C. Rios’s research while affiliated with Rutgers, The State University of New Jersey and other places

What is this page?


This page lists works of an author who doesn't have a ResearchGate profile or hasn't added the works to their profile yet. It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping. If you are this author and want this page removed, please let us know.

Publications (3)


Figure 2. Performance on individual tasks of animals of highest and lowest general cognitive abilities. General learning abilities were determined by factor scores (of individual animals) derived from a principal component analysis of the acquisition data from all five learning tasks. Illustrated is the mean performance of animals of high and low general learning abilities (for clarity, animals of intermediate abilities are not illustrated.) Animals with high general learning abilities outperformed animals of low general learning abilities in each of the five individual tasks (Lashley Maze [A], odor-guided discrimination [B], Morris Water Maze [C], fear conditioning [D], and passive avoidance (E]). Brackets indicate standard error of the mean.
Figure 3. 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).
Figure 6. Performance in the binary decision tree. Plotted is the average streak length of all 47 animals tested in this maze, where a streak of 24 would reflect optimal efficiency. Animals' performance was initially erratic, but stabilized within six trials and remained stable thereafter. Although several animals performed at optimal efficiency during the last four trials, other animals exhibited unsystematic performance. Streak lengths (across animals) ranged from 4-24 on each of the last four trials. Brackets indicate standard error of the mean.
Figure 7. Individual animals' performance in the binary decision maze is predicted by their aggregate (general) learning ability. Panel A: Factor scores for each animal were derived from a principal component analysis of all animals' performance on five learning tasks. These scores reflect animals' aggregate performance across all five tasks. (Note that lower factor scores better aggregate learning performance.) A significant correlation was observed between animals' factor scores and the number of node crossings prior to unnecessarily crossing a node ( " streak " performance ) on the last four (of 10) test trials. Thus, the efficacy of an animal's search (a form of inductive reasoning) was predicted by their general learning ability. Panel B: Three groups of animals were formed based on the upper, middle, and bottom third of factor scores (reflective of general learning performance). The average streak length (indicative of search efficacy) differed across these three groups. Plotted is the animals that performed best (High), intermediate, and worst (Low) on the battery of learning tasks. Brackets indicate standard errors. Panel C: After an animal reached its first low-level terminal node, the adjacent entry point was blocked with a sliding door. This was intended to disrupt any rote path (i.e., algorithmic strategy) that an animal may have developed in lieu of comprehension of the overall structure of the maze. Plotted is the average streak during three such probe trials against factor scores obtained from the principal component analysis of learning performance. (Note that lower factor scores better aggregate learning performance.) Again, a significant correlation between general learning abilities and search efficacy was observed.  
Covariation of Learning and "Reasoning" Abilities in Mice: Evolutionary Conservation of the Operations of Intelligence
  • Article
  • Full-text available

March 2012

·

869 Reads

·

27 Citations

Journal of Experimental Psychology Animal Behavior Processes

·

Alexander Denman-Brice

·

Chris Rios

·

[...]

·

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.

Download


Covariation of learning and "reasoning" abilities in mice: evolutionary conservation of the operations of intelligence

January 2004

·

47 Reads

·

33 Citations

Journal of Experimental Psychology Animal Behavior Processes

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

Citations (2)


... 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. ...

Reference:

Retention and impairment of neurocognitive functions in mild cognitive impairment and Alzheimer’s disease with a comprehensive neuropsychological test
Covariation of learning and "reasoning" abilities in mice: evolutionary conservation of the operations of intelligence
  • Citing Article
  • January 2004

Journal of Experimental Psychology Animal Behavior Processes

... 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. ...

Covariation of Learning and "Reasoning" Abilities in Mice: Evolutionary Conservation of the Operations of Intelligence

Journal of Experimental Psychology Animal Behavior Processes