The flight of the locus of selection: Some intricate relationships between evolutionary elements

Becker, April M.

doi:10.1016/j.beproc.2018.01.002

Cited by 6 publications

(3 citation statements)

References 96 publications

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“…Although the conceptual framework of selection by consequences has a long tradition in behavior analysis and remains a popular narrative (e.g., Baum, 2023; Becker, 2019; Donahoe, 2011; Donahoe et al, 1993; Hull et al, 2001; McDowell, 2013, 2023; Simon, 2020; Simon & Hessen, 2019), it seems to have had few effects on the actual practices of many behavior analysts (but see McDowell, 2019, and Li et al, 2018). One possible reason for this gap between theory and practice may be that operant selection is sometimes used as a mere synonym for what is traditionally called “reinforcement.” Of course, adopting the language and vocabulary of evolutionary biology alone does not add much to the theoretical foundations of the experimental analysis of behavior.…”

Section: Introductionmentioning

confidence: 99%

Model‐based estimates for operant selection

Borgstede,

Anselme

2024

J Exper Analysis Behavior

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We present a new methodology to partition different sources of behavior change within a selectionist framework based on the Price equation—the multilevel model of behavioral selection. The multilevel model of behavioral selection provides a theoretical background to describe behavior change in terms of operant selection. Operant selection is formally captured by the covariance‐based law of effect and accounts for all changes in individual behavior that involve a covariance between behavior and predictors of evolutionary fitness (e.g., food). In this article, we show how the covariance‐based law of effect may be applied to different components of operant behavior (e.g., allocation, speed, and accuracy of responding), thereby providing quantitative estimates for various selection effects affecting behavior change using data from a published learning experiment with pigeons.

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Section: Introductionmentioning

confidence: 99%

Model‐based estimates for operant selection

Borgstede,

Anselme

2024

J Exper Analysis Behavior

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“…Although the conceptual framework of selection by consequences has a long tradition in behavior analysis and remains a popular narrative (e.g., Baum, 2023; Becker, 2019; Donahoe, 2011; Donahoe, Burgos, & Palmer, 1993; Hull, Langman & Glenn, 2001; Simon & Hessen, 2019; Simon, 2020), it seems to have had little effects on the actual practices of many behavior analysts. One possible reason for this gap between theory and practice may be that operant selection is sometimes used as a mere synonym for what is traditionally called “reinforcement.” Of course, adopting the language and vocabulary of evolutionary biology alone does not add much to the theoretical foundations of the experimental analysis of behavior.…”

Section: Introductionmentioning

confidence: 99%

Model-Based Estimates for Operant Selection

Borgstede

Anselme

2022

Preprint

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We present a new methodology to partition different sources of learning within a selectionist framework based on the Price equation – the Multilevel Model of Behavioral Selection (MLBS). The MLBS provides a theoretical and formal background to disentangle behavior changes due to selection effects ('reinforcement') from behavior changes due to non-selection effects (e.g., “effort” or “skill”). The model allows to quantify the causal effect of reinforcement even in the presence of opposing sources of behavior change and to test whether the reinforcement effect is statistically significant for a given subject. We demonstrate our method using training data from a reinforcement experiment with nine pigeons (Columba livia). The pigeons were trained to search for food in small holes on the ground of a special cage. In each session, the holes in only one half of the cage were filled with food (reinforced region), while the holes in the other half of the cage remained empty (nonreinforced region). Although the total time spent in the reinforced region between training sessions tends to decrease between training sessions, our method reveals positive behavioral selection (reinforcement effect) in most of the pigeons. However, this selection effect is masked by a strong non-selection component that consists in pigeons becoming more efficient foragers (skill effect) and in an increased peck frequency (effort effect). The partitioning of behavior change into selection and non-selection components allows to identify the causal effects of reinforcement even if they cannot be isolated experimentally. Our method is especially useful in the context of non-equilibrium behavior analysis (such as the change between training sessions), and in the context of behavior observations in naturalistic settings.

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“…The analogy between natural selection and reinforcement learning was further developed by Skinner (1981) who even claims that natural selection and reinforcement learning are two instances of the same underlying causal principle: selection by consequences. The selectionist account of learning has also been adopted in neuronal accounts of reinforcement (Donahoe, Burgos, & Palmer, 1993), and Bayesian learning models (Richerson, 2019), and remains popular among behavioral psychologists (Baum, 2017(Baum, , 2018a; A. M. Becker, 2019;Hull, Langman, & Glenn, 2001;Simon & Hessen, 2019;Staddon, 2016) 1 .…”

Section: Introductionmentioning

confidence: 99%

An evolutionary model of reinforcer value

Borgstede

2021

Preprint

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Within the field of evolutionary biology, natural selection is often thought to favor traits that lead to individuals behaving as if they were maximizing their evolutionary fitness. The concept of the individual as a maximizer is also popular in behavioral psychology, especially when it comes to theories of operant learning. Here, the individual is taken to adapt its behavior to the local environment, such that the expected amount of reinforcer value is maximized.Whereas there is a considerable consensus concerning the formal properties of an evolutionary maximand ('fitness'), there is no generally accepted conceptualization of a corresponding behavioral maximand ('reinforcer value'). However, such theoretical clarification is crucial to the development and empirical testing of learning theories, since it is impossible to decide whether the concept of reinforcer maximization is adequate, as long as the maximand is not well defined. This paper presents a formal model of reinforcer value that is consistent with existing work on the nature of reinforcement and provides an explicit link between behavioral psychology and evolutionary biology. The main result is that the reinforcer value of an additional time unit spent at a behavior equals its expected marginal effects on evolutionary fitness. Applying the model to matching behavior, it is further demonstrated how the established link between reinforcer value and evolutionary fitness can be used to derive new hypotheses.

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The flight of the locus of selection: Some intricate relationships between evolutionary elements

Cited by 6 publications

References 96 publications

Model‐based estimates for operant selection

Model‐based estimates for operant selection

Model-Based Estimates for Operant Selection

An evolutionary model of reinforcer value

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