Conformity does not perpetuate suboptimal traditions in a wild population of songbirds

Abstract

Social learning is important to the life history of many animals, helping individuals to acquire new adaptive behavior. However despite long-running debate, it remains an open question whether a reliance on social learning can also lead to mismatched or maladaptive behavior. In a previous study, we experimentally induced traditions for opening a bidirectional door puzzle box in replicate subpopulations of the great tit Parus major Individuals were conformist social learners, resulting in stable cultural behaviors. Here, we vary the rewards gained by these techniques to ask to what extent established behaviors are flexible to changing conditions. When subpopulations with established foraging traditions for one technique were subjected to a reduced foraging payoff, 49% of birds switched their behavior to a higher-payoff foraging technique after only 14 days, with younger individuals showing a faster rate of change. We elucidated the decision-making process for each individual, using a mechanistic learning model to demonstrate that, perhaps surprisingly, this population-level change was achieved without significant asocial exploration and without any evidence for payoff-biased copying. Rather, by combining conformist social learning with payoff-sensitive individual reinforcement (updating of experience), individuals and populations could both acquire adaptive behavior and track environmental change.

Keywords: Parus major; animal culture; conformity; social learning.

Fig. 1.

(A) A puzzle box where visiting individuals can slide the door open from either the blue/left side (variant A) or the red/right side (variant B) to access a reward in a concealed feeder behind the door. The individual pictured is solving using variant A. Solving using either option can give the same (equal condition) or different (unequal condition) rewards. Puzzle boxes record identity, contact duration, and solution choice and reset after each visit. (B) Proportion of variant A or B used in each replicate (T1–T4) in three sequential conditions after variant A (T1–T2) or variant B (T3–T4) was initially introduced by a trained demonstrator: (i) equally high payoffs for each solving option, proportion for last 5 days shown; (ii) equally low payoffs for each solving option (2 days); and (iii) unequal payoffs, with the established tradition leading to a lower reward (14 days). Solid circles and error bars show mean and 95% CI of the probability of individuals’ first solve in each condition being the uncommon variant.

Fig. S1.

(A) Photo of puzzle box in condition 1. (B) Photo of puzzle box in condition 2, showing door modification. (C) The latency for individuals to solve the puzzle box after contacting the openly accessible peanut-granule feeder does not consistently differ between the last day of condition 1 and the first day of condition 2 across four replicates (treatments 1–4). LMM: z = −1.05, P = 0.29. The openly accessible feeder was positioned ∼1 m from the puzzle box and contained peanut granules (a less preferred food source). Similarly, there was also no consistent difference between conditions 1 and 2 in whether individuals contacted the puzzle box before or after the peanut-granule feeder (GLMM, z = −0.97, P = 0.33). This suggests that individuals did not exhibit a neophobic response toward the modified puzzle box in condition 2.

Fig. 2.

The proportion of solutions using the seeded technique decreased over time in each replicate, with individuals moving toward preferring the previously uncommon technique. Each replicate is shown in a different color/shape, and solid and open symbols represents the two distinct clusters of individuals identified in the longitudinal clustering algorithm (solid symbols, cluster 1; open symbols, cluster 2). Lines show the generalized estimating equation model fit for each cluster/replicate.

Fig. S2.

The distribution of individual preferences for the high-payoff solution technique across the four replicates (T1–T4) on the last 2 days of condition 3. The distribution is clearly not unimodal, and a test for deviance from unimodality on this (R package dip-test) gives a clear affirmative result for the combined data: Hartigans’ dip test (HDT) = 0.07, P < 0.001. When analyzed separately, three of four replicates show a significant or near-significant signal of bimodality (T1, HDT = 0.059, P = 0.057; T2, HDT = 0.089, P = 0.20; T3, HDT = 0.103, P = 0.002; T4, HDT = 0.185, P < 0.001).

Fig. S3.

Older individuals were less likely to prefer the high-payoff variant at the end of the experimental period. Box and whisker plots show age (in years) by preference for solving using the high-payoff technique for all individuals, summed over the last 2 days of condition 3 (unequal payoffs).

Fig. 3.

Individual parameter estimates for the mechanistic learning model. Each circle represents the posterior mean for an individual bird. (A) Strength of conformity ($\lambda$) is negatively correlated with reliance on social learning ($s$), but most individuals show some conformist bias (exponent above 1). (B) Implied social learning influence functions (expression 5). The diagonal line represents unbiased social learning. S-shaped curves are conformist individuals. The weak influence of payoff bias shifts these curves upward in the lower left corner. (C) Reliance on social cues tends to decline with age, explained mainly by the presence of large values of $s$ in the youngest individuals. Individuals with low values are present at all ages. (D) Updating rate $g$ tends to decline with age. The youngest individuals can be highly responsive to individual experience, whereas the oldest individuals change their attraction scores more slowly.

Fig. 4.

Simulations of the population consequences of mixes of conformist social learning and individual reinforcement. In each plot, each row is an individual agent and each column is a time period. Open and solid circles represent alternative behavior. Before the vertical dashed line at turn 30, solid is adaptive. After turn 30, open is adaptive. All groups of learners initialized with nonadaptive attraction scores, $s=0.5$, $g=0.6$, and $y=0$. (A) $\lambda =1$, no conformity. (B) $\lambda =10$, high conformity. (C) $\lambda =5$, intermediate conformity. (D) Ten birds sampled from posterior distribution of the fitted model.

Fig. 5.

Selection gradient on social learning weight and conformity, represented as a vector field for social learning weight (horizontal axis) and conformity (vertical axis). Selection increases conformity below the outer red contours. Selection increases social learning above and to the left of the central blue contour. For these parameter values, selection does not favor much social learning, unless social learning is also conformist.

Fig. S4.

Individual parameter estimates for the mechanistic learning model. Each circle represents the posterior mean for an individual bird. (A) Reliance on social learning (s) is correlated with the weight of new experience (g), across individuals. (B) Strength of conformity ($\lambda$) is negatively correlated with reliance on social learning (s). (C) The influence payoff bias (y) is small overall. (D) Implied social learning influence functions (main text, expression 5). The diagonal dashed line represents unbiased social learning. S-shaped curves are conformist individuals. The weak influence of payoff bias shifts these curves upward in the lower right corner. B and D are replicated in Fig. 3 A and D.