Overview: In the four major sections of this chapter, we examine various approaches to evaluating what a reinforcer is, and what role it plays in learning. The first section presents what might be termed intrinsic reinforcement theories. This section is primarily devoted to Hull's Drive Reduction Theory of reinforcement, but also discusses Sheffield's Consummatory Response Hypothesis. Both theories discuss biological approaches to reinforcement. Next, the second section discusses what might be termed contextual reinforcement theories. Here, whether something counts as a reinforcer varies with the individual situation facing the animal; under certain circumstances, an event that reinforces in one situation will fail to reinforce in another, in contrast to Skinner's claim that reinforcement ought to be transsituational. Theories presented in this section include Optimal Stimulation Theory, the Premack Principle, and Timberlake and Allison's Equilibrium Theory. The third section addresses theories that claim reinforcement is not needed for learning. At issue in this section is why reinforcement seems to influence the course of acquisition. Guthrie's Contiguity Theory is discussed here, along with more cognitive approaches arising from Tolman's work. In particular, this section examines associational versus expectancy theories of avoidance learning. Finally, the last section briefly visits the issue of expectancy-based versus habit-based behavior.
We start with a venerable claim by Hull that ties reinforcement to important
Before discussing some of these intervening variables and principles, however, let us point out some important preliminary assumptions of the theory. The first of these is that the theory is a reinforcement theory: Learning requires the presence of a reinforcer following a response. The second is that this is a molecular response theory: What gets acquired is one or more peripheral muscle movements, in contrast to Skinner's functional definition of a response. The third is that this is an associational S-R theory: Learning involves modifying the strength of the association between a stimulus and a molecular response (or chain of molecular responses in the case of complex behavior such as navigating a maze). The fourth is that learning is incremental or continuous: The association between a stimulus and response strengthens with each additional reinforcer, rather than being all or none. Fifth, Hull's theory posits permanent learning: An association can stay as it is or strengthen; it will never weaken. And sixth, what appears to be inhibition (in extinction, for example) is also due to an association that strengthens because of the presence of a reinforcer. This last assumption, of course, arises as a special case of the initial assumption that all learning involves reinforcement.
Habit strength will always be 0 or greater. In the theory, each additional reinforcer will increase the habit strength by an increment. The formula for determining the amount of habit strength as a function of number of reinforced trials is:
SHR = 1 - 10-iN
In this formula, i is a rate parameter that refers to the individual learning speed of a given animal, and N is the number of continuously reinforced trials. Hull did measure the individual learning speed in one experiment, and found it to be .0305, but he clearly held out the possibility that it would be a different number with different individuals. Using the .0305 parameter, we would expect that the learning curve over 20 trials ought to be identical to the bottom (solid) line in Figure 1. As you can see from this figure, the formula correctly predicts the general shape of the acquisition curve as being negatively accelerated (i.e., a diminishing returns curve).
Another feature of this graph is noteworthy: The maximum value for habit strength will be equal to 1. However, we may modify this formula slightly to illustrate its similarity with the Rescorla-Wagner delta rule. Thus, if we multiply a lambda by (1 - 10-iN), then we see that both rules have a mechanism for evaluating how much of the remaining association gets conditioned on each additional trial. There is, of course, no variable for CS salience in Hull's formula. If you recall, CS salience in the delta rule determined how rapidly the curve approached asymptote. In contrast, the individual rate parameter i determines the speed with which asymptote is approached in Hull's formula. You can see this graphically by comparing the two lines in Figure 1. The top line corresponds to an i value of .1, and the result is a much quicker approach to asymptote than when the i value is smaller.
Finally, although it may not seem so, Hull's formula also takes into account how much prior learning has occurred. This is done through the N parameter. Unlike the delta rule, of course, prior learning involves just the current stimulus and response, and not multiple stimuli predicting the association. Thus, while there are some clear differences between the two rules, they are also more similar than would appear to be the case on first comparing them.
In addition to habit strength, Hull also posited an intervening variable of drive level, symbolized as D. The drive level is posited to be related to the amount of deprivation of a basic biological need the animal has undergone. Thus, informally, time since being fed translates into drive level (for the drive of hunger), with a longer time corresponding to a greater hunger drive. The higher the drive level, the more strongly motivated the animal is to perform a response that will satisfy the drive. Or put in slightly different fashion, the higher the drive level, the more value the reinforcer has. Drive level will always be 0 or greater.
With these two intervening variables, we are now able to develop a principle that interrelates them. This is the principle of reaction potential which states how strongly excited a specific response is in the presence of a given stimulus and a given level of drive. The excitation is:
SER = D * SHR
The principle states two essential conditions for a stimulus to excite a response: (1) that the stimulus and response be associated together (i.e., that habit strength be above 0), and (2) that the drive level be positive. An animal that is not hungry will not perform any behavior that has been associated with quelling that hunger. And in like fashion, an animal that is not fearful in an avoidance situation (involving a non-zero level of the fear drive) will not perform an avoidance response to get away from the fear.
In addition to excitation, however, Hull also discusses two types of inhibition involving not doing a response. One is conditioned inhibition, and is symbolized as SIR. As is clear both from the use of the word conditioned and from the presence of the S and R subscripts, this type of inhibition involves learning. The other is reactive inhibition, and is unlearned. It is symbolized as IR (note the absence of the S subscript!). Reactive inhibition is a drive state that arises immediately after performing a response: For a short period of time thereafter, a factor such as fatigue sets in that makes the response difficult to do, and that tends to result in the animal resting until reactive inhibition has gone away.
