Brain size predicts learning abilities in bees.
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ABSTRACT:
SUBMITTER: Collado MA
PROVIDER: S-EPMC8131939 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Brain structure and learning capacities both vary with experience, but the mechanistic link between them is unclear. Here, we investigated whether experience-dependent variability in learning performance can be explained by neuroplasticity in foraging honey bees. The mushroom bodies (MBs) are a brain center necessary for ambiguous olfactory learning tasks such as reversal learning. Using radio frequency identification technology, we assessed the effects of natural variation in foraging activity, and the age when first foraging, on both performance in reversal learning and on synaptic connectivity in the MBs. We found that reversal learning performance improved at foraging onset and could decline with greater foraging experience. If bees started foraging before the normal age, as a result of a stress applied to the colony, the decline in learning performance with foraging experience was more severe. Analyses of brain structure in the same bees showed that the total number of synaptic boutons at the MB input decreased when bees started foraging, and then increased with greater foraging intensity. At foraging onset MB structure is therefore optimized for bees to update learned information, but optimization of MB connectivity deteriorates with foraging effort. In a computational model of the MBs sparser coding of information at the MB input improved reversal learning performance. We propose, therefore, a plausible mechanistic relationship between experience, neuroplasticity, and cognitive performance in a natural and ecological context.
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Project description:It has become increasingly clear that a larger brain can confer cognitive benefits. Yet not all of the numerous aspects of cognition seem to be affected by brain size. Recent evidence suggests that some more basic forms of cognition, for instance colour vision, are not influenced by brain size. We therefore hypothesize that a larger brain is especially beneficial for distinct and gradually more complex aspects of cognition. To test this hypothesis, we assessed the performance of brain size selected female guppies (Poecilia reticulata) in two distinct aspects of cognition that differ in cognitive complexity. In a standard reversal-learning test we first investigated basic learning ability with a colour discrimination test, then reversed the reward contingency to specifically test for cognitive flexibility. We found that large-brained females outperformed small-brained females in the reversed-learning part of the test but not in the colour discrimination part of the test. Large-brained individuals are hence cognitively more flexible, which probably yields fitness benefits, as they may adapt more quickly to social and/or ecological cognitive challenges. Our results also suggest that a larger brain becomes especially advantageous with increasing cognitive complexity. These findings corroborate the significance of brain size for cognitive evolution.
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Project description:Reversal learning assays are commonly used across a wide range of taxa to investigate associative learning and behavioural flexibility. In serial reversal learning, the reward contingency in a binary discrimination is reversed multiple times. Performance during serial reversal learning varies greatly at the interspecific level, as some animals adopt a rule-based strategy that enables them to switch quickly between reward contingencies. A larger relative brain size, generating enhanced learning ability and increased behavioural flexibility, has been proposed to be an important factor underlying this variation. Here, we experimentally tested this hypothesis at the intraspecific level. We used guppies (Poecilia reticulata) artificially selected for small and large relative brain size, with matching differences in neuron number, in a serial reversal learning assay. We tested 96 individuals over 10 serial reversals and found that learning performance and memory were predicted by brain size, whereas differences in efficient learning strategies were not. We conclude that variation in brain size and neuron number is important for variation in learning performance and memory, but these differences are not great enough to cause the larger differences in efficient learning strategies observed at higher taxonomic levels.
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Project description:BackgroundDespite reasonable accuracy with preoperative templating, the search for an optimal planning tool remains an unsolved dilemma. The purpose of the present study was to apply machine learning (ML) using preoperative demographic variables to predict mismatch between templating and final component size in primary total knee arthroplasty (TKA) cases.MethodsThis was a retrospective case-control study of primary TKA patients between September 2012 and April 2018. The primary outcome was mismatch between the templated and final implanted component sizes extracted from the operative database. The secondary outcome was mismatch categorized as undersized and oversized. Five supervised ML algorithms were trained using 6 demographic features. Prediction accuracies were obtained as a metric of performance for binary mismatch (yes/no) and multilevel (undersized/correct/oversized) classifications.ResultsA total of 1801 patients were included. For binary classification, the best-performing algorithm for predicting femoral and tibial mismatch was the stochastic gradient boosting model (area under the curve: 0.76/0.72, calibration intercepts: 0.05/0.05, calibration slopes: 0.55/0.7, and Brier scores: 0.20/0.21). For multiclass classification, the best-performing algorithms had accuracies of 83.9% and 82.9% for predicting the concordance/mismatch of the femoral and tibial implant, respectively. Model predictions of greater than 51.0% and 47.9% represented high-risk thresholds for femoral and tibial sizing mismatch, respectively.ConclusionsML algorithms predicted templating mismatch with good accuracy. External validation is necessary to confirm the performance and reliability of these algorithms. Predicting sizing mismatch is the first step in using ML to aid in the prediction of final TKA component sizes. Further studies to optimize parameters and predictions for the algorithms are ongoing.
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Project description:BACKGROUND:Artificial rearing of honey bee larvae is an established method which enables to fully standardize the rearing environment and to manipulate the supplied diet to the brood. However, there are no studies which compare learning performance or neuroanatomic differences of artificially-reared (in-lab) bees in comparison with their in-hive reared counterparts. METHODS:Here we tested how different quantities of food during larval development affect body size, brain morphology and learning ability of adult honey bees. We used in-lab rearing to be able to manipulate the total quantity of food consumed during larval development. After hatching, a subset of the bees was taken for which we made 3D reconstructions of the brains using confocal laser-scanning microscopy. Learning ability and memory formation of the remaining bees was tested in a differential olfactory conditioning experiment. Finally, we evaluated how bees reared with different quantities of artificial diet compared to in-hive reared bees. RESULTS:Thorax and head size of in-lab reared honey bees, when fed the standard diet of 160 µl or less, were slightly smaller than hive bees. The brain structure analyses showed that artificially reared bees had smaller mushroom body (MB) lateral calyces than their in-hive counterparts, independently of the quantity of food they received. However, they showed the same total brain size and the same associative learning ability as in-hive reared bees. In terms of mid-term memory, but not early long-term memory, they performed even better than the in-hive control. DISCUSSION:We have demonstrated that bees that are reared artificially (according to the Aupinel protocol) and kept in lab-conditions perform the same or even better than their in-hive sisters in an olfactory conditioning experiment even though their lateral calyces were consistently smaller at emergence. The applied combination of experimental manipulation during the larval phase plus subsequent behavioral and neuro-anatomic analyses is a powerful tool for basic and applied honey bee research.
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Project description:Human infants in the first year of life possess an intuitive sense of number. This preverbal number sense may serve as a developmental building block for the uniquely human capacity for mathematics. In support of this idea, several studies have demonstrated that nonverbal number sense is correlated with mathematical abilities in children and adults. However, there has been no direct evidence that infant numerical abilities are related to mathematical abilities later in childhood. Here, we provide evidence that preverbal number sense in infancy predicts mathematical abilities in preschool-aged children. Numerical preference scores at 6 months of age correlated with both standardized math test scores and nonsymbolic number comparison scores at 3.5 years of age, suggesting that preverbal number sense facilitates the acquisition of numerical symbols and mathematical abilities. This relationship held even after controlling for general intelligence, indicating that preverbal number sense imparts a unique contribution to mathematical ability. These results validate the many prior studies purporting to show number sense in infancy and support the hypothesis that mathematics is built upon an intuitive sense of number that predates language.
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