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Covariation of learning and "reasoning" abilities in mice: evolutionary conservation of the operations of intelligence
Abstract
Contemporary descriptions of human intelligence hold that this trait influences a broad range of cognitive abilities, including learning, attention, and reasoning. Like humans, individual genetically heterogeneous mice express a "general" cognitive trait that influences performance across a diverse array of learning and attentional tasks, and it has been suggested that this trait is qualitatively and structurally analogous to general intelligence in humans. However, the hallmark of human intelligence is the ability to use various forms of "reasoning" to support solutions to novel problems. Here, we find that genetically heterogeneous mice are capable of solving problems that are nominally indicative of inductive and deductive forms of reasoning, and that individuals' capacity for reasoning covaries with more general learning abilities. Mice were characterized for their general learning ability as determined by their aggregate performance (derived from principal component analysis) across a battery of five diverse learning tasks. These animals were then assessed on prototypic tests indicative of deductive reasoning (inferring the meaning of a novel item by exclusion, i.e., "fast mapping") and inductive reasoning (execution of an efficient search strategy in a binary decision tree). The animals exhibited systematic abilities on each of these nominal reasoning tasks that were predicted by their aggregate performance on the battery of learning tasks. These results suggest that the coregulation of reasoning and general learning performance in genetically heterogeneous mice form a core cognitive trait that is analogous to human intelligence
... Nevertheless, as in humans, the derived g factors have been shown to covary with executive functions, such as selective attention Matzel et al. 2011a) and working memory (particularly working memory capacity: Kolata et al. 2005;Matzel et al. 2008;Sauce et al. 2014) as well as performance in tests of reasoning. For instance, g derived from a standard mouse test battery predicted performance in inductive (finding efficient search strategies in a complex maze) and deductive reasoning (inferring the meaning of a novel item by exclusion, i.e., "fast mapping": Wass et al. 2012). Working memory training did increase g Matzel et al. 2011a), mainly through its positive effect on selective attention ; see also Sauce et al. 2014). ...
... Equally promising is to focus on unusually difficult problems relative to individual performance (i.e., problems that cannot be solved in a routine way). For instance, performance in difficult problems such as fast mapping or inductive reasoning was correlated with independently assessed g in mice (Wass et al. 2012). Particularly strong evidence would include the demonstration that individuals recruit the same basic cognitive processes for such difficult problems that are also strongly correlated with g, such as selective attention or working memory capacity Geary 2009). ...
... Burkart et al. claim that "recent studies are consistent with the presence of general intelligence in mammals" (in the Abstract), which is defined as the ability to reason, plan, and think abstractly (Gottfredson 1997). However, the only cited reasoning study outside of rodents (Anderson 1993;Wass et al. 2012) has not found evidence for g (Herrmann & Call 2012). The author of this commentary has found evidence for reasoning by exclusion in several human animals (Aust et al. 2008;Huber 2009;O'Hara et al. 2015;, but so far, evidence for g in these species is lacking. ...
Burkart et al.'s impressive synthesis will serve as a valuable resource for intelligence research. Despite its strengths, the target article falls short of offering compelling explanations for the evolution of intelligence. Here, we outline its shortcomings, illustrate how these can lead to misguided conclusions about the evolution of intelligence, and suggest ways to address the article's key questions.
... Nevertheless, as in humans, the derived g factors have been shown to covary with executive functions, such as selective attention Matzel et al. 2011a) and working memory (particularly working memory capacity: Kolata et al. 2005;Matzel et al. 2008;Sauce et al. 2014) as well as performance in tests of reasoning. For instance, g derived from a standard mouse test battery predicted performance in inductive (finding efficient search strategies in a complex maze) and deductive reasoning (inferring the meaning of a novel item by exclusion, i.e., "fast mapping": Wass et al. 2012). Working memory training did increase g Matzel et al. 2011a), mainly through its positive effect on selective attention ; see also Sauce et al. 2014). ...
... Equally promising is to focus on unusually difficult problems relative to individual performance (i.e., problems that cannot be solved in a routine way). For instance, performance in difficult problems such as fast mapping or inductive reasoning was correlated with independently assessed g in mice (Wass et al. 2012). Particularly strong evidence would include the demonstration that individuals recruit the same basic cognitive processes for such difficult problems that are also strongly correlated with g, such as selective attention or working memory capacity Geary 2009). ...
... Burkart et al. claim that "recent studies are consistent with the presence of general intelligence in mammals" (in the Abstract), which is defined as the ability to reason, plan, and think abstractly (Gottfredson 1997). However, the only cited reasoning study outside of rodents (Anderson 1993;Wass et al. 2012) has not found evidence for g (Herrmann & Call 2012). The author of this commentary has found evidence for reasoning by exclusion in several human animals (Aust et al. 2008;Huber 2009;O'Hara et al. 2015;, but so far, evidence for g in these species is lacking. ...
Here, we specifically discuss why and to what extent we agree with Burkart et al. about the coexistence of general intelligence and modular cognitive adaptations, and why we believe that the distinction between primary and secondary modules they propose is indeed essential.
... Nevertheless, as in humans, the derived g factors have been shown to covary with executive functions, such as selective attention Matzel et al. 2011a) and working memory (particularly working memory capacity: Kolata et al. 2005;Matzel et al. 2008;Sauce et al. 2014) as well as performance in tests of reasoning. For instance, g derived from a standard mouse test battery predicted performance in inductive (finding efficient search strategies in a complex maze) and deductive reasoning (inferring the meaning of a novel item by exclusion, i.e., "fast mapping": Wass et al. 2012). Working memory training did increase g Matzel et al. 2011a), mainly through its positive effect on selective attention ; see also Sauce et al. 2014). ...
... Equally promising is to focus on unusually difficult problems relative to individual performance (i.e., problems that cannot be solved in a routine way). For instance, performance in difficult problems such as fast mapping or inductive reasoning was correlated with independently assessed g in mice (Wass et al. 2012). Particularly strong evidence would include the demonstration that individuals recruit the same basic cognitive processes for such difficult problems that are also strongly correlated with g, such as selective attention or working memory capacity Geary 2009). ...