Although reactive inhibition may seem a concept whose motivation is puzzling, you will see (in the chapter on extinction and partial reinforcement) that reactive inhibition is central to the notion of extinction depending on learning. For now, one piece of evidence that fits with the notion of reactive inhibition is the spacing effect discussed in Chapter 2. To remind you, massed practice (in which trials or responses occur in rapid succession) results in weaker learning or memory than spaced practice (in which there is more of a breather between trials). From Hull's point of view, the explanation for this is that reactive inhibition has not completely dissipated in massed practice. In addition, spontaneous recovery may be explained by claiming that with time, reactive inhibition decreases to the point that excitation is slightly stronger, eventuating in a response.
Given the potential presence of both excitation and inhibition, we may now posit a second principle that adds in the notion of inhibition. This principle, the principle of effective reaction potential, involves the notion of algebraic summation: The net excitation will be equal to excitation minus inhibition. Net excitation is represented as excitation with a bar over the symbol. Thus,
SER = (D * SHR) - IR - SIR
If the two types of inhibition sum to a greater value than excitation, then the response will be inhibited; otherwise (assuming non-zero excitation), the response will be expressed. Thus, how strongly a response is performed will depend on both excitation and inhibition. When we add in the many factors that in addition to learning affect the strength of a response (drive level and inhibition, in particular), we see a clear distinction in Hull's system between learning and performance: Performance is determined by the principle of effective reaction potential, whereas learning is restricted strictly to SHR.
One more point now becomes important: the nature of reinforcement. This involves the notion of a need-drive cycle. Hull claimed that reinforcement was ultimately dependent on satisfying an organism's biological needs. In time, a state of deprivation arises that places the animal in a physiological state of need. This need state gives rise to a psychological state that may be termed a drive. The psychological state has motive properties in that it impels the animal towards appetitive responses that will reduce the drive, and ultimately, reduce the need. Thus, at some point in time, deprivation of nourishment results in a need for nourishment that is associated with a hunger drive. The appetitive responses associated with the hunger drive most directly include the process of eating and ingesting food, but also include the search for food. Eating and ingesting food reduce the drive. With time, the food is broken down, and nutrients are carried through the blood stream to be absorbed at the cellular level. When that happens, the need diminishes. Then, in time, the cycle starts over.
Within this cycle, it is the reduction of the drive that Hull identifies as the reinforcer. The reason is that it takes too long for the need to go away. Hence, on the basis of contiguity, a response such as ingesting food can more-or-less immediately get rid of the hunger drive, but will take longer to have an effect on the need for food. Thus, in Hull's system, any response that results in drive reduction will cause the association that triggered that response (the SHR) to strengthen. If you go back to the formula for habit strength, you will see that there is no role assigned to the amount of reinforcement that occurs. That is consistent with Hull's later approach (in 1952). But in the earlier (1943) model, he did believe that amount of drive reduction influenced the amount of strengthening of the association. Thus, in the early version, there was a principle having to do with amount of reinforcement: Bigger reinforcers (i.e., greater drive reduction) result in stronger learning.
One more point about the need-drive cycle: A need can also arise from aversive stimulation rather than deprivation. Pain administered through shock can cause a need to restore the organism to a pain-free state, and the pain itself may be associated with a drive state such as fear.
A further idea we may add to this collection of theoretical notions is Hull's claim that animals acquire habit hierarchies. Put simply, in any given situation, a stimulus may become associated with a number of different responses, so that it may have links to many responses. According to Hull, which response it is most likely to make will then depend on which response is most strongly excited by the principle of effective reaction potential. Since associations never really weaken in Hull's system, a response that fails to work in a given situation may acquire some conditioned inhibition, so that its net excitation (its effective reaction potential) decreases relative to another response. And a response that does work will have its habit strength increased, resulting in stronger net excitation, relative to some other response. Thus, associated with each stimulus condition are a number of responses that may be ordered in terms of their relative effective reaction potentials. This ordering constitutes the hierarchy of available responses. The animal will perform the responses in order of this hierarchy, when it is in a state of drive, until some response finally works.
As a sample experiment, relevant to this, consider a sample study by Tolman and Honzik using a 3-arm maze of the sort represented in Figure 2. In this maze, the start box is the yellow oval at the bottom, and the chamber to which the animal must run for its reinforcement (the goal box) is the yellow oval at the top. There are three routes to the goal box: the route directly between the two ovals (the speckled route); the triangular route (solid grey in black and white; light blue on a color monitor); and the rectangular route (dark blue or black, depending on your monitor). Let us call these three routes Pathways 1, 2, and 3, respectively. Hull's notion of a habit hierarchy would predict that running down Pathway 1 would be at the top of the hierarchy associated with being in the start box; running down Pathway 2 would be the next strongest association; and running down Pathway 3 would be the weakest. There are several reasons we can use to justify this predicted ordering, but the most obvious two involve the effort in making the response, and the delay of reinforcement. The straight path involves the least effort, and gets the animal to the food the soonest. The triangular path involves more effort and a greater delay, and the rectangular path involves yet more work, and an even longer delay. Thus, following initial exploration of the maze, we ought to find that our animals generally settle into the routine of running Pathway 1.
But let us specifically ask what would happen if, following acquisition, Pathway 1 were blocked at a point corresponding to, say, A on the figure? If you look at the maze, you will see that such a blockage occurs after the other two paths diverge, but before either of them comes back into the central path. In this case, Hull's habit hierarchy predicts the animal ought to back up to where the other two paths diverge, and run down Pathway 2, since it is the next most-favored response on the hierarchy. And that is what generally does happen.
