Back

The Role of Similarity of Stimuli and Responses in Learning by Nectar-Foraging Bumble Bees: A Test of Osgood's Model

Baek, M.; Papaj, D. R.

2024-04-28 animal behavior and cognition
10.1101/2024.04.25.591145 bioRxiv
Show abstract

Learning stimulus - response associations helps animals to adjust to changing environments. Sequentially learned associations may interact with each other, either reinforcing memory, a process referred to as transfer, or hindering memory, a process referred to as interference. According to Osgoods (1949) model, close similarity between new and previously learned stimuli can enhance the transfer of memory through a process of stimulus generalization. In contrast, the model proposes that if responses are different from those previously learned, generalizing stimuli may lead to confusion, resulting in the interference of memory. Except for some work in humans, the interaction between stimulus similarity and response similarity is poorly documented. Here, we tested Osgoods model using bumble bees (Bombus impatiens) foraging for sucrose on artificial flowers with varied colours (= stimuli) that required either legitimate visitation or nectar robbing (= responses). Bees were first allowed to forage on one type of flower, were then switched to another, and finally were returned to the initial flower type. We measured learning performance via flower handling time and the number of failed visits. Consistent with Osgoods model, bees made more failed visits when they switched between similarly coloured flowers requiring different foraging techniques but made fewer failed visits when switching between similarly coloured flowers with the same technique. Regardless of similarities in stimuli or responses, however, experienced bees were faster in handling flowers than were naive bees. Results taken together thus provided mixed support for Osgoods model. Possible explanations for the mixed results are discussed.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"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.