- Email: [email protected]
All thumbs?
Behavioural Processes 69 (2005) 143–145
Commentary
All thumbs? Federico Sanabria ∗ , Peter R. Killeen Department of Psychology, Box 871104, Arizona State University, Tempe, AZ 85287-1104, USA
Abstract In this otherwise insightful line of investigation, some aspects of rules of thumb (RoT) remain underspecified: their evolution and function, how they are selected for a task, how they are learned, and the dependence of their apparent simplicity on their embodiment and context. Theories of operant and respondent conditioning may serve as the theoretical framework required to flesh out those details. © 2005 Elsevier B.V. All rights reserved. Keywords: Conditioning (psychology); Decision making; Animal models
What a beautiful report of a brilliant line of investigation! We have little to offer, but touches of needlepoint on an historic tapestry. We couch them as rules of thumb about rules of thumb. Most remain underspecified—though not ignored—under the heuristics research program advanced by the ABC group.
1. Rules of thumb need rules By their definition, rules of thumb (RoT) are simpler than the algorithms they are contrasted with. But some RoT that seem simple to us are not adopted by our animals and vice-versa. Consider the response-initiated delay schedule, in which the first response starts a clock
and after the clock chimes the animal can get reinforced. A simple RoT is “Make the first response as soon as you can to start the clock, then goof off until you hear the chime.” Rats and pigeons can’t learn that RoT (Shull, 1970). Or consider the RoT “Just say NO! [to drugs and alcohol],” a rule followed by many of our rats and pigeons, but harder for a lot of humans to exploit. We may be all thumbs, yet not be able to infer the simplest RoT to exploit them. Question for students of behavior (SoB): is Hutchinson and Gigerenzer’s (2005) contention—that RoT that evolved in one context may look like misbehavior in another—adequate to carry the explanatory burden of the sagacities and stupidities of our subjects? 2. Rules of thumb need thumbs
∗ Corresponding author. Tel.: +1 480 965 0756; fax: +1 480 965 8544. E-mail address: [email protected] (F. Sanabria).
0376-6357/$ – see front matter © 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.beproc.2005.02.015
The opposable thumb was not the result of natural selection acting to conform that strange but handy digit
144
F. Sanabria, P.R. Killeen / Behavioural Processes 69 (2005) 143–145
to the inch. The thumb was exploited for measurement, like the forearm and foot and fathom. Such exploitation required that its variance be low and that it be generally adamant to modification—unlike the extremities of the hapless spider in Hutchison and Gigerenzer’s cautionary tale. Evolution, like the trades, always starts by exploiting existing structures; if their utility is above threshold, they get improved; or replaced by things such as pocket rules. Most of the cognitive heuristics noted by Hutchinson and Gigerenzer involve simple exploitation of complex computations, computations which, like the thumb, often evolved for other reasons and found some general utility. The secondary benefits of some structures outweigh their original function and drive the remaining course of their evolution. Instructions to reap those benefits may come with the machinery or may be learned. Question for SoB: what do they think they are studying in their experiments: exploitation, exaptation or education?
3. Rules of thumb need rules of thumb It is as ill-advised to ask a cabinetmaker to build your fence as to ask a carpenter to build your furniture. “Go for the Gold” is a good motivational RoT that leaves hundreds disappointed in their Bronzes. When is it wise to do what comes natural or easy because of an available RoT; and when is it unwise? Even though humans frequently make one-reason decisions—or, perhaps it is better to say they frequently give but one reason for their decisions— they also use complex and precise combinations of rules to guide behavior, be it driving in city traffic or following a recipe. A complete description of behavior cannot be had by indicating that one-reason decisions are common and even rational. It must also specify how and when information about the environment’s structure is sought and how that information is mapped to a meta-decision between the two strategies—fast and frugal versus slow and sound. Decision models require specific meta-decision models, meta-decision models require specific meta-metadecision models and so on, until no more variance can be reduced with additional levels. Such an approach is consistent with ecological rationality (Martignon and Hoffrage, 1999), but until it is articulated in the form of explicit axioms, it does not afford much more than the ad-hoc construction of decision models. But, then