Other findings consistent with Hull (in addition to the shape of the acquisition curve, spacing effect, spontaneous recovery, and choice of response as indicated by habit hierarchies) include the effects of delay of reinforcement and amount of deprivation. Because Hull's is a contiguity theory, reinforcement ought to work at short delays following a response. Grices's results cited in the previous chapter serve as an example consistent with this prediction. And because deprivation should translate directly into drive level, Hull's theory predicts that depriving an animal of food for a longer period of time should result in more vigorous responding. That result has also been verified.
Let us start with the issue of drive reduction as constituting reinforcement. Hull became convinced that drive reduction per se would not work as an account of reinforcement. That change of heart was due in part to the work that Sheffield and his colleagues were publishing at the time. To see the problem, let's consider a sample experiment by Sheffield and Roby. They taught rats a response using a reinforcer of saccharine. As you may remember from earlier chapters, rats do indeed react to saccharine. In the absence of any prior experience with saccharine, they will nevertheless treat it as a primary, unlearned reinforcer.
Why is this a problem for Hull's theory? In a nutshell, the problem (acknowledged by Hull) is that saccharine serves no nutritional value. It can neither satisfy the need for food, nor the hunger drive. And because it has not been experienced before by the rat, it is not a learned or secondary reinforcer. Even if it were (due to generalization, for example), it should readily extinguish because it fails to satisfy any nutritional need. Hence, the use of saccharine cannot involve the operation of drive reduction.
How did the theory change in response to this? Hull handled such findings by altering the focus from drive reduction to moderation of the drive stimuli. He claimed that there were stimulus components associated with the various drive states. When you are hungry, for example, you may experience stomach contractions and growlings. When you are thirsty, you may experience a sensation of dryness about the lips (the 'parched, cracked lips' syndrome). In the later theory, reinforcement occurs when these stimuli are removed or moderated to less intense levels. Thus, eating saccharine will work because there is some bulk substance moving into the stomach which 'satisfies' the drive stimuli associated with an empty stomach.
But what about contrast effects? As you know from several of the studies mentioned in the previous chapter (see particularly Crespi's work on incentive contrasts), a change in the amount of reinforcement will influence behavior after acquisition of the association. We discussed such effects as evidence for a distinction between learning and performance, a distinction that Hull's theory also maintains. But if you look at the variables that affect performance in the earlier version of the theory, you will find none that is capable of predicting a contrast effect. Thus, animals in Crespi's experiment who are shifted from 64 to 16 food pellets immediately respond less vigorously following the shift. Because associations never weaken in Hull's system, a change in the size of the reinforcer cannot decrease the habit strength, but can only result in smaller increases on subsequent trials, a result at odds with what actually happens in a negative contrast.
Moreover, Crespi's results also displayed a positive contrast for the group shifted from 4 to 16 pellets (see Figure 3 in the previous chapter): They displayed a much larger increase in responding than would be expected, according to Hull's account of how habit strength increases, on this relatively late conditioning trial. That finding, in turn, suggested that perhaps size of reinforcer had its primary influence on performance rather than learning.
To accommodate results such as these, Hull's theory included two new changes. The first, briefly discussed above, got rid of the notion that size of reinforcement had anything to do with how much learning occurred on any given trial. So long as there was an effective reinforcement (defined in terms of reducing the drive stimuli, of course), the habit strength increased by a certain amount. That amount of change was calculated by the formula for SHR you have already been introduced to: That formula contains no variable that refers to size of the reinforcement.
And the second change involved introducing a new intervening variable called incentive motivation, and symbolized by K. Incentive motivation was an attempt to capture the notion that bigger reinforcers would result in more vigorous responding, because they were of greater value to the animal. For food, incentive motivation could be operationally defined as the amount or weight of the reinforcer. Greater values of K ought to result in stronger net excitation, whereas weaker values ought to result in weaker net excitation. Thus, incentive motivation may be regarded as an addition to the notion of reaction potential, the principle of amount of excitation. Our revised formula for reaction potential (and you can add in a similar change for the effective reaction potential formula) may now be expressed as follows:
SER = D * K * SHR
Note here that very low levels of K should depress responding whereas high levels should magnify responding. Thus, the formula can now handle contrast effects involving positive and negative contrasts. Moreover, when incentive motivation is at 0, then no excitation will be evident.
One more change is worth discussing. As in CS intensity effects in classical conditioning, it became evident that stimulus intensity in instrumental conditioning influenced responding. Thus, Hull added in yet another intervening variable termed stimulus-intensity dynamism, symbolized by V. Stimulus intensity could be non-existent (0), or positive to various levels, with higher levels generating more excitation. You should be able to predict that such an effect on performance forms part of the principle of excitation. Hence, we may now present a reasonably complete formula for how strongly a response is excited (the principle of reaction potential) as:
SER = D * K * V * SHR
and the formula for effective reaction potential as:
SER = (D * K * V * SHR) - IR - SIR
As you may see from this latter formula, the relationship between learning and performance in Hull's later theory turns out to be quite complex. In particular, a minimal level of drive, incentive motivation, or stimulus intensity will turn off the expression of the response, as will sufficiently high levels of inhibition. There were other principles and intervening variables in Hull's system, but the ones we have discussed should give you a good basis for understanding and evaluating Hull's approach.
