Popa, A., & McDowell, J J. (2009). A Computational Model of Adaptive Behavior Dynamics. Emory University, Department of Psychology, Annual Research Festival. Atlanta, GA
Mathematical models such as the matching equations describe behavior. Computational theories are essentially computer programs that generate behavior. They implement theoretical assumptions about how learning works in biological agents. Mathematical models can be used to verify these assumptions.
Popa, A., & McDowell, J J. (2009). Hamming cliffs in a computational model of selection by consequences. Poster presented at the 32nd Annual Meeting of the Society for the Quantitative Analysis of Behavior, Phoenix, AZ.
https://www.sqab.org/programs/2009.pdfMcDowell et. al. (2008) hypothesized that the Hamming distance between the target classes may be functionally equivalent to a changeover delay. Exploring this parallel became the topic of my Masters' thesis.
McDowell, J. J, & Popa, A. (2009). Beyond continuous mathematics and traditional scientific analysis: Understanding and mining Wolfram's A new kind of science. Behavioural Processes, 81, 343-352.
http://www.ncbi.nlm.nih.gov/pubmed/19429227https://www.researchgate.net/publication/24414894_Beyond_continuous_mathematics_and_traditional_scientific_analysis_Understanding_and_mining_Wolfram's_A_New_Kind_of_ScienceIn 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 amachine known as a cellular automaton as the prototype generator of complex phenomena such as those we see in nature. The simplest cellular automaton consists of a row of cells, each existing in one of two states. The states of the cells are updated from moment to moment by simple rules. Wolfram shows that these machines and their many variations can generate a host of outcomes ranging from very simple to extremely complex.He argues that among these outcomes representations of all the phenomena in the universe will be found, including presumably the behavior of organisms. The output of cellular automata can be mapped to behavior by considering, for example, one of the states of a cell to represent the emission of a behavior. For some cellular automaton rules, these mappings generate cumulative records and inter-response time distributions that are similar to those produced by live organisms. In addition, at least one cellular automaton generates the Herrnstein hyperbola as an emergent outcome. These results suggest that Wolfram’s program and its mainstream version, which is known as complexity theory, is worth pursuing as a possible means of understanding and accounting for the behavior of organisms.
Popa, A. (2009). The Effects of Hamming Distances in a Computational Model of Selection by Consequences. Masters thesis. Retrieved:
https://etd.library.emory.edu/concern/etds/8k71nj12p?locale=en
https://etd.library.emory.edu/concern/etds/8k71nj12p?locale=enMcDowell (2004) instantiated the Darwinian principles of selection, recombination, and mutation in a computational model of selection by consequences. The model forces a population of behaviors to evolve under the selection pressure of the environment, by applying low-level Darwinian principles; it has been tested under a variety of conditions and the quantitative outcomes are remarkably similar to those obtained in experiments with live organisms (McDowell et al., 2008). The computational model animates a virtual organism with a repertoire of 100 behaviors, represented by binary strings; this raises the specific issue of Hamming distances, the number of digits in a binary string that must be changed in order to obtain another bit string of equal length (Hamming, 1950). McDowell (2008) hypothesized that in environments that reinforce two alternatives the Hamming distance may be computationally equivalent to a changeover delay (COD). In experiments with live organisms that reinforce two alternatives, an interesting phenomenon is sometimes observed: instead of responding to the alternatives, the organism behaves "as if" switching itself is reinforced. One way to prevent this phenomenon is the use of a changeover delay, a procedure that prevents the organism from acquiring reinforcement if it switches too often (Findley, 1958). The computational model places the target classes next to each other, and, traditionally, they are separated by a large Hamming cliff, which makes it more difficult for a behavior to switch from one target class to the other. In order to investigate the effects of smaller cliffs between the target classes, they were positioned at different locations along the continuum; in addition, other parameters were systematically varied. Results confirmed McDowell et al.'s Hamming-Distance-As-Changeover-Delay hypothesis and also revealed a robust rule about the effects of Hamming distances within the model. The steady state outcome is, therefore, a product of the reiteration of Darwinian rules, and not an artifact of conveniently choosing an exceptional location for the target classes. This study constitutes another argument for the robustness of the computational model of selection by consequences as a valid account of the behavioral dynamics.
