Achieving arithmetic learning in honeybees and examining how individuals learn.
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ABSTRACT:
SUBMITTER: Howard SR
PROVIDER: S-EPMC6802933 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The honeybee has to detect, process and learn numerous complex odours from her natural environment on a daily basis. Most of these odours are floral scents, which are mixtures of dozens of different odorants. To date, it is still unclear how the bee brain unravels the complex information contained in scent mixtures.This study investigates learning of complex odour mixtures in honeybees using a simple olfactory conditioning procedure, the Proboscis-Extension-Reflex (PER) paradigm. Restrained honeybees were trained to three scent mixtures composed of 14 floral odorants each, and then tested with the individual odorants of each mixture. Bees did not respond to all odorants of a mixture equally: They responded well to a selection of key odorants, which were unique for each of the three scent mixtures. Bees showed less or very little response to the other odorants of the mixtures. The bees' response to mixtures composed of only the key odorants was as good as to the original mixtures of 14 odorants. A mixture composed of the other, non-key-odorants elicited a significantly lower response. Neither an odorant's volatility or molecular structure, nor learning efficiencies for individual odorants affected whether an odorant became a key odorant for a particular mixture. Odorant concentration had a positive effect, with odorants at high concentration likely to become key odorants.Our study suggests that the brain processes complex scent mixtures by predominantly learning information from selected key odorants. Our observations on key odorant learning lend significant support to previous work on olfactory learning and mixture processing in honeybees.
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Project description:Acquisition of numerical knowledge and understanding of numerical information are crucial for coping with the changing demands of our digital society. In this study, we assessed arithmetic learning in older and younger individuals in a training experiment including brain imaging. In particular, we assessed age-related effects of training intensity, prior arithmetic competence, and neuropsychological variables on the acquisition of new arithmetic knowledge and on the transfer to new, unknown problems. Effects were assessed immediately after training and after 3 months. Behavioural results showed higher training effects for younger individuals than for older individuals and significantly better performance after 90 problem repetitions than after 30 repetitions in both age groups. A correlation analysis indicated that older adults with lower memory and executive functions at baseline could profit more from intensive training. Similarly, training effects in the younger group were higher for those individuals who had lower arithmetic competence and executive functions prior to intervention. In younger adults, successful transfer was associated with higher executive functions. Memory and set-shifting emerged as significant predictors of training effects in the older group. For the younger group, prior arithmetic competence was a significant predictor of training effects, while cognitive flexibility was a predictor of transfer effects. After training, a subgroup of participants underwent an MRI assessment. A voxel-based morphometry analysis showed a significant interaction between training effects and grey matter volume of the right middle temporal gyrus extending to the angular gyrus for the younger group relative to the older group. The reverse contrast (older group vs. younger group) did not yield any significant results. These results suggest that improvements in arithmetic competence are supported by temporo-parietal areas in the right hemisphere in younger participants, while learning in older people might be more widespread. Overall, our study indicates that arithmetic learning depends on the training intensity as well as on person-related factors including individual age, arithmetic competence before training, memory, and executive functions. In conclusion, we suggest that major progress can be also achieved by older participants, but that interventions have to take into account individual variables in order to provide maximal benefit.
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Project description:Learning of stimulus sequences is considered as a characteristic feature of episodic memory since it contains not only a particular item but also the experience of preceding and following events. In sensorimotor tasks resembling navigational performance, the serial order of objects is intimately connected with spatial order. Mammals and birds develop episodic(-like) memory in serial spatio-temporal tasks, and the honeybee learns spatio-temporal order when navigating between the nest and a food source. Here I examine the structure of the bees' memory for a combined spatio-temporal task. I ask whether discrimination and generalization are based solely on simple forms of stimulus-reward learning or whether they require sequential configurations. Animals were trained to fly either left or right in a continuous T-maze. The correct choice was signaled by the sequence of colors (blue, yellow) at four positions in the access arm. If only one of the possible 4 signals is shown (either blue or yellow), the rank order of position salience is 1, 2 and 3 (numbered from T-junction). No learning is found if the signal appears at position 4. If two signals are shown, differences at positions 1 and 2 are learned best, those at position 3 at a low level, and those at position 4 not at all. If three or more signals are shown these results are corroborated. This salience rank order again appeared in transfer tests, but additional configural phenomena emerged. Most of the results can be explained with a simple model based on the assumption that the four positions are equipped with different salience scores and that these add up independently. However, deviations from the model are interpreted by assuming stimulus configuration of sequential patterns. It is concluded that, under the conditions chosen, bees rely most strongly on memories developed during simple forms of associative reward learning, but memories of configural serial patterns contribute, too.
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Project description:Unexpected outcomes can reflect noise in the environment or a change in the current rules. We should ignore noise but shift strategy after rule changes. How we learn to do this is unclear, but one possibility is that it relies on learning to learn in uncertain environments. We propose that acquisition of latent task structure during learning to learn, even when not necessary, is crucial. We report results consistent with this hypothesis. Macaque monkeys acquired adaptive responses to feedback while learning to learn serial stimulus-response associations with probabilistic feedback. Monkeys learned well, decreasing their errors to criterion, but they also developed an apparently nonadaptive reactivity to unexpected stochastic feedback, even though that unexpected feedback never predicted problem switch. This surprising learning trajectory permitted the same monkeys, naïve to relearning about previously learned stimuli, to transfer to a task of stimulus-response remapping at immediately asymptotic levels. Our results suggest that learning new problems in a stochastic environment promotes the acquisition of performance rules from latent task structure, providing behavioral flexibility. Learning to learn in a probabilistic and volatile environment thus appears to induce latent learning that may be beneficial to flexible cognition.