RESPONSE-ORIENTED ISSUES. Let us start with the theme of responses. One of the central tenets of Hulls' theory was that learning involved a habit whereby a stimulus triggered specific muscle movements. (This may be presented as the issue of the molecular nature of responding.) As you know from a previously discussed experiment by Macfarlane, however, animals who learn to run a maze will later be able to swim it to the goal box. As running and swimming involve different muscle movements, learning to run should not transfer, in Hull's system, to learning to swim. Similarly, experiments by Morris and by Morris, Garrud, Rawlins, and O'Keefe using the Morris Maze demonstrated that rats who learned to swim in one direction were able to immediately swim in another direction to get to a hidden platform, when released from a novel location in the maze. Being released from a different location should either have involved a stimulus situation so different as not to trigger any effective response at all, or the same response of swimming at a certain angle relative to the point of release (which ought to have resulted in the rat swimming to the wrong location).
Another issue having to do with responses concerns the claim (met earlier in classical conditioning: recall the experiment by Light and Gantt involving temporarily paralyzing a dog's paw!) by Hull that a physical response has to occur in order for habit strength to increase. (This may be presented as the issue of the need to make a response.) But we saw earlier in numerous observational learning studies that animals could learn by observing others. Different types of observational learning occurred. One of these involved the construction of cognitive maps in the absence of a response. For example, recall the experiment by MacNamara, Long, and Wike in which a group of rats drawn through a maze learned to run it faster than a group without this experience. Recall, too, the experiment by Menzel in which a chimp watching an experimenter hide bananas in a field remembered where these could be found, but located them in a different order -- hence made different responses -- than that in which they were hidden. Another type of observational learning we looked at involved the acquiring of responses prior to their actual performance. As an example of this type, recall the Bandura study in which kids learned how to play with toys by watching a clown play with them, and the Kohn and Dennis study in which rats learned a discrimination faster when they watched other rats learn the same discrimination. In both of these experiments, a stimulus-response association was apparently acquired without the animal actually having had first to make a response.
A final study which we also learned about in Chapter 4 is relevant here: the Seward and Levy study on latent extinction. In this study, animals that were put through a pre-extinction phase in which they were placed in the goal location without the usual reinforcement extinguished more rapidly. Although we have not really discussed Hull's theory of extinction yet (see the next chapter), it also requires that the animal make a response, since extinction is a form of learning. In Seward and Levy's experiment, however, the animal is not making any sort of running response, so that response ought not to become associated with conditioned inhibition. Thus, the Seward and Levy study also demonstrates a form of learning that is not dependent on first performing a certain physical movement.
A final issue that concerns Hull's conception of responses has to do with the order in which an animal will execute responses. We may term this the issue of whether responses are arranged in a strict habit hierarchy. A classic experiment by Tolman and Honzik will illustrate this issue. They took the 3-arm maze used to illustrate the concept of a habit hierarchy (review Figure 2 and the surrounding discussion), and blocked it at a slightly different point. Specifically, their maze looked somewhat like that in Figure 3. The thin black area in the central corridor represents where the block is located. As you can see, this block also blocks Pathway 2 (the triangular pathway). Thus, on the notion of a strict habit hierarchy, the rats ought to run down Pathway 1 (the central corridor), hit the barrier, back up to where the other two pathways diverge, run down Pathway 2 (the triangular path), hit the barrier again, back up again to where the pathways diverge, and only then take Pathway 3 (the rectangular path), the weakest habit in their hierarchy. But of course, that is not what Tolman and Honzik found. Consistent with Tolman's theory of purpose behavior built upon knowledge contained in cognitive maps, they found that their rats avoided Pathway 2, and took Pathway 3 as their second choice. These rats apparently were able to use their maps to realize that the barrier also blocked Pathway 2, and thus did not elect that path when given a chance to do so.
Results such as these are not always obtained with this type of setup. Keller and Hull, for example, find that physical features of the maze in addition to where it is blocked may determine whether the animal will choose Pathway 2 or Pathway 3. But the fact that results like Tolman and Honzik's are sometimes obtained nevertheless poses a problem for Hull.
REINFORCEMENT-ORIENTED ISSUES. The next set of problems concerns the nature of reinforcement. We will start with whether reducing the drive stimuli (as per Hull's 1952 theory) is all that is needed for reinforcement. (We may refer to this as the issue of the sufficiency of drive stimuli moderation.) A series of experiments by Miller and his colleagues is informative here, but let us consider one study by Miller and Kessen. They had several groups of rats learn a T-maze. Two groups are of particular interest: Their reinforcer was milk. However, while one group was allowed to drink the milk in the normal fashion, the other had the milk pumped directly to their stomachs. Both groups learned, but the group that drank in the normal fashion learned more rapidly, and displayed a stronger response. Since both groups had their stomach-based drive stimuli satisfied, each, on Hull's theory, ought to have had the same effective reinforcer. But instead, taste resulted in a stronger effect.
Moreover, Miller and Kessen ran a third group of rats, but pumped a saline-based solution into their stomachs, instead of milk. Both 'pumped' groups ought to have had the same amount of drive reduction from satisfying the drive stimuli, and the same 'negative' consequences, if any, from the process of sending fluids down an inserted tube. But the group with the saline solution displayed the worst learning.
Thus, an aspect of reinforcement that involves tasting the reinforcer in this experiment proved important. While there certainly was some evidence that satisfying drive stimuli could serve as a reinforcer, there were other things going on besides, as is evident from the different results obtained in the three groups.
