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Review of General Psychology 2006, Vol. 10, No. 4, 365–376 Copyright 2006 by the American Psychological Association 1089-2680/06/$12.00 DOI: 10.1037/1089-2680.10.4.365 Classical Conditioning Since Pavlov M. E. Bitterman University of Hawaii In the three quarters of a century following Pavlov’s work, the accumulation of factual information about classical conditioning has continued, but there has been little conceptual progress. The only thing we have now that approximates a workable general theory of conditioning was introduced more than 30 years ago and continues to receive a good deal of respectful consideration despite a variety of generally recognized shortcomings that little has been done to repair; nor does a systematic review of recent papers in leading journals give any good reason to think that a more satisfactory theory is in the making. A remedial strategy, recommended long ago by C. L. Hull and by E. R. Hilgard, is proposed and exemplified by some research with honeybees. Keywords: classical conditioning, Pavlov, Hilgard, fishes, honeybees Today, we remember Ernest R. Hilgard, a scholar of enormous accomplishment, whose first experimental work was on classical conditioning (Hilgard, 1931). Early in his career, he published with Donald Marquis a pivotal analysis of classical and instrumental conditioning that continues to be required reading (Hilgard & Marquis, 1940). I first came under his influence soon after my arrival as a graduate student in Howard Liddell’s laboratory at Cornell, when I was referred to that important book. Liddell’s laboratory was an old farm, called the Behavior Farm, and the subjects were farm animals (Liddell, James, & Anderson, 1934). In my first semester there, I began to earn my Pavlovian credentials in an experiment on aversive conditioning in a sheep, with brief, unavoidable shock to a limb as the unconditioned stimulus. Various responses of the animal, skeletal and autonomic, were captured by what was then a state-of-the-art recorder that put as much ink on me as on the recording paper but even so was preferable to the smoked drums we still were using in the physiology laboratory. At the same time, I read Pavlov, starting with his Nobel lecture of almost exactly a century ago (Pavlov, 1904/1967), in which he described his initial observations of conditioning in the course of the work on the digestive glands for which the prize was given. Then I settled down with Conditioned Reflexes (Pavlov, 1927), a review of the quarter century or so of intensive work in Pavlov’s laboratory that followed his initial observations. Liddell, who had close ties to the psychiatric community, was interested primarily in the pathogenic possibilities of what was called defensive conditioning, and so it remained for Hilgard and Marquis to provide me with a broader psychological perspective. Where We Are Now The work with honeybees that is described was supported by a series of grants from the National Science Foundation, currently Grant IBN03-46546. This article is based on an address by the author, as recipient of the Ernest R. Hilgard Award for Distinguished Contributions to General Psychology, at the 113th Annual Convention of the American Psychological Association in Washington, DC, August 2005. Correspondence concerning this article should be addressed to M. E. Bitterman, Békésy Laboratory of Neurobiology, University of Hawaii, 1993 East-West Road, Honolulu, HI 96822. E-mail: [email protected] Pavlov learned a great deal about conditioning, and it is interesting to ask how much more we have learned about it since then. In my opinion, we have made little conceptual progress. We are still today, as Hilgard (1948) regretted in the first edition of his influential Theories of Learning, and, as Gray (1979) put it so well several decades later, in the uncomfortable position of having many facts that seem to provide significant clues to the underlying processes but no adequate theory to account for them. Other people working in the field are 365 366 BITTERMAN more complacent—Rescorla (1988), for example, has claimed “dramatic” conceptual progress— but the complacency is not well founded. Some ideas that are supposed to be new are not at all new, as a reading of Pavlov would show. Some really are new, but untenable. A common claim is that Pavlov was wrong about the importance of conditioned stimulus– unconditioned stimulus (CS-US) contiguity. The new idea is that conditioning is based not on contiguity but on contingency—that conditioning occurs only when the probability of the US in the presence of the CS is different than in its absence. In the primordial contingency experiment advertised by Rescorla (1988), one group of rats was shocked only in the presence of a CS, whereas a second group was shocked with the same probability both in the presence of the CS and in its absence. Although the number of CS-US parings was the same for both groups, the animals of the first group responded differentially to the CS but the poor animals of the second group (shocked five times as often as the first) did not, which Pavlov might have found perfectly understandable since, for those animals, both the CS and