- Email: [email protected]
SQAB 2009: Flying high
Behavioural Processes 84 (2010) 353–355
Contents lists available at ScienceDirect
Behavioural Processes journal homepage: www.elsevier.com/locate/behavproc
Preface
SQAB 2009: Flying high
This special issue of Behavioral Processes reports the proceedings of the 32nd Annual Meeting of the Society for Quantitative Analysis of Behavior (SQAB) held in Phoenix Arizona May 21st–23rd 2009. Since 2000, SQAB registrants have received a physical copy of the special issue reporting the proceedings of the previous year. Starting with this issue, registrants will receive electronic access to the special issue for 1 month after the conference. This transition will allow SQAB to continue the rapid 1-year turn around between the meeting and publication of peer-reviewed articles reporting the proceedings. In addition, electronic access likely will be better aligned with the manner in which most users access the scientific literature. The special issue is comprised of 8 full-length articles stemming from presentations by invited speakers and 20 short communications based upon poster presentations. Both full-length papers and short communications go through the usual peer-review process and represent original contributions to the scientific literature. As is typical for SQAB, the roster of speakers was an eclectic group of researchers addressing issues in the quantitative analyses of behavior from a variety of traditions including behavior analysis, associative learning, economic game theory, and comparative cognition. The keynote address was delivered by Nobel Laureate Gerald M. Edelman. Dr. Edelman described his influential theory of Neural Darwinism and the selectionist principles it suggests provide the basis for brain function. Dr. Edelman also discussed how higher brain functioning like consciousness might be understood within the framework of Neural Darwinism. He also noted similarities between Neural Darwinism and McDowell’s (2004) model of adaptive behavior dynamics based on the computational model of selection by consequences. At the request of the editors of this special issue, Dr. McDowell has contributed an article describing the relationship between Neural Darwinism and the evolutionary theory of behavior dynamics. As noted by McDowell, the theories together provide a framework for quantitatively understanding adaptive behavior extending from basic brain functioning to the behavior of whole organisms. Such a comprehensive system based on basic Darwinian principles appears to hold great promise as a biological based and integrative approach for understanding brain, behavior, and cognition. The article by Erev, Ingram, Raz and Shany presents a provocative analysis of rule enforcement based on a co-mingling of economic game theory and reinforcement learning. They address the question of whether ‘gentle continuous punishment’ (gentle COP) policies can be effective in controlling human behavior. The basic idea of gentle COP is that if the first M violators of a rule
0376-6357/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.beproc.2010.02.019
(out of a potential number of violators N > M) are punished with sufficiently high probability with a mild sanction, then most of the population will decide rationally to follow the rule. Erev et al. describe both a theoretical analysis of this situation in terms of a model for choice that they had previously developed (Erev and Haruvy, in press), as well as two experiments, which test the effectiveness of gentle COP in the laboratory and the real world. The theoretical analysis outlines the conditions under which gentle COP can be expected to be effective, and the experiments confirm predictions of the model for both humans in a laboratory gambling task, and choices by students taking an exam whether or not to cheat. Erev et al.’s article provides an excellent illustration of how core SQAB topics such as quantitative analysis of reinforcement and punishment can have wide application when integrated with perspectives from other disciplines. Moore’s article is an interesting commentary on contemporary research on behavioral choice. He suggests that by providing economical descriptions of data in terms of variables like average rate and immediacy of reinforcement, quantitative models for choice (e.g., Grace, 1994; Mazur, 2001) may have prematurely foreclosed attention to other factors which may be important determiners of behavior. Moore defines a ‘procedural variable’ as any factor which determines how ‘subjects contact stimulus, response, and reinforcer variables in an experiment’. As a case in point, he describes how the effects of reinforcement rate, measured in terms of the average interval between reinforcers, can differ between two widely used choice procedures, concurrent schedules and concurrent chains (Moore, 1984). A second example is how preference between variable-interval (VI) terminal links in concurrent chains can vary depending on whether terminal links consist of a delay to a single reinforcer, or a fixed-period of access to a VI schedule in which a variable number of reinforcers may be earned (Grace