Perhaps a more serious problem, however, involves whether reinforcers exist that don't rely on drive stimuli at all. Certainly in terms of the earlier version of Hull's theory, studies by Sheffield's group demonstrated reinforcement without drive reduction. These included the Sheffield and Roby study mentioned above, in which saccharine was used as a reinforcer, and a study by Sheffield, Wulff, and Backer (discussed more fully below) in which male rats were reinforced by being allowed to mount female rats in heat, but were removed prior to ejaculation (and thus, prior to reduction of the sex drive). Arguably, however, each of these studies was associated with removal or moderation of the drive stimuli. More troublesome for Hull's theories were studies like Butler's (see previous chapter), in which monkeys learned responses for reinforcers that involved looking into a laboratory, or out on a parking lot. What are the drive stimuli that get moderated here? Or to take another example, consider a study by Premack in which young kids learned a response whose reinforcer was playing a pinball game. The notion of a pinball drive and pinball drive stimuli does not bear extended scrutiny, to put it politely.
LEARNING-ORIENTED ISSUES. Perhaps the most serious set of problems, however, concerned the issue of when learning would occur, and how it would progress. As we have seen from the latent learning experiment of Tolman and Honzik, learning may occur without the presence of any apparent reinforcement whatsoever, in direct contradiction to Hull's claim that habit strength depends on a response being followed by a reinforcer. This could be termed the issue of non-reinforced learning. And in a related issue, the issue of incremental learning, Voeks demonstrated that sometimes incremental learning was the exception, rather than the rule. Following up on Guthrie's approach (see below), Voeks demonstrated all-or-none learning: Her animals either didn't show an evidence of an association, or demonstrated evidence that they had acquired the association at full strength.
In short, while there may certainly have been some situations in which drive reduction or moderation of drive stimuli served to influence learning, Hull's limitation of learning to these situations (and to the acquisition of individual muscle movements) proved too restrictive.
The theory was in trouble.
As this brief description makes clear, both Hull and Sheffield tied reinforcement to biological states associated with drives, although they were quite different in other respects. Thus, although Hull at one point described the amount or quality of reinforcement as being dependent on amount of drive reduction, Sheffield equated quality of reinforcement with vigor of the consummatory response. The more vigorous the behavior, the better the reinforcement (and learning). Thus, rats will escape a strong shock more rapidly (more vigorously) than a weak shock, and will lap up sweeter liquids more vigorously than less sweet liquids.
As you know, Sheffield and Roby demonstrated learning using a reinforcer of saccharine. Rats will engage in vigorous consummation of the saccharine, which is why it serves as a reinforcer. Moreover, different concentrations of saccharine (compared to plain water) result in different levels of the consummatory response, and not surprisingly, different levels of instrumental responding (recall Kraeling's results with different sucrose concentrations). So, if we set up a contingency between instrumental and consummatory responding such that certain instrumental responses will be followed by an opportunity to engage in a consummatory response, we ought to find (1) that acquisition of the instrumental behavior occurs, and (2) that it depends on the vigor of the consummatory response.
A famous experiment illustrating these simple but compelling ideas was performed by Sheffield, Wulff, and Backer. We have talked about this experiment briefly, but let us present it in a bit more detail. This was the study using a female rat in heat as the reinforcer. Sheffield et al. used rats that were sexually naive (i.e., that had never mounted a female), and placed them in the start box of a maze. Presumably, their sex drives were not at high levels immediately prior to the start of this experiment: The female's pheromones would have induced a high drive level (so that we see yet another mechanism, aside from temporal deprivation, by which a drive state may ensue). And to remind you, they allowed the male rats to mount the female, but prevented them from ejaculating (i.e., the rats engaged in a consummatory response that did not lead to drive reduction). So what happened?
Sheffield and his colleagues noted that the rats apparently differed in drive level. Those rats who were little interested in the female (i.e., those rats who did not engage in vigorous consummatory responding) showed much lowered levels of learning compared to those who were highly interested. Thus, as predicted, learning depended on the vigor of the consummatory response, and not the satisfaction of a drive. In a real sense, vigor of a consummatory response may be taken as a measure of the extent to which a drive state has been induced. For our rats who were uninterested in the female, a vigorous consummatory response did not occur because their sexual drives were not brought up to a sufficiently high level.
It is an interesting idea, but not everyone has been able to track a simple correlation between vigor and learning or performance. And many of the studies that posed problems for Hull also pose problems for Sheffield. What is the consummatory response in the Miller and Kessen study in which rats were fed through a tube, for example? How do we assess a difference in vigor for a saline solution versus a milk solution piped into a rat's stomach? What is the consummatory response in Butler's experiment with monkeys? What consummatory response occurs on the first ten trials with Tolman and Honzik's latent learning group?
Reinforcement may have multiple
effects and causes, including drive reduction and drive induction. But
results such as those above suggest the presence of additional factors
that may eventuate in reinforcement. We turn next to claims that reinforcers,
at least sometimes, are context dependent.
The notion of differing arousal levels may also apply to individuals, as the optimal level will differ individual to individual. Within the area of personality theory, for example, some theorists have claimed that extraverts are people who are understimulated (and thus seek greater stimulation), whereas introverts are overstimulated, and generally seek decreased stimulation.
In any case, although the theory can obviously handle both drive reduction and drive induction reinforcers, it is a difficult theory to test in practice, due to the need to know what an animal's optimal level of arousal is, and what its current level is. Obviously, some simple predictions can be made: Depriving an animal of food for a long enough period should place it in a higher-than-optimal level of arousal, so that decreases in arousal (as provided by food rewards, for example), ought to be reinforcing. But even here, things get complicated. Animals that are hungry are more likely to respond to novel stimuli. That appears to be the wrong prediction, since novel stimuli increase the level of stimulation overall.
