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Fast mapping test performance, Experiment 1A. Three groups of animals were formed based on the upper, middle, and bottom third of factor scores (reflective of general learning performance) obtained from the principal component analysis of learning test performance in Experiment 1A. Plotted is average number of errors ( standard error) on the fast mapping test trial of the animals that performed best (High), intermediate, and worst (Low) on the battery of learning tasks. For this task, one error (on average) would be expected in a random search (assuming that repeated errors were not committed, in which case, the number of errors could increase).
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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 ha...
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Citations
... Although the negative correlation loses statistical significance after correction, we still regard it as a correlated trend worth discussing. Reasoning and problemsolving function was assessed using the NAB maze tracking task, which involves inductive reasoning-a crucial aspect of generating predictions and one of the most significant problemsolving activities (Wass et al., 2012). In terms of the relationship between reasoning and problem-solving function and diffusion indicators, Zahr et al. (2009) discovered a positive correlation between problem-solving function and FA values in genu and fornix. ...
Background and objective
Peak width of skeletonized mean diffusivity (PSMD), a fully automated diffusion tensor imaging (DTI) biomarker of white matter (WM) microstructure damage, has been shown to be associated with cognition in various WM pathologies. However, its application in schizophrenic disease remains unexplored. This study aims to investigate PSMD along with other DTI markers in first-episode schizophrenia patients compared to healthy controls (HCs), and explore the correlations between these metrics and clinical characteristics.
Methods
A total of 56 first-episode drug-naive schizophrenia patients and 64 HCs were recruited for this study. Participants underwent structural imaging and DTI, followed by comprehensive clinical assessments, including the Positive and Negative Syndrome Scale (PANSS) for patients and cognitive function tests for all participants. We calculated PSMD, peak width of skeletonized fractional anisotropy (PSFA), axial diffusivity (PSAD), radial diffusivity (PSRD) values, skeletonized average mean diffusivity (MD), average fractional anisotropy (FA), average axial diffusivity (AD), and average radial diffusivity (RD) values as well as structural network global topological parameters, and examined between-group differences in these WM metrics. Furthermore, we investigated associations between abnormal metrics and clinical characteristics.
Results
Compared to HCs, patients exhibited significantly increased PSMD values (t = 2.467, p = 0.015), decreased global efficiency (Z = −2.188, p = 0.029), and increased normalized characteristic path length (lambda) (t = 2.270, p = 0.025). No significant differences were observed between the groups in the remaining metrics, including PSFA, PSAD, PSRD, average MD, FA, AD, RD, local efficiency, normalized cluster coefficient, small-worldness, assortativity, modularity, or hierarchy (p > 0.05). After adjusting for relevant variables, both PSMD and lambda values exhibited a significant negative correlation with reasoning and problem-solving scores (PSMD: r = −0.409, p = 0.038; lambda: r = −0.520, p = 0.006). No statistically significant correlations were observed between each PANSS score and the aforementioned metrics in the patient group (p > 0.05). Multivariate linear regression analysis revealed that increased PSMD (β = −0.426, t = −2.260, p = 0.034) and increased lambda (β = −0.490, t = −2.994, p = 0.007) were independently associated with decreased reasoning and problem-solving scores respectively (Radj2 = 0.295, F = 2.951, p = 0.029). But these significant correlations did not withstand FDR correction (p_FDR > 0.05).
Conclusion
PSMD can be considered as a valuable neuroimaging biomarker that complements conventional diffusion measurements for investigating abnormalities in WM microstructural integrity and cognitive functions in schizophrenia.
... Wass et al. (2012) show evidence for deductive and inductive reasoning of mice.Russell et al. (1996) summarize information on the capability of drawing deductive transitive inference of monkeys, pigeons, rats and chimpanzees.Sauce and Matzel (2017) enumerate examples of inductive reasoning of sea slugs, rodents, dogs, cats, chimpanzees, chicks, pigeons etc. ...
Following ideas of Ch. S. Peirce on continuity of mind (synechism) and universality of semiotic processes (pansemiotism) as well as development of the understanding of manipulative abduction in works of L. Magnani the thesis of possibility of abductive reasoning in non-human animal minds is defended. The animal capacity to form explanatory hypotheses is demonstrated by instances of grasping regularities in environment, behavior of conspecifics and even self-knowledge. In the framework of debate on instinctual or rather inferential nature of abductive capacity questions of innate and acquired mechanisms of learning, the role of language in development of explanations and priority of inner (emotional) or outer (referential) perspectives in genesis of first explanatory hypotheses are considered.
... Although there is no nal de nition yet, a generally accepted concept of intelligence might be "a measure of an agent's ability to achieve goals in a wide range of environments", based on Legg and Hutter's synthesis of more than seventy de nitions (Legg and Hutter 2007a, b; Youse an et al. 2016). Generally, intelligence includes the ability to adapt to the environment and to learn quickly, with adaptability and exibility (Zador et al. 2023; Wass et al. 2012). In the nature, many animals have developed such skills to cope with changing environments and unpredictable events, especially when it comes to the most critical survival situations such as predation and escape. ...