the context were conditioned. The general acceptance of the contingency idea is discouraging, not only in view of the limitations of the work that first suggested it, but also because it is directly contradicted by the results of many subsequent experiments (Papini & Bitterman, 1990). Consider only one of them, an autoshaping experiment by Brandon (1981): A single group of pigeons is trained with three colors that follow each other in haphazard sequence. In the presence of one color, free food is given twice per minute on average; in the presence of the second color, once per minute; and in the presence of the third color, never. The pigeons come to respond differentially to the second color (less than to the first, more than to the third), although the probability of reinforcement is exactly the same in its presence as in its absence. Like results for goldfish have been obtained in analogous experiments both with food and with shock as reinforcement (Brandon, Satake, & Bitterman, 1982). Almost everybody points proudly to the discovery of blocking (Kamin, 1969), which is said again to demonstrate the insufficiency of contiguity. The prototypical design of a blocking experiment, given uncritically in the textbooks, is this: A blocking group is conditioned first to stimulus A and then to a compound of two stimuli, A and B; a control group is conditioned only to the compound; and then both groups are tested with B. Although the contiguity of B and reinforcement is the same for both groups, there is less responding to B in the first group than in the second. A variety of other control procedures have been used in efforts to establish that it is the conditioning of A which is critical, rather than the prior reinforcement apart from experience with A, or the prior experience with A apart from reinforcement. A has been backwardly paired or explicitly unpaired with reinforcement, or reinforced trials with a different stimulus, C, have been substituted for reinforced trials with A, all with the same reported outcome: less responding by the blocking animals than by control animals in the test with B. The traditional explanation is that learning about B on reinforced trials with the AB compound is somehow impaired by the conditioning of A, which competes with B for associative strength or for attention, but similar results have been obtained in recent work with B entirely absent in the training (Blaser, Couvillon, & Bitterman, 2006). The new results suggest that differential responding to B in conventional between-groups blocking experiments may be traced, not to different learning about B, but to generalized effects of the different treatments of other stimuli in the blocking and control groups. It now seems fair to say that no such demonstration of blocking is credible unless accompanied by null results for B-absent controls. After a good many years of work on blocking, we are pretty much back at the beginning. In any case, Pavlov knew very well that performance in conditioning situations cannot be understood in terms of contiguity alone. It was he, after all, who discovered overshadowing: strong responding to B after reinforced training with B alone but very little after reinforced training with AB. Diminished responding to B alone after the AB training did not mean, Pavlov noted, that little had been learned about it; clear evidence to the contrary was provided by greater responding to AB than to A alone. We are still not sure about what causes overshadowing or its opposite, called potentiation, which sometimes occurs. CLASSICAL CONDITIONING SINCE PAVLOV Of course, we can all agree that there has been at least some conceptual progress in the years since Pavlov. (a) We now have a somewhat better appreciation than he had of the generality of Pavlovian conditioning, both over animals and over systems within animals. With a drop of sucrose solution as the US and extension of the proboscis as the response, harnessed honeybees perform like Pavlov’s dogs in a wide range of comparative experiments (Bitterman, Menzel, Fietz, & Schäfer, 1983), although the nearest common ancestor lived hundreds of millions of years ago and probably had only an extremely simple central nervous system. (b) There is better methodological understanding. Pavlovian conditioning now is studied not only in classical situations (with reinforcement independent of response), but also in Thorndikian or instrumental situations, as Hilgard and Marquis (1940) termed them. Awkward early efforts to treat classical training as instrumental training in disguise were put to rest after a time by omission experiments as conceived by Sheffield (1965). We understand now that quite the opposite is true—that Pavlovian processes are embedded in Thorndikian procedures, which afford new opportunities to study them; when an animal makes an instrumental response, it is, in effect, giving itself a classical conditioning trial. (c) We are clearer about the nature of the associations that are formed. Pavlov (1904/1967) speculated from the very outset on the possibility of connections between sensory centers in the brain—what later were called S-S, as distinct from stimulus–response (S-R) connections—and the idea has since been amply supported by several lines of work dating back to an experiment by Brogden (1939) on sensory preconditioning. (d) Thinking about inhibition has advanced. Pavlov believed that a stimulus becomes inhibitory in the course of continued action, but the modern idea is that a stimulus becomes inhibitory only when encountered without reinforcement in an otherwise excitatory context (Konorski, 1948). (e) Discrimination experiments have long pointed to the need for some such concept as attention and its modification with training (Sutherland & Mackintosh, 1971). Over the years, there have been many interesting suggestions as to how the idea might be formalized, although their potential still is largely unrealized. 