The evolution of sensitive periods in a model of incremental development

Abstract

Sensitive periods, in which experience shapes phenotypic development to a larger extent than other periods, are widespread in nature. Despite a recent focus on neural-physiological explanation, few formal models have examined the evolutionary selection pressures that result in developmental mechanisms that produce sensitive periods. Here, we present such a model. We model development as a specialization process during which individuals incrementally adapt to local environmental conditions, while receiving a constant stream of cost-free, imperfect cues to the environmental state. We compute optimal developmental programmes across a range of ecological conditions and use these programmes to simulate developmental trajectories and obtain distributions of mature phenotypes. We highlight four main results. First, matching the empirical record, sensitive periods often result from experience or from a combination of age and experience, but rarely from age alone. Second, individual differences in sensitive periods emerge as a result of stochasticity in cues: individuals who obtain more consistent cue sets lose their plasticity at faster rates. Third, in some cases, experience shapes phenotypes only at a later life stage (lagged effects). Fourth, individuals might perseverate along developmental trajectories despite accumulating evidence suggesting the alternate trajectory is more likely to match the ecology.. . .we all begin with the natural equipment to live a thousand kinds of life but end in the end having lived only one.-Clifford Geertz, 1973

Echoes of Early Life: Recent Insights From Mathematical Modeling

“…Cues are often imperfect: They reduce uncertainty but do not eliminate it. Moreover, individuals are likely to sample different cues, resulting in estimates that diverge even for individuals in the same environment, which might produce variation in phenotypes (Fischer, Van Doorn, Dieckmann, & Taborsky, ; Frankenhuis & Panchanathan, ; Panchanathan & Frankenhuis, ). Animals also use social cues (Taborsky, ).…”

“…State‐dependent optimality modeling is a suitable method that is used in biology to determine adaptive decisions in a particular environment, when decisions are interdependent over time, meaning that past decisions affect future options (Houston & McNamara, ; Mangel & Clark, ; for explanation tailored to psychologists, see Frankenhuis, Panchanathan, & Barto, ; Frankenhuis et al., ). Such modeling clearly shows that even if an environment is completely stable within lifetimes, noisy environmental cues often produce substantial mismatch between some individuals' phenotypes and actual conditions (Frankenhuis & Panchanathan, ; Meacham & Bergstrom, ; Panchanathan & Frankenhuis, ). Such mismatch even occurs when: (a) individuals have the opportunity to repeatedly sample cues, (b) individuals obtain cues at no cost throughout their entire lifetimes, and (c) there is a cost to being mismatched.…”

“…Such mismatch is likely to occur when it is adaptive to commit to a developmental trajectory early in life, even if doing so implies having had few opportunities to learn about the environmental state, thus increasing the risk of mismatch. Committing early might be favored, for example, when it takes time to build conditional adaptations (e.g., predator defenses) that yield high fitness in specialized form, if they are well matched (Frankenhuis & Panchanathan, ; Panchanathan & Frankenhuis, ). A general lesson is that natural selection maximizes the fitness of developmental systems (not of individuals), and these systems are likely to produce some mismatched individuals, even when these systems function optimally in a context they are evolutionarily adapted to.…”

“…The reason is that widespread and severe suffering is unlikely to happen except when conditions are harsh, whereas some occasional suffering could happen even in beneficial conditions, if there is a streak of bad luck due to chance (e.g., minor injuries happening to different people in the same village). Children experiencing more reliable cues might reduce their uncertainty about environmental conditions faster, reducing their plasticity earlier in life than children experiencing less reliable cues (Frankenhuis & Panchanathan, ; Panchanathan & Frankenhuis, ). The “reliability” of cues (the extent to which the frequency of a cue varies between different states of the environment) should be distinguished from their “information value” (the extent to which they reduce uncertainty; this depends both on the cue reliability and on current knowledge) and their “fitness value” (the extent to which optimal use of the cue increases fitness; Donaldson‐Matasci, Bergstrom, & Lachmann, ; McNamara & Dall, ; Pike, McNamara, & Houston, ).…”

Emergent buffering balances evolvability and robustness in the evolution of phenotypic flexibility

“…; Stamps and Krishnan ; Nettle and Bateson ; Beaman et al. ; Panchanathan and Frankenhuis ; Stamps and Frankenhuis ). Indeed, recent studies of age‐dependent phenotypic adjustments that integrated life‐history, developmental, genetic, and informational perspectives have provided some of the most illuminating insights into both the mechanistic basis of within‐generation modifications (e.g., Fagiolini et al.…”