again, perhaps this is old-algorithmic-think, balking at fast and frugal theorizing. 4. Complex algorithms are rules of thumb on steroids A difference between carpentry and cabinet making is whether an inch is a thumb’s-width or an interval on a ruler. As the demands for precision grow, so too does the sophistication of measurement. But to check the cabinet’s width using laser interferometry is a parody of nerdly incompetence. Too fine a point too often breaks. The heart of the heuristics program is its insistence on the ubiquitous utility of fast and frugal. But speed and parsimony should never have been treated as an externality. The Olympian view of maximum expected utility has always been flawed by not costing the delay of local optimization and the impossibility of global optimization. When the value of the next decimal gets balanced against its cost in an appropriate model, both heuristics and MEU can join hands in justifiably laying claim to that model. The economic advantage of information, so often assumed, should not be taken for granted. Research on observing behavior has demonstrated that stimuli signaling reinforcement are more reinforcing than stimuli signaling no reinforcement, even though both provide the same amount of information (Fantino, 1977; Killeen et al., 1980)—indeed, animals will turn off stimuli that convey unwanted information. Not just rats; many hominids find it difficult to watch the news in these difficult times. Decision making couched as an information-seeking cost–benefit tradeoff may overinterpret a “Good news is better than no news; no news is better than bad news” RoT. 5. Whose rules? Behavior analysts are alert to the confusion of scientific descriptions with subjective mechanisms. Humans readily give reasons for their behavior, whether or not they are relevant to their behavior (Nesbitt and Wilson, 1977; Bargh and Chartrand, 1999). ABC are careful to validate the reality of the heuristics their subjects use. We also must be careful to distinguish between characterizations of behavior (such as ROT) and the causes of behavior. Descriptions such as “reinforcement”
F. Sanabria, P.R. Killeen / Behavioural Processes 69 (2005) 143–145
or “matching” too easily become implicit mechanisms such as “win-stay/lose-shift.” Will all RoT for non-verbal organisms better describe the scientist’s models than the animal’s machinery? Two friends always choose restaurants by their proximity. Friend A does so explicitly, sometimes using a map. Friend B was surprised when I mentioned this regularity in his behavior, which he had never realized. Do both friends use the same RoT? If not, do any animals use RoT?
6. Rules of thumb need to be learned Is learning nothing more or less than a process designed to identify and exploit RoT? Animals attend to, and become conditioned to, stimuli that are good predictors of reinforcement. Stimuli that are redundant or that are poorly coupled with reinforcement (“is there a stadium in this city?”) are blocked or overshadowed by more general or more generally reliable stimuli (“have I heard of this city?”) (Hall et al., 1977; Kamin, 1969). It may be that “Follow Salient Signposts to Reinforcement” is the simplest RoT from which all secondary environment-specific RoT derive. If such is the case, the constellation of RoT observed in nature may be cast in the mold of operant and classical conditioning theories, providing them with a theoretical and empirical context that would advance our understanding of behavior both in the field and in the laboratory. The laws of causality—precedence, contiguity, frequency and similarity—are the laws of conditioning. Conditioning evolved to detect and exploit biologically salient stimuli. Are good RoT just sagacious identifications of stimuli and responses that lead to
145
reinforcement? We think so, but it took the sagacity of the ABC group to teach us this lesson. Now then, what can we teach the teachers? Acknowledgements Preparation of this commentary was supported by NSF IBN 0236821 and NIMH 1R01MH066860.
References Bargh, J.A., Chartrand, T.L., 1999. The unbearable automaticity of being. Am. Psychol 54, 462–479. Fantino, E., 1977. Conditioned reinforcement: choice and information. In: Honig, W.K., Staddon, J.E.R. (Eds.), Handbook of Operant Behavior. Prentice-Hall, Englewood Cliffs, pp. 313–339. Hall, G., Mackintosh, N.J., Goodall, G., Dal Martello, M., 1977. Loss of control by a less valid or by a less salient stimulus compounded with a better predictor of reinforcement. Learn. Motiv. 8, 145–158. Hutchinson, J.M.C., Gigerenzer, G., 2005. Simple heuristics and rules of thumb; where psychologists and behavioural biologists might meet. Behav. Proc. 69, 97–124. Kamin, L.J., 1969. Predictability, surprise, attention and conditioning. In: Campbell, B.A., Church, R.M. (Eds.), Punishment and Aversive Behavior. Appleton-Century-Crofts, New York, pp. 279–296. Killeen, P., Wald, B., Cheney, C.D., 1980. Observing behavior and information. Psychol. Rec. 30, 181–190. Martignon, L., Hoffrage, U., 1999. Why does one-reason decision making work? A case study in ecological rationality. In: Gigerenzer, G., Todd, P.M. (Eds.), Simple Heuristics that Make us Smart. Oxford University Press, New York, pp. 119–140. Nesbitt, R.E., Wilson, T.D., 1977. Telling more than we can know: verbal reports on mental processes. Psychol. Rev. 84, 231–259. Shull, R.L., 1970. A response-initiated fixed-interval schedule of reinforcement. J. Exp. Anal. Behav. 13, 13–15.
