We agree with Burkart et al. that general intelligence and specialized modules likely coexist in nonhuman animals. In mammals, similar cognitive skills have evolved independently in different phylogenetic lineages (Barton & Harvey Reference Barton and Harvey2000; de Winter & Oxnard Reference de Winter and Oxnard2001), suggesting the existence of independently evolving modules. These specialized modules likely reflect fitness-enhancing adaptations to specific socioecological challenges (Shettleworth Reference Shettleworth2010b). However, as Burkart et al. correctly argue, nonhuman animals also solve problems flexibly across domains – something impossible for a strictly modular brain. Therefore, general intelligence and specialized modules likely coexist, at least in mammals: Although cognitive modules are the response to domain-specific socioecological challenges (Shettleworth Reference Shettleworth2010b), general intelligence may allow behavioural flexibility across domains – something especially useful in novel or unpredictable environments (Lefebvre et al. Reference Lefebvre, Reader and Sol2013; Sol Reference Sol2009a).
From a neurological perspective, general intelligence and independent domain-specific cognitive skills compatibly coexist. Some properties of the human brain (e.g., amount of grey matter, neuronal speed of transmission) affect multiple brain regions, so that performance in different domains may correlate even if cognitive processes are localized in discrete regions (e.g., Jensen Reference Jensen1993; Lee Reference Lee2007; Pennington et al. Reference Pennington, Filipek, Lefly, Chhabildas, Kennedy, Simon, Filley, Galaburda and DeFries2000). In our view, specific cognitive processes may be localized in specific brain regions also in other mammals, whereas other properties are intercorrelated across brain regions and affect all cognitive domains. Lee (Reference Lee2007), for instance, proposed that more synaptic connections might enhance the overall processing power of the brain, regardless of the brain regions involved. Therefore, having specific cognitive modules and more synaptic connections are two different brain characteristics that likely coexist.
In our view, Burkart et al. downplayed the importance of multifactor (as opposed to g-based) approaches in human intelligence (e.g., Kaufman Reference Kaufman1979; Sternberg Reference Sternberg1988; Gardner Reference Gardner1993). The concept of g, originally postulated by Spearman (Reference Spearman1927), has been challenged on countless occasions and its current use in human IQ assessment is marginal at best, having been largely replaced by multifactor theories (see Kaufman Reference Kaufman2009). An excessive reliance on g prevented Burkart et al. from considering multifactor approaches that may better capture interspecific cognitive diversity without necessarily invoking modularity. Several studies in nonhuman mammals have failed to find g and instead support a multifactor view of intelligence (e.g., Amici et al. Reference Amici, Barney, Johnson, Call and Aureli2012; Herrmann et al. Reference Herrmann, Call, Hernández-Lloreda, Hare and Tomasello2007, Reference Herrmann, Hernandez-Lloreda, Call, Hare and Tomasello2010b; Herrmann & Call, Reference Herrmann and Call2012; Kolata et al. Reference Kolata, Light and Matzel2008; Schmitt et al. Reference Schmitt, Pankau and Fischer2012). We suspect that the attractiveness of g stems from its simplicity and its use as a bastion against radical modularity. However, a multifactor view of intelligence should not be conflated with a modular view of the mind, at least not the kind of modularity defended by some evolutionary psychologists (e.g., Cosmides & Tooby, Reference Cosmides, Tooby, Sternberg and Kaufman2002). The multifactor view is general in spirit, as its factors subserve multiple cognitive problems, but each factor is specialized in particular operations (e.g., inference) or capacities (e.g., working memory). We think that a substantial portion of interspecific (and interindividual) variation in cognition can be captured by a multifactor theory without invoking modules, and as such, the multifactor approach is more germane with the notion of g than that of radical modularity.
We agree with Burkart et al. that different experimental and statistical approaches may lead to different results. Thus, finding g may, at least partly, depend on which data are included and how they are analysed. In particular, Herrmann and Call (Reference Herrmann and Call2012) argued that task selection may inflate the relative importance of general intelligence (a point that Burkart et al. also made) by, for instance, selecting tasks that share a key feature (e.g., associative learning). Burkart et al. also argued that the allocation of tasks to specific domains (as done in confirmatory analyses and some Bayesian approaches) may be problematic, although it is possible to limit the drawbacks of a priori allocation by selecting multiple basic tasks with low cognitive demands (see Amici et al. Reference Amici, Barney, Johnson, Call and Aureli2012). Meta-analyses based on large data sets are especially useful for large-scale interspecific comparisons, but they often entail missing information (e.g., no interindividual variation), rely on data that are not evenly distributed across species, and disregard potentially important methodological differences across studies. These problems remain a challenge for future research, also because it is not easy to conceive tasks in which single cognitive skills are required.