Popa, A., & McDowell, J. J. (2010). The effect of Hamming distances in a computational model of selection by consequences. Behavioural Processes, 84, 428-434.
https://www.researchgate.net/publication/41427297_The_effect_of_Hamming_distances_in_a_computational_model_of_selection_by_consequenceshttp://www.ncbi.nlm.nih.gov/pubmed/20152891McDowell (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 outcome is quantitatively indistinguishable from that displayed by live organisms. The computational model animates a virtual organism with a repertoire of 100 behaviors, selected from the integers from 0 to 1023, where the corresponding binary representations constitute the behavior’s genotypes. Using strings of binary digits raises the specific problem of Hamming distances: the number of bits that must be changed from 1 to 0 or from 0 to 1 in order to obtain another string of equal length.McDowell hypothesized that the Hamming distance may be computationally equivalent to the changeover delay used in experiments with live organisms.The results of the present experiments confirmed this hypothesis and revealed a robust rule about the effects of Hamming distances within the model, namely, in order to obtain good matching, the difference between the Hamming distance that separates the target classes and the largest Hamming distance comprised within a class must be equal to or larger than three.
Popa, A., & McDowell, J J. (2010). Absolute reinforcement rates in ETBD. Poster presented at the 33rd Annual Meeting of the Society for the Quantitative Analysis of Behavior, San Antonio, TX.
https://www.sqab.org/programs/2010.pdfMcDowell and Popa (in press) showed that when running concurrent schedules, experimental settings designed to reasonably sample the reinforcement ratio domain may not adequately sample the absolute reinforcement rate domain. For example, a concurrent RI 10 RI 20 has a reinforcement ratio of 2:1, equal to a concurrent RI 100 RI 200; evidently, the absolute reinforcement rates differ considerably. McDowell and Popa suggested that the overall absolute reinforcement rate in a concurrent schedule might affect behavior generated by the computational model. Systematic and thorough sampling of the absolute reinforcement rate domain in a series of computational experiments confirmed this hypothesis. <b>The results</b> showed that as the overall absolute reinforcement rate increased, behavior generated by the computational model became less sensitive to changes in parameters related to reinforcer value, changeover delay, and impulsivity.
Popa, A., & McDowell, J J. (2010). Evolutionary Behavior Dynamics causally generates both molar and molecular properties of behavior. Symposium: The Molar-Molecular debate: A further discussion on the unresolved issue. Discussant: William M. Baum, Ph.D. Presented at the 36th Annual Convention of the Association for Behavior Analysis International, San Antonio, TX.
https://www.abainternational.org/events/program-details/event-detail.aspx?sid=8950&by=ByArea#s436_0Both molar and molecular causality may be illusory. Molar and molecular features of behavior are emergent properties of an evolutionary theory of behavior dynamics that instantiates the idea that behavior evolves in response to selection pressure provided by reinforcement from the environment. This theory consists of Darwinian rules of selection, reproduction, and mutation that operate on a population of potential behaviors over time to generate a continuous stream of emitted behavior. The evolutionary theory has been shown to generate behavior on single random interval (RI) and concurrent RI RI schedules that has molar properties consistent with matching theory, and that are quantitatively indistinguishable from molar properties of live-organism behavior. At the same time, the theory has been shown to generate molecular inter-response time (IRT) distributions that can be studied in the form of log-survivor plots, and that are similar to log-survivor IRT distributions from live organisms. A parallel selectionist theory of neural functioning has been discussed as a plausible material mechanism for this evolutionary theory of behavior dynamics.
McDowell, J. J, & Popa, A. (2010). Toward a mechanics of adaptive behavior: Evolutionary dynamics and matching theory statics. Journal of the Experimental Analysis of Behavior, 94, 241-260.
http://www.ncbi.nlm.nih.gov/pubmed/20152891http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2929088/https://www.researchgate.net/publication/50937156_Toward_a_Mechanics_of_Adaptive_Behavior_Evolutionary_Dynamics_and_Matching_Theory_StaticsOne 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 behavior of virtual organisms animated by this theory may be studied in any experimental environment. The evolutionary theory was tested by comparing the steady-state behavior it generated on concurrent schedules to the description of steady state behavior provided by modern matching theory. Ensemble fits of modern matching theory that enforced its constant-k requirement and the parametric identities required by its equations, accounted for large proportions of data variance, left random residuals, and yielded parameter estimates with values and properties similar to those obtained in experiments with live organisms. These results indicate that the dynamics of the evolutionary theory and the statics of modern matching theory together constitute a good candidate for a mechanics of adaptive behavior.
