Abstract
We examined how bees solve a visual discrimination task with stimuli commonly used in numerical cognition studies. Bees performed well on the task, but additional tests showed that they had learned continuous (non-numerical) cues. A network model using biologically plausible visual feature filtering and a simple associative rule was capable of learning the task using only continuous cues inherent in the training stimuli, with no numerical processing. This model was also able to reproduce behaviours that have been considered in other studies indicative of numerical cognition. Our results support the idea that a sense of magnitude may be more primitive and basic than a sense of number. Our findings highlight how problematic inadvertent continuous cues can be for studies of numerical cognition. This remains a deep issue within the field that requires increased vigilance and cleverness from the experimenter. We suggest ways of better assessing numerical cognition in non-speaking animals, including assessing the use of all alternative cues in one test, using cross-modal cues, analysing behavioural responses to detect underlying strategies, and finding the neural substrate.
Highlights
Mapping specific cognitive capacities to the behaviour of any animal is rarely straightforward
The difficulty is that animals may not be solving the task the way we think they are. One example of this is in our own recent work where we had bees discriminate different shapes based on relative size [1]
Using the same two-dimensional visual stimulus set as a paradigmatic honeybee study [28], and similar to stimulus sets used for other animals (e.g. [8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40]), we first asked whether honeybees use numerosity to solve a numericbased discrimination task
Summary
Mapping specific cognitive capacities to the behaviour of any animal is rarely straightforward. Analysis of first and sequential choices during training bouts and tests revealed that the bees switched to a simpler strategy in the middle of training: win-stay/lose-switch These results, along with other works suggesting animals are able to solve tasks in unexpected ways Several works show that animals use non-numerical cues to solve numeric-based tasks when not controlled for, e.g. size of elements [54], total area [55] and convex hull [56], and even when they are controlled Because we assume that the difference of the decision neuron’s responses to the positive (sp) and negative stimuli (sn) must be increased during the training phase, the locally optimal synaptic weights, Wopt, can be obtained from maximizing the objective function: Xm L 1⁄4 1⁄2D(stp) À D(stn) rt, t1⁄41 where t and m are the index over the paired stimuli and the number of presented stimuli, respectively. : After exposing the model to conditioned stimuli in learning paradigms, the behavioural outcomes of the model presented with a pair of the test stimuli were evaluated as a simple subtraction of the decision neuron’s responses to both test stimuli
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