367 So there has been some conceptual progress, although not very much to show for three quarters of a century of work. It is interesting, too, that none of these post-Pavlovian developments is very new. The only thing we have now that begins to approximate a general theory of conditioning was introduced more than 30 years ago by Rescorla and Wagner (1972). One interesting (and widely credited) postulate of the theory is that the components of a compound stimulus share the changes in associative strength generated by reinforcement and nonreinforcement of the compound. Another is that excitation and inhibition lie on a single continuum—that inhibition is negative excitation— which (with little empirical support) contradicts Pavlov’s assumption that a stimulus can have both excitatory and inhibitory properties at the same time. An especially attractive feature of the theory is its statement in equational form, the old linear equation of Bush and Mosteller (1951) in a different and now much more familiar notation, which opens the door to quantitative prediction. That door, unfortunately, remains unentered. Without values for the several parameters of the equation, associative strength cannot be computed, which means that predictions from the theory can be no more than ordinal, and even those predictions are made on the naı̈ve assumption of a one-to-one relation between associative strength and performance. Despite a variety of shortcomings (Miller, Barnet, & Grahame, 1995), the theory has continued to receive a great deal of respectful consideration, generating forests of competing bar graphs. Looking for alternatives, we find only a disjointed array of rather insular conceptions (Pearce & Bouton, 2001; Wasserman & Miller, 1997). If we do not have a satisfactory general theory of conditioning, are we at least on the way to developing one? I am afraid not. We simply do not begin to have what we need for the purpose, which is first of all a homogeneous body of data, exactly replicable and readily extendable, to which people working in different laboratories contribute. That, of course, would entail agreement on a well-characterized model animal and on a set of highly standardized and efficient training techniques, but there is no indication that the need is even recognized. A few months ago, I suggested to Rachel Blaser, a doctoral candidate at the University of Hawaii, 368 BITTERMAN that it might be useful to have a systematic look at relevant papers—their subjects, methods, and topics—published during the past 10 years in some of our leading journals. Table 1 summarizes what she found in 236 papers appearing from 1995 to 2004 in Animal Learning & Behavior (Animal now has been dropped from the title) and in the Journal of Experimental Psychology: Animal Behavior Processes. As should come as no surprise, the experiments were conducted mostly with rats and pigeons, although there are bits and pieces of data also for an odd assortment of other animals, the reasons for the choice of which are not often evident. Training techniques varied widely, some of them terribly crude. In experiments on conditioned food aversion, an animal ingests some food that waits in its gut for a poison given later, or sometimes even before the food, to take effect. In any case, there is no meaningful control either of the CS, the US, or the CS-US interval. In “freezing” experiments, an animal is shocked in a box, removed, and then returned some time later, when any restriction of its movement is noted. I remember, in a report of one such experiment, an earnest attempt to describe exactly how the animals were carried to the box, certainly a possible source of unspecified conditioned stimuli. More sophisticated techniques also were used, but they differed so much in detail as to make it impossible to pool results from different laboratories. It is interesting to note a decline (relative to my own count of papers in the same journals for the years 1981–1990) in the use of the old conditioned suppression technique, which not only is rather inefficient (most of the experimenter’s time is spent in preparing for a few terminal measurements) but also has the not-inconsiderable disadvantage, as commonly used, of failing to track the course of acquisition. The topics also were rather diverse. There were some notable convergences of interest, but the concerns so scattered that fewer than two thirds of them could be meaningfully classified, even with rather broad categories. Some of the results