... Burkart et al. claim that "recent studies are consistent with the presence of general intelligence in mammals" (in the Abstract), which is defined as the ability to reason, plan, and think abstractly (Gottfredson 1997). However, the only cited reasoning study outside of rodents (Anderson 1993;Wass et al. 2012) has not found evidence for g (Herrmann & Call 2012). The author of this commentary has found evidence for reasoning by exclusion in several human animals (Aust et al. 2008;Huber 2009;O'Hara et al. 2015;, but so far, evidence for g in these species is lacking. ...
We welcome the cross-disciplinary approach taken by Burkart et al. to probe the evolution of intelligence. We note several concerns: the uses of g and G , rank-ordering species on cognitive ability, and the meaning of general intelligence. This subject demands insights from several fields, and we look forward to cross-disciplinary collaborations.
... Nevertheless, as in humans, the derived g factors have been shown to covary with executive functions, such as selective attention Matzel et al. 2011a) and working memory (particularly working memory capacity: Kolata et al. 2005;Matzel et al. 2008;Sauce et al. 2014) as well as performance in tests of reasoning. For instance, g derived from a standard mouse test battery predicted performance in inductive (finding efficient search strategies in a complex maze) and deductive reasoning (inferring the meaning of a novel item by exclusion, i.e., "fast mapping": Wass et al. 2012). Working memory training did increase g Matzel et al. 2011a), mainly through its positive effect on selective attention ; see also Sauce et al. 2014). ...
... Equally promising is to focus on unusually difficult problems relative to individual performance (i.e., problems that cannot be solved in a routine way). For instance, performance in difficult problems such as fast mapping or inductive reasoning was correlated with independently assessed g in mice (Wass et al. 2012). Particularly strong evidence would include the demonstration that individuals recruit the same basic cognitive processes for such difficult problems that are also strongly correlated with g, such as selective attention or working memory capacity Geary 2009). ...
... Burkart et al. claim that "recent studies are consistent with the presence of general intelligence in mammals" (in the Abstract), which is defined as the ability to reason, plan, and think abstractly (Gottfredson 1997). However, the only cited reasoning study outside of rodents (Anderson 1993;Wass et al. 2012) has not found evidence for g (Herrmann & Call 2012). The author of this commentary has found evidence for reasoning by exclusion in several human animals (Aust et al. 2008;Huber 2009;O'Hara et al. 2015;, but so far, evidence for g in these species is lacking. ...
Are the mechanisms underlying variations in the performance of animals on cognitive test batteries analogous to those of humans? Differences might result from procedural inconsistencies in test battery design, but also from differences in how animals and humans solve cognitive problems. We suggest differentiating associative-based ( learning ) from rule-based ( knowing ) tasks to further our understanding of cognitive evolution across species.
... Nevertheless, as in humans, the derived g factors have been shown to covary with executive functions, such as selective attention Matzel et al. 2011a) and working memory (particularly working memory capacity: Kolata et al. 2005;Matzel et al. 2008;Sauce et al. 2014) as well as performance in tests of reasoning. For instance, g derived from a standard mouse test battery predicted performance in inductive (finding efficient search strategies in a complex maze) and deductive reasoning (inferring the meaning of a novel item by exclusion, i.e., "fast mapping": Wass et al. 2012). Working memory training did increase g Matzel et al. 2011a), mainly through its positive effect on selective attention ; see also Sauce et al. 2014). ...
... Equally promising is to focus on unusually difficult problems relative to individual performance (i.e., problems that cannot be solved in a routine way). For instance, performance in difficult problems such as fast mapping or inductive reasoning was correlated with independently assessed g in mice (Wass et al. 2012). Particularly strong evidence would include the demonstration that individuals recruit the same basic cognitive processes for such difficult problems that are also strongly correlated with g, such as selective attention or working memory capacity Geary 2009). ...
... Burkart et al. claim that "recent studies are consistent with the presence of general intelligence in mammals" (in the Abstract), which is defined as the ability to reason, plan, and think abstractly (Gottfredson 1997). However, the only cited reasoning study outside of rodents (Anderson 1993;Wass et al. 2012) has not found evidence for g (Herrmann & Call 2012). The author of this commentary has found evidence for reasoning by exclusion in several human animals (Aust et al. 2008;Huber 2009;O'Hara et al. 2015;, but so far, evidence for g in these species is lacking. ...
The goal of our target article was to lay out current evidence relevant to the question of whether general intelligence can be found in nonhuman animals in order to better understand its evolution in humans. The topic is a controversial one, as evident from the broad range of partly incompatible comments it has elicited. The main goal of our response is to translate these issues into testable empirical predictions, which together can provide the basis for a broad research agenda.
... Nevertheless, as in humans, the derived g factors have been shown to covary with executive functions, such as selective attention Matzel et al. 2011a) and working memory (particularly working memory capacity: Kolata et al. 2005;Matzel et al. 2008;Sauce et al. 2014) as well as performance in tests of reasoning. For instance, g derived from a standard mouse test battery predicted performance in inductive (finding efficient search strategies in a complex maze) and deductive reasoning (inferring the meaning of a novel item by exclusion, i.e., "fast mapping": Wass et al. 2012). Working memory training did increase g Matzel et al. 2011a), mainly through its positive effect on selective attention ; see also Sauce et al. 2014). ...
... Equally promising is to focus on unusually difficult problems relative to individual performance (i.e., problems that cannot be solved in a routine way). For instance, performance in difficult problems such as fast mapping or inductive reasoning was correlated with independently assessed g in mice (Wass et al. 2012). Particularly strong evidence would include the demonstration that individuals recruit the same basic cognitive processes for such difficult problems that are also strongly correlated with g, such as selective attention or working memory capacity Geary 2009). ...
