Dataset Information

Number-based visual generalisation in the honeybee.

ABSTRACT: Although the numerical abilities of many vertebrate species have been investigated in the scientific literature, there are few convincing accounts of invertebrate numerical competence. Honeybees, Apis mellifera, by virtue of their other impressive cognitive feats, are a prime candidate for investigations of this nature. We therefore used the well-established delayed match-to-sample paradigm, to test the limits of honeybees' ability to match two visual patterns solely on the basis of the shared number of elements in the two patterns. Using a y-maze, we found that bees can not only differentiate between patterns containing two and three elements, but can also use this prior knowledge to differentiate three from four, without any additional training. However, bees trained on the two versus three task could not distinguish between higher numbers, such as four versus five, four versus six, or five versus six. Control experiments confirmed that the bees were not using cues such as the colour of the exact configuration of the visual elements, the combined area or edge length of the elements, or illusory contours formed by the elements. To our knowledge, this is the first report of number-based visual generalisation by an invertebrate.

SUBMITTER: Gross HJ

PROVIDER: S-EPMC2629729 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature

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Number-based visual generalisation in the honeybee.

Gross Hans J HJ Pahl Mario M Si Aung A Zhu Hong H Tautz Jürgen J Zhang Shaowu S

PloS one 20090128 1

Although the numerical abilities of many vertebrate species have been investigated in the scientific literature, there are few convincing accounts of invertebrate numerical competence. Honeybees, Apis mellifera, by virtue of their other impressive cognitive feats, are a prime candidate for investigations of this nature. We therefore used the well-established delayed match-to-sample paradigm, to test the limits of honeybees' ability to match two visual patterns solely on the basis of the shared n ...[more]

PMID: 19173008

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Project description:Multisensory integration plays a central role in perception, as all behaviors usually require the input of different sensory signals. For instance, for a foraging honeybee the association of a food source includes the combination of olfactory and visual cues to be categorized as a flower. Moreover, homing after successful foraging using celestial cues and the panoramic scenery may be dominated by visual cues. Hence, dependent on the context, one modality might be leading and influence the processing of other modalities. To unravel the complex neural mechanisms behind this process we studied honeybee mushroom body output neurons (MBON). MBONs represent the first processing level after olfactory-visual convergence in the honeybee brain. This was physiologically confirmed in our previous study by characterizing a subpopulation of multisensory MBONs. These neurons categorize incoming sensory inputs into olfactory, visual, and olfactory-visual information. However, in addition to multisensory units a prominent population of MBONs was sensitive to visual cues only. Therefore, we asked which visual features might be represented at this high-order integration level. Using extracellular, multi-unit recordings in combination with visual and olfactory stimulation, we separated MBONs with multisensory responses from purely visually driven MBONs. Further analysis revealed, for the first time, that visually driven MBONs of both groups encode detailed aspects within this individual modality, such as light intensity and light identity. Moreover, we show that these features are separated by different MBON subpopulations, for example by extracting information about brightness and wavelength. Most interestingly, the latter MBON population was tuned to separate UV-light from other light stimuli, which were only poorly differentiated from each other. A third MBON subpopulation was neither tuned to brightness nor to wavelength and encoded the general presence of light. Taken together, our results support the view that the mushroom body, a high-order sensory integration, learning and memory center in the insect brain, categorizes sensory information by separating different behaviorally relevant aspects of the multisensory scenery and that these categories are channeled into distinct MBON subpopulations.

Project description:Humans can learn and store multiple visuomotor mappings (dual-adaptation) when feedback for each is provided alternately. Moreover, learned context cues associated with each mapping can be used to switch between the stored mappings. However, little is known about the associative learning between cue and required visuomotor mapping, and how learning generalises to novel but similar conditions. To investigate these questions, participants performed a rapid target-pointing task while we manipulated the offset between visual feedback and movement end-points. The visual feedback was presented with horizontal offsets of different amounts, dependent on the targets shape. Participants thus needed to use different visuomotor mappings between target location and required motor response depending on the target shape in order to "hit" it. The target shapes were taken from a continuous set of shapes, morphed between spiky and circular shapes. After training we tested participants performance, without feedback, on different target shapes that had not been learned previously. We compared two hypotheses. First, we hypothesised that participants could (explicitly) extract the linear relationship between target shape and visuomotor mapping and generalise accordingly. Second, using previous findings of visuomotor learning, we developed a (implicit) Bayesian learning model that predicts generalisation that is more consistent with categorisation (i.e. use one mapping or the other). The experimental results show that, although learning the associations requires explicit awareness of the cues' role, participants apply the mapping corresponding to the trained shape that is most similar to the current one, consistent with the Bayesian learning model. Furthermore, the Bayesian learning model predicts that learning should slow down with increased numbers of training pairs, which was confirmed by the present results. In short, we found a good correspondence between the Bayesian learning model and the empirical results indicating that this model poses a possible mechanism for simultaneously learning multiple visuomotor mappings.

Dataset Information

Number-based visual generalisation in the honeybee.

Publications

Number-based visual generalisation in the honeybee.

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