Most animals must reserve their limited intelligence for the most important situations, such as predation and escape, in order to have a better chance of survival. As a highly sequentially programmed behavior driven by innate desire, one of the most challenging parts of predation is how the predator can pursue and capture an escaping prey that is also running for its own survival. This requires the predator to synthesize environmental and prey information to make dynamic decisions in real time to guide appropriate behavior. However, it is still largely unclear whether and how mice can cope with such challenge. Here, we developed a real-time interactive platform to study the pursuit behavior during predation in rodents. An artificial prey was magnetically controlled by a closed-loop system that attempts to escape an approaching predator (e.g., a hungry mouse) in real time. By recording the time costs, trajectories and other parameters of both predator and prey, we found that not only were the mice able to complete predation tasks of varying difficulty, but that they could also improve their predation efficiency over trials, mainly due to the improvements in the pursuit phase. Further investigation revealed that the increase in pursuit performance may not entirely achieved by physical improvement, but rather by optimization of velocity control as well as a change of navigation strategy. In conclusion, this study reveals that mice are capable of making dynamic decisions during predatory pursuit, and the transition from novice to veteran can be used to study the biological mechanisms of dynamic decision making in mice.
... In other words, this inductive reasoning optimizes strategies and decision-making by deriving the "whole" from samples of the component parts. Wass et al. (2012) studied a form of inductive reasoning for foraging in mice using a Binary Tree Maze, inspired by procedures developed in human decision analysis for identifying the most efficient strategies to reach a goal. The Binary Tree Maze is a decision tree that bifurcates (at decision points) into branches. ...
... (However, some mice still performed poorly, which is indicative of wide variability in those mice's inductive reasoning.) Furthermore, Wass et al. (2012) also determined if mice were relying on rote paths through the maze or whether they were engaging in an active search of the maze (a requisite for inductive reasoning). To make this determination, each mouse was allowed to begin its exploration of the maze, and upon making its first entry into a second level branch, the adjacent branch was blocked by lowering a black guillotine door. ...
... Upon completion of testing in the Lashley maze, an additional cognitive test was administered in a distinct piece of apparatus (a decision tree). The Decision Tree maze is a "tree" shaped maze constructed from black Plexiglass with a start box and series of bifurcating arms at seven symmetric locations, "nodes, " after an initial split dividing the maze in two symmetrical halves (see Wass et al., 2012, for an illustration of the maze). Before the initial division in the maze sits an alley that originates from a starting box with a removable door where mice begin the test. ...
Although genetically heterogeneous laboratory mice express individual differences in general cognitive ability (c.f., “intelligence”), it is unknown whether these differences are translated into behaviors that would promote survival. Here, genetically heterogeneous laboratory CD-1 mice were administered a series of cognitive tests from which their aggregate general cognitive ability was estimated. Subsequently, all animals were tested on nine (unlearned) tasks designed to assess behaviors that could contribute to survival in the wild. These tests included nest building (in the home and a novel environment), exploration, several indices of food finding, retrieval, and preference, and predator avoidance. Like general cognitive ability, a principal component analysis of these measures of survival-related behaviors (survival-readiness) yielded a general factor that accounted for ∼25% of the variance of mice across all of the tasks. An aggregate metric of general cognitive ability predicted an aggregate metric of general survival-readiness (r = 0.64), suggesting that more intelligent animals would be more suited for survival in natural environments. The nature of the pattern of correlations between general cognitive ability and performance on individual tests of survival-readiness (where tests conducted in previously unexplored contexts were more closely related to general cognitive ability) suggests the possibility that heightened attention (which is taxed in a novel environment) may be the common mediator of both of these classes of abilities, although other potential mediators are discussed. In total, these results suggest that performance on tasks that are explicitly intended to assess the likelihood of survival can be impacted by cognitive abilities.
... Mice with high general intelligence would explore the maze in efficient paths (i.e., cross the same node only en route to an unexplored node) while mice with lower intelligences would take meandering paths and make many unnecessary node crossings (errors) in exploring the maze. The efficiency with which an animal searched the maze has been said to be emblematic of inductive reasoning, and performance in this maze (efficiency of search for food) has previously been shown to load heavily (0.49) on a factor analysis describing a general intelligence factor (Wass et al., 2012). ...
... We used one measures from the maze, an animal's "streak", defined as the number of necessary node crossings an animal made before making an unnecessary crossing over a node it had previously explored. For additional details about the construction of this maze, see Wass et al. (2012). ...
... Animals were tasked with exploring each of the maze nodes (labeled 1-14 above) for a food reward during each trial. Four to eight random nodes were baited with food during each trial following an initial acclimation trial with all nodes baited, The efficiency of animals' maze navigation was determined by measuring the "streak" of necessary node crossings made before making an unnecessary crossing (figure source: Wass et al., 2012). ...
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.
... 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 batteries 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’.