The Qualifying exam is the last requirement on the way to PhD.
Popa, A., & McDowell, J J. (2011). A Computational Model of Selection by Consequences: Effects of the changeover delay (COD) on the impulsive behavior of a virtual organism. Poster presented at the 34th Annual Meeting of the Society for the Quantitative Analysis of Behavior, Denver, CO.
https://www.sqab.org/programs/2011.pdfPopa & McDowell (2010) showed that a partcular feature of ETBD is computationally equivalent to to the changeover delay (COD) used in experiments with live organisms. McDowell & Popa (2010) suggested that mutation, one of the Darwinian rules implemented in ETBD, is computationally equivalent to impulsivity. The present study clarified how Hamming distances affect ETBD behavior in concurrent schedules, at different mutation rates. The results were qualitatively and quantitatively congruent with those recently reported in the ADHD literature (Taylor et al, 2010): high mutation rates (impulsivity) resulted in low exponents (a) and increased changeover frequency; the maladaptive effects were meliorated by increasing the COD requirement - namely the average Hamming distance between the two target classes.
Popa, A., & McDowell, J J. (2011). A computational Model of selection by consequences: evidence that mutation is computationally equivalent to impulsivity. Paper presented at the 37th Annual Convention of the Association for Behavior Analysis International, Denver, CO.
https://www.abainternational.org/events/program-details/event-detail.aspx?sid=13494&by=ByArea#s445_0... The present experiments systematically investigated the effects of various mutation levels, rates of reinforcement, changeover delay (COD) values, and reinforcers' magnitudes on the behavior of the virtual organism on concurrent RI RI schedules. The purpose was twofold: to clarify the parallel between mutation and impulsivity and to explore methods of counteracting the undesired effects of high mutation rates. The results showed that improved performance in concurrent-schedules performance (e.g. increased sensitivity to reinforcement, reduced rate of switching, etc.) under high levels of mutation is attainable by increasing the rate or magnitude of the reinforcement and/or the COD requirement; the results accurately parallel those reported in the attention deficit/hyperactivity disorder literature (Kollins, Lane, and Shapiro, 1997; Aase and Sagvolden, 2006; Taylor, Lincoln, and Foster, 2010), indicating that mutation is the computational equivalent of impulsivity.
McDowell, J, J., Popa, A., & Calvin, N. (2012). Selection dynamics in joint matching to rate and magnitude of reinforcement. Journal of the Experimental Analysis of Behavior, 98, 199-212.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3449856/https://www.researchgate.net/publication/231176507_Selection_dynamics_in_joint_matching_to_rate_and_magnitude_of_reinforcementVirtual 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 genetic algorithm. An extension of the power function matching equation, which expresses behavior allocation as a joint function of exponentiated reinforcement rate and reinforcer magnitude ratios, was fitted to the virtual organisms’ data, and over a range of moderate mutation rates was found to provide an excellent description of their behavior without residual trends.
Popa, A., & McDowell, J J. (2012). The computational theory of behavior dynamics predicts effects of COD on behavioral variability: evidence from experiments with human participants. Paper presented at the 38th Annual Convention of the Association for Behavior Analysis International, Seattle, WA.
https://www.abainternational.org/events/program-details/event-detail.aspx?sid=17728&by=ByArea#s28_0https://www.abainternational.org/events/program-details/event-detail.aspx?sid=17728&by=ByArea#s28_0Popa and McDowell (2010) showed that the Hamming Distance, a particular mathematical feature of McDowell’s Evolutionary Theory of Behavior Dynamics (ETBD; McDowell, 2004, 2010) is computationally equivalent to the changeover delay (COD; Findley, 1954) used in experiments with live organisms. Popa and McDowell (2011) suggested that increasing the computational COD requirement reduces behavioral variability in a virtual organism animated by the ETBD. The present paper further investigated a wide range of computational COD values and used the results to formulate predictions about the effects of COD requirements on the behavioral variability of humans in concurrent-schedules environments. As predicted by ETBD, the rate of switching between alternatives was systematically decreased by increased COD requirements. These results provide further support for the ETBD as a valid account of behavior dynamics, showing that it is not only able to produce outcomes congruent with known behavior statics (the Matching Law; McDowell & Popa, 2010), but it can also formulate predictions about the behavior of live organisms. Moreover, these findings suggest that high behavioral variability may be a natural outcome of the selection pressure exerted by unstructured environments. The implications for ADHD-symptoms are discussed within the framework provided by previous research (Neuringer, 2010; Taylor et al, 2010).
