Bridging Evolutionary Biology and Developmental Psychology: Toward An Enduring Theoretical Infrastructure

Expectable Environments in Early Life

Humans develop in the context of environmental information that can be considered either experience-expectant or experience-dependent. Though the exact timing of sensitive period closures and consequences of environmental experiences have not been well delineated, early life is a period of increased vulnerability. While some forms of care (e.g., institutional care for children; representing the absence of experience-expectant caregiving) are not present in the evolutionary history of humans, it is likely that what is considered significant hardship today may have been more typical experience-dependent environmental information in the evolutionary timescale. Thus, assumptions that threatening or neglectful experiences are unexpected for the human child may not fit well in the scope of the broader timescale of human history. We argue that it is important to consider early caregiving experiences from the context of what has been expected in our evolutionary past rather than what is expected in modern sociocultural terms.

Cognitive gadgets: A provocative but flawed manifesto

The argument against innatism at the heart of Cognitive Gadgets is provocative but premature, and is vitiated by dichotomous thinking, interpretive double standards, and evidence cherry-picking. I illustrate my criticism by addressing the heritability of imitation and mindreading, the relevance of twin studies, and the meaning of cross-cultural differences in theory of mind development. Reaching an integrative understanding of genetic inheritance, plasticity, and learning is a formidable task that demands a more nuanced evolutionary approach.

Competition for novelty reduces information sampling in a research game-a registered report

Incentive structures shape scientists' research practices. One incentive in particular, rewarding priority of publication, is hypothesized to harm scientific reliability by promoting rushed, low-quality research. Here, we develop a laboratory experiment to test whether competition affects information sampling and guessing accuracy in a game that mirrors aspects of scientific investigation. In our experiment, individuals gather data in order to guess true states of the world and face a tradeoff between guessing quickly and increasing accuracy by acquiring more information. To test whether competition affects accuracy, we compare a treatment in which individuals are rewarded for each correct guess to a treatment where individuals face the possibility of being 'scooped' by a competitor. In a second set of conditions, we make information acquisition contingent on solving arithmetic problems to test whether competition increases individual effort (i.e. arithmetic-problem solving speed). We find that competition causes individuals to make guesses using less information, thereby reducing their accuracy (H1a and H1b confirmed). We find no evidence that competition increases individual effort (H2, inconclusive evidence). Our experiment provides proof of concept that rewarding priority of publication can incentivize individuals to acquire less information, producing lower-quality research as a consequence.

Open Science Is Liberating and Can Foster Creativity

Some scholars think that Open Science practices constrain researchers in ways that reduce their creativity, arguing, for instance, that preregistration discourages data exploration and so stifles discovery. In this article, we argue the opposite: Open Science practices are liberating and can foster creativity. Open Science practices are liberating because they (a) enable us to explore data transparently and comfortably; (b) reward quality, which is under our control, rather than outcomes, which are not; and (c) reduce the choke hold of needing to find "positive" results for career advancement. Open Science practices can foster creativity because they cultivate an open and flexible mind-set, create a more collaborative and constructive climate, and generate more accurate information and make it more accessible. In sum, Open Science liberates researchers more than it constrains them.

Enriching behavioral ecology with reinforcement learning methods

This article focuses on the division of labor between evolution and development in solving sequential, state-dependent decision problems. Currently, behavioral ecologists tend to use dynamic programming methods to study such problems. These methods are successful at predicting animal behavior in a variety of contexts. However, they depend on a distinct set of assumptions. Here, we argue that behavioral ecology will benefit from drawing more than it currently does on a complementary collection of tools, called reinforcement learning methods. These methods allow for the study of behavior in highly complex environments, which conventional dynamic programming methods do not feasibly address. In addition, reinforcement learning methods are well-suited to studying how biological mechanisms solve developmental and learning problems. For instance, we can use them to study simple rules that perform well in complex environments. Or to investigate under what conditions natural selection favors fixed, non-plastic traits (which do not vary across individuals), cue-driven-switch plasticity (innate instructions for adaptive behavioral development based on experience), or developmental selection (the incremental acquisition of adaptive behavior based on experience). If natural selection favors developmental selection, which includes learning from environmental feedback, we can also make predictions about the design of reward systems. Our paper is written in an accessible manner and for a broad audience, though we believe some novel insights can be drawn from our discussion. We hope our paper will help advance the emerging bridge connecting the fields of behavioral ecology and reinforcement learning.