Still, the theory has the
benefit of making a number of predictions, and thus being easily testable.
We would predict, for example, that mildly hungry animals ought to find
a moderate amount of food more reinforcing than a large amount,
whereas very hungry animals ought to exhibit the reverse effect.
(For moderately hungry animals, a small amount ought to bring them closer
to their optimal levels; for very hungry animals, the large amount ought
to bring them closer.) Moreover, stimulus intensity, novelty,
and complexity do seem to have an effect on arousal, as measured
by aspects of the orienting response. Longer or more intense orienting
responses are associated with greater levels of these three qualities,
as predicted by the theory. Thus, the theory in principle can handle some
of the findings we looked at earlier in which 'non-traditional' reinforcers
were present. Being able to look out on a lab is an increase in stimulation
for monkeys that are understimulated (as in Butler's experiments);
and being able to explore a novel maze qualifies as an increase in stimulation
for, say, Tolman and Honzik's latent learning group. And as you
will see shortly, the theory has some similarity to later work by Timberlake
and Allison (see below).
The notion, posited by Premack, is quite simple: Let us observe animal behavior in free-choice situations. Let us further assume that the relative amount of time the animal devotes to different responses may serve as a measure of how preferred a response is. We may then establish the animal's preference ordering of responses. This ordering will reflect the relative strength of a response, with the response at the top of the ordering being the most preferred response, and that at the bottom being the least preferred. The Premack Principle may then be succinctly stated as follows: Stronger responses will reinforce weaker responses.
Thus, for any response that is not at the top or the bottom of the preference ordering, there will be at least one response that it fails to reinforce, and at least one other response that it will reinforce. For example, given the following preference ordering of six responses,
A > B > C > D > E > F
response A will reinforce all the other responses because it is stronger than all the others, and response F will reinforce none of the others, because it is weaker than all the others. Response C, however, will reinforce D, E, or F, but not A, or B. The theory thus finds a natural mechanism to explain the belongingness effects of the type Shettleworth found with hamsters (see previous chapter). Indeed, now that we know the three responses that were not reinforced by food and the three responses that were, we could test the theory out as an explanation of Shettleworth's results by making a prediction about what a hamster's preference ordering would look like. The prediction would be:
(face washing, scent marking, hind leg scratching) >
eating the reinforcer provided by Shettleworth >
(digging, rearing, front paw scraping)
Note particularly that reinforcers on Premack's account do not have to be tied to biological drive states, and do not have to involve such activities as eating or drinking. Thus, as was true of optimal stimulation theory, Premack's approach can handle 'non-traditional' reinforcers such as exploring a maze (which is a response) or checking out what's happening in the lab (which is also a response).
Here is a sample study that illustrates the Premack principle. Premack observed first-graders in a free-choice situation involving candy-dispensing machines (let's call them gumball machines, for short), and pinball machines. Each child's preference was established, and the kids were subsequently identified as players or eaters. Then, 4 groups were set up in which a contingency between the gumball and pinball responses was established, to determine whether a child's behavior could be modified. The design of the experiment was as follows:
Group Preference Learning Task
gumball response followed by chance to pinball
1b player pinball response followed by chance to gumball
2a eater gumball response followed by chance to pinball
2b eater pinball response followed by chance to gumball
In this experiment, the prediction, of course, was that playing should act as a reinforcer for the players but not the eaters, and that eating should act as a reinforcer for the eaters, but not the players. In harmony with this prediction, a congruity effect was obtained in which the learning that was successful in the two types of children differed: Which type of learning occurred was congruent with the child's type. So, only Groups 1a and 2b displayed any increase in responding on the first machine during the learning task. The other 2 groups exhibited the same amount of responding on the first machine as they had during the pre-learning session.
Although I have presented a simple version of the Premack Principle, there is more to it than what we have discussed so far. In particular, the Premack Principle explains reinforcement size or quality effects in terms of relative preference. Thus, to take our example of the preference ordering presented three paragraphs earlier (6 activities ordered from Response A to Response F), while Response E will reinforce Response F (since it is stronger), Response A should be a much better reinforcer because it is so much stronger. As an illustration of this notion, consider another study by Premack that involved rats learning to press a lever to get a chance to run in an activity wheel, or to dispense a certain amount of drinking solution. Premack used 3 different concentrations of sucrose in the drinking solution (64%, 32%, and 16%), and 2 different levels of difficulty in running in the activity wheel (it was weighted with an 80 gram or an 18 gram weight). By observing rats in a free choice situation involving these 5 responses, he established the following preference ordering:
16% Drink > 32% Drink > 18 g Run > 64% Drink > 80 g Run
Lever pressing, of course, is considerably lower in the preference ordering. So, we run an experiment using 5 groups of rats in which one of the 5 responses above is used as the reinforcer for lever pressing.
Premack found, in accord with the theory, that the average number of lever responses per 10-minute session (following learning) depended on which response was used as the reinforcer. As the second (contingent) response increased in preference, so did performance of the first (learned) response. Rats allowed a brief run in a heavily weighted activity wheel pressed the lever about 20 times per session, but rats allowed a quick drink of the 16% sucrose concentration pressed the lever about 37 times per session.