reported are trivial, others are intuitively interesting although largely undigested, and there are numerous unresolved contradictions. Consider this problem in compound conditioning: One group of animals has reinforced training with three individual stimuli, A, B, and C. A second group has reinforced training with the three two-component compounds composed of the same three stimuli, AB, AC, and BC. Then both groups are tested with the three-stimulus compound, ABC. Which will respond more? (Note the ordinal nature of the question.) Rescorla–Wagner theory suggests that the first group will respond more, whereas Pearce’s (2002) configurational variant of the theory suggests that the second will respond more. What actually happens depends, presumably, on whether you do a rabbit eyelid-conditioning experiment with light, tone, and tactile stimuli in New Haven, where the first group responds Table 1 Summary of 236 Papers—Animals, Techniques, and Topics—From 1995 to 2004 Animal Rat Pigeon Rabbit Mouse Quail Miscellaneous (snail, ferret, shrew, drosophila, gerbil, wasp, goldfish, etc.) Technique 76 11 4 3 3 3 Foodbox approach or entry Food aversion Conditioned suppression Autoshaping Freezing Mazes or runways Eyelid conditioning Sign-tracking Heart rate Miscellaneous behavioral observations (activity, startle, rearing, retching, orienting, shuttling, etc.) Topic 21 15 15 11 9 8 5 3 1 12 Compound conditioning Extinction Latent inhibition ITI and ISI effects Sensory preconditioning Conditioned inhibition US properties Miscellaneous (not readily classified) 21 11 10 7 5 4 4 38 Note. The classification is by Rachel E. Blaser of 236 papers in Animal Learning & Behavior and the Journal of Experimental Psychology: Animal Behavior Processes (1995–2004). The data are percentages of papers. ITI ⫽ intertrial interval; ISI ⫽ interstimulus interval. CLASSICAL CONDITIONING SINCE PAVLOV A Way Forward The time may have come for us to turn again to a once widely admired strategy pioneered long ago by Hull (1935), who unfortunately did not have the computer resources necessary to implement it properly. The strategy was forcefully recommended by Hilgard (1948, 1956), who called attention to the importance of “miniature systems” in the early development of the physical sciences. The idea is to begin with (a) a carefully chosen model animal; (b) some basic questions; (c) an efficient, objective, and highly standardized training technique designed to yield results that are exactly reproducible; and (d) the simplest conceivable theoretical account of the results in the form of a set of equations with meaningful parameters that are carefully evaluated. Then there are new experiments designed to test exact predictions from the theory, which is modified systematically as required by expansion of the database and (Hilgard’s phrase) progressive broadening of the boundary conditions. Some contemporary work with honeybees, still in an early stage, may provide a useful example. Individual subjects are pretrained to forage at a laboratory window, where on arrival they find two targets— disposable petri dishes labeled with different colors or odors—that are set out on the deep shelf of the window (Couvillon & Bitterman, 1985). One target contains a drop of 50% sucrose solution and the other contains a drop of water, which is unacceptable and can be distinguished from the sucrose only by taste. If the animal goes first to the water, it is permitted to correct its choice. After filling its “social stomach,” the animal leaves for the hive to deposit the sucrose it has collected, returning to the window a few minutes later to make another choice. Of course, the lateral arrangement of the two targets is changed quasirandomly from visit to visit. Figure 1 shows a 1.0 CHOICE OF A more (Myers, Vogel, Shin, & Wagner, 2001), or a pigeon key-pecking experiment with differently positioned colored lights in Cardiff, where the second group responds more (Pearce, Aydin, & Redhead, 1997). A third conception of compounding (Wagner, 2003)—the “replaced elements” conception—provides an explanation of either outcome, although only after the fact. How are we to move on? 369 0.8 0.6 0.4 Obtained Simulated 0.2 0 0 5 10 VISITS 15 20 Figure 1. Obtained and simulated proportions of honeybees choosing A, the originally reinforced of two odors, on each trial of a l0-trial reversal problem (Couvillon & Bitterman, 1985). typical acquisition curve for a discrimination between two odors, with the positive and negative alternatives reversed after 10 trials; the performance of a group of 8 subjects is plotted there in terms of the probability of correct choice on each trial. To explain the results of this and several other rather basic discrimination experiments performed at the same time, we began with a simple contiguity theory consisting of two postulates in equational form, one dealing with learning and the second with performance. The first postulate is that the attractiveness of a target is given by the strength of its association with sucrose, which increases with reinforcement to some asymptotic value and decreases to zero with nonreinforcement. The second is that the probability of choosing a given alternative is a power function