... Burkart et al. claim that "recent studies are consistent with the presence of general intelligence in mammals" (in the Abstract), which is defined as the ability to reason, plan, and think abstractly (Gottfredson 1997). However, the only cited reasoning study outside of rodents (Anderson 1993;Wass et al. 2012) has not found evidence for g (Herrmann & Call 2012). The author of this commentary has found evidence for reasoning by exclusion in several human animals (Aust et al. 2008;Huber 2009;O'Hara et al. 2015;, but so far, evidence for g in these species is lacking. ...
Burkart et al. present a paradox – general factors of intelligence exist among individual differences ( g ) in performance in several species, and also at the aggregate level ( G ); however, there is ambiguous evidence for the existence of g when analyzing data using a mixed approach, that is, when comparing individuals of different species using the same cognitive ability battery. Here, we present an empirical solution to this paradox.
... Nevertheless, as in humans, the derived g factors have been shown to covary with executive functions, such as selective attention Matzel et al. 2011a) and working memory (particularly working memory capacity: Kolata et al. 2005;Matzel et al. 2008;Sauce et al. 2014) as well as performance in tests of reasoning. For instance, g derived from a standard mouse test battery predicted performance in inductive (finding efficient search strategies in a complex maze) and deductive reasoning (inferring the meaning of a novel item by exclusion, i.e., "fast mapping": Wass et al. 2012). Working memory training did increase g Matzel et al. 2011a), mainly through its positive effect on selective attention ; see also Sauce et al. 2014). ...
... Equally promising is to focus on unusually difficult problems relative to individual performance (i.e., problems that cannot be solved in a routine way). For instance, performance in difficult problems such as fast mapping or inductive reasoning was correlated with independently assessed g in mice (Wass et al. 2012). Particularly strong evidence would include the demonstration that individuals recruit the same basic cognitive processes for such difficult problems that are also strongly correlated with g, such as selective attention or working memory capacity Geary 2009). ...
... Burkart et al. claim that "recent studies are consistent with the presence of general intelligence in mammals" (in the Abstract), which is defined as the ability to reason, plan, and think abstractly (Gottfredson 1997). However, the only cited reasoning study outside of rodents (Anderson 1993;Wass et al. 2012) has not found evidence for g (Herrmann & Call 2012). The author of this commentary has found evidence for reasoning by exclusion in several human animals (Aust et al. 2008;Huber 2009;O'Hara et al. 2015;, but so far, evidence for g in these species is lacking. ...
The authors evaluate evidence for general intelligence ( g ) in nonhumans but lean heavily toward mammalian data. They mention, but do not discuss in detail, evidence for g in nonmammalian species, for which substantive material exists. I refer to a number of avian studies, particularly in corvids and parrots, which would add breadth to the material presented in the target article.
... Nevertheless, as in humans, the derived g factors have been shown to covary with executive functions, such as selective attention Matzel et al. 2011a) and working memory (particularly working memory capacity: Kolata et al. 2005;Matzel et al. 2008;Sauce et al. 2014) as well as performance in tests of reasoning. For instance, g derived from a standard mouse test battery predicted performance in inductive (finding efficient search strategies in a complex maze) and deductive reasoning (inferring the meaning of a novel item by exclusion, i.e., "fast mapping": Wass et al. 2012). Working memory training did increase g Matzel et al. 2011a), mainly through its positive effect on selective attention ; see also Sauce et al. 2014). ...
... Equally promising is to focus on unusually difficult problems relative to individual performance (i.e., problems that cannot be solved in a routine way). For instance, performance in difficult problems such as fast mapping or inductive reasoning was correlated with independently assessed g in mice (Wass et al. 2012). Particularly strong evidence would include the demonstration that individuals recruit the same basic cognitive processes for such difficult problems that are also strongly correlated with g, such as selective attention or working memory capacity Geary 2009). ...
... Burkart et al. claim that "recent studies are consistent with the presence of general intelligence in mammals" (in the Abstract), which is defined as the ability to reason, plan, and think abstractly (Gottfredson 1997). However, the only cited reasoning study outside of rodents (Anderson 1993;Wass et al. 2012) has not found evidence for g (Herrmann & Call 2012). The author of this commentary has found evidence for reasoning by exclusion in several human animals (Aust et al. 2008;Huber 2009;O'Hara et al. 2015;, but so far, evidence for g in these species is lacking. ...
Across taxonomic subfamilies, variations in intelligence ( G ) are sometimes related to brain size. However, within species, brain size plays a smaller role in explaining variations in general intelligence ( g ), and the cause-and-effect relationship may be opposite to what appears intuitive. Instead, individual differences in intelligence may reflect variations in domain-general processes that are only superficially related to brain size.
... Nevertheless, as in humans, the derived g factors have been shown to covary with executive functions, such as selective attention Matzel et al. 2011a) and working memory (particularly working memory capacity: Kolata et al. 2005;Matzel et al. 2008;Sauce et al. 2014) as well as performance in tests of reasoning. For instance, g derived from a standard mouse test battery predicted performance in inductive (finding efficient search strategies in a complex maze) and deductive reasoning (inferring the meaning of a novel item by exclusion, i.e., "fast mapping": Wass et al. 2012). Working memory training did increase g Matzel et al. 2011a), mainly through its positive effect on selective attention ; see also Sauce et al. 2014). ...
... Evidence for g (explaining 27%-32% of variance); g correlated with inductive and deductive reasoning performance. Wass et al. (2012) Mice (26) 4 learning tasks: odor discrimination, reinforced alternation, fear conditioning, radial arm maze plus attention battery consisting of 4 tasks: Mouse-Stroop (conflicting visual and olfactory cues), T-maze reversal, coupled latent inhibition, and dual radial arm maze Evidence for g (explaining 37% of variance); different types of attention (external: selective attention; internal: inhibition) contributed independently to variation in g. Sauce et al. (2014) Primates Rhesus macaques (30+23) 6 non-social tasks (n = 30): delayed non-matching to sample (acquisition time and performance after 120 sec delay), delayed recognition span task (spatial and color condition), and reversal learning task (spatial and object condition) ...
