When does selection favor learning from the old? Social Learning in age-structured populations

Culture and demography jointly facilitate flexible human adaptation, yet it still remains unclear how social learning operates in populations with age structure. Specifically, how do demographic processes affect the adaptive value of culture, cultural adaptation and population growth and when does selection favor copying the behavior of older vs. younger individuals? Here, we develop and analyze a mathematical model of the evolution of social learning in a population with different age classes. We find that adding age structure alone does not resolve Rogers' paradox, i.e. the finding that social learning can evolve without increasing population fitness. Cultural transmission in combination with demographic filtering, however, can lead to much higher adaptation levels. This is because by increasing proportions of adaptive behavior in older age classes, demographic filtering constitutes an additional adaptive force that social learners can benefit from. Moreover, older age classes tend to have higher proportions of adaptive behavior when the environment is relatively stable and adaptive behavior is hard to acquire but confers large survival advantages. Through individual-based simulations comparing temporal and spatial variability in the environment, we find a ``copy older over younger models''-strategy only evolves readily when social learning is erroneous. The opposite ``copy the younger''-strategy is adaptive when the environment fluctuates frequently but still maintains large proportions of social learners. Our results demonstrate that age structure can substantially alter cultural dynamics and should be addressed in further theoretical and empirical work

A causal framework for cross-cultural generalizability

Behavioral researchers increasingly recognize the need for more diverse samples that capture the breadth of human experience. Current attempts to establish generalizability across populations focus on threats to validity, constraints on generalization, and the accumulation of large, cross-cultural data sets. But for continued progress, we also require a framework that lets us determine which inferences can be drawn and how to make informative cross-cultural comparisons. We describe a generative causal-modeling framework and outline simple graphical criteria to derive analytic strategies and implied generalizations. Using both simulated and real data, we demonstrate how to project and compare estimates across populations and further show how to formally represent measurement equivalence or inequivalence across societies. We conclude with a discussion of how a formal framework for generalizability can assist researchers in designing more informative cross-cultural studies and thus provides a more solid foundation for cumulative and generalizable behavioral research

Holevo's bound from a general quantum fluctuation theorem

We give a novel derivation of Holevo's bound using an important result from nonequilibrium statistical physics, the fluctuation theorem. To do so we develop a general formalism of quantum fluctuation theorems for two-time measurements, which explicitly accounts for the back action of quantum measurements as well as possibly non-unitary time evolution. For a specific choice of observables this fluctuation theorem yields a measurement-dependent correction to the Holevo bound, leading to a tighter inequality. We conclude by analyzing equality conditions for the improved bound.Comment: 5 page

Single ion heat engine with maximum efficiency at maximum power

We propose an experimental scheme to realize a nano heat engine with a single ion. An Otto cycle may be implemented by confining the ion in a linear Paul trap with tapered geometry and coupling it to engineered laser reservoirs. The quantum efficiency at maximum power is analytically determined in various regimes. Moreover, Monte Carlo simulations of the engine are performed that demonstrate its feasibility and its ability to operate at maximum efficiency of 30% under realistic conditions.Comment: 5 pages, 3 figure

Do the more flexible individuals rely more on causal cognition? Observation versus intervention in causal inference in great-tailed grackles

Behavioral flexibility, the ability to change behavior when circumstances change based on learning from previous experience, is thought to play an important role in a species’ ability to successfully adapt to new environments and expand its geographic range. However, it is possible that causal cognition, the ability to understand relationships beyond their statistical covariations, could play a significant role in rapid range expansions by allowing one to learn faster by making better predictions about outcomes and by exerting more control over events. We aim to determine whether great-tailed grackles (Quiscalus mexicanus), a species that is rapidly expanding its geographic range, use causal inference and whether this ability relates to their behavioral flexibility (flexibility measured in these individuals by Logan et al. 2019: reversal learning of a color discrimination and solution switching on a puzzle box). We found that grackles showed no evidence of making causal inferences when given the opportunity to intervene on observed events using a touchscreen apparatus, and that performance on the causal cognition task did not correlate with behavioral flexibility measures. This could indicate that causal cognition is not implicated as a key factor involved in a rapid geographic range expansion, though we suggest further exploration of this hypothesis using larger sample sizes and multiple test paradigms before considering this a robust conclusion

