Search bioRxiv⌕ Search

Biology subjects

Schweiger, J.

Publications and source records attributed to Schweiger, J..

2 recordsLinked to original sources

Testing the peak-end rule in bumblebees: lack of preference for a higher-reward sequence when the final reward is disappointing

The peak-end rule describes the tendency to evaluate experiences by their most intense and final moments, rather than considering the entire experience as a whole. While this cognitive bias is well-established in humans, studies on nonhuman animals are very limited. Bumblebees make foraging decisions largely based on past experiences, but whether peak-end effects influence their subsequent flower choices is still unknown. Here, we trained individual Bombus terrestris workers on two artificial flower types, alternating blue and yellow, over 12 consecutive foraging bouts. One flower type offered a sequence of three high-quality rewards (25 L drops of 50% w/w sucrose solution: "consistent" sequence), while the other provided the same sequence but ended with an additional, lower-quality reward (25 L drop of 20% w/w sucrose solution: "poor end" sequence). We then tested the bees flower type preference in a final binary choice. Bees showed a strong preference for blue flowers, both in their initial and overall visits. Across all visits during a 1-minute period, they also favoured flowers associated with the "consistent" sequence, though this preference was significant only when these flowers were yellow. Interestingly, despite offering more sucrose per foraging bout, bees did not favour the "poor end" sequence flower. This study is, to our knowledge, the first to investigate peak-end effects in an insect. How bees evaluate sequential rewards when foraging remains largely unexplored, yet could provide valuable insights into nectar distribution and plant-pollinator co-evolution.

animal behavior and cognition↗

Generative network models identify biological mechanisms of altered structural brain connectivity in schizophrenia

BackgroundAlterations in the structural connectome of schizophrenia patients have been widely characterized, but the mechanisms leading to those alterations remain largely unknown. Generative network models have recently been introduced as a tool to test the biological underpinnings of the formation of altered structural brain networks.\n\nMethodsWe evaluated different generative network models to investigate the formation of structural brain networks in healthy controls (n=152), schizophrenia patients (n=66) and their unaffected first-degree relatives (n=32), and we identified spatial and topological factors contributing to network formation. We further investigated the association of these factors to cognition and to polygenic risk for schizophrenia.\n\nResultsStructural brain networks can be best accounted for by a two-factor model combining spatial constraints and topological neighborhood structure. The same wiring model explained brain network formation for all groups analyzed. However, relatives and schizophrenia patients exhibited significantly lower spatial constraints and lower topological facilitation compared to healthy controls. The model parameter for spatial constraint was correlated with the polygenic risk for schizophrenia and predicted reduced cognitive performance.\n\nConclusionsOur results identify spatial constraints and local topological structure as two interrelated mechanisms contributing to normal brain development as well as altered connectomes in schizophrenia. Spatial constraints were linked to the genetic risk for schizophrenia and general cognitive functioning, thereby providing insights into their biological basis and behavioral relevance.

neuroscience↗