and Nevin, 2000; Moore, 2008). Moore analyzes these situations and suggests that researchers may have overlooked an important factor: the delay to the first reinforcer on an alternative. Whether these effects can be accommodated in terms of a single general model for choice remains to be seen. Moore’s contribution performs the useful service of reminding us that when quantitative models are able to account for 95% of phenomena (optimistically speaking), the remaining 5% is likely to be even more important in terms of achieving a more general understanding of behavior. The article by Elliffe and Davison describes an experiment with pigeons extending the study of choice to a situation with four response options available. They use a procedure in which the reinforcement ratio across four keys is always 27:9:3:1, but which reinforcement rates were assigned to key changes within
354
Preface / Behavioural Processes 84 (2010) 353–355
the session. The allocation of pigeons’ behavior quickly tracked the changing reinforcement allocations. But, the data suggested that preference was more extreme for options arranging higher reinforcement rates but the same reinforcement ratio (e.g., greater perference for 27:3 than for 9:1). Such an outcome is inconsistent with the constant-ratio rule and with the assumption of mathcing theory that relative reinforcement should govern choice independently of absolute rate of reinforcement. Elliffe and Davison suggest that the data are consistent with a contingency-discriminability approach to choice which does not depend upon the constantratio rule. This work by Elliffe and Davison is a good reminder of the importance of examining the assumptions and boundary conditions of our existing quantitative models by stepping beyond the simplified two-alternative situations that have been typical of studies examining the matching law. Curry, Foxall and Sigurdsson’s article is an example of translational research on choice. An important question studied in their research area, consumer behavior analysis, is whether the matching law can apply to everyday economic decisions, for example, the choices of customers in supermarkets between which of several brands of shampoo and conditioner to purchase. One method in which the matching law has been applied to consumer behavior is to examine the relationship between the relative amount purchased of two substitutable commodities (e.g., brands of shampoo) and the relative amount spent. In this approach, known as ‘amount matching’, money spent is viewed similarly as responding in basic laboratory research, while amount purchased is analogous to reinforcement. However, one potential criticism of amount matching is that it is tautologous—that is, that the relationship between relative amount spent and relative amount purchased is constrained by the ratio-like contingencies of the marketplace. Curry et al. provide a broad perspective on the tautology issue in consumer behavior analysis, as well as a discussion of previous work on the matching law. They show that when data are aggregated across consumers and brands, ratios of amount spent and amount purchased are not constrained to strict matching, and hence that amount matching is not formally tautologous. Curry et al. accomplish this by deriving an ‘error term’ which represents the discrepancy between observed and strict amount matching. They also discuss the challenges that other translational research on choice-specifically the application of the matching law to sports behavior-has faced. Overall, Curry et al.’s article is noteworthy for depicting the logical challenges that translational research on matching must confront, and describing a possible solution. The aritcle by Podlesnik and Shahan explores how the relapse of operant behavior can be understood in terms of behavioral momentum theory. They review experiments in which operant responding
is more resistant to disruption in the presence of stimuli previously associated with the delivery of response-independent reinforcers (a Pavlovian operation). Of interest, relapse of operant behavior following extinction is greater when responding was maintained by higher rates of reinforcement in baseline training, in the same way that resistance to extinction is greater when baseline responding is maintained by higher reinforcer rates. Podlesnik and Shahan offer a new quantitative model which explains the relapse effect in terms of a reduction in the effects of a disruptor which normally hastens the course of extinction. The model accounts well for data from several experiments they review. The notion that relative resistance to change and relapse are a function of the same variables has important implications for other areas where behavioral momentum has relevance, such as drug addiction. Todd, Winterbauer, and Bouton report three experiments examining the generality of a previously reported asymmetry in Pavlovian temporal discrimination learning. Previous work by this group has shown that the asymmetry occurs when the duration of the intertrial interval (ITI) signals whether or not a subsequent target conditioned stimulus (CS) will be