Neural networks reveal emergent properties of collective learning in democratic but not despotic groups
Abstract
Collective learning, the improvement of behaviours through experience of collective actions, is an area of animal learning that has received little attention to date. We investigated how individual learning during collective actions could produce improvements in collective performance, and how collective decision-making processes, including leadership dynamics, could impact upon learning. We trained artificial neural networks, either solo or paired, at an orientation task, based upon collective navigation in animals. In pairs, we implemented two rules of collective decision-making: "democratic" (weighted average of individual propositions) or "despotic" (one individual's proposition, determined randomly with weighted probabilities in each trial). Decision-making weightings were varied between pairs, but fixed for a given pair, with asymmetric weightings generating "leaders" and "followers". We found nearly all pairs improved their orientation, but more slowly than solo learners. Within pairs, leaders learnt more quickly than followers ("the passenger-driver effect"). In democratic pairs, collective performance improved through individuals learning to compensate for partner error. This emergent process was not observed in pairs with despotic decision-making, in which individuals learnt similarly to solo learners. Our model helps to clarify the links between individual learning, collective decision-making and collective performance, in the context of collective navigation, and collective behaviour, more generally.
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BibTeX
@article{morford2026neural,
title = {{Neural networks reveal emergent properties of collective learning in democratic but not despotic groups}},
author = {Joe Morford and Patrick Lewin and Dora Biro and Tim Guilford and Oliver Padget and Julien Collet},
journal = {Animal Behaviour},
year = {2026},
month = oct,
doi = {10.1016/j.anbehav.2022.09.020},
url = {https://hal.science/hal-05797553v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Neural networks reveal emergent properties of collective learning in democratic but not despotic groups
AU - Joe Morford
AU - Patrick Lewin
AU - Dora Biro
AU - Tim Guilford
AU - Oliver Padget
AU - Julien Collet
PY - 2026
DA - 2026/10/13
JO - Animal Behaviour
DO - 10.1016/j.anbehav.2022.09.020
UR - https://hal.science/hal-05797553v1
AB - Collective learning, the improvement of behaviours through experience of collective actions, is an area of animal learning that has received little attention to date. We investigated how individual learning during collective actions could produce improvements in collective performance, and how collective decision-making processes, including leadership dynamics, could impact upon learning. We trained artificial neural networks, either solo or paired, at an orientation task, based upon collective navigation in animals. In pairs, we implemented two rules of collective decision-making: "democratic" (weighted average of individual propositions) or "despotic" (one individual's proposition, determined randomly with weighted probabilities in each trial). Decision-making weightings were varied between pairs, but fixed for a given pair, with asymmetric weightings generating "leaders" and "followers". We found nearly all pairs improved their orientation, but more slowly than solo learners. Within pairs, leaders learnt more quickly than followers ("the passenger-driver effect"). In democratic pairs, collective performance improved through individuals learning to compensate for partner error. This emergent process was not observed in pairs with despotic decision-making, in which individuals learnt similarly to solo learners. Our model helps to clarify the links between individual learning, collective decision-making and collective performance, in the context of collective navigation, and collective behaviour, more generally.
SN - 0003-3472
SN - 1095-8282
ER -