Chemotactile perception and associative learning of amino acids in yellowjacket workers
Mattiacci, A.; Pietrantuono, A. L.; Corley, J. C.; Masciocchi, M.
Show abstract
Learning and memory are essential for animal survival, influencing preferences, decision-making, and foraging behaviour. In this study, we explore the perceptual and learning abilities of Vespula germanica (yellowjacket wasps) to various amino acids. We hypothesize that V. germanica can qualitatively evaluate various amino acid solutions, given their scavenging habits and the possibility of metabolizing amino acids to fuel energy. Through chemo-tactile differential conditioning, we studied worker wasp maxilla labium extension response (MaLER) to essential (Lysine, Tryptophan, Arginine) and non-essential amino acids (Ornithine, Aspartic acid, Glycine). Conditioning sessions included individual amino acids against water and comparisons between different amino acids. Additionally, we tested retention, discrimination, and generalization abilities, 30 minutes later with conditioned and novel stimuli. Our results show that wasps exhibit the ability to learn and discriminate various amino acids. The discrimination capacity extended to differentiating between pairs of amino acids. Memory retention was generally robust, but certain associations observed during conditioning did not persist after a 30-minute interval. Moreover, when wasps were trained with essential amino acids, the acquired learning did not generally extend to other non-essential amino acids, except for Arginine, which exhibited generalization when tested with its precursor, Ornithine. Conversely, when trained with non-essential amino acids, the acquired learning generalized to other essential amino acids. These results suggest that, unlike other hymenopterans, wasps can detect, discriminate, and generalize free amino acids, crucial for their foraging decisions. This knowledge contributes to understanding the cognitive dimensions of V. germanica and their implications for targeted pest management. Summary statementThis study on Vespula germanicas amino acid perception enhances our understanding of foraging behaviours. The findings contribute to insect cognition studies, with potential implications for pest management.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Honey bees can store and retrieve independent memory traces after complex experiences that combine appetitive and aversive associations 95%
- Learning a non-neutral conditioned stimulus: place preference in the crab Neohelice granulata 95%
- Landmark knowledge overrides optic flow in honeybee waggle dance distance estimation 95%
Similar papers in this journal
- A systematic examination of learning in the invasive ant Linepithema humile reveals very rapid development of short and long-term memory 95%
- The role of visual and olfactory cues in social decisions of guppies and zebrafish 95%
- Great tits show serial reversal learning in the perseverance phase but not in the new learning phase 95%
Similar papers in this journal
- Geometry-based navigation in the dark: Layout symmetry facilitates spatial learning in the house cricket, Acheta domesticus, in the absence of visual cues 95%
- Cleaner wrasse failed in early testing stages of both visual and spatial Working Memory paradigms 94%
- Reward Value Is More Important Than Physical Saliency During Bumblebee Visual Search For Multiple Rewarding Targets 94%
Similar papers in this journal
- Detection of conspicuous and cryptic food by marmosets (Callithrix jacchus): An evaluation of the importance of color and shape cues 94%
- Social investigation and social novelty in zebrafish: Roles of salience and novelty 94%
- Understanding feeding competition under laboratory conditions: Rohu (Labeo rohita) versus Amazon sailfin catfish (Pterygoplichthys spp.) 93%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.