reinforced. Rats acquire Long+/Short− discriminations in which the target is reinforced after a long ITI (4 min) and not reinforced after a short ITI (1 min), but fail to acquire the reverse Long−/Short+ discrimination. Experiment 1 demonstrates that the same asymmetry occurs when Long versus Short stimuli differentially signal reinforcement within trials (i.e., Long versus Short white noise followed by a target tone all separated by an ITI). Experiment 2 shows that when the temporal stimuli are decreased to 60 s and 15 s, the superior performance with the Long+/Short− arrangement disappears. Experiment 3 shows that when the associative value of the target is decreased by including additional non-reinforced presentations of the target CS, the Long+/Short− asymmetry returns, even with 60 s and 15 s durations. The authors suggest that the results can be understood by combining elemental models of conditioning and a temporal elements hypothesis which characterizes the passage of time as a series of discrete stimulus elements (A, B, C, . . .). Based on this hypothesis, the Long+/Short− asymmetry can be understood as occurring because the Long+/Short− situation is akin to a feature positive discrimination (AB+/A−) and Long−/Short+ as a feature negative discrimination (A−/AB+). This account appears to provide an integrative approach within which to explain the data from the present experiments other related findings in the literature. The article by Weisman et al. describes a network model which successfully predicts frequency-range discriminations in songbirds. Data in five species of songbirds show impressive regularities in their accurate ability to perceive pitches without an external referent. The network model of Weisman et al. accounts nicely for
Fig. 1. A “word cloud” visualization (http://www.wordle.net/) generated from the text of all of the short communications included in this special issue. The size of a word is proportional to the relative frequency of occurrence for the 150 most frequent words. Common words like “the”, “and”, and “to” were excluded.
Preface / Behavioural Processes 84 (2010) 353–355
individual differences between species, and also for discriminations made by two species of non-songbirds where performance is less accurate. A virtue of the model is its simplicity, and it highlights some beautiful cross-species data on avian auditory perception. The 20 short communication articles based on poster presentations span an impressive range of topics including, timing, delay discounting, the gamblers’ fallacy, a rat model of ADHD, deception, the effect of drugs on choice and timing, timing, artifical neural networks, computational selectionist models, and activity based anorexia. Along with the full-length articles, the wide range of short communications and their reference to applications of behavior analysis shows that the quantitative analysis of behavior is indeed ‘flying high. In the spirit of the elegant and succinct treatments of behavior provided by the quantitative analyses serving as the core of SQAB, Fig. 1 presents a “word cloud” visualization (http://www.wordle.net/) constructed from the text of all of the short communications included in this issue. In the visualization, the size of a word is proportional to the relative frequency of occurrence of the 150 most frequent words excluding common words like “the”, “and”, and “to”. The resulting word cloud is an interesting mix of science and art that provides a snapshot of the written products of the SQAB poster sessions. Unlike the quantitative models that are the focus of SQAB, this simple visualization does not capture the relations between the concepts addressed by the papers upon which it is based. Nonetheless, there is a sort of poetry in this picture generated from words scientists have used to describe their interactions with nature. Like good science though, good poetry is also better at capturing the essence of things than this simple visualization based on relative frequencies of word use. The ability to capture the essence of things by creating both beautiful science and
355
beautiful art is rare among us, but is embodied in the work of the late John L. Falk. We begin this special issue with a remembrance of an accomplished scientist and poet. References Erev, I., Haruvy, E., in press. Learning and the economics of small decisions. In: Kagel, J.H., Roth, A.E. (Eds.), The Handbook of Experimental Economics. Princeton University Press, Princeton, NJ. Grace, R.C., 1994. A contextual model of concurrent-chains choice. J. Exp. Anal. Behav. 61, 113–129. Grace, R.C., Nevin, J.A., 2000. Comparing preference and resistance to change in constant- and variable-duration schedule components. J. Exp. Anal. Behav. 74, 165–188. Mazur, J.E., 2001. Hyperbolic value addition and general models of animal choice. Psychol. Rev. 108, 96–112. McDowell, J.J., 2004. A computational model of selection by consequences. J. Exp. Anal. Behav. 81, 297–317. Moore, J., 1984. Choice and transformed interreinforcement intervals. J. Exp. Anal. Behav. 42, 321–335. Moore, J., 2008. Choice and the initial delay to a reinforcer. Psychol. Rec. 58, 193–216.
