The Computational Challenges of Means Selection Problems: Network Structure of Goal Systems Predicts Human Performance.

We study human performance in two classical NP-hard optimization problems: Set Cover and Maximum Coverage. We suggest that Set Cover and Max Coverage are related to means selection problems that arise in human problem-solving and in pursuing multiple goals: The relationship between goals and means is expressed as a bipartite graph where edges between means and goals indicate which means can be used to achieve which goals. While these problems are believed to be computationally intractable in general, they become more tractable when the structure of the network resembles a tree. Thus, our main prediction is that people should perform better with goal systems that are more tree-like. We report three behavioral experiments which confirm this prediction. Our results suggest that combinatorial parameters that are instrumental to algorithm design can also be useful for understanding when and why people struggle to choose between multiple means to achieve multiple goals.

Using large-scale experiments and machine learning to discover theories of human decision-making.

Predicting and understanding how people make decisions has been a long-standing goal in many fields, with quantitative models of human decision-making informing research in both the social sciences and engineering. We show how progress toward this goal can be accelerated by using large datasets to power machine-learning algorithms that are constrained to produce interpretable psychological theories. Conducting the largest experiment on risky choice to date and analyzing the results using gradient-based optimization of differentiable decision theories implemented through artificial neural networks, we were able to recapitulate historical discoveries, establish that there is room to improve on existing theories, and discover a new, more accurate model of human decision-making in a form that preserves the insights from centuries of research.

Recommendation as generalization: Using big data to evaluate cognitive models.

The explosion of data generated during human interactions online presents an opportunity for psychologists to evaluate cognitive models outside the confines of the laboratory. Moreover, the size of these online data sets can allow researchers to construct far richer models than would be feasible with smaller in-lab behavioral data. In the current article, we illustrate this potential by evaluating 3 popular psychological models of generalization on 2 web-scale online data sets typically used to build automated recommendation systems. We show that each psychological model can be efficiently implemented at scale and in certain cases can capture trends in human judgments that standard recommendation systems from machine learning miss. We use these results to illustrate the opportunity Internet-scale data sets offer to psychologists and to underscore the importance of using insights from cognitive modeling to supplement the standard predictive-analytic approach taken by many existing machine learning approaches. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Evolution in Mind: Evolutionary Dynamics, Cognitive Processes, and Bayesian Inference.

Evolutionary theory describes the dynamics of population change in settings affected by reproduction, selection, mutation, and drift. In the context of human cognition, evolutionary theory is most often invoked to explain the origins of capacities such as language, metacognition, and spatial reasoning, framing them as functional adaptations to an ancestral environment. However, evolutionary theory is useful for understanding the mind in a second way: as a mathematical framework for describing evolving populations of thoughts, ideas, and memories within a single mind. In fact, deep correspondences exist between the mathematics of evolution and of learning, with perhaps the deepest being an equivalence between certain evolutionary dynamics and Bayesian inference. This equivalence permits reinterpretation of evolutionary processes as algorithms for Bayesian inference and has relevance for understanding diverse cognitive capacities, including memory and creativity.

microRNAs regulate human embryonic stem cell division.

microRNAs (miRNAs) regulate numerous physiological processes such as cell division and differentiation in many tissue types including stem cells. To probe the role that miRNAs play in regulating processes relevant to embryonic stem cell biology, we used RNA interference to silence DICER and DROSHA, the two main miRNA processing enzymes. Consistent with a role for miRNAs in maintaining normal stem cell division and renewal, we found that perturbation of miRNA pathway function in human embryonic stem cells (hESCs) attenuates cell proliferation. Normal cell growth can be partially restored by introduction of the mature miRNAs miR-195 and miR-372. These miRNAs regulate two tumor suppressor genes, respectively: WEE1, which encodes a negative G2/M kinase modulator of the CycB/CDK complex and CDKN1A, which encodes p21, a CycE/CDK cyclin dependent kinase inhibitor that regulates the G1/S transition. We show that in wild-type hESCs, WEE 1 levels control the rate of hESC division, whereas p21 levels must be maintained at a low level for hESC division to proceed. These data support a model for hESC cell cycle control in which miRNAs regulate negative cell cycle modulators at two phases of the cell cycle to ensure proper replenishment of the stem cell population.