of its relative associative strength. Each of the equations has several parameters to which values must be assigned if performance is to be computed; for the learning equation, they are the salience of the CS, the rates of growth and decline in associative strength with reinforcement and nonreinforcement, and the asymptotic level of associative strength determined by the magnitude of reinforcement; for the performance equation, slope and curvature parameters. The question then asked was whether we could find values for the parameters that would yield a close simulation of all the data; the answer, as Figure 1 illustrates, is that we could, which is perhaps not very surprising. (Who was it who once said that, 370 BITTERMAN given a few free parameters, he could draw a picture of an elephant?) A more challenging question, hardly ever asked, was whether, with the same parameter-values, we could exactly predict the results of new experiments; the answer again is that we could. Figure 2 shows, for example, the predicted and obtained results of a new experiment with the same stimuli in which four initial training trials were followed by seven 4-trial reversals (Couvillon & Bitterman, 1986). Because the question of how learning is translated into performance is so often ignored, it may be interesting to look at the choice function (intermediate between what have been described as “maximizing” and “matching”) that yielded the best fit to the original data. The function is plotted in Figure 3, along with the good predictions it provided of the results of a subsequent experiment on probability learning that was designed especially to constrain it (Fischer, Couvillon, & Bitterman, 1993). Seven of nine groups of honeybees (individually trained, as always) began with one of two alternatives (odors) consistently reinforced, the other never reinforced (a 100:0 problem), and then were either continued at that ratio or shifted to one of several others (90:10, 80:20, 70:30, 50:50, 20:80, or 0:100). An eighth group began at 70:30 and was shifted to 30:70. For a ninth group, the ratio was 50:50 throughout. The terminal choice-ratios, predicted and obtained, show very good agreement. To see how well the theory could deal with performance in somewhat more complex problems, we turned from simple color or odor learning to learning about color-odor com- CHOICE OF A 1.0 Obtained Predicted 0.8 0.6 0.4 0.2 0 0 4 8 12 16 20 VISITS 24 28 pounds (Couvillon & Bitterman, 1988). In experiments with two colors, A and B, and two odors, X and Y, honeybees were trained to discriminate between a compound and its components, the compound reinforced in some cases (AX⫹/A⫺/X⫺) and the separate components reinforced in others (A⫹/X⫹/AX⫺). When the results made it clear that, even for these animals, a compound is more than the sum of its components, we considered the simplest alternative that had yet been suggested in the vertebrate literature—the Rescorla–Wagner assumption that the components of a compound stimulus interact to generate a new, compoundunique component, Q, which functions like any other component. With an intermediate value for the salience of Q, the results of the entire set of compounding experiments could be simulated readily. Consider an experiment on conditional discrimination in which the task was, for example, to choose A in the presence of X, but B in the presence of Y: As Figure 4 shows, the performance of the animals in this difficult problem was captured rather nicely. Although the theory provides a rigorous quantitative account of a now quite substantial set of data, it is still rather simple. For one thing, the components of a compound, including the compound-unique component, do not compete for associative strength, but gain and lose it independently with reinforcement and nonreinforcement. Furthermore, there is no inhibitory assumption; the effect of nonreinforcement is simply to reduce associative strength, which never falls below zero. Now, however, there are some data for honeybees that do suggest an inhibitory process (Couvillon, Bumanglag, & Bitterman, 2003), as well as some sort of competition for attention (Shapiro & Bitterman, 1998), data that point inescapably to the inadequacy of the theory, which must either be elaborated or abandoned. We would be happy for colleagues in other laboratories with facilities for working with honeybees to join in the effort, and we would be glad to provide any information that would promote the growth of a common pool both of data and ideas. 