**Abstract:**

Prior work has suggested that environmental variability can drive the evolution of social learning strategies, such as a frequency dependent or payoff-biased learning (Kendal et al. 2018, Aoki & Feldman 2014). Furthermore, moving to a novel environment might increase neurological plasticity (Park et al. 2021) and an individual’s sensitivity to social information (Deffner et al. 2020, Morchen et al. 2023). A prior experiment tested how great tits (Parus major) respond to temporal variability in payoffs (Aplin et al. 2017), although little is known about how tits respond to spatial variability. Here, we present results from a large scale cultural diffusion experiment with 18 micro-populations of great tits (N=144). We simulated an immigration event between pairs of populations with contrasting preferences for solving an automated foraging puzzle. Furthermore, the experiment had a 2x2 factorial design, where environmental cues were either symmetric or asymmetric, and rewards for solving preferences were also symmetric or asymmetric. We find that immigrant birds were most likely to adopt the residents’ preference when both environments and rewards were asymmetric. Using a bayesian dynamic learning model, we found that these birds were most sensitive to social information, and relied on a payoff-biased social learning strategy. In other conditions, birds were less sensitive to social information and did not evidence any strategic learning. This study provides empirical support for existing theory related to social learning strategies in a non-human animal. It also gives insight into when and why great tits evaluate the rewards of conspecifics.

Aoki K, Feldman MW. Evolution of learning strategies in temporally and spatially variable environments: a review of theory. Theoretical population biology. 2014 Feb 1;91:3-19.

Aplin LM, Sheldon BC, McElreath R. Conformity does not perpetuate suboptimal traditions in a wild population of songbirds. Proceedings of the National Academy of Sciences. 2017 Jul 25;114(30):7830-7.

Deffner D, Kleinow V, McElreath R. Dynamic social learning in temporally and spatially variable environments. Royal Society open science. 2020 Dec 2;7(12):200734.

Kendal RL, Boogert NJ, Rendell L, Laland KN, Webster M, Jones PL. Social learning strategies: Bridge-building between fields. Trends in cognitive sciences. 2018 Jul 1;22(7):651-65.

Mörchen J, Luhn F, Wassmer O, Kunz J, Kulik L, Van Noordwijk M, Van Schaik C, Rianti P, Atmoko SS. Migrant orangutan males use social learning to adapt to new habitat after dispersal. Frontiers Ecology And Evolution. 2023 Jul 5;11.

Park AJ, Harris AZ, Martyniuk KM, Chang CY, Abbas AI, Lowes DC, Kellendonk C, Gogos JA, Gordon JA. Reset of hippocampal–prefrontal circuitry facilitates learning. Nature. 2021 Mar 25;591(7851):615-9.

**Speaker's social media:**

twitter: @evogom; bluesky: mchimento.bsky.social

RECORDING - Great tits use payoff-biased learning when encountering spatial variability

**Author(s):** Barrett, B; Kandler, A; Aplin, L <br><br>Cultural evolution is usually framed in terms of transmission biases or behavioral transformation via cognitive biases, although there is a third dimension that is often ignored: learning through reward. This process of internal selection that happens when socially learned behaviors are used by individuals, has been hypothesized to play a critical role in the evolution of adaptiveness of cultural traits, as they are either maintained or abandoned over the course of repeated production. To date, this has remained a verbal argument. To test this argument, we construct an agent based model where acquisition and production are linked, as acquisition is conditioned on association, as well as behavioral frequencies of associates. We show that variation in the learning rules that govern production influence the diffusion of a novel behavior. We further show that these effects can affect the inferential power of two popular models in the field of animal culture: Network Based Diffusion Analysis and Experience Weighted Attraction models. We then allow for dynamic networks of populations, and show how the social process of population turnover interacts with reinforcement learning to drive the cultural evolution of efficiency. Different regimes of population turnover generate optimal, neutral or sub-optimal selection for a more efficient behavior, when compared against static populations. These two studies generate new predictions for how reinforcement learning can influence cultural outcomes, and highlight un-discussed consequences for current practices in the field. The formulation of our model also encourages more rigorous definitions for social learning strategies and biases, as they might target the process of acquisition or production, or both, yet have been discussed interchangeably to date. They also provide theoretical support for the hypothesis that naive learners, introduced through population turnover, play a crucial role in resampling payoff landscapes.

The interaction of learning rules and population turnover can drive cultural evolution

Selection for more efficient socially learned behaviors over alternatives is crucial for cultural evolution, however our understanding of this process of cultural selection in animals is limited. Using artificial populations of wild-caught great tits (Parus major), we conducted a cultural diffusion experiment to ask whether more efficient foraging traditions can replace an established tradition, and how this process might be affected by turnover. We show that gradual replacement of individuals greatly increases the probability that a more efficient behavior will out-compete an established inefficient behavior. Turnover does not increase innovation rates, but instead increases adoption rates, as immigrants are more susceptible to the novel, more efficient behavior. We go on to show that this effect might extend beyond birds by using an an agent based model to test the effect of turnover across populations of different types of learners. Altogether, these results provide strong evidence for cultural selection for efficiency in animals, and highlight mechanisms that link population turnover to this process.

The importance of population turnover for the evolution of efficiency

Michael Chimento

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