... A "Y" in column g indicates that the authors describe results as evidence for a "general learning ability" or a factor analogous to g, an "N" indicates that the authors conclude that there is no evidence for a g factor Table 1). These studies have reported that the general factor underpinning performance in this test battery is positively correlated with inferential exclusion (measured as fast-mapping task performance; Wass et al. 2012), working memory capacity and selective attention (Kolata et al. 2005(Kolata et al. , 2007Light et al. 2010). To date, chimpanzee test batteries have included the largest number of tasks across the most domains (Herrmann et al. 2010;Herrmann and Call 2012), but have found little evidence for a general factor underlying task performance (but see Hopkins et al. 2014). ...
... The greatest challenge in terms of quantifying interindividual variation across several cognitive domains lies in designing an appropriate test battery. In contrast to the languagebased approach used in psychometric studies of human PCA principal components analysis with an unrotated solution, unless otherwise specified, PC1 principal component 1, CFA confirmatory factor analysis a This test battery included two "non-cognitive" measures, "stress" and "activity" b These test batteries all contained the five original tasks used by Matzel et al. (2003), but included additional measures of working memory (Kolata et al. 2005), visual discrimination, short term memory capacity and duration, and selective attention (Kolata et al. 2007), spatial win-stay and reinforced alteration (Kolata et al. 2008), as well as visual fast-mapping (Wass et al. 2012) c Subjects tested in their natural habitat in the wild Content courtesy of Springer Nature, terms of use apply. Rights reserved. ...
For the past two decades, behavioural ecologists have documented consistent individual differences in behavioural traits within species and found evidence for animal “personality”. It is only relatively recently, however, that increasing numbers of researchers have begun to investigate individual differences in cognitive ability within species. It has been suggested that cognitive test batteries may provide an ideal tool for this growing research endeavour. In fact, cognitive test batteries have now been used to examine the causes, consequences and underlying structure of cognitive performance within and between many species. In this review, we document the existing attempts to develop cognitive test batteries for non-human animals and review the claims that these studies have made in terms of the structure and evolution of cognition. We argue that our current test battery methods could be improved on multiple fronts, from the design of tasks, to the domains targeted and the species tested. Refining and optimising test battery design will provide many benefits. In future, we envisage that well-designed cognitive test batteries may provide answers to a range of exciting questions, including giving us greater insight into the evolution and structure of cognition.
... Despite forcing the mice to deviate from their initial path, the correlation between their maze performance and factor scores from the learning battery remained strong, r .51 ( Wass et al., 2012). After testing in the Binary Tree Maze, we tested the same mice on a second reasoning task based on the concept of fast mapping, a process whereby a new concept can be acquired based on logical elimination, corresponding with Aristotle's description of deductive reasoning. ...
... Following a series of four such trials (interspersed among trials with known paired associates), the number of fast mapping errors (incorrect choices) committed were compared to factor scores indicative of each mice's general learning ability. Better learners tended to make fewer fast mapping errors, with a correlation of .44 between factor scores and fast mapping performance ( Wass et al., 2012). It is also worth noting that the average performance of all mice exceeded what would be expected were the animals choosing randomly, suggesting that along with humans and dogs ( Kaminski, Call, & Fischer, 2004;Pilley & Reid, 2011;Tomasello & Kaminski, 2009), fast mapping is within the cognitive capacity of rodents. ...
Early in the 20th century, individual differences were a central focus of psychologists. By the end of that century, studies of individual differences had become far less common, and attention to these differences played little role in the development of contemporary theory. To illustrate the important role of individual differences, here we consider variations in intelligence as a compelling example. General intelligence (g) has now been demonstrated in at least 2 distinct genera: primates (including humans, chimpanzees, bonobos, and tamarins) and rodents (mice and rats). The expression of general intelligence varies widely across individuals within a species; these variations have tremendous functional consequence, and are attributable to interactions of genes and environment. Here we provide evidence for these assertions, describe the processes that contribute to variations in general intelligence, as well as the methods that underlie the analysis of individual differences. We conclude by describing why consideration of individual differences is critical to our understanding of learning, cognition, and behavior, and illustrate how attention to individual differences can contribute to more effective administration of therapeutic strategies for psychological disorders.
... In other words, this inductive reasoning optimizes strategies and decision-making by deriving the "whole" from samples of the component parts. Wass et al. (2012) studied a form of inductive reasoning for foraging in mice using a Binary Tree Maze, inspired by procedures developed in human decision analysis for identifying the most efficient strategies to reach a goal. The Binary Tree Maze is a decision tree that bifurcates (at decision points) into branches. ...
... (However, some mice still performed poorly, which is indicative of wide variability in those mice's inductive reasoning.) Furthermore, Wass et al. (2012) also determined if mice were relying on rote paths through the maze or whether they were engaging in an active search of the maze (a requisite for inductive reasoning). To make this determination, each mouse was allowed to begin its exploration of the maze, and upon making its first entry into a second level branch, the adjacent branch was blocked by lowering a black guillotine door. ...