32 Figure 2. Obtained and predicted proportions of honeybees choosing A, the originally reinforced of two odors, on each trial of a four-trial reversal problem (Couvillon & Bitterman, 1986). A New Model Animal It is only the strategy represented by this work, however, that I (along with Hull and Hilgard) am recommending again to the larger 1.0 0.9 Maximizing Best Fit 0.8 Matching 0.7 371 1.0 CHOICE RATIO PROBABILITY OF CHOICE CLASSICAL CONDITIONING SINCE PAVLOV 0.6 0.8 Obtained Predicted 0.6 0.4 0.2 0.5 0.0 0.5 0.6 0.7 0.8 0.9 1.0 0.0 0.2 0.4 0.6 0.8 1.0 RELATIVE ASSOCIATIVE STRENGTH REINFORCEMENT RATIO Figure 3. (Left) Three functions relating probability of choice to relative associative strength: maximizing, matching, and the (intermediate) function of best fit to the data of all previous choice experiments with honeybees. (Right) Obtained and predicted relations between asymptotic choice ratio and reinforcement ratio in an array of choice problems; the straight line of best fit to the obtained data also is shown (Fischer et al., 1993). world, and not the honeybee as a model animal. Among its other limitations, the foraging honeybee is a hive-bound creature that must be depended on to come to the laboratory of its own accord and to expose itself to the stimuli. In proboscis-extension conditioning with harnessed foragers, there is better stimulus control, but the technique is of limited value, because extension of the proboscis is evoked only by a very narrow range of conditioned stimuli, and because one must be concerned about the wellbeing of subjects restrained for too long a time. In any case, we certainly will want to continue to study learning in vertebrates. Given the long history of work with rats and pigeons, it might seem reasonable to begin with one of those animals, but since we would be starting all over again with newly standardized techniques to build a common body of data, my own prefer- CORRECT CHOICE 1.0 0.8 0.6 0.4 Obtained Simulated 0.2 0 0 4 8 12 16 20 VISITS 24 28 32 Figure 4. Obtained and simulated proportions of honeybees choosing correctly on each trial of a conditional color– odor problem (Couvillon & Bitterman, 1988). ence would be to begin with a fish (Powers, 1989). Small freshwater fish are abundant, relatively inexpensive, and live well in the laboratory. We already have for them several excellent conditioning techniques that compare favorably indeed with those commonly used for rats and pigeons—techniques that are readily standardized and fully automated, that require little advance preparation of the subjects, and that provide reliable, trial-by-trial measures of responding to a wide range of stimuli. A good deal of work has already been conducted with a modern version of a simple appetitive technique conceived long ago for the study of discriminative learning in fish (Washburn & Bentley, 1906). As illustrated in Figure 5, a plastic disk in the training tank has at its center a nipple through which thickened liquid food can be pumped. The disk is suspended from a thin rod running to a strain gauge so that contacts with it can be recorded, and stimuli paired with food pumped through the nipple soon begin to produce anticipatory responses to the nipple. Consider some sample results for a group of small goldfish in an experiment on the generalization of excitation (Tennant & Bitterman, 1975). After reinforced trials with a tone of either 200 or 800 Hz, nonreinforced tests with tones of the original and several different frequencies yielded the generalization gradient plotted in Figure 6. Useful also is a modern version of a technique, pioneered by Froloff (1925), that is sketched in Figure 7. The CS is a light or a tone, 372 BITTERMAN Figure 5. Apparatus for the study of appetitive conditioning in fish. Liquid food is pumped through the nipple in the center of a disk mounted on a thin rod. Strikes at the disk in response to the food, or to stimuli paired with food, are recorded. kin, & Bitterman, 1963). For one group, the pairing was consistent; for a second group, the US occurred on only half the trials. When reinforcement was terminated altogether, responding in both groups declined, more precipitously in the consistently reinforced group—the familiar partial reinforcement extinction effect. The shuttlebox, designed originally for the study of avoidance conditioning in rats (Warner, 1932), is suitable also for the study of classical conditioning in fish, as well as of instrumental conditioning and the relation be- MEAN RESPONSE MAGNITUDE RELATIVE RESPONSE FREQUENCY and the US is a brief shock that activates the animal, which is confined in a small chamber. The activity, both conditioned and unconditioned, is reflected by movement in the water of a paddle linked to a strain gauge, and a simple integrating circuit provides a measure of response-magnitude. The results for two groups of small Tilapia, trained in daily 10-trial sessions with light as the CS and a 10-s CS-US interval, are shown in Figure 8 (Gonzalez, Es- Figure 7. Apparatus for the study of aversive conditioning in fish. General activity produced by brief shock, or by stimuli paired with the shock, generates water currents that move the paddle. 35 30 25 20 15 0 1 2 3 200-HZ STEPS FROM TRAINING STIMULUS Figure 6. Auditory frequency generalization in a group of goldfish after classical appetitive conditioning with a tone as the CS and food as the US (Tennant & Bitterman, 1975). 