Popa, A., Calvin, N., & McDowell, J J. (2013). Complexity, emergence, and the behavior of organisms. Paper presented at the 39th Annual Convention of the Association for Behavior Analysis International, Minneapolis, MN.
https://www.abainternational.org/events/program-details/event-detail.aspx?sid=28880&by=ByArea#s166_0Complexity 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-level rules that can give rise to novel, emergent features or behaviors. High-level, emergent properties are not readily reducible to the rules that produce them. They appear to be stand-alone entities and behavioral and psychological sciences have traditionally studied them as such (e.g. impact of divorce on children's risk of depression). However, if they are emergent features of a dynamic system, the relations between them cannot, in principle, be causal (divorce → depression). Their co-occurrence is incidental, both being produced by underlying simple rules reiterated over long periods of time. In order to fully understand an emergent property, one must identify the rules that govern the system and the specific conditions under which the property emerges (McDowell & Popa, 2009). This implies a complete shift in focus, from high-level properties to low-level rules and characteristics, opening a fascinating doorway for scholars interested in the behavior of organisms.
Popa (2013). An evolutionary theory of behavior dynamics: complexity, darwinism, and the emergence of high-level phenotypes. Doctoral Dissertation. Retrieved:
https://etd.library.emory.edu/concern/etds/9880vr10s?locale=en. ISBNxxxxxxxxxxxxxxx
https://etd.library.emory.edu/concern/etds/9880vr10s?locale=enThe main purpose of this project was to explore the effects of mutation and the environment's value and conduciveness on various dimensions of behavioral variability, in continuous choice environments. Secondly, qualitative predictions made by the Evolutionary Theory about the effects of changeover delays (COD) on behavior variability were verified against the behavior of college students in equivalent environments. The continuous choice behavior of college students was correctly predicted on eight out of eight behavioral dimensions. Thirdly, low-level characteristics of students' continuous choice behavior were compared with traditional measures of impulsivity and sustained attention, in an effort to investigate the potential equivalence between mutation and a property of the nervous system that produces impulsivity-like symptoms. The results were inconclusive, likely due to a lack of extreme impulsivity scores in the human sample. The findings presented in this paper provided significant additional evidence for the selectionist account as a valid mechanism of behavior change. In addition, the knowledge generated by the Evolutionary Theory provided important insights about clinically-relevant phenomena, such as disordered variability (or impulsivity) and raise the possibility of using the theory as a platform for simulating the emergence of specific high-level phenotypes. These implications appear even more fascinating considering that a connection with mental health was not explicitly sought, nor can it be traced to the inner-workings of the theory. This challenges our current understanding of mental illness and provides a new way of thinking about the evolution of behavioral repertoires and their emergent high-level characteristics.
Popa, A., Calvin, N., & McDowell, J J. (2014). Multifinality and equifinality in an evolutionary theory of behavior dynamics. Paper presented at the 40th Annual Convention of the Association for Behavior Analysis International, Chicago, IL.
https://www.abainternational.org/events/program-details/event-detail.aspx?sid=40586&by=ByArea#s30_0Skinner (1981) suggested that natural selection operates not only at the biological level, but is also responsible for the evolution of behavioral repertoires throughout an organism’s lifetime. McDowell (2004) implemented the selectionist account in a computational theory of behavior dynamics. The theory causes a population of behaviors to evolve through time under the selection pressure exerted by the environment. It has been tested under a variety of conditions and the emergent outcomes were repeatedly shown to be qualitatively and quantitatively indistinguishable from those displayed by live organisms (McDowell, in press). The present project investigated the effects of various environmental variables (e.g. reward magnitude) on the behavior variability of virtual organisms characterized by various mutation rates. High mutation rates produced behavioral constellations similar to those displayed by ADHD-diagnosed children. These effects were counteracted by arranging richer or more structured environments (higher changeover delay). Interestingly, arranging low-value environments caused organisms characterized by low mutation rates to display abnormally high levels of variability. These findings suggest that similar high-level phenotypes such ADHD may be caused by various combinations of organismic and environmental features (equifinality), during a dynamic process governed by Darwinian forces.