Timothy A. Shahan ∗ Utah State University, USA K. Geoffrey White University of Otago, New Zealand Randolph C. Grace University of Canterbry, New Zealand ∗ Corresponding author. E-mail address: [email protected] (T.A. Shahan)
Contents lists available at ScienceDirect
Behavioural Processes journal homepage: www.elsevier.com/locate/behavproc
Preface
SQAB 2009: Flying high
This special issue of Behavioral Processes reports the proceedings of the 32nd Annual Meeting of the Society for Quantitative Analysis of Behavior (SQAB) held in Phoenix Arizona May 21st–23rd 2009. Since 2000, SQAB registrants have received a physical copy of the special issue reporting the proceedings of the previous year. Starting with this issue, registrants will receive electronic access to the special issue for 1 month after the conference. This transition will allow SQAB to continue the rapid 1-year turn around between the meeting and publication of peer-reviewed articles reporting the proceedings. In addition, electronic access likely will be better aligned with the manner in which most users access the scientific literature. The special issue is comprised of 8 full-length articles stemming from presentations by invited speakers and 20 short communications based upon poster presentations. Both full-length papers and short communications go through the usual peer-review process and represent original contributions to the scientific literature. As is typical for SQAB, the roster of speakers was an eclectic group of researchers addressing issues in the quantitative analyses of behavior from a variety of traditions including behavior analysis, associative learning, economic game theory, and comparative cognition. The keynote address was delivered by Nobel Laureate Gerald M. Edelman. Dr. Edelman described his influential theory of Neural Darwinism and the selectionist principles it suggests provide the basis for brain function. Dr. Edelman also discussed how higher brain functioning like consciousness might be understood within the framework of Neural Darwinism. He also noted similarities between Neural Darwinism and McDowell’s (2004) model of adaptive behavior dynamics based on the computational model of selection by consequences. At the request of the editors of this special issue, Dr. McDowell has contributed an article describing the relationship between Neural Darwinism and the evolutionary theory of behavior dynamics. As noted by McDowell, the theories together provide a framework for quantitatively understanding adaptive behavior extending from basic brain functioning to the behavior of whole organisms. Such a comprehensive system based on basic Darwinian principles appears to hold great promise as a biological based and integrative approach for understanding brain, behavior, and cognition. The article by Erev, Ingram, Raz and Shany presents a provocative analysis of rule enforcement based on a co-mingling of economic game theory and reinforcement learning. They address the question of whether ‘gentle continuous punishment’ (gentle COP) policies can be effective in controlling human behavior. The basic idea of gentle COP is that if the first M violators of a rule
0376-6357/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.beproc.2010.02.019
(out of a potential number of violators N > M) are punished with sufficiently high probability with a mild sanction, then most of the population will decide rationally to follow the rule. Erev et al. describe both a theoretical analysis of this situation in terms of a model for choice that they had previously developed (Erev and Haruvy, in press), as well as two experiments, which test the effectiveness of gentle COP in the laboratory and the real world. The theoretical analysis outlines the conditions under which gentle COP can be expected to be effective, and the experiments confirm predictions of the model for both humans in a laboratory gambling task, and choices by students taking an exam whether or not to cheat. Erev et al.’s article provides an excellent illustration of how core SQAB topics such as quantitative analysis of reinforcement and punishment can have wide application when integrated with perspectives from other disciplines. Moore’s article is an interesting commentary on contemporary research on behavioral choice. He suggests that by providing economical descriptions of data in terms of variables like average rate and immediacy of reinforcement, quantitative models for choice (e.g., Grace, 1994; Mazur, 2001) may have prematurely foreclosed attention to other factors which may be important determiners of behavior. Moore defines a ‘procedural variable’ as any factor which determines how ‘subjects contact stimulus, response, and reinforcer variables in an experiment’. As a case in point, he describes how the effects of reinforcement rate, measured in terms of the average interval between reinforcers, can differ between two widely used choice procedures, concurrent schedules and concurrent chains (Moore, 1984). A second example is how preference between variable-interval (VI) terminal links in concurrent chains can vary depending on whether terminal links consist of a delay to a single reinforcer, or a fixed-period of access to a VI schedule in which a variable number of reinforcers may