Are the more flexible great-tailed grackles also better at behavioral inhibition? (In principle acceptance by PCI Ecology of the version on 6 Mar 2019)

Behavioral flexibility should theoretically be positively related to behavioral inhibition (hereafter referred to as inhibition) because one should need to inhibit a previously learned behavior to change their behavior when the task changes (the flexibility component;). However, several investigations show no or mixed support of this hypothesis, which challenges the assumption that inhibition is involved in making flexible decisions. We aimed to test the hypothesis that behavioral flexibility (measured as reversal learning and solution switching on a multi-access box by Logan et al. 2019) is associated with inhibition by measuring both variables in the same individuals and three inhibition tests (a go/no go task on a touchscreen, a detour task, and a delay of gratification experiment). We set out to measure grackle inhibition to determine whether those individuals that are more flexible are also better at inhibition. Because touchscreen experiments had never been conducted in this species, we additionally validated that a touchscreen setup is functional for wild-caught grackles who learned to use the touchscreen and completed the go/no go inhibition task on it. Results showed that only performance on the go/no go inhibition task correlated with the two flexibility measures: positively with the number of trials to reverse a preference in the reversal learning experiment, and negatively with the average latency to attempt a new option on the multi-access box. That is, individuals who were faster to update their behavior in the reversal experiment were also faster to reach criterion in the go/no go task, but took more time to attempt a new option in the multi-access box experiment. Performance on the detour inhibition task did not correlate with either measure of flexibility, suggesting that detour performance and the flexibility experiments may measure separate traits. We were not able to run the delay of gratification experiment because the grackles never habituated to the apparatuses. Performance on the go/no go and detour inhibition tests did not correlate with each other, indicating that they did not measure the same trait. We conclude that behavioral flexibility is associated with certain types of inhibition, but not others, in great-tailed grackles

Engineered swift equilibration of a Brownian particle

A fundamental and intrinsic property of any device or natural system is its relaxation time relax, which is the time it takes to return to equilibrium after the sudden change of a control parameter [1]. Reducing $tau$ relax , is frequently necessary, and is often obtained by a complex feedback process. To overcome the limitations of such an approach, alternative methods based on driving have been recently demonstrated [2, 3], for isolated quantum and classical systems [4--9]. Their extension to open systems in contact with a thermostat is a stumbling block for applications. Here, we design a protocol,named Engineered Swift Equilibration (ESE), that shortcuts time-consuming relaxations, and we apply it to a Brownian particle trapped in an optical potential whose properties can be controlled in time. We implement the process experimentally, showing that it allows the system to reach equilibrium times faster than the natural equilibration rate. We also estimate the increase of the dissipated energy needed to get such a time reduction. The method paves the way for applications in micro and nano devices, where the reduction of operation time represents as substantial a challenge as miniaturization [10]. The concepts of equilibrium and of transformations from an equilibrium state to another, are cornerstones of thermodynamics. A textbook illustration is provided by the expansion of a gas, starting at equilibrium and expanding to reach a new equilibrium in a larger vessel. This operation can be performed either very slowly by a piston, without dissipating energy into the environment, or alternatively quickly, letting the piston freely move to reach the new volume

Hierarchical Equations of Motion Approach to Quantum Thermodynamics

We present a theoretical framework to investigate quantum thermodynamic processes under non-Markovian system-bath interactions on the basis of the hierarchical equations of motion (HEOM) approach, which is convenient to carry out numerically "exact" calculations. This formalism is valuable because it can be used to treat not only strong system-bath coupling but also system-bath correlation or entanglement, which will be essential to characterize the heat transport between the system and quantum heat baths. Using this formalism, we demonstrated an importance of the thermodynamic effect from the tri-partite correlations (TPC) for a two-level heat transfer model and a three-level autonomous heat engine model under the conditions that the conventional quantum master equation approaches are failed. Our numerical calculations show that TPC contributions, which distinguish the heat current from the energy current, have to be take into account to satisfy the thermodynamic laws.Comment: 9 pages, 4 figures. As a chapter of: F. Binder, L. A. Correa, C. Gogolin, J. Anders, and G. Adesso (eds.), "Thermodynamics in the quantum regime - Recent Progress and Outlook", (Springer International Publishing