We thought that the distinction between primary and secondary modules was useful. Burkart et al. argue that, through ontogeny, individuals may specialize in a certain domain, learning specific skills that become automatized and therefore appear to be domain-specific, even if they are not. The experimental distinction between primary and secondary modules is not easy, and relates to the more general problem of disentangling the relative contribution of evolutionary forces and developmental experience to cognition. Although the epigenesis of cognitive skills in nonhuman mammals is still largely unexplored, cross-fostering experiments would be a powerful tool to differentiate between evolutionarily selected and developmentally acquired behaviour. Experimental studies have shown that young macaques change their reconciliation tendencies (which are usually considered species-specific) depending on the social context in which they are raised (de Waal & Johanowicz Reference de Waal and Johanowicz1993). Evolutionary forces and developmental experience are intertwined in complex ways: Differentiating between primary modules and ontogenetically acquired skills is an essential point that future research will need to address.
Finally, concerning the relative contribution of general intelligence and primary modules across taxa, there are various hypotheses as to how they should vary. On the one hand, an ecologically oriented approach suggests that taxa living in more unpredictable environments could especially benefit from behavioural flexibility across domains, and thus more strongly rely on general intelligence (Lefebvre et al. Reference Lefebvre, Reader and Sol2013; Sol Reference Sol2009a). On the other hand, a more socially oriented approach suggests that taxa showing social learning can more efficiently acquire relevant skills through ontogeny without having to mainly rely on cognitive modules for their survival (Herrmann et al. Reference Herrmann, Call, Hernández-Lloreda, Hare and Tomasello2007; van Schaik & Burkart Reference van Schaik and Burkart2011; van Schaik et al. Reference van Schaik, Isler and Burkart2012). Future research will need to find creative ways to contrast these hypotheses, while controlling for the existence of secondary modules.
We agree with Burkart et al. that general intelligence and specialized modules likely coexist in nonhuman animals. In mammals, similar cognitive skills have evolved independently in different phylogenetic lineages (Barton & Harvey Reference Barton and Harvey2000; de Winter & Oxnard Reference de Winter and Oxnard2001), suggesting the existence of independently evolving modules. These specialized modules likely reflect fitness-enhancing adaptations to specific socioecological challenges (Shettleworth Reference Shettleworth2010b). However, as Burkart et al. correctly argue, nonhuman animals also solve problems flexibly across domains – something impossible for a strictly modular brain. Therefore, general intelligence and specialized modules likely coexist, at least in mammals: Although cognitive modules are the response to domain-specific socioecological challenges (Shettleworth Reference Shettleworth2010b), general intelligence may allow behavioural flexibility across domains – something especially useful in novel or unpredictable environments (Lefebvre et al. Reference Lefebvre, Reader and Sol2013; Sol Reference Sol2009a).
From a neurological perspective, general intelligence and independent domain-specific cognitive skills compatibly coexist. Some properties of the human brain (e.g., amount of grey matter, neuronal speed of transmission) affect multiple brain regions, so that performance in different domains may correlate even if cognitive processes are localized in discrete regions (e.g., Jensen Reference Jensen1993; Lee Reference Lee2007; Pennington et al. Reference Pennington, Filipek, Lefly, Chhabildas, Kennedy, Simon, Filley, Galaburda and DeFries2000). In our view, specific cognitive processes may be localized in specific brain regions also in other mammals, whereas other properties are intercorrelated across brain regions and affect all cognitive domains. Lee (Reference Lee2007), for instance, proposed that more synaptic connections might enhance the overall processing power of the brain, regardless of the brain regions involved. Therefore, having specific cognitive modules and more synaptic connections are two different brain characteristics that likely coexist.