be earned (Grace and Nevin, 2000; Moore, 2008). Moore analyzes these situations and suggests that researchers may have overlooked an important factor: the delay to the first reinforcer on an alternative. Whether these effects can be accommodated in terms of a single general model for choice remains to be seen. Moore’s contribution performs the useful service of reminding us that when quantitative models are able to account for 95% of phenomena (optimistically speaking), the remaining 5% is likely to be even more important in terms of achieving a more general understanding of behavior. The article by Elliffe and Davison describes an experiment with pigeons extending the study of choice to a situation with four response options available. They use a procedure in which the reinforcement ratio across four keys is always 27:9:3:1, but which reinforcement rates were assigned to key changes within
354
Preface / Behavioural Processes 84 (2010) 353–355
the session. The allocation of pigeons’ behavior quickly tracked the changing reinforcement allocations. But, the data suggested that preference was more extreme for options arranging higher reinforcement rates but the same reinforcement ratio (e.g., greater perference for 27:3 than for 9:1). Such an outcome is inconsistent with the constant-ratio rule and with the assumption of mathcing theory that relative reinforcement should govern choice independently of absolute rate of reinforcement. Elliffe and Davison suggest that the data are consistent with a contingency-discriminability approach to choice which does not depend upon the constantratio rule. This work by Elliffe and Davison is a good reminder of the importance of examining the assumptions and boundary conditions of our existing quantitative models by stepping beyond the simplified two-alternative situations that have been typical of studies examining the matching law. Curry, Foxall and Sigurdsson’s article is an example of translational research on choice. An important question studied in their research area, consumer behavior analysis, is whether the matching law can apply to everyday economic decisions, for example, the choices of customers in supermarkets between which of several brands of shampoo and conditioner to purchase. One method in which the matching law has been applied to consumer behavior is to examine the relationship between the relative amount purchased of two substitutable commodities (e.g., brands of shampoo) and the relative amount spent. In this approach, known as ‘amount matching’, money spent is viewed similarly as responding in basic laboratory research, while amount purchased is analogous to reinforcement. However, one potential criticism of amount matching is that it is tautologous—that is, that the relationship between relative amount spent and relative amount purchased is constrained by the ratio-like contingencies of the marketplace. Curry et al. provide a broad perspective on the tautology issue in consumer behavior analysis, as well as a discussion of previous work on the matching law. They show that when data are aggregated across consumers and brands, ratios of amount spent and amount purchased are not constrained to strict matching, and hence that amount matching is not formally tautologous. Curry et al. accomplish this by deriving an ‘error term’ which represents the discrepancy between observed and strict amount matching. They also discuss the challenges that other translational research on choice-specifically the application of the matching law to sports behavior-has faced. Overall, Curry et al.’s article is noteworthy for depicting the logical challenges that translational research on matching must confront, and describing a possible solution. The aritcle by Podlesnik and Shahan explores how the relapse of operant behavior can be understood in terms of behavioral momentum theory. They review experiments in which operant responding
is more resistant to disruption in the presence of stimuli previously associated with the delivery of response-independent reinforcers (a Pavlovian operation). Of interest, relapse of operant behavior following extinction is greater when responding was maintained by higher rates of reinforcement in baseline training, in the same way that resistance to extinction is greater when baseline responding is maintained by higher reinforcer rates. Podlesnik and Shahan offer a new quantitative model which explains the relapse effect in terms of a reduction in the effects of a disruptor which normally hastens the course of extinction. The model accounts well for data from several experiments they review. The notion that relative resistance to change and relapse are a function of the same variables has important implications for other areas where behavioral momentum has relevance, such as drug addiction. Todd, Winterbauer, and Bouton report three experiments examining the generality of a previously reported asymmetry in Pavlovian temporal discrimination learning. Previous work by this group has shown that the asymmetry occurs when the duration of the intertrial interval (ITI) signals whether or not a subsequent target conditioned stimulus (CS) will be reinforced. Rats acquire Long+/Short− discriminations in which the target is reinforced after a long ITI (4 