... Equally promising is to focus on unusually difficult problems relative to individual performance (i.e., problems that cannot be solved in a routine way). For instance, performance in difficult problems such as fast mapping or inductive reasoning was correlated with independently assessed g in mice (Wass et al. 2012). Particularly strong evidence would include the demonstration that individuals recruit the same basic cognitive processes for such difficult problems that are also strongly correlated with g, such as selective attention or working memory capacity Geary 2009). ...
Conceptualizing intelligence in its biological context, as the expression of manifold adaptations, compels a rethinking of measuring this characteristic in humans, relying also on animal studies of analogous skills. Mental manipulation , as an extension of object manipulation, provides a continuous, biologically based concept for studying G as it pertains to individual differences in humans and other species.
... Nevertheless, as in humans, the derived g factors have been shown to covary with executive functions, such as selective attention Matzel et al. 2011a) and working memory (particularly working memory capacity: Kolata et al. 2005;Matzel et al. 2008;Sauce et al. 2014) as well as performance in tests of reasoning. For instance, g derived from a standard mouse test battery predicted performance in inductive (finding efficient search strategies in a complex maze) and deductive reasoning (inferring the meaning of a novel item by exclusion, i.e., "fast mapping": Wass et al. 2012). Working memory training did increase g Matzel et al. 2011a), mainly through its positive effect on selective attention ; see also Sauce et al. 2014). ...
... Equally promising is to focus on unusually difficult problems relative to individual performance (i.e., problems that cannot be solved in a routine way). For instance, performance in difficult problems such as fast mapping or inductive reasoning was correlated with independently assessed g in mice (Wass et al. 2012). Particularly strong evidence would include the demonstration that individuals recruit the same basic cognitive processes for such difficult problems that are also strongly correlated with g, such as selective attention or working memory capacity Geary 2009). ...
... Burkart et al. claim that "recent studies are consistent with the presence of general intelligence in mammals" (in the Abstract), which is defined as the ability to reason, plan, and think abstractly (Gottfredson 1997). However, the only cited reasoning study outside of rodents (Anderson 1993;Wass et al. 2012) has not found evidence for g (Herrmann & Call 2012). The author of this commentary has found evidence for reasoning by exclusion in several human animals (Aust et al. 2008;Huber 2009;O'Hara et al. 2015;, but so far, evidence for g in these species is lacking. ...
... I illustrate this argument with a brief discussion of a number of recent studies. Mice Matzel and colleagues (2011; Wass et al. 2012 ) have regularly found a g factor which accounts for around 40 % of the variance in the performance of mice on a variety of tasks. They summarize their research program as follows; ''In total, this work indicates that learning abilities, attentional control, and the capacity for reasoning, features that Behav Genet 123 ...
... Nippak and Milgram (2005) also demonstrated, using beagles, strong correlations between response latencies to several cognitive tasks that assessed cognitive difficulty. Fast mapping has been shown in mice and related to mouse g (Wass et al. 2012). There are some 500 stray dogs living in the Moscow metro stations. ...
I argue that the g factor meets the fundamental criteria of a scientific construct more fully than any other conception of intelligence. I briefly discuss the evidence regarding the relationship of brain size to intelligence. A review of a large body of evidence demonstrates that there is a g factor in a wide range of species and that, in the species studied, it relates to brain size and is heritable. These findings suggest that many species have evolved a general-purpose mechanism (a general biological intelligence) for dealing with the environments in which they evolved. In spite of numerous studies with considerable statistical power, we know of very few genes that influence g and the effects are very small. Nevertheless, g appears to be highly polygenic. Given the complexity of the human brain, it is not surprising that that one of its primary faculties-intelligence-is best explained by the near infinitesimal model of quantitative genetics.
... It is one of the most important and ubiquitous of all problem-solving activities. 46,47 Baghel et al determined that the integration of multiple relations between mental representations is critical for higher-level cognition. Relational integration may be a basic common factor that connects various abilities that depend on prefrontal function, including problem-solving, for which an intact prefrontal cortex is essential. ...
Aim:
MATRICS Consensus Cognitive Battery was developed by the National Institute of Mental Health to establish acceptance criteria for measuring cognitive changes in schizophrenia and can be used to assess cognitive functions in other psychiatric disorders. We used a Japanese version of MATRICS Consensus Cognitive Battery to explore the changes in multiple cognitive functions in patients with mild cognitive impairment and mild Alzheimer's disease.
Methods:
We administered the Japanese version of MATRICS Consensus Cognitive Battery to 11 patients with mild cognitive impairment (MCI), 11 patients with Alzheimer's disease, and 27 healthy controls. All Japanese versions of MATRICS Consensus Cognitive Battery domain scores were converted to t-scores using sample means and standard deviations and were compared for significant performance differences among healthy control, MCI, and mild Alzheimer's disease groups.
Results:
Compared with healthy controls, patients with MCI and mild Alzheimer's disease demonstrated the same degree of impairment to processing speed, verbal learning, and visual learning. Reasoning and problem-solving showed significant impairments only in mild Alzheimer's disease. Verbal and visual abilities in working memory showed different performances in the MCI and mild Alzheimer's disease groups, with the Alzheimer's disease group demonstrating significantly more deficits in these domains. No significant difference was found among the groups in attention/vigilance and social cognition.
Conclusions:
The Japanese version of MATRICS Consensus Cognitive Battery can be used to elucidate the characteristics of cognitive dysfunction of normal aging, MCI, and mild dementia in clinical practice.
... Further evidence for domain generality in humans comes from our ability to transfer and combine information across different domains 7,10-12 . In animals, while there is some evidence for 'g' [13][14][15][16] , this remains controversial 1,7,[17][18][19][20] , and there is currently little evidence for cross-modular integration of information 7,10,18,21 . This has led to claims that such integration is unique to humans 1,21-24 and dependent on language 10,11 . ...