Of course, the theory also applies to punishment. Specifically, Premack's claim is that punishment involves a situation in which a more preferred response is followed by the requirement that the animal engage in a less preferred response. (You should note that this is not what our players and eaters in Groups 1b and 2a did: They had the opportunity to engage in the less preferred response following the more preferred response, but they were not forced to do so.) Premack has done some studies illustrating that such a setup decreases the amount of time the animal does the more preferred response, consistent with a punishment-like interpretation. The same response may thus reinforce a weaker response but punish a stronger response.
A final experiment will illustrate one additional feature of this theory. Let us take two activities such as running in an activity wheel, and drinking. For 2 groups of rats, we will deprive the animals of activity wheel running for a day; for 2 other groups, we will deprive them of drinking for a day. That amount of deprivation ought to change their momentary preferences for drinking versus running. Let us call the drink-deprived rats drinkers, and the activity-wheel-deprived rats runners. Our experiment may then be set up as follows:
Group Preference Learning Task
running response followed by chance to drink
1b drinker drinking response followed by chance to run
2a runner running response followed by chance to drink
2b runner drinking response followed by chance to run
If you compare this design to the one used for the kids playing pinball or gumball machines, you will see it is formally identical. And as predicted, a congruity effect occurs whereby rats deprived of water will treat water as a reinforcer (and will thus increase their running), and rats deprived of running will treat running as their reinforcer. A rat's drinking will not be affected by whether drinking is followed by the opportunity to engage in running when it is the drinking that has been deprived, and not the running. Similarly, a rat's running will not be affected by drinking when running has been deprived. Thus, if we run 2 other control groups as follows:
Group Preference Pseudo Learning Task
2c runner running response
we should see no difference in amount of drinking between groups 1c and 1b, and no difference in amount of running between groups 2a and 2c. In the 'c' groups, we measure baseline drinking or running in free choice after deprivation, to see how much catching up they do. (That is why this task is referred to as pseudo learning. There's no real learning going on here, it's just a control so that we can assess changes from normal levels in our experimental groups.) In short, as was true with the earlier study using kids, only Groups 1a and 2b exhibit learning. They will increase their drinking or running relative to the control groups.
The message in this latter
study is that deprivation will momentarily increase the preference or strength
of the deprived response: Accordingly, an animal's preference ordering
may temporarily change because of deprivation.
In order to handle some of these situations, Timberlake and Allison have developed (separately and jointly) what is sometimes referred to as the response deprivation hypothesis (Allison's term). This hypothesis takes off from some of the studies Premack performed, in which weak responses can be turned into reinforcers of strong responses through deprivation. The relevant point to be made here, however, is that many -- if not most -- studies of instrumental learning have a component of deprivation. Establishing a contingency between two responses such that one has to be performed in order to get a chance to do the other often means that the second response is somewhat restricted or deprived. In the gumball-pinball experiment, for example, players who had to eat to play were being deprived of playing. Thus, in a modification of the Premack Principle, the response deprivation hypothesis generalized the principle to the claim that an animal will perform a non-deprived or less-deprived activity in order to get a chance to do a more-deprived activity.
Note the implicit claim of this principle: If the strong and the weak responses are not deprived, then the strong response ought not to reinforce the weak response. And in contrast (as in the activity wheel study of Premack), if the weak response is deprived, then it will reinforce the strong response. One of the advantages of such an approach, particularly considering the latter case, is that we need not try to assess the increase in strength of a weak response due to deprivation, in order to see whether it is now stronger than the normally more preferred response. Instead, we need simply assess which response is the more deprived.
Let us consider an experiment by Allison and Timberlake. They chose two levels of a saccharine solution such that their rats displayed a preference for the higher level. We will refer to these levels as dry and sweet for the sake of convenience. (Note, however, that a rat's preference for sweet solutions isn't strictly linear: As you can see from a previous study by Premack, it is possible to create solutions so intensely sweet that they are no longer preferred to less sweet solutions). In any case, consistent with the Premack Principle, Allison and Timberlake verified that their animals would learn a task involving sipping the dry liquid in order to sip the sweet liquid. So far, so good. But Allison and Timberlake then asked another question. Suppose we ask animals to sip the sweet to get a shot at the dry. Would the animals do it? On Premack's theory, they shouldn't. But in Allison and Timberlake's experiment, if the rats had to take ten sips of the sweet to get one sip of the dry, then they actually displayed learning: In apparent contradiction to Premack, a less preferred response reinforced a more preferred response.
Why did this happen? The basic idea behind Timberlake and Allison's approach is that animals in a free choice situation are in a state of equilibrium: They are able to engage in various activities at their preferred levels. These various levels are the bliss points. Or to put it slightly differently, the bliss points are the levels at which you are happiest. You have a bliss point for how much ice cream you would like to eat, for example. Being at the bliss point keeps you at equilibrium. But, being below or above the bliss point gives you a motive for returning to the bliss point. And if you consider the implications of being below or above the bliss point for ice cream, you will see how these states may lead to appetitive or aversive responding. Being below your bliss point gives you a reason for doing something that will get you more ice cream. But, being above (being forced to eat too much ice cream!) will give you a reason for doing something that lets you beg off having to eat more ice cream. At this point, for example, you might even offer to go into the kitchen and start washing dishes, so you don't have to eat more.
Both optimal stimulation theory and the response deprivation hypothesis involve homeostasis, whereby an animal or person in a state of disequilibrium attempts to return to the ideal level. In both, the same outcome may serve as a reinforcer when the organism is below its ideal level or bliss point, and as a punisher when it is above. Thus, the approaches ought to strike you as quite similar.