Popa, A. (2015). Towards an Evolutionary Account for the Dynamics of Cognizant Systems. Darwin Day Lecture Series, Georgia State University, Atlanta, GA.
Before Darwin, biology was dominated by essentialism, the view that each species has an immutable prototype, an ideal form from which all individuals depart to some extent. Darwins insight, now known as population thinking, was that ideal forms do not exist and averages are illusions. The phenotipic diversity in a population is not imperfection, or decay, but its natural state. We have a similar problem in psychology.
Popa, A. (2016). Population Thinking, Psychodiversity, and Human Development. Darwin Day Lecture Series, Georgia State University, Atlanta, GA.
Darwin proposed that the biodiversity we observe today emerged gradually, from a common root, by means of natural selection: when individuals reproduce, they pass on their genes. Those who reproduce more often will pass on more copies of their genetic material. Adaptation, therefore, is a byproduct of sexual reproduction.
Popa, A., & McDowell, J, J. (2016). Behavioral Variability in an Evolutionary Theory of Behavior Dynamics. The Journal of the Experimental Analysis of Behavior, 105 (2), 270-290.
http://onlinelibrary.wiley.com/doi/10.1002/jeab.199/abstracthttps://www.researchgate.net/publication/299382923_Behavioral_variability_in_an_evolutionary_theory_of_behavior_dynamicsMcDowell’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 mutation.The emergent patterns can be analyzed and compared to those produced by biological organisms.The purpose of this project was to explore the effects of high mutation rates on behavioral variability in environments that arranged different reinforcer rates and magnitudes.Behavioral variability increased with the rate of mutation. High reinforcer rates and magnitudes reduced these effects; low reinforcer rates and magnitudes augmented them. These results are in agreement with live - organism research on behavioral variability. Various combinations of mutation rates, reinforcer rates, and reinforcer magnitudes produced similar high-level outcomes(equifinality).These findings suggest that the independent variables that describe an experimental condition interact; that is, they do not influence behavior independently.These conclusions have implications for the interpretation of high levels of variability, mathematical undermatching, and the matching theory. The last part of the discussion centers on a potential biological counterpart for the rate of mutation, namely spontaneous fluctuations in the brain’s default mode network.
UAdekunle, O. & Popa, A. (2016). Higher Payoff or More Options? Poster presented at the Spring Annual Research Conference, Agnes Scott College, Decatur, GA.
https://www.agnesscott.edu/sparc/index.htmlhttps://www.agnesscott.edu/sparc/Having multiple options is appealing in our everyday lives, arguably because it allows for a flexible future. Some studies, however, showed that people may be less satisfied when presented with multiple options. This project investigated whether people preferred to keep their options open (so to speak) by arranging an asymmetrical, continuous choice environment. It was hypothesized that people will work to keep their options open even if it limits acquiring the maximum number of points. Seven Agnes Scott students responded on concurrent Random Interval (RI) schedules of reinforcement. The overall rate of reinforcement was constant, but one target class delivered higher-magnitude reinforcers (5 points vs. 1 point). However, the target class with the lower reinforcer magnitude (1 reinforcer = 1 point) shrank in size when not selected for ten or more consecutive seconds. Every ten-second interval would reduce its size by one fourth of its original size and every new response would increase its size by one fourth (or, if at original size, reset the ten-second interval). Results showed preference for the shrinking class (b ~ 0.9), even though reinforcer magnitude on this class was five times smaller. This suggests that participants preferred to keep their options open, even if meant acquiring a lower overall payoff.
UBooher, C. & Popa, A. (2016). Choice Behavior in Unsignaled Environments With and Without Immediate Negative Feedback. Poster presented at the Spring Annual Research Conference, Agnes Scott College, Decatur, GA.