One key aspect of domain-general thought is the ability to integrate information across different cognitive domains. Here, we tested whether kea (Nestor notabilis) can use relative quantities when predicting sampling outcomes, and then integrate both physical information about the presence of a barrier, and social information about the biased sampling of an experimenter, into their predictions. Our results show that kea exhibit three signatures of statistical inference, and therefore can integrate knowledge across different cognitive domains to flexibly adjust their predictions of sampling events. This result provides evidence that true statistical inference is found outside of the great apes, and that aspects of domain-general thinking can convergently evolve in brains with a highly different structure from primates. This has important implications not only for our understanding of how intelligence evolves, but also for research focused on how to create artificial domain-general thought processes.
... Because this G measure is tightly correlated with brain size (Deaner, Isler, Burkart, & Van Schaik, 2007) and inhibitory control (MacLean et al., 2014; see Burkart et al., 2017), both known correlates of g in humans (Deary, Penke, & Johnson, 2010;Meldrum, Petkovsek, Boutwell, & Young, 2017), it probably expresses very similar abilities as the intraspecific g measure. Additionally, an increasing number of studies addresses the existence of a psychometric g within a variety of different taxa (reviews: Chabris, 2007, Matzel, Wass, & Kolata, 2011, Burkart et al., 2017, including dogs (Arden & Adams, 2016), mice (Galsworthy, Paya-Cano, Monleon, & Plomin, 2002;Locurto, Fortin, & Sullivan, 2003;Matzel et al., 2003;Matzel et al., 2011;Matzel, Kolata, Light, & Sauce, 2017;Wass et al., 2012), rats (Anderson, 1993), bowerbirds (Keagy, Savard, & Borgia, 2011), New Zealand Robins (Shaw, Boogert, Clayton, & Burns, 2015), cotton-top tamarins (Banerjee et al., 2009), rhesus macaques (Herndon, Moss, Rosene, & Killiany, 1997), and chimpanzees (Herrmann, Hernández-Lloreda, Call, Hare, & Tomasello, 2010;Hopkins, Russell, & Schaeffer, 2014;Woodley of Menie, Fernandes, & Hopkins, 2015). ...
For over a century, theories of human intelligence have concentrated on a single general factor, the psychometric g, which is used to estimate reasoning ability and cognitive flexibility, i.e. general intelligence. To better understand the evolution of general intelligence, it is important to identify the presence of a psychometric g in nonhuman animals, especially in primates, and to further disentangle the influences affecting its development. We therefore investigated the cognitive abilities of 53 Bornean and Sumatran orangutans to assess the presence of a psychometric g, and to explore possible influences on its expression. We did so using a set of carefully selected physical cognition tasks addressing abilities of inhibitory control, behavioral flexibility, causal reasoning, tool use, and associative- and reversal learning, and presented tasks to the subjects in the absence of human experimenters. A principal component analysis of the individuals' performances revealed a single component, which accounted for 31% of the individual variation in task performance. This g could not be explained by non-cognitive confounding variables, such as health status, island of origin, or rearing background. Furthermore, we found a modest correlation between an individual's independently assessed curiosity and g, which is consistent with the notion that accumulating experience affects the developmental construction of g. Together, our results suggest there is evidence for general intelligence in orangutans comparable to humans and chimpanzees, and thus evolutionary continuity in this trait.
... Mice that do well in one task of the battery tend to perform well in other tasks within the battery too, revealing a positive correlation of each animal's learning across all tasks. The 'general learning' scores derived from a factor analysis also covaries with other cognitive domains, such as inductive and deductive reasoning [17], spatial ability [18] and working memory and attention [19]. That means that the common factor behind performance in the learning bat- teries is capturing something cognitively more general (across domains) than simply learning. ...
General cognitive ability can be highly heritable in some species, but at the same time, is very malleable. This apparent paradox could potentially be explained by gene–environment interactions and correlations that remain hidden due to experimental limitations on human research and blind spots in animal research. Here, we shed light on this issue by combining the design of a sibling study with an environmental intervention administered to laboratory mice. The analysis included 58 litters of four full-sibling genetically heterogeneous CD-1 male mice, for a total of 232 mice. We separated the mice into two subsets of siblings: a control group (maintained in standard laboratory conditions) and an environmental-enrichment group (which had access to continuous physical exercise and daily exposure to novel environments). We found that general cognitive ability in mice has substantial heritability (24% for all mice) and is also malleable. The mice that experienced the enriched environment had a mean intelligence score that was 0.44 standard deviations higher than their siblings in the control group (equivalent to gains of 6.6 IQ points in humans). We also found that the estimate of heritability changed between groups (55% in the control group compared with non-significant 15% in the enrichment group), analogous to findings in humans across socio-economic status. Unexpectedly, no evidence of gene–environment interaction was detected, and so the change in heritability might be best explained by higher environmental variance in the enrichment group. Our findings, as well as the ‘sibling intervention procedure’ for mice, may be valuable to future research on the heritability, mechanisms and evolution of cognition.
This article is part of the theme issue ‘Causes and consequences of individual differences in cognitive abilities’.
... One major source of evidence in favor of domain-general abilities in nonhuman animals is provided by interspecific patterns, where species differences in cognitive performance on a wide variety of tests are captured by a single variable , to which we refer here as G to distinguish it from the intraspecific g. Additionally, an increasing number of studies addresses the existence of a psychometric g in a variety of different taxa (reviews: Chabris 2007, Matzel et al. 2011, including dogs (Arden and Adams 2016), mice (favorite mouse psychometric test Galsworthy et al. 2002, Locurto et al. 2003, Matzel et al. 2003, Matzel et al. 2011, Wass et al. 2012, Matzel et al. 2017 , rats (Anderson 1993), bowerbirds (Keagy et al. 2011), New ...
... However, not all mice perform similarly, and while some exhibit perfect performance, some consistently make incorrect choices. The likelihood of a mouse's success in this fast mapping task is correlated with their performance on other more elemental cognitive tasks (e.g., associative learning, spatial learning, operant learning), suggesting that as in humans, this form of reasoning ability is related to more general cognitive abilities (Wass et al. 2012). ...
