How cognitive biases select for imperfect mimicry: a study of asymmetry in learning with bumblebees

Abstract

Imperfect mimicry presents a paradox of incomplete adaptation - intuitively, closer resemblance should improve performance. Receiver psychology can often explain why mimetic signals do not always evolve to match those of their models. Here, we explored the influence of a pervasive and powerful cognitive bias where associative learning depends upon an asymmetric interaction between the cue (stimulus) and consequence (reinforcer), such as in rats, which will associate light and tone with shock, and taste with nausea, but not the converse. Can such biases alter selection for mimicry? We designed an artificial mimicry system where bees foraged on artificial flowers, so that colours could be switched between rewarding or aversive. We found that when the colour blue was paired with a sucrose reward, other cues were ignored, but not when blue was paired with aversive compounds. We also tested the hypothesis that costs of errors affect how receivers sample imperfect mimics. However, costs of errors did not affect bee visits to imperfect mimics in our study. We propose a novel hypothesis for imperfect mimicry, in which the pairing between specific cues and reinforcers allows an imperfect mimic to resemble multiple models simultaneously. Generally, our results emphasize the importance of receiver psychology for the evolution of signal complexity and specificity.

Keywords: Bombus impatiens; floral mimicry; multicomponent signal; overshadowing; prepared learning.

Figure A1.

(a) Spectral reflectance curves of colours used in this study. (b) The ambient light source (default in Avicol v.6, Gomez, 2006). (c) Visualization of test colours on the bee colour hexagon. The experiment was carried out indoors under white fluorescent light (daylight spectrum lamps).

Figure A2.

Relative visiting rates to the first N flowers visited by bees in the test trial after differential (experiment 1) and absolute conditioning (experiment 2). The pattern of visits was consistent following differential conditioning but changed dramatically with each new flower visited following absolute conditioning. B = blue; C = cyan; G = green; K = grey. This suggests that any apparent trend towards visiting CK flowers in experiment 2 is an artefact.

Figure 1.

The range of mimetic precision across different mimicry complexes. The top row contains model species, the middle row contains relatively good mimics and the bottom row contains poor mimics. (a–c) A floral mimicry complex where a Malpighiaceae is mimicked by orchids (from Papadapulos et al., 2013, with permission). (d–f) The Arizona coral snake is mimicked by two nonvenomous colubrid snakes (photos: D. W. Kikuchi, Tom Brennan and David Pfennig, respectively, with permission). (g–j) In Australia, defended ants are models for other members of the so-called ‘golden mimicry complex’, typified by ant-like appearance and golden abdomens (from Pekár et al., 2017, with permission). In this particular golden mimicry ring, these mimics appear intermediate in phenotype between two species of models.

Figure 2.

The four flower types used in our experiment. We chose this 2×2 design so that flowers would be defined by two orthogonal axes: blue versus grey and cyan versus green.

Figure 3.

The proportion of rewarding flowers visited (out of both rewarding and aversive flowers visited) across the four training trials of experiment 1. Grey symbols: blue–green rewarding; black symbols: cyan–grey rewarding.

Figure 4.

Relative visiting rates to each type of flower among the first five flowers visited during the test trial of experiment 1. (a) Grey–cyan rewarding. (b) Blue–green rewarding. B = blue; C = cyan; G = green; K = grey.

Figure 5.

Relative visiting rates to each type of flower among the first five flowers visited during the test trial of experiment 2. B = blue; C = cyan; G = green; K = grey.

Figure 6.

An illustration of how asymmetric learning could lead to imperfect mimics that have higher fitness than perfect mimics of either of two models (A or B) or imperfect mimics that do not have a feature that is subject to asymmetric learning. In this illustration, imperfect mimics with blue get the full benefit of mimicking model A, due to blue having an advantage in cue competition. However, blue does not outcompete other colours when paired with punishment, so it does not completely prevent the mimic from being partially associated with model B. This leads to an elevated rate of response to the mimic’s signal. Percentages listed on the arrows are arbitrary and could be substituted for others that have the same relative ordering.