The purpose of this study was to explore properties of choice behavior when the target classes that have the potential for reinforcement were hidden. Twenty-four Agnes Scott students were randomly assigned to two experimental conditions. In one condition an unpleasant sound was made contingent on each response that occurred outside a target region. In the second condition, extraneous responses were not signaled. In both conditions, reinforced responses resulted in one point and a pleasant sound. Target responses that were not reinforced were never signaled. We hypothesized that the condition with feedback 1) will elicit higher accuracy in locating the target classes and 2) will produce lower levels of behavioral variability. Preliminary results appear to confirm the hypotheses.
UForbes, V., & Popa, A. (2016). Human Choice Behaviors Before and After Extinction. Poster presented at the Spring Annual Research Conference, Agnes Scott College, Decatur, GA.
One way to eliminate behaviors form an organism’s repertoire is to identify and remove the reinforcing contingencies that maintain them. This procedure is referred to as extinction. Extinction is known to be accompanied by a short increase in behavior frequency, intensity, and variability. In this project, we examined various properties of choice behavior before and after extinction was implemented. Preliminary results showed that the frequency of both target and extraneous (non-target) responses increased during the extinction phase. The effect was more pronounced for extraneous responses, possibly for their exploratory potential. Future analysis will focus on various measures of variability before and after reinforcement is withdrawn.
UGrissom, M. K., & Popa, A. (2016). Effects of Extinction on Human Escape Behavior. Paper presented at the Spring Annual Research Conference, Agnes Scott College, Decatur, GA.
The purpose of this study was to explore the effects of negative reinforcement on the frequency and variability of continuous choice behavior. The concurrent-schedule procedure was implemented via a computer program developed by the second author. Participants began the experiment with a fixed number of points (e.g., 1,000). As time passed, the number of points decreases at a rate of 4 points per second. Responses (mouse clicks) on two target regions stopped the loss of points for a small amount of time (e.g., 4 seconds). The first specific aim was to verify if escape behavior (stop the loss of points) become avoidance behavior (prevent loss of points). The second was to observe if avoidance behavior continued when it was no longer necessary. The third specific aim was to verify to what extent responding continued when it was no longer adaptive (i.e., when loss of points could not be avoided). The fourth specific aim was to observe if responding resurged once its adaptive function - preventing loss of points - was restored. The fifth aim was to explore possible relations between specific, low-level properties of choice behavior (e.g., bout frequency, variability in inter-response intervals) and personality traits (e.g., conscientiousness).
UShen, R. & Popa, A. (2016). Is Competition Sufficient to Increase the Motivation to “Do Well”? Paper presented at the Spring Annual Research Conference, Agnes Scott College, Decatur, GA.
In concurrent schedule procedures, humans exhibit lower sensitivity to reinforcement than non-humans (McDowell, 2013), possibly because points may not be as reinforcing for humans as food is for non- humans. We hypothesized that an environment that creates the impression of competition may increase the reinforcing value of points. Two groups of participants (competition vs. non-competition) responded for 18 minutes in a continuous-choice procedure that arranged concurrent, independent Random Interval (RI) schedules; the target classes were invisible to both groups. Preliminary analyses showed that sensitivity to reinforcement, contrary to our hypothesis, was not noticeably higher in the competition condition. These results showed that competition by itself may not be sufficient to increase motivation. Several potential explanations are discussed, including the perceived relevance (or lack thereof) of the activity.
UTang, X., & Popa, A. (2016). Choice behavior in Low discriminability conditions: effects of the operant class size. Poster presented at the Spring Annual Research Conference, Agnes Scott College, Decatur, GA.
The purpose of the study was to explore basic properties of choice behavior when the target classes varied in size, in low-discriminability conditions (invisible target classes). 24 Agnes Scott students responded in environments that arranged symmetrical, concurrent, Random Interval (RI) schedules of reinforcement. The target regions (or classes) were hidden. In one condition (N = 12) the target classes were small (about 4% of the experimental area). In the second condition the target classes occupied approximately 20% of the experimental area. Sensitivity to reinforcement was larger when the classes were small ( a ~ 0.70) than when they were large ( a ~ 0.19). The same was true for spatial variability, but not for temporal variability, which was larger when the target classes were small.