... Nevertheless, as in humans, the derived g factors have been shown to covary with executive functions, such as selective attention Colas-Zelin, Denman-Brice, Waddel & Kolata 2011) and working memory (in particular working memory capacity: Kolata et al. 2005;Matzel et al. 2008;Sauce et al. 2014) as well as performance in tests of reasoning. For instance, g derived from a standard mouse test battery predicted performance in inductive (finding efficient search strategies in a complex maze) and deductive reasoning (inferring the meaning of a novel item by exclusion, i.e. "fast mapping": Wass et al. 2012). Working memory training did increase g , mainly through its positive effect on selective attention ; see also Sauce et al. 2014). ...
The presence of general intelligence poses a major evolutionary puzzle, which has led to increased interest in its presence in nonhuman animals. The aim of this review is to critically evaluate this puzzle, and to explore the implications for current theories about the evolution of cognition. We first review domain-general and domain-specific accounts of human cognition in order to situate attempts to identify general intelligence in nonhuman animals. Recent studies are consistent with the presence of general intelligence in mammals (rodents and primates). However, the interpretation of a psychometric g-factor as general intelligence needs to be validated, in particular in primates, and we propose a range of such tests. We then evaluate the implications of general intelligence in nonhuman animals for current theories about its evolution and find support for the cultural intelligence approach, which stresses the critical importance of social inputs during the ontogenetic construction of survival-relevant skills. The presence of general intelligence in nonhumans implies that modular abilities can arise in two ways, primarily through automatic development with fixed content and secondarily through learning and automatization with more variable content. The currently best-supported model, for humans and nonhuman vertebrates alike, thus construes the mind as a mix of skills based on primary and secondary modules. The relative importance of these two components is expected to vary widely among species, and we formulate tests to quantify their strength.
... In this regard, the use of mammalian animal models such as the mouse can be especially useful because individuals can be safely housed individually after the time of weaning (and long prior to adolescence). In the past, we have developed behavioral and analysis methods with which it is possible to characterize the general cognitive ability of outbred laboratory mice (Kolata et al., 2005;Matzel et al., 2003;Matzel et al., 2006;Wass et al., 2012), and this cognitive trait has been described as qualitatively analogous to what is described in humans as intelligence (Blinkhorn, 2003). This approach makes it possible to ascertain the degree to which social submission and general cognitive ability are related. ...
... In parallel with research on the evolution of behavioral plasticity and cognition, there is an increasing amount of work indicating that repeatable individual differences in cognitive abilities measured across batteries of standardized cognitive tests are underpinned by the existence of a general process factor referred to as a general intelligence factor, g (Matzel et al., 2003;Matzel, Wass, & Kolata, 2011;Sauce 24 A.S. Griffin and D. Guez Advances in the Study of Behavior, First Edition, 2016, 1e40 & Matzel, 2013; but see Locurto, Benoit, Crowley, & Miele (2006)). This latent process explains between 30% and 40% of interindividual variation in performance and is typically operationalized by faster learning Wass et al., 2012). A substantial body of experimental work in rodents manipulating the processes that contribute to g has established that individual differences in g are related to enhanced selective attention, one component of working memory (Colas-Zelin et al., 2012;Light, Grossman, Kolata, & Matzel, 2011;Light, Kolata, Hale, Grossman, & Matzel, 2008;Matzel, Muzzio, & Talk, 1996;Matzel et al., 2011;Wass et al., 2013). ...
Behavioral innovations, the invention of new behaviors or the use of preexisting ones in new contexts, are increasingly considered an essential source of behavioral plasticity, yet the mechanisms by which they arise are poorly understood. In recent years, much emphasis has been placed on the role of cognition. Here, we review briefly key findings from large-scale comparative research and, in more detail, those from experimental work on innovation. We draw attention to the high degree of inconsistency among empirical findings relating interindividual variation in innovativeness to interindividual variation in learning performance. We go on to propose a model that reconciles the possible (but perhaps controversial) existence of positive associations between cognition and innovation at the cross-taxon level with inconsistent associations at the within-species level. A key component of the model is the possibility that the association between cognition (ie, brain size) and innovativeness is not causal. Rather, it arises as a consequence of correlated evolution because both cognition and diet generalism evolve in response to environmental variability. Furthermore, motor diversity constitutes a proximate link between diet generalism and innovativeness.
... In order to test whether cognitive abilities are correlated or not, individual-level data on task performance need to be collected, in a sample of reasonable size. This has been done in mice (Galsworthy, Paya-Cano, Monleon, & Plomin, 2002; Locurto, Fortin, & Sullivan, 2002; Matzel et al., 2003; Wass et al., 2012), where a g factor was found, and in chimpanzees (Banerjee et al., 2009; Herrmann & Call, 2012; Hopkins, Russell, & Schaeffer, 2014) where a g factor was found in two out of three studies. We tested the structure of measured cognitive abilities in dogs. ...
... They tested individual mice from a single outbred line using a battery of five to nine different learning tasks, and found that 32-48% of the variance across tasks could be attributable to a single factor, which they described as 'general learning ability'. In addition, they reported that scores on this general learning factor were positively related, across individuals, with scores on various tests of reasoning ability ( Wass et al., 2012). A scattering of studies on other species have used similar techniques to ask whether there are individual differences in the rates at which individuals learn different tasks. ...
