More complex brains are not always better: rats outperform humans in implicit category-based generalization by implementing a similarity-based strategy

Vermaercke, Ben; Cop, Elsy; Willems, Sam; D’Hooge, Rudi; Beeck, Hans Op de

doi:10.3758/s13423-013-0579-9

Cited by 21 publications

(21 citation statements)

References 26 publications

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“…In other situations, surprisingly, rodents can sometimes perform better on visual tasks than humans. For example, Vermaercke, Cop, Willems, D'Hooge, and Op de Beeck (2014) have shown that rats significantly outperformed humans in terms of generalization in an informationintegration categorization task. This study provides evidence that category learning and generalization in humans are dimension based, whereas rats use a similarity-based generalization strategy.…”

Section: Introductionmentioning

confidence: 99%

Face categorization and behavioral templates in rats

et al. 2019

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Rodents have become a popular model in vision science. It is still unclear how vision in rodents relates to primate vision when it comes to complex visual tasks. Here we report on the results of training rats in a face-categorization and generalization task. Additionally, the Bubbles paradigm is used to determine the behavioral templates of the animals. We found that rats are capable of face categorization and can generalize to previously unseen exemplars. Performance is affected-but remains above chance-by stimulus modifications such as upside-down and contrast-inverted stimuli. The behavioral templates of the rats overlap with a pixel-based template, with a bias toward the upper left parts of the stimuli. Together, these findings significantly expand the evidence about the extent to which rats learn complex visual-categorization tasks.

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Section: Introductionmentioning

confidence: 99%

Face categorization and behavioral templates in rats

et al. 2019

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“…Literature has also shown that there is a strikingly similar range of cognitive abilities between rodents and humans, as well as a remarkably high degree of anatomical overlap in their brain functions 47 . Rodents are even able to outperform humans in some learning tasks 48 but we do believe that our slight differing kinetics are mainly accounted by task design variability. Rodents possess preserved metacognitive abilities, which is an essential aspect of the IGT 49 .…”

Section: Discussionmentioning

confidence: 82%

Modeling decision-making under uncertainty: a direct comparison study between human and mouse gambling data

Álvarez

Giustiniani

Chabin

et al. 2019

Preprint

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Decision-making is a conserved evolutionary process enabling to choose one option among several alternatives, and relying on reward and cognitive control systems. The Iowa Gambling Task allows to assess human decision-making under uncertainty by presenting four cards decks with various cost-benefit probabilities. Participants seek to maximize their monetary gains by developing long-term optimal choice strategies. Animal versions have been adapted with nutritional rewards but interspecies data comparisons are still scarce. Our study directly compared physiological decision-making performances between humans and wild-type C57BL/6 mice. Human subjects fulfilled an electronic Iowa Gambling Task version while mice performed a maze-based adaptation with four arms baited in a probabilistic way. Our data show closely matching performances among species with similar patterns of choice behaviors. Moreover, both populations clustered into good, intermediate, and poor decision-making categories with similar proportions. Remarkably, mice good decision-makers behaved as humans of the same category, but slight differences among species have been evidenced for the other two subpopulations. Overall, our direct comparative study confirms the good face validity of the rodent gambling task. Extended behavioral characterization and pathological animal models should help strengthen its construct validity and disentangle determinants of decision-making in animals and humans.

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“…Vandist et al (2009) compared the effects of this semisupervised learning process to supervised and unsupervised learning processes by using the information-integration structure. This category structure is frequently used in category learning research (e.g., Ashby et al, 2002;Ashby & Ell, 2001;Ell & Ashby, 2006;Maddox, Ashby, Ing, & Pickering, 2004a;Maddox & Filoteo, 2011;Maddox & Ing, 2005;Maddox, Pacheco, Reeves, Zhu, & Schnyer, 2010b;Paul, Boomer, Smith, & Ashby, 2011;Spiering & Ashby, 2008a, b;Vermaercke, Cop, Willems, D'Hooge, & Op de Beeck, 2014). Figure 1 shows an example of the informationintegration category structure used in the experiments reported in the current study.…”

Section: For Amentioning

confidence: 99%

Semisupervised category learning facilitates the development of automaticity

Vandist

Storms

Bussche

2018

Atten Percept Psychophys

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In the human category of learning, learning is studied in a supervised, an unsupervised, or a semisupervised way. The rare human semisupervised category of learning studies all focus on early learning. However, the impact of the semisupervised category learning late in learning, when automaticity develops, is unknown. Therefore, in Experiment 1, all participants were first trained on the information-integration category structure for 2 days until they reached an expert level. Afterwards, half of the participants learned in a supervised way and the other half in a semisupervised way over two successive days. Both groups received an equal number of feedback trials. Finally, all participants took part in a test day where they were asked to respond as quickly as possible. Participants were significantly faster on this test in the semisupervised group than in the supervised group. This difference was not found on day 2, implying that the no-feedback trials in the semisupervised condition facilitated automaticity. Experiment 2 was designed to test whether the higher number of trials in the semisupervised condition of Experiment 1 caused the faster response times. We therefore created an almost supervised condition where participants almost always received feedback (95%) and an almost unsupervised condition where participants almost never received feedback (5%). All conditions now contained an equal number of trials to the semisupervised condition of Experiment 1. The results show that receiving feedback almost always or almost never led to slower response times than the semisupervised condition of Experiment 1. This confirms the advantage of semisupervised learning late in learning.

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More complex brains are not always better: rats outperform humans in implicit category-based generalization by implementing a similarity-based strategy

Cited by 21 publications

References 26 publications

Face categorization and behavioral templates in rats

Face categorization and behavioral templates in rats

Modeling decision-making under uncertainty: a direct comparison study between human and mouse gambling data

Semisupervised category learning facilitates the development of automaticity

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