UWaithe, O. Y., & Popa, A. (2016). The Effects of Imagery on Psychological Discomfort. Paper presented at the Spring Annual Research Conference, Agnes Scott College, Decatur, GA.
https://www.agnesscott.edu/sparc/index.htmlhttps://www.agnesscott.edu/sparc/This study examines the effectiveness of art by means of geometric and spatial relations as a means of therapy for anxiety-based disorders. Recent studies on the nature of trypophobia (fear of holes) suggest that specific geometrical arrangements, such as high contrast midrange spatial frequency images, may automatically trigger feelings of discomfort (Cole & Wilkins, 2013). The purpose of this study is twofold: to replicate the findings reported by Cole and Wilkins (2013) and to explore the extent to which the phenomenon can be reversed. Participants will be exposed to similar stimuli as those used by Cole & Wilkins (2013) via a computer program. They will be asked to rate their level of discomfort and provide a short explanation of why. They will then be asked to manipulate the images using (i.e., re-arrange the elements) using the mouse until the level of discomfort decreases. Each resulting image, as well as the stroke paths, will be recorded and analyzed for concurrences of basic geometric shapes and/or arrangements that help reduce anxiety. The overarching goal is to be able to produce personalized visual stimuli that reduce anxiety, thus increasing the level of personalization and effectiveness of therapeutic approaches to anxiety.
Popa, A. & Grissom, M. K. (2017). Extinction-Induced Variability on Negatively Reinforced Human Behavior. Paper presented at the Winter Conference on Animal Learning & Behavior, Park City, UT.
https://static1.squarespace.com/static/57b732f259cc68697145ea01/t/5b7dbd231ae6cf8700f3d604/1534967075831/Past+Programs+2017.pdfWhen a previously reinforced behavior suddenly becomes ineffective, it will eventually disappear from an organism’s repertoire. Its extinction, however, is often preceded by an extinction burst: a short-lived period of frenetic activity, during which the targeted behavior increases in frequency, intensity, and variability, and often accompanied by the presence of novel behaviors. The effects of extinction on behavioral variability have been mostly investigated in the context of positively reinforced behaviors. In this paper, we explored its effects on the frequency and variability of negatively reinforced human behavior. Extinction caused a ten-fold increase in behavior frequency, high changeover frequency, a surge in the frequency of extraneous behavior, and higher inter-response variability, in both time and space. These results suggested, among other things, that negatively reinforced behavior was inherently variable, that behavioral novelty observed during extinction may have had little adaptive potential (if any), and that negative reinforcement may have stifled exploratory tendencies.
Popa, A., (2017). Towards a Dynamic Account of Human Development. Paper presented at the 43rd Annual Convention of the Association for Behavior Analysis International, Denver, CO.
https://www.abainternational.org/events/program-details/event-detail.aspx?sid=53139&by=ByArea#s196_0https://www.abainternational.org/events/annual-2017.aspxThe biopsychosocial model acknowledges that high-level phenotypes (e.g., impulsivity, good at math, etc.) are multiply-caused by a plethora of contributing, interacting factors, such as specific genetic configurations, socio-economic status, culture, ethnicity, gender, and so on. However, the model does not explain how these variables interact with each other or how, exactly, they contribute to a specific outcome. I submit to the reader a potential explanation. Drawing on theoretical advancements from the field of complex systems and on computational research on the dynamics of behavioral repertoires (McDowell, 2013; Popa, 2013; Popa & McDowell, 2016), the theory discussed here proposes that the interaction between agents and their environment consists of a continuous-choice process during which agents adapt to environmental changes. This process molds an individual’s context (e.g., income, culture, etc.) into robust collections of cognitive, emotional, and behavioral manifestations like “impulsivity”, “authoritative parent”, etc. The factors typically associated with impulsivity (for example) contribute to its emergence indirectly, by altering the relative value of existing options, and, by extension, the moment-to-moment probability of choosing one course of action over another. High-level phenotypes, therefore, cannot be directly explained by the contributing factors themselves, but by the moment-to-moment changes said factors produce in cognition and action.
Popa, Andrei. 2019. Psychology 2.0: The Emergence of Individuality. PsyArXiv. September 8. doi:10.31234/osf.io/m87an.
Popa, Andrei. 2019. Imperfect Automata: Effects of “mutation” on the Evolution of Automaton 01101110 (rule 110). PsyArXiv. September 30. doi:10.31234/osf.io/wgp4e.