Interest in individual differences in animal behavioural plasticities has surged in recent years, but research in this area has been hampered by semantic confusion as different investigators use the same terms (e.g. plasticity, flexibility, responsiveness) to refer to different phenomena. The first goal of this review is to suggest a framework for categorizing the many different types of behavioural plasticities, describe examples of each, and indicate why using reversibility as a criterion for categorizing behavioural plasticities is problematic. This framework is then used to address a number of timely questions about individual differences in behavioural plasticities. One set of questions concerns the experimental designs that can be used to study individual differences in various types of behavioural plasticities. Although within-individual designs are the default option for empirical studies of many types of behavioural plasticities, in some situations (e.g. when experience at an early age affects the behaviour expressed at subsequent ages), 'replicate individual' designs can provide useful insights into individual differences in behavioural plasticities. To date, researchers using within-individual and replicate individual designs have documented individual differences in all of the major categories of behavioural plasticities described herein. Another important question is whether and how different types of behavioural plasticities are related to one another. Currently there is empirical evidence that many behavioural plasticities [e.g. contextual plasticity, learning rates, IIV (intra-individual variability), endogenous plasticities, ontogenetic plasticities) can themselves vary as a function of experiences earlier in life, that is, many types of behavioural plasticity are themselves developmentally plastic. These findings support the assumption that differences among individuals in prior experiences may contribute to individual differences in behavioural plasticities observed at a given age. Several authors have predicted correlations across individuals between different types of behavioural plasticities, i.e. that some individuals will be generally more plastic than others. However, empirical support for most of these predictions, including indirect evidence from studies of relationships between personality traits and plasticities, is currently sparse and equivocal. The final section of this review suggests how an appreciation of the similarities and differences between different types of behavioural plasticities may help theoreticians formulate testable models to explain the evolution of individual differences in behavioural plasticities and the evolutionary and ecological consequences of individual differences in behavioural plasticities.
© 2015 Cambridge Philosophical Society.
... For tasks utilizing food reinforcers, animals were food-deprived 48 h prior to training by allowing only 90 min of access to food within 2 h of the end of the light cycle. All of the procedures for the five tasks have been recently described in Wass et al. (2012); therefore, only brief descriptions of the tasks, as well as some of the neuroanatomical areas implicated in the processing of each task, will be discussed here. ...
A common source of variance (i.e., "general intelligence") underlies an individual's performance across diverse tests of cognitive ability, and evidence indicates that the processing efficacy of working memory may serve as one such source of common variance. One component of working memory, selective attention, has been reported to co-vary with general intelligence, and dopamine D1 signaling in prefrontal cortex can modulate attentional abilities. Based on their aggregate performance across five diverse tests of learning, here we characterized the general cognitive ability (GCA) of CD-1 outbred mice. In response to a D1 agonist (SKF82958, 1 mg/kg), we then assessed the relationship between GCA and activation of D1 receptor (D1R)-containing neurons in the prelimbic region of the medial prefrontal cortex, the agranular insular cortex, and the dorsomedial striatum. Increased activation of D1R-containing neurons in the prelimbic cortex (but not the agranular insular cortex or dorsomedial striatum) was observed in animals of high GCA relative to those of low GCA (quantified by c-Fos activation in response to the D1 agonist). However, a Western blot analysis revealed no differences in the density of D1Rs in the prelimbic cortex between animals of high and low GCA. Last, it was observed that working memory training promoted an increase in animals' GCA and enhanced D1R-mediated neuronal activation in the prelimbic cortex. These results suggest that the sensitivity (but not density) of D1Rs in the prelimbic cortex may both regulate GCA and be a target for working memory training.
Great ape cognition is used as a reference point to specify the evolutionary origins of complex cognitive abilities, including in humans. This research often assumes that great ape cognition consists of cognitive abilities (traits) that account for stable differences between individuals, which change and develop in response to experience. Here, we test the validity of these assumptions by assessing repeatability of cognitive performance among captive great apes (Gorilla gorilla, Pongo abelii, Pan paniscus, Pan troglodytes) in five tasks covering a range of cognitive domains. We examine whether individual characteristics (age, group, test experience) or transient situational factors (life events, testing arrangements or sociality) influence cognitive performance. Our results show that task-level performance is generally stable over time; four of the five tasks were reliable measurement tools. Performance in the tasks was best explained by stable differences in cognitive abilities (traits) between individuals. Cognitive abilities were further correlated, suggesting shared cognitive processes. Finally, when predicting cognitive performance, we found stable individual characteristics to be more important than variables capturing transient experience. Taken together, this study shows that great ape cognition is structured by stable cognitive abilities that respond to different developmental conditions.
Substantial improvements in factors such as microbiological quality have been noted in laboratory rodent (mouse [Mus musculus] and rat [Rattus norvegicus]) populations over the last 140 years, since domestication of laboratory strains started. These environmental improvements may have caused Flynn effect-like cognitive changes to occur in these populations, perhaps if these improvements enhanced cognitive plasticity and, consequently, learning potential. While lack of relevant data precludes cross-temporal comparison of cognitive performance means of laboratory rodent populations, it is possible to estimate changes in the proportion of cognitive performance variance attributable to general cognitive ability (GCA) over time. This “differentiation effect” has been found to occur along with the Flynn effect in human populations, suggesting that environmental factors, possibly mediated by their effects on life history speed, may weaken the manifold of GCA across time, allowing for greater cultivation of specialized abilities. Meta-analysis of the literature on mouse and rat cognition yielded 25 mouse studies from which 28 GCA effect sizes could be estimated, and 10 rat studies from which 11 effect sizes could be estimated. Cross-temporal meta-analysis yielded evidence of significant “differentiation effects” spanning approximately a century in both mice and rats, which were independent of age, sex, factor estimation technique, and task number in the case of the mice, and both factor estimation technique and task number in the case of the rats. These trends were also independent of the random effect of strain in both cases. While this is suggestive of the presence of the Flynn effect in captive populations of non-human animals, there are still factors that might be confounding these results. This meta-analysis should be followed up with experimental investigation.
