Andrei Popa

Andrei Popa
Emory University | EU · Department of Psychology

PhD

About

11
Publications
1,786
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112
Citations
Citations since 2017
2 Research Items
62 Citations
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Publications

Publications (11)
Preprint
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Cellular automata are discreet mathematical models. They consist of cells; each cell can exist in a limited number of mutually exclusive states, like 0 or 1. The state of each cell at time t is determined by simple rules, based on the states of its neighboring cells. While exploring their relevance to behavioral sciences (McDowell & Popa, 2009) one...
Preprint
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Physical forces acting on particles explain how physical systems change over time. Evolutionary forces acting on populations of genomes explain change in the genetic structure of populations across generations. The dynamics of human development - i.e., learning, or change in psychological systems, are not yet understood. This is a step in that dire...
Article
McDowell’s evolutionary theory of behavior dynamics (McDowell, 2004) instantiates populations of behaviors (abstractly represented by integers) that evolve under the selection pressure of the environment in the form of positive reinforcement. Each generation gives rise to the next via low-level Darwinian processes of selection, recombination, and m...
Conference Paper
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Skinner (1981) suggested that natural selection operates not only at the biological level, but is also responsible for the evolution of behavioral repertoires throughout an organisms lifetime. McDowell (2004) implemented the selectionist account in a computational theory of behavior dynamics. The theory causes a population of behaviors to evolve th...
Thesis
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McDowell (2004) instantiated low-level Darwinian processes in a computational theory of behavior dynamics. The theory causes a population of behaviors to evolve through time under the selection pressure of the environment. It has been tested under a variety of conditions and the emergent outcomes were repeatedly shown to be qualitatively and quanti...
Conference Paper
Full-text available
Complexity science is rapidly becoming the "spoiled child" of the scientific community, promising to dissolve interdisciplinary barriers and open a new chapter in our understanding of the natural world (Mitchell, 2009). Complex systems are dynamic, adaptive systems, composed from a large number of interconnected parts, and governed by simple, low-l...
Article
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Virtual organisms animated by a selectionist theory of behavior dynamics worked on concurrent random interval schedules where both the rate and magnitude of reinforcement were varied. The selectionist theory consists of a set of simple rules of selection, recombination, and mutation that act on a population of potential behaviors by means of a gene...
Conference Paper
Full-text available
McDowell (2004) instantiated the Darwinian principles of selection, recombination, and mutation in a computational model of selection by consequences. The model has been tested under a variety of conditions and the emergent outcome is quantitatively indistinguishable from that displayed by live organisms. McDowell (2010) suggested that mutation may...
Article
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One theory of behavior dynamics instantiates the idea that behavior evolves in response to selection pressure from the environment in the form of reinforcement. This computational theory implements Darwinian principles of selection, reproduction, and mutation, which operate on a population of potential behaviors by means of a genetic algorithm. The...
Article
Full-text available
In A New Kind of Science, Stephen Wolfram recommends abandoning traditional scientific analysis and the continuous mathematical description that it affords in favor of the study of simple rules. He focuses on a machine known as a cellular automaton as the prototype generator of complex phenomena such as those we see in nature. The simplest cellular...

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Projects

Project (1)
Project
Previous work on choice behavior (i.e., concurrent schedules) suggested that sensitivity to reinforcement rate (ar ~ 0.8; Davison and McCarthy, 1988) was higher than sensitivity to reinforcement magnitude (am = 0.65; Cording, McLean, & Grace, 2011), results replicated by McDowell’s computational model of behavior dynamics (McDowell, Popa, & Calvin, 2012). These studies, however, approached rate and magnitude asymmetrically: the magnitude of the reinforcers was always fixed, from trial to trial, just like time intervals in Fixed Interval schedules. On the other hand, the intervals between reinforcers was always different from trial to trial, even when the average rates were equal between the two alternatives. This project examines choice behavior of virtual agents animated by evolutionary tendencies in environments that allow both rate and magnitude to vary from trial to trial. To the author’s knowledge, this manipulation has not been investigated in the live organisms literature. As such, they will become epistemologically pure predictions of the evolutionary paradigm, predictions that can be then verified in human matching procedures (e.g., Popa, 2013, pp. 50-59).