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Project description:Gains and losses have previously been found to differentially modulate Executive Functions and cognitive performance depending on performance contingency. Following recent findings suggesting that random gains and losses modulate arithmetic performance, the current study aimed to investigate the effect of perceived performance-contingent gains and losses on arithmetic performance. In the current study, an arithmetic equation judgment task was administered, with perceived performance-contingent gain, loss, and error feedback presented upon each trial. The results from two experiments suggest that when perceiving gain and loss as performance-contingent, the modulation of arithmetic performance, seen previously under random contingency conditions was entirely eliminated. In addition, another type of feedback was examined in the context of an arithmetic task: post-error adjustments. When performance after error feedback was compared to performance after other aversive performance feedback such as loss signals, only errors, but not other aversive feedback, modulated performance in the subsequent trial. These findings further extend the knowledge regarding the influence of gain and loss situations, as well as errors, on arithmetic performance.
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Project description:Fluency with simple arithmetic, typically achieved in early elementary school, is thought to be one of the building blocks of mathematical competence. Behavioral studies with adults indicate that math anxiety (feelings of tension or apprehension about math) is associated with poor performance on cognitively demanding math problems. However, it remains unclear whether there are fundamental differences in how high and low math anxious individuals approach overlearned simple arithmetic problems that are less reliant on cognitive control. The current study used functional magnetic resonance imaging to examine the neural correlates of simple arithmetic performance across high and low math anxious individuals. We implemented a partial least squares analysis, a data-driven, multivariate analysis method to measure distributed patterns of whole-brain activity associated with performance. Despite overall high simple arithmetic performance across high and low math anxious individuals, performance was differentially dependent on the fronto-parietal attentional network as a function of math anxiety. Specifically, low-compared to high-math anxious individuals perform better when they activate this network less-a potential indication of more automatic problem-solving. These findings suggest that low and high math anxious individuals approach even the most fundamental math problems differently.
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Project description:The honeybee has been established as an important model organism in studies on visual learning. So far the emphasis has been on appetitive conditioning, simulating floral discrimination, and homing behavior, where bees perform exceptionally well in visual discrimination tasks. However, bees in the wild also face dangers, and recent findings suggest that what is learned about visual percepts is highly context dependent. A stimulus that follows an unpleasant period, is associated with the feeling of relief- or safety in humans and animals, thus acquiring a positive meaning. Whether this is also the case in honeybees is still an open question. Here, we conditioned bees aversively in a walking arena where each half was illuminated by light of a specific wavelength and intensity, one of which was combined with electric shocks. In this paradigm, the bees' preferences to the different lights were modified through nine conditioning trials, forming robust escape, and avoidance behaviors. Strikingly, we found that while 465 nm (human blue) and 590 nm (human yellow) lights both could acquire negative valences (inducing avoidance response), 525 nm (human green) light could not. This indicates that green light holds an innate meaning of safety which is difficult to overrule even through intensive aversive conditioning. The bees had slight initial preferences to green over the blue and the yellow lights, which could be compensated by adjusting light intensity. However, this initial bias played a minor role while the chromatic properties were the most salient characteristics of the light stimuli during aversive conditioning. Moreover, bees could learn the light signaling safety, revealing the existence of a relief component in aversive operant conditioning, similar to what has been observed in other animals.
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Project description:BackgroundRecent studies on colour discrimination suggest that experience is an important factor in how a visual system processes spectral signals. In insects it has been shown that differential conditioning is important for processing fine colour discriminations. However, the visual system of many insects, including the honeybee, has a complex set of neural pathways, in which input from the long wavelength sensitive ('green') photoreceptor may be processed either as an independent achromatic signal or as part of a trichromatic opponent-colour system. Thus, a potential confound of colour learning in insects is the possibility that modulation of the 'green' photoreceptor could underlie observations.Methodology/principal findingsWe tested honeybee vision using light emitting diodes centered on 414 and 424 nm wavelengths, which limit activation to the short-wavelength-sensitive ('UV') and medium-wavelength-sensitive ('blue') photoreceptors. The absolute irradiance spectra of stimuli was measured and modelled at both receptor and colour processing levels, and stimuli were then presented to the bees in a Y-maze at a large visual angle (26°), to ensure chromatic processing. Sixteen bees were trained over 50 trials, using either appetitive differential conditioning (N?=?8), or aversive-appetitive differential conditioning (N?=?8). In both cases the bees slowly learned to discriminate between the target and distractor with significantly better accuracy than would be expected by chance. Control experiments confirmed that changing stimulus intensity in transfers tests does not significantly affect bee performance, and it was possible to replicate previous findings that bees do not learn similar colour stimuli with absolute conditioning.ConclusionOur data indicate that honeybee colour vision can be tuned to relatively small spectral differences, independent of 'green' photoreceptor contrast and brightness cues. We thus show that colour vision is at least partly experience dependent, and behavioural plasticity plays an important role in how bees exploit colour information.
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