General cognitive ability (or general intelligence; g) is a latent variable that describes performance across a broad array of cognitive skills. This general influence on cognitive ability varies between individuals and shares a similar structure in both humans and mice. Evidence suggests that much of the variation in general intelligence is related to the efficacy of the working memory system. We have previously observed that one component of the working memory system, selective attention, disproportionately accounts for the relationship between working memory and general intelligence in genetically heterogeneous mice. In the three studies reported here, we test a hypothesis that emerges from human behavioral studies which suggests that attentional disengagement, a sub-component of selective attention, critically mediates its relationship with g. Studies 1 and 2 both assess the factor loadings (on a principal component analysis) of the performance of mice on an array of learning tasks as well as tasks designed to make explicit demands on attentional disengagement. We find that attentional disengagement tasks load more highly than measures of cognitive performance that place less explicit demands on disengagement and that performance in these disengagement tasks is strongly predictive of the general cognitive performance of individual animals. In Study 3 we observed that groups of mice (young and old) with known differences in general cognitive abilities differ more on a discrimination task that requires attentional disengagement than on a simple discrimination task with fewer demands on disengagement. In total, these results provide support for the hypothesis that attentional disengagement is strongly related to general intelligence, and that variations in this ability may contribute to both individual differences in intelligence as well as age-related cognitive declines.
In both humans and mice, performance on tests of intelligence or general cognitive ability (GCA) is related to dopamine D1 receptor-mediated activity in the prelimbic cortex, and levels of DRD1 mRNA predict the GCA of mice. Here we assessed the turnover rate of D1 receptors as well as the expression level of the D1 chaperone protein (DRiP78) in the medial PPC (mPFC) of mice to determine whether rate of receptor turnover was associated with variations in the GCA of genetically heterogeneous mice. Following assessment of GCA (aggregate performance on four diverse learning tests) mice were administered an irreversible dopamine receptor antagonist (EEDQ), after which the density of new D1 receptors were quantified. GCA was positively correlated with both the rate of D1 receptor recovery and levels of DRiP78. Additionally, the density of D1 receptors was observed to increase within 60 min (or less) in response to intense demands on working memory, suggesting that a pool of immature receptors was available to accommodate high cognitive loads. These results provide evidence that innate general cognitive abilities are related to D1 receptor turnover rates in the prefrontal cortex, and that an intracellular pool of immature D1 receptors are available to accommodate cognitive demands.
This commentary contrasts evolutionary plausibility with empirical evidence and cognitive continuity with radiation and convergent evolution. So far, neither within-species nor between-species comparisons on the basis of rigorous experimental and species-appropriate tests substantiate the claims made in the target article. Caution is advisable on meta-analytical comparisons that primarily rely on publication frequencies and overgeneralizations (from murids and primates to other nonhuman animals).
Attention is a component of the working memory system, and as such, is responsible for protecting task-relevant information from interference. Cognitive performance (particularly outside of the laboratory) is often plagued by interference, and the source of this interference, either external or internal, might influence the expression of individual differences in attentional ability. By definition, external attention (also described as “selective attention”) protects working memory against sensorial distractors of all kinds, while internal attention (also called “inhibition”) protects working memory against emotional impulses, irrelevant information from memory, and automatically-generated responses. At present, it is unclear if these two types of attention are expressed independently in non-human animals, and how they might differentially impact performance on other cognitive processes, such as learning. By using a diverse battery of four attention tests (with varying levels of internal and external sources of interference), here we aimed both to explore this issue, and to obtain a robust and general (less task-specific) measure of attention in mice. Exploratory factor analyses revealed two factors (external and internal attention) that in total, accounted for 73% of the variance in attentional performance. Confirmatory factor analyses found an excellent fit with the data of the model of attention that assumed an external and internal distinction (with a resulting correlation of 0.43). In contrast, a model of attention that assumed one source of variance (i.e., “general attention”) exhibited a poor fit with the data. Regarding the relationship between attention and learning, higher resistance against external sources of interference promoted better new learning, but tended to impair performance when cognitive flexibility was required, such as during the reversal of a previously instantiated response. The present results suggest that there can be (at least) two types of attention that contribute to the common variance in attentional performance in mice, and that external and internal attentions might have opposing influences on the rate at which animals learn.
Contrast agents are commonly used in combination with magnetic resonance imaging (MRI) to monitor the distribution of molecules in the brain. Recent experiments conducted in our laboratory have shown that co-infusion of recombinant Adeno-associated virus serotype 5 (rAAV5) and the MRI contrast agent gadoteridol (Gd) enhances vector transduction in the rat striatum. The goal of this study was to determine whether gadoteridol may also be used as a tool to enhance transduction efficiency of rAAV1 and rAAV5 within the rat hippocampus. We show that Gd/rAAV1-GFP but not Gd/rAAV5-GFP co-infusion results in significantly higher distribution of the transgene both in the injected hemisphere as well as in the contralateral side and adjacent areas of cortex along the injection track. We also show that Gd/rAAV1-GFP co-infusion has no deleterious effect on hippocampal function as assessed by two tests of spatial memory formation. This work indicates that Gd can be exploited as a method to increase transduction efficiency of AAV1 in the hippocampus for animal studies.Gene Therapy advance online publication, 19 September 2013; doi:10.1038/gt.2013.47.
An individual’s performance across multiple cognitive tests tends to co-vary. This ubiquitous observation suggests that various cognitive domains are regulated in common, and this co-variance underlies the interpretation of many quantitative tests of “intelligence”. As in humans, we find that differences in intelligence exist across genetically heterogeneous mice. Specifically, we have observed a co-variance in the performance of mice across diverse tests of learning, reasoning, and attention. As in humans, the processing efficacy of working memory is both correlated with animals’ general cognitive ability and may in some instances serve to regulate behaviors indicative of intelligence. Beyond its axiomatic significance in demonstrating the evolutionary conservation of a cognitive trait, studies of mice may provide unique opportunities to assess the molecular (e.g., brain-specific RNA expression; transgenics) and neuroanatomic substrates for intelligence. One such approach will be briefly described here, with which we have determined that the signaling efficacy of the dopamine D1 receptor in the prefrontal cortex is one potential link between performance on both working memory tasks and tests of intelligence. In combination, studies of both humans and non-human animals provide converging lines of evidence that might evade either approach in isolation.
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