70 Consistent 60 50 40 30 Partial 20 10 0 0 5 10 15 20 SESSIONS 25 Figure 8. Performance in the activity-conditioning situation of two groups of Tilapia extinguished after partial versus consistent reinforcement with light as the CS and brief shock as the US (Gonzalez, Eskin, & Bitterman, 1963). CLASSICAL CONDITIONING SINCE PAVLOV 373 Figure 9. A shuttlebox for fish. Shuttling responses generated by shock, or by stimuli paired with shock, are detected by the photocells. PROBABILITY OF RESPONSE tween the two. A shuttlebox for fish is diagrammed in Figure 9. Plotted in Figure 10 are the results for a group of small goldfish classically trained with general illumination as the CS and brief, unavoidable shock as the US (Woodard & Bitterman, 1971). Two colors were used— one reinforced and the other not (a standard control for sensitization)—and differential responding to them soon appeared. I should emphasize that the procedure was purely clas- 1.0 CS+ 0.8 0.6 CS- 0.4 0.2 0 0 5 10 15 SESSIONS 20 Figure 10. Differential classical conditioning of a group of goldfish in a shuttlebox with colored lights as stimuli (CS⫹ and CS⫺) and brief (unavoidable) shock as the US (Woodard & Bitterman, 1971). sical, the structure of the apparatus serving to channel the conditioned activity generated by the CS. Some promising first steps in the development of a quantitative two-process theory of shuttlebox conditioning in goldfish have already been taken (Zhuikov, Couvillon, & Bitterman, 1994). I began a long time ago to develop these conditioning techniques for fish in the belief that it would be useful to have some systematic comparative information about learning in older vertebrate lines (Bitterman, Wodinsky, & Candland, 1958), and you might wonder why I have not continued to do much work with them. The answer is simple: The funding ran out. At a time when the influence of the ethologists loomed large and there was a lot of loose talk about “evolutionary constraints” on learning, the funding agencies were willing to support an inquiry into the possibility that learning in fish might be different in some fundamental respects from learning in the birds (pigeons) and mammals (rats) with which most people were working, but their interest waned as little indisputable evidence of differences in learning, qua learning, turned up (Bitterman, 1975). The fact, for example, that the choice ratios of several species of fish in experiments on probability learning tended closely to approximate the reinforcement ratios, whereas rats either max- 374 BITTERMAN imized or behaved “hypothetically” in the old Krechevskian manner, could be taken to reflect a difference only in the use rather than in the acquisition of information; the fact that large and sudden decrement in reward magnitude did not produce in goldfish the dramatic disruption of performance found in rats could be put down to a difference in temperament. It was then, with even Konrad Lorenz himself joining the ranks of the general-process learning theorists (Lorenz, 1977), that P. A. Couvillon and I turned to the study of learning in honeybees (Couvillon & Bitterman, 1980), a still more divergent species. For that work, paradoxically enough, we have found continuing support precisely because of many unexpected and quite detailed similarities in the learning of honeybees and vertebrates (Bitterman, 1988, 1996) that have captured the interest of funding agencies. Now, perhaps, it may be worth thinking again about work with fish. The conditioning techniques that I have described can be readily adapted for the zebra fish, which would be a wonderful model animal. Support for work with it should not be difficult to find, given that it is now, and probably for a long time will continue to be, the darling of the biomedical research community, which can be counted on to welcome the results; nor is it unreasonable to suppose that success with the zebra fish would attract support for equally sophisticated comparative work with judiciously selected vertebrates of other classes. The central question (Hull, 1945) would be whether the equations that describe learning in the various animals are of the same form, differing only in their constants—as, in Hull’s memorable example, the gravitational constant at Hammerfest and Madras— or whether the equations themselves are different. In concluding, I should say that I regret not being able to bring you a happier message about progress to date in work on classical conditioning, but only the sort of message that the messenger is likely to be shot for bringing. It may be of some comfort to those of us involved in that work to know that we have not been alone in our floundering. In a review of research on human memory and verbal learning, Tulving and Madigan (1970) concluded that, at least as of then, “nothing very much [had] changed over the past hundred years,” or even since Aristotle, “in the understanding of how people learn and remember things” (pp. 476 – 477), although Tulving now tells me that there has been some significant progress at least in the understanding of episodic memory. In any case, recognition of shortcomings is the first step on the road to improvement, and there does seem to be a way forward for us, as Hull and Hilgard advised, that promises more rapid advance than in the past. I would like to think that, together, we will begin at last to act on that advice. References Bitterman, M. E. (1975). The comparative analysis of learning. Science, 188, 699 –709. Bitterman, M. E. (1988). Vertebrate-invertebrate comparisons. In H. J. Jerison & I. Jerison (Eds.), Intelligence and evolutionary biology (pp. 251– 276). Berlin: Springer. 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