Commentary
All thumbs? Federico Sanabria ∗ , Peter R. Killeen Department of Psychology, Box 871104, Arizona State University, Tempe, AZ 85287-1104, USA
Abstract In this otherwise insightful line of investigation, some aspects of rules of thumb (RoT) remain underspecified: their evolution and function, how they are selected for a task, how they are learned, and the dependence of their apparent simplicity on their embodiment and context. Theories of operant and respondent conditioning may serve as the theoretical framework required to flesh out those details. © 2005 Elsevier B.V. All rights reserved. Keywords: Conditioning (psychology); Decision making; Animal models
What a beautiful report of a brilliant line of investigation! We have little to offer, but touches of needlepoint on an historic tapestry. We couch them as rules of thumb about rules of thumb. Most remain underspecified—though not ignored—under the heuristics research program advanced by the ABC group.
1. Rules of thumb need rules By their definition, rules of thumb (RoT) are simpler than the algorithms they are contrasted with. But some RoT that seem simple to us are not adopted by our animals and vice-versa. Consider the response-initiated delay schedule, in which the first response starts a clock
and after the clock chimes the animal can get reinforced. A simple RoT is “Make the first response as soon as you can to start the clock, then goof off until you hear the chime.” Rats and pigeons can’t learn that RoT (Shull, 1970). Or consider the RoT “Just say NO! [to drugs and alcohol],” a rule followed by many of our rats and pigeons, but harder for a lot of humans to exploit. We may be all thumbs, yet not be able to infer the simplest RoT to exploit them. Question for students of behavior (SoB): is Hutchinson and Gigerenzer’s (2005) contention—that RoT that evolved in one context may look like misbehavior in another—adequate to carry the explanatory burden of the sagacities and stupidities of our subjects? 2. Rules of thumb need thumbs
∗ Corresponding author. Tel.: +1 480 965 0756; fax: +1 480 965 8544. E-mail address: [email protected] (F. Sanabria).
0376-6357/$ – see front matter © 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.beproc.2005.02.015
The opposable thumb was not the result of natural selection acting to conform that strange but handy digit
144
F. Sanabria, P.R. Killeen / Behavioural Processes 69 (2005) 143–145
to the inch. The thumb was exploited for measurement, like the forearm and foot and fathom. Such exploitation required that its variance be low and that it be generally adamant to modification—unlike the extremities of the hapless spider in Hutchison and Gigerenzer’s cautionary tale. Evolution, like the trades, always starts by exploiting existing structures; if their utility is above threshold, they get improved; or replaced by things such as pocket rules. Most of the cognitive heuristics noted by Hutchinson and Gigerenzer involve simple exploitation of complex computations, computations which, like the thumb, often evolved for other reasons and found some general utility. The secondary benefits of some structures outweigh their original function and drive the remaining course of their evolution. Instructions to reap those benefits may come with the machinery or may be learned. Question for SoB: what do they think they are studying in their experiments: exploitation, exaptation or education?
3. Rules of thumb need rules of thumb It is as ill-advised to ask a cabinetmaker to build your fence as to ask a carpenter to build your furniture. “Go for the Gold” is a good motivational RoT that leaves hundreds disappointed in their Bronzes. When is it wise to do what comes natural or easy because of an available RoT; and when is it unwise? Even though humans frequently make one-reason decisions—or, perhaps it is better to say they frequently give but one reason for their decisions— they also use complex and precise combinations of rules to guide behavior, be it driving in city traffic or following a recipe. A complete description of behavior cannot be had by indicating that one-reason decisions are common and even rational. It must also specify how and when information about the environment’s structure is sought and how that information is mapped to a meta-decision between the two strategies—fast and frugal versus slow and sound. Decision models require specific meta-decision models, meta-decision models require specific meta-metadecision models and so on, until no more variance can be reduced with additional levels. Such an approach is consistent with ecological rationality (Martignon and Hoffrage, 1999), but until it is articulated in the form of explicit axioms, it does not afford much more than the ad-hoc construction of decision models. But, then
