Gil-Guevara_etal2022_PoIE_JEB243832

Published: 13 May 2022| Version 1 | DOI: 10.17632/tm6wsmpb3t.1
Contributor:
Oswaldo Gil-Guevara

Description

Readme file for JEB243832 Journal of Experimental Biology (2022) 225, jeb243832. doi:10.1242/jeb.243832 Honey bees respond to multimodal stimuli following the Principle of Inverse Effectiveness Oswaldo Gil-Guevara1, *, Hernan A. Bernal2 and Andre J. Riveros1,3, * Readme: Data was collected using a variation of the conditioning of the proboscis extension response (PER) protocol (Giurfa and Sandoz, 2012; Matsumoto et al., 2012). We adapted a training apparatus that allows both precise and automatic delivery of olfactory and visual stimuli (Fig. 1A) (Jernigan et al., 2014; Riveros and Gronenberg, 2009; Riveros et al., 2020). We employed custom software in Processing and Arduino UNO to control airflow output from a set of pumps limited by electronic valves and electric currents connected to a set of LED lights (see methods). Data was processed directly into excel sheets and from there into R (RStudio) (R Core Team, 2021). The first three columns are categorical variables describing treatment distribution. The first column represents the treatments employed divided into modalities (olfaction, visual and bimodal) at three different intensities (low, mid, and high). The second column divides data into modalities (without differentiating the intensity) and the third column describes intensity (without differentiating the modality). The fourth column counts the number of individuals per individual treatment; the fifth column counts the consecutive number of individuals employed in the experiments. The sixth column represents the consecutive number of trials in which individual bees received the stimuli during the PER protocol. The seventh column shows the binary response (PER response). Finally, the eight-column shows the latency time to PER response.

Files

Institutions

Universidad del Rosario Facultad de Ciencias Naturales y Matematicas

Categories

Animal Behavior, Behavioral Neuroscience, Animal Learning, Appetitive Learning, Multimodality

Licence