min) and not reinforced after a short ITI (1 min), but fail to acquire the reverse Long−/Short+ discrimination. Experiment 1 demonstrates that the same asymmetry occurs when Long versus Short stimuli differentially signal reinforcement within trials (i.e., Long versus Short white noise followed by a target tone all separated by an ITI). Experiment 2 shows that when the temporal stimuli are decreased to 60 s and 15 s, the superior performance with the Long+/Short− arrangement disappears. Experiment 3 shows that when the associative value of the target is decreased by including additional non-reinforced presentations of the target CS, the Long+/Short− asymmetry returns, even with 60 s and 15 s durations. The authors suggest that the results can be understood by combining elemental models of conditioning and a temporal elements hypothesis which characterizes the passage of time as a series of discrete stimulus elements (A, B, C, . . .). Based on this hypothesis, the Long+/Short− asymmetry can be understood as occurring because the Long+/Short− situation is akin to a feature positive discrimination (AB+/A−) and Long−/Short+ as a feature negative discrimination (A−/AB+). This account appears to provide an integrative approach within which to explain the data from the present experiments other related findings in the literature. The article by Weisman et al. describes a network model which successfully predicts frequency-range discriminations in songbirds. Data in five species of songbirds show impressive regularities in their accurate ability to perceive pitches without an external referent. The network model of Weisman et al. accounts nicely for
Fig. 1. A “word cloud” visualization (http://www.wordle.net/) generated from the text of all of the short communications included in this special issue. The size of a word is proportional to the relative frequency of occurrence for the 150 most frequent words. Common words like “the”, “and”, and “to” were excluded.
Preface / Behavioural Processes 84 (2010) 353–355
individual differences between species, and also for discriminations made by two species of non-songbirds where performance is less accurate. A virtue of the model is its simplicity, and it highlights some beautiful cross-species data on avian auditory perception. The 20 short communication articles based on poster presentations span an impressive range of topics including, timing, delay discounting, the gamblers’ fallacy, a rat model of ADHD, deception, the effect of drugs on choice and timing, timing, artifical neural networks, computational selectionist models, and activity based anorexia. Along with the full-length articles, the wide range of short communications and their reference to applications of behavior analysis shows that the quantitative analysis of behavior is indeed ‘flying high. In the spirit of the elegant and succinct treatments of behavior provided by the quantitative analyses serving as the core of SQAB, Fig. 1 presents a “word cloud” visualization (http://www.wordle.net/) constructed from the text of all of the short communications included in this issue. In the visualization, the size of a word is proportional to the relative frequency of occurrence of the 150 most frequent words excluding common words like “the”, “and”, and “to”. The resulting word cloud is an interesting mix of science and art that provides a snapshot of the written products of the SQAB poster sessions. Unlike the quantitative models that are the focus of SQAB, this simple visualization does not capture the relations between the concepts addressed by the papers upon which it is based. Nonetheless, there is a sort of poetry in this picture generated from words scientists have used to describe their interactions with nature. Like good science though, good poetry is also better at capturing the essence of things than this simple visualization based on relative frequencies of word use. The ability to capture the essence of things by creating both beautiful science and
355
beautiful art is rare among us, but is embodied in the work of the late John L. Falk. We begin this special issue with a remembrance of an accomplished scientist and poet. References Erev, I., Haruvy, E., in press. Learning and the economics of small decisions. In: Kagel, J.H., Roth, A.E. (Eds.), The Handbook of Experimental Economics. Princeton University Press, Princeton, NJ. Grace, R.C., 1994. A contextual model of concurrent-chains choice. J. Exp. Anal. Behav. 61, 113–129. Grace, R.C., Nevin, J.A., 2000. Comparing preference and resistance to change in constant- and variable-duration schedule components. J. Exp. Anal. Behav. 74, 165–188. Mazur, J.E., 2001. Hyperbolic value addition and general models of animal choice. Psychol. Rev. 108, 96–112. McDowell, J.J., 2004. A computational model of selection by consequences. J. Exp. Anal. Behav. 81, 297–317. Moore, J., 1984. Choice and transformed interreinforcement intervals. J. Exp. Anal. Behav. 42, 321–335. Moore, J., 2008. Choice and the initial delay to a reinforcer. Psychol. Rec. 58, 193–216.
Timothy A. Shahan ∗ Utah State University, USA K. Geoffrey White University of Otago, New Zealand Randolph C. Grace University of Canterbry, New Zealand ∗ Corresponding author. E-mail address: [email protected] (T.A. Shahan)