DOI: 10.5061/dryad.08kprr543
title: Label-based expectations affect reward perception in bumblebees

description: While classic models of animal decision-making assume that individuals
 objectively assess the absolute value of options, decades of research have
 shown that rewards are often evaluated relative to prior experience,
 creating ‘contrast effects’. Contrast effects are often assumed to be
 purely sensory, yet consumer psychology tells us that label-based
 expectations can affect value perception. However, this has rarely been
 tested in non-model systems. Bumblebees forage on a variety of flower
 types that vary in their signals and rewards and show clear contrast
 effects when rewards are lower than their immediate previous experience.
 Here we manipulated bees’ expectations of a stimulus’ quality, before
 downshifting the reward to induce incentive contrast. We found that
 contrast effects were not solely driven by prior experience with a better
 reward, but also influenced by experience with associated stimuli. Bees
 were faster to accept the lower-quality reward when it was paired with a
 novel rather than a familiar stimulus. We then explored the boundaries of
 these label-based expectations by testing bees along a stimulus gradient
 and found that expectations generalized to similar stimuli. Such
 reference-dependent evaluations may play an important role in bees’
 foraging choices, with the potential to impact floral evolution and
 plant community dynamics.
description: <em>Data collection:</em> Data
 were collected between November 2020 to May 2022 at the University of
 Texas at Austin. Experiments were conducted by CTH.
 Experiment 1: We used
 worker bumblebees <i>Bombus impatiens</i> from
 commercially-reared colonies (n=3) (Koppert, USA). We used 20 bees per
 treatment (10 for each colour combination), with treatments equally
 represented across colonies (Table S2). Individuals were trained to a
 colour (blue or yellow) paired with a high-quality reward (8ml of 50% w/w
 sucrose) over three consecutive trials spaced 5 minutes apart. Within each
 trial, individuals visited ~10 rewarding flowers and consumed all rewards;
 the number of flowers visited did not differ across treatments (for
 additional information see Supplementary Material). Following these three
 training trials, bees were presented with a lower quality reward in a
 ‘test’ trial (8ml of 30% w/w sucrose) paired with either the familiar or a
 novel colour (Figure 1a). Our experimental nectar concentrations were
 designed to match natural variation found in bumblebee-visited flowers
 [33]. In the test trial, we recorded bees’ responses to downshifted
 rewards over their first 20 visits to flowers. We chose 20 successive
 visits based on our expectation that bees would change their acceptance of
 the downshifted reward over this timescale. Acceptance was measured as
 bees consuming the solution, while rejection was characterized by bees
 probing the solution and exiting without imbibing (video S1).
 Experiment 2: To determine
 how similar stimuli needed to be to bear the cost of higher expectations,
 we tested bees using a range of colour stimuli. Individuals
 (<i>n</i>= 85 from 5 colonies; Table S1) were trained to a
 blue stimulus paired with a high-quality reward across three training
 trials (Figure 1b). We then presented individuals with one of five
 possible colours. Four of these stimuli ranged from blue to green and were
 increasingly different from the originally-trained colour whilst still
 being discriminable to foragers [34] (chromatic contrasts calculated in
 the bee colour space model [35,36]; Table S1). The fifth colour was
 yellow, serving as a ‘novel’ stimulus as in Experiment 1. We used a
 slightly different blue stimulus in Experiment 2 than Experiment 1, while
 the yellow was the same across both experiments. Again, we measured
 individuals’ acceptance of the downshifted reward over their first 20
 visits. <em>Data analysis</em>
 We used generalized linear models (GLMs) and linear mixed-effect
 models (GLMMs) with the glm() and glmer() functions in the lme4 package.
 We first analysed the differences in initial acceptance of the downshifted
 reward in the first visits between treatments using binomial GLMs with
 ‘acceptance’ as the main response and ‘stimulus type’ as the explanatory
 variable. We also looked at acceptance over the first five visits using
 binomial GLMMs with the response variable ‘accept’ or ‘reject’, the
 explanatory variables ‘stimulus type’ (different for each experiment) and
 ‘visit number’ (continuous variable), and ‘bee’ and ‘colony’ as random
 factors. To determine whether the number of visits before acceptance
 differed across treatments, we carried out GLMs with a quasi-Poisson
 distribution using ‘number of visits until acceptance’ as the response
 variable and ‘stimulus type’ as the explanatory variable. Finally, to
 analyse acceptance across all 20 visits we ran binomial GLMMs with the
 response variable ‘accept’ or ‘reject’, the explanatory variables
 ‘stimulus type’ (different for each experiment) and ‘visit number’
 (continuous variable), and ‘bee’ as a random factor. We also included
 ‘colony’ as a random factor in Experiment 2, but not Experiment 1 due to a
 singularity issue.