright et al. Reference Wright, Kirschman, Rozen and Maynard1996). This would allow core functions of a neural circuit to remain intact, while enabling the circuit to produce different dynamic states, corresponding to the neural exploitation theory.

A nice example of changes in neural modulation that leads to large changes in behavior has been documented in the social behavior of voles (Donaldson & Young Reference Donaldson and Young2008; McGraw & Young Reference McGraw and Young2010). Prairie voles pair-bond after mating, whereas meadow voles do not. In addition to displaying partner preference, pair-bonding involves a number of complex behavioral traits, including increased territoriality and male parental care. The difference in the behavior of male voles can largely be accounted for by the neural expression pattern of vasopressin V1a receptors. These receptors are highly expressed in the ventral pallidum of prairie voles, but not in non-monogamous species. Using viral gene expression to express the V1a receptor in the ventral forebrain of the meadow vole substantially increased its partner-preference behavior (Lim et al. Reference Lim, Wang, Olazabal, Ren, Terwilliger and Young2004).

The evolutionary mechanism for differences in gene expression patterns in voles has been traced to an unstable stretch of repetitive microsatellite domains upstream from the coding region of the V1a receptor gene (Hammock & Young Reference Hammock and Young2005). Although similar genetic mechanisms do not play a role in the expression pattern in primates (Donaldson et al. Reference Donaldson, Kondrashov, Putnam, Bai, Stoinski, Hammock and Young2008), monogamous primate species such as the common marmoset display high levels of V1a receptor expression in ventral forebrain regions, whereas non-monogamous species such as rhesus macaques do not (Young Reference Young1999). This suggests that similar social behaviors have arisen independently through changes in the expression of V1a receptors in the ventral forebrains of rodents and primates. Once again, this supports the neural exploitation model: The basic connectivity of the brain has not been altered; instead, there is change in the expression of a particular receptor, which can modulate the dynamics of the activity through that area. The ventral forebrain areas are involved in more than pair-bonding; they also play a role in addiction and reward-based learning ( Kalivas & Volkow Reference Kalivas and Volkow2005; Schultz et al. Reference Schultz, Dayan and Montague1997). Pair-bonding results from these types of reward-learning processes being applied to a mate. This further supports the neural exploitation theory.

Anderson expresses several ideas relating to the “age” of a particular brain area influencing its ability to undergo evolutionary change. This notion smacks of Scala Natura because it assigns youngest age to structures that are found in humans and not in other animals. The fallacy of this line of thinking can be seen with the above example. By all accounts, the ventral forebrain areas predate mammals. Yet, even closely related voles can exhibit behavioral differences caused by evolutionary change to this “older” region of the forebrain. Furthermore, the ventral forebrain area is also involved in learning in birds (Jarvis et al. Reference Jarvis, Gunturkun, Bruce, Csillag, Karten, Kuenzel, Medina, Paxinos, Perkel, Shimizu, Striedter, Wild, Ball, Dugas-Ford, Durand, Hough, Husband, Kubikova, Lee, Mello, Powers, Siang, Smulders, Wada, White, Yamamoto, Yu, Reiner and Butler2005; Perkel Reference Perkel2004).

In summary, comparative studies offer important insights into how brains evolved. There are surely many mechanisms that can be found. It is clear, however, that assigning a function to a particular brain structure is a gross simplification and can lead to false conclusions about its evolution. Neural circuitry is multifunctional and dynamic. Anything that changes the dynamics of the circuit will alter the behavioral output.