Insects modify their behaviour depending on the feedback sensor used when walking on a trackball in virtual reality.
When using virtual-reality paradigms to study animal behaviour, careful attention must be paid to how the animal's actions are detected. This is particularly relevant in closed-loop experiments where the animal interacts with a stimulus. Many different sensor types have been used to measure aspects of behaviour, and although some sensors may be more accurate than others, few studies have examined whether, and how, such differences affect an animal's behaviour in a closed-loop experiment. To investigate this issue, we conducted experiments with tethered honeybees walking on an air-supported trackball and fixating a visual object in closed-loop. Bees walked faster and along straighter paths when the motion of the trackball was measured in the classical fashion - using optical motion sensors repurposed from computer mice - than when measured more accurately using a computer vision algorithm called 'FicTrac'. When computer mouse sensors were used to measure bees' behaviour, the bees modified their behaviour and achieved improved control of the stimulus. This behavioural change appears to be a response to a systematic error in the computer mouse sensor that reduces the sensitivity of this sensor system under certain conditions. Although the large perceived inertia and mass of the trackball relative to the honeybee is a limitation of tethered walking paradigms, observing differences depending on the sensor system used to measure bee behaviour was not expected. This study suggests that bees are capable of fine-tuning their motor control to improve the outcome of the task they are performing. Further, our findings show that caution is required when designing virtual-reality experiments, as animals can potentially respond to the artificial scenario in unexpected and unintended ways.
Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia gavin.taylor@biol.lu.se.
Gavin J
GJ
Taylor
Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia.
Angelique C
AC
Paulk
Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia.
Thomas W J
TW
Pearson
Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia.
Richard J D
RJ
Moore
Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia.
Jacqui A
JA
Stacey
Science and Engineering Faculty, Queensland University of Technology, Brisbane, QLD 4001, Australia.
David
D
Ball
Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia.
Bruno
B
van Swinderen
Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia.
Mandyam V
MV
Srinivasan
D000465:Algorithms; D000818:Animals; D001516:Bees / Q000502:physiology*; D001522:Behavior, Animal / Q000502:physiology; D003198:Computer Simulation; D055096:Optical Devices; D016138:Walking / Q000502:physiology
Adaptive control; Closed-loop; Computer mouse sensor; FicTrac; Free-walking; Honeybee; Sensor accuracy; Tethered-walking; Visual fixation