again, perhaps this is old-algorithmic-think, balking at fast and frugal theorizing. 4. Complex algorithms are rules of thumb on steroids A difference between carpentry and cabinet making is whether an inch is a thumb’s-width or an interval on a ruler. As the demands for precision grow, so too does the sophistication of measurement. But to check the cabinet’s width using laser interferometry is a parody of nerdly incompetence. Too fine a point too often breaks. The heart of the heuristics program is its insistence on the ubiquitous utility of fast and frugal. But speed and parsimony should never have been treated as an externality. The Olympian view of maximum expected utility has always been flawed by not costing the delay of local optimization and the impossibility of global optimization. When the value of the next decimal gets balanced against its cost in an appropriate model, both heuristics and MEU can join hands in justifiably laying claim to that model. The economic advantage of information, so often assumed, should not be taken for granted. Research on observing behavior has demonstrated that stimuli signaling reinforcement are more reinforcing than stimuli signaling no reinforcement, even though both provide the same amount of information (Fantino, 1977; Killeen et al., 1980)—indeed, animals will turn off stimuli that convey unwanted information. Not just rats; many hominids find it difficult to watch the news in these difficult times. Decision making couched as an information-seeking cost–benefit tradeoff may overinterpret a “Good news is better than no news; no news is better than bad news” RoT. 5. Whose rules? Behavior analysts are alert to the confusion of scientific descriptions with subjective mechanisms. Humans readily give reasons for their behavior, whether or not they are relevant to their behavior (Nesbitt and Wilson, 1977; Bargh and Chartrand, 1999). ABC are careful to validate the reality of the heuristics their subjects use. We also must be careful to distinguish between characterizations of behavior (such as ROT) and the causes of behavior. Descriptions such as “reinforcement”
F. Sanabria, P.R. Killeen / Behavioural Processes 69 (2005) 143–145
or “matching” too easily become implicit mechanisms such as “win-stay/lose-shift.” Will all RoT for non-verbal organisms better describe the scientist’s models than the animal’s machinery? Two friends always choose restaurants by their proximity. Friend A does so explicitly, sometimes using a map. Friend B was surprised when I mentioned this regularity in his behavior, which he had never realized. Do both friends use the same RoT? If not, do any animals use RoT?
6. Rules of thumb need to be learned Is learning nothing more or less than a process designed to identify and exploit RoT? Animals attend to, and become conditioned to, stimuli that are good predictors of reinforcement. Stimuli that are redundant or that are poorly coupled with reinforcement (“is there a stadium in this city?”) are blocked or overshadowed by more general or more generally reliable stimuli (“have I heard of this city?”) (Hall et al., 1977; Kamin, 1969). It may be that “Follow Salient Signposts to Reinforcement” is the simplest RoT from which all secondary environment-specific RoT derive. If such is the case, the constellation of RoT observed in nature may be cast in the mold of operant and classical conditioning theories, providing them with a theoretical and empirical context that would advance our understanding of behavior both in the field and in the laboratory. The laws of causality—precedence, contiguity, frequency and similarity—are the laws of conditioning. Conditioning evolved to detect and exploit biologically salient stimuli. Are good RoT just sagacious identifications of stimuli and responses that lead to
145
reinforcement? We think so, but it took the sagacity of the ABC group to teach us this lesson. Now then, what can we teach the teachers? Acknowledgements Preparation of this commentary was supported by NSF IBN 0236821 and NIMH 1R01MH066860.
References Bargh, J.A., Chartrand, T.L., 1999. The unbearable automaticity of being. Am. Psychol 54, 462–479. Fantino, E., 1977. Conditioned reinforcement: choice and information. In: Honig, W.K., Staddon, J.E.R. (Eds.), Handbook of Operant Behavior. Prentice-Hall, Englewood Cliffs, pp. 313–339. Hall, G., Mackintosh, N.J., Goodall, G., Dal Martello, M., 1977. Loss of control by a less valid or by a less salient stimulus compounded with a better predictor of reinforcement. Learn. Motiv. 8, 145–158. Hutchinson, J.M.C., Gigerenzer, G., 2005. Simple heuristics and rules of thumb; where psychologists and behavioural biologists might meet. Behav. Proc. 69, 97–124. Kamin, L.J., 1969. Predictability, surprise, attention and conditioning. In: Campbell, B.A., Church, R.M. (Eds.), Punishment and Aversive Behavior. Appleton-Century-Crofts, New York, pp. 279–296. Killeen, P., Wald, B., Cheney, C.D., 1980. Observing behavior and information. Psychol. Rec. 30, 181–190. Martignon, L., Hoffrage, U., 1999. Why does one-reason decision making work? A case study in ecological rationality. In: Gigerenzer, G., Todd, P.M. (Eds.), Simple Heuristics that Make us Smart. Oxford University Press, New York, pp. 119–140. Nesbitt, R.E., Wilson, T.D., 1977. Telling more than we can know: verbal reports on mental processes. Psychol. Rev. 84, 231–259. Shull, R.L., 1970. A response-initiated fixed-interval schedule of reinforcement. J. Exp. Anal. Behav. 13, 13–15.