In our view, Burkart et al. downplayed the importance of multifactor (as opposed to g-based) approaches in human intelligence (e.g., Kaufman Reference Kaufman1979; Sternberg Reference Sternberg1988; Gardner Reference Gardner1993). The concept of g, originally postulated by Spearman (Reference Spearman1927), has been challenged on countless occasions and its current use in human IQ assessment is marginal at best, having been largely replaced by multifactor theories (see Kaufman Reference Kaufman2009). An excessive reliance on g prevented Burkart et al. from considering multifactor approaches that may better capture interspecific cognitive diversity without necessarily invoking modularity. Several studies in nonhuman mammals have failed to find g and instead support a multifactor view of intelligence (e.g., Amici et al. Reference Amici, Barney, Johnson, Call and Aureli2012; Herrmann et al. Reference Herrmann, Call, Hernández-Lloreda, Hare and Tomasello2007, Reference Herrmann, Hernandez-Lloreda, Call, Hare and Tomasello2010b; Herrmann & Call, Reference Herrmann and Call2012; Kolata et al. Reference Kolata, Light and Matzel2008; Schmitt et al. Reference Schmitt, Pankau and Fischer2012). We suspect that the attractiveness of g stems from its simplicity and its use as a bastion against radical modularity. However, a multifactor view of intelligence should not be conflated with a modular view of the mind, at least not the kind of modularity defended by some evolutionary psychologists (e.g., Cosmides & Tooby, Reference Cosmides, Tooby, Sternberg and Kaufman2002). The multifactor view is general in spirit, as its factors subserve multiple cognitive problems, but each factor is specialized in particular operations (e.g., inference) or capacities (e.g., working memory). We think that a substantial portion of interspecific (and interindividual) variation in cognition can be captured by a multifactor theory without invoking modules, and as such, the multifactor approach is more germane with the notion of g than that of radical modularity.
We agree with Burkart et al. that different experimental and statistical approaches may lead to different results. Thus, finding g may, at least partly, depend on which data are included and how they are analysed. In particular, Herrmann and Call (Reference Herrmann and Call2012) argued that task selection may inflate the relative importance of general intelligence (a point that Burkart et al. also made) by, for instance, selecting tasks that share a key feature (e.g., associative learning). Burkart et al. also argued that the allocation of tasks to specific domains (as done in confirmatory analyses and some Bayesian approaches) may be problematic, although it is possible to limit the drawbacks of a priori allocation by selecting multiple basic tasks with low cognitive demands (see Amici et al. Reference Amici, Barney, Johnson, Call and Aureli2012). Meta-analyses based on large data sets are especially useful for large-scale interspecific comparisons, but they often entail missing information (e.g., no interindividual variation), rely on data that are not evenly distributed across species, and disregard potentially important methodological differences across studies. These problems remain a challenge for future research, also because it is not easy to conceive tasks in which single cognitive skills are required.
We thought that the distinction between primary and secondary modules was useful. Burkart et al. argue that, through ontogeny, individuals may specialize in a certain domain, learning specific skills that become automatized and therefore appear to be domain-specific, even if they are not. The experimental distinction between primary and secondary modules is not easy, and relates to the more general problem of disentangling the relative contribution of evolutionary forces and developmental experience to cognition. Although the epigenesis of cognitive skills in nonhuman mammals is still largely unexplored, cross-fostering experiments would be a powerful tool to differentiate between evolutionarily selected and developmentally acquired behaviour. Experimental studies have shown that young macaques change their reconciliation tendencies (which are usually considered species-specific) depending on the social context in which they are raised (de Waal & Johanowicz Reference de Waal and Johanowicz1993). Evolutionary forces and developmental experience are intertwined in complex ways: Differentiating between primary modules and ontogenetically acquired skills is an essential point that future research will need to address.
Finally, concerning the relative contribution of general intelligence and primary modules across taxa, there are various hypotheses as to how they should vary. On the one hand, an ecologically oriented approach suggests that taxa living in more unpredictable environments could especially benefit from behavioural flexibility across domains, and thus more strongly rely on general intelligence (Lefebvre et al. Reference Lefebvre, Reader and Sol2013; Sol Reference Sol2009a). On the other hand, a more socially oriented approach suggests that taxa showing social learning can more efficiently acquire relevant skills through ontogeny without having to mainly rely on cognitive modules for their survival (Herrmann et al. Reference Herrmann, Call, Hernández-Lloreda, Hare and Tomasello2007; van Schaik & Burkart Reference van Schaik and Burkart2011; van Schaik et al. Reference van Schaik, Isler and Burkart2012). Future research will need to find creative ways to contrast these hypotheses, while controlling for the existence of secondary modules.