But how do we explain rats being willing to take 10 sips of sweet to get one sip of dry? How does this fit in with the notion of bliss points? Assume, for the moment, that in a free choice situation involving what to drink, the animal drinks the sweet stuff 60% of the time, and the dry stuff the other 40% of the time (these made up figures refer to proportion of drinking choice, not the animal's overall activities). By forcing it into a situation where it has to engage in drinking the sweet at 10 times the rate of the dry, we have effectively forced it into a level below its bliss point for the dry. Hence, we have given it some motivation for doing something that would return it to the dry bliss point.
If you have followed the reasoning so far, however, you will also realize that we have done something else in this experiment: We have potentially moved the animal far above its bliss point for the sweet, and thus given it some motivation to decrease drinking of the sweet liquid. And that demonstrates one of the most powerful ideas in Timberlake and Allison's approach. There will be a tradeoff between the response level and the outcome level, if both are out of equilibrium. In that case, an animal's behavior will tend to reflect an optimization strategy in which it engages in the two responses at a level that is closest to their joint bliss points. Thus, while we have been used to thinking about how learning is defined in terms of moderation of the response frequency, Timberlake and Allison provide us with a new perspective in which we also look at moderation of the outcome behavior.
We can illustrate this principle graphically. In Figure 4, we have drawn a graphical representation corresponding to our hypothetical rat's 60% preference for sweet. For the sake of argument, let us say that this translates into 60 sips of sweet per day in free choice, and 40 sips per day of dry. The orange starburst is the animal's joint bliss point for these two activities. In contrast, the two lines reflect two different contingencies that we might set up between sweet and dry. A one-to-one contingency is represented by the solid blue line, and a 10 sweet-to-1 dry contingency is represented by the dotted red line. In each case, the closest point on the line to the starburst is the best the animal can do by way of getting both responses back to their joint bliss point. And if you draw in the shortest line from the starburst to the dotted red line, you will see that it involves taking more than 60 sips of sweet, in agreement with Allison and Timberlake's finding above.
This work, along with other work on relative choice such as Herrnstein's Matching Law and optimal foraging theory, represents an interesting trend towards analyzing instrumental behavior in terms of economics. Economic theories also discuss the various tradeoffs involved in choices, and present mathematical models concerned with the relative costs and benefits of such choices, and how one might optimize the cost-benefit ratios. It is a very different and a very useful way of looking at the nature of learning and reinforcement.
Guthrie's writings were rather informal, and sometimes ambiguous. One of his students, Voeks, formalized them into a system of principles. Three of these principles are of particular interest to us. These are the principle of association, the principle of postremity, and the principle of response probability. You have already been introduced to the principle of association. Voeks added that a stimulus presented within half a second of the animal making a response would likely be associated with that response. (For comparison, review Guthrie's principle of conditioning presented in the previous chapter.)
There is a point about this principle that may not be obvious to you. According to both Guthrie and Voeks, when an association forms, it is a full strength. Thus, learning is not incremental in this approach. Instead, the learning ought to be sudden (occurring on a given trial). Such learning is referred to as all-or-none learning. As we will see in Chapter 7, all-or-none learning is also posited in hypothesis-testing approaches to discrimination learning. These approaches claim that animals adopt a hypothesis about what to pay attention to in a discrimination. They operate according to a win-stay lose-shift strategy: If the hypothesis works, keep it; if it doesn't, replace it with another. Thus, on this account, there ought to come a trial on which the animal selects the proper hypothesis. When that happens, the learning occurs, and is complete. Attentional hypothesis-testing theories and Guthrian theories agree on this sudden and complete nature of learning, although they claim radically different processes underlying the learning.
Figure 5 presents an example of what an individual learning curve might look like. Prior to learning, there ought to be some relatively random behavior, some of which may sometimes by chance involve doing the correct response, and some of which may not, so long as the association has not formed. When this random behavior is averaged over small sessions, the result ought to be a relatively flat baseline level of responding (but that might be slightly above 0 due to chance fluctuations). Then (in this case, in Session 7), the association forms, and thereafter the animal ought to have the association in full strength ,so long as no other association forms that competes with it: see the next principle below.
If you compare this acquisition curve to the incremental curve in Figure 1 or to Figures 3 and 4 in the chapter on classical conditioning (Chapter 2), you will see that the all-or-none curve appears to differ from the idealized and actual obtained diminishing returns curves. However, there are several reasons for why such a difference may be more apparent than real. One of these is that learning curves are typically averaged over a group of animals. Because the animals learn at different rates, they will form the association on different trials. Thus, a curve averaged over a group of animals includes both animals who have formed the association on a given trial, and animals who have not. Since the probability increases that more and more animals get the association as trials progress, the average goes up, giving the appearance of gradual, continuous, incremental learning. Hence, an important lesson to be learned from Guthrie's approach is that we ought to look at what individual animals do, and not just at what the group as a whole does: Averaged learning may not represent the type of learning found with individuals.
The principle of postremity represents one of the more lasting contributions of a Guthrian approach. This principle handles the fact that multiple responses may become associated with a single stimulus, since learning is constantly occurring. According to this principle, if there are incompatible responses linked to the same stimulus, then the more recent response is the one that will be expressed.
Guthrie laid particular stress on the notion of response competition. Learning is continually occurring, but new learning constantly overwrites or interferes with old learning. Hence, the learning may be difficult to see simply because response competition is likely to hide it from us. The notion of response competition has come to play a major role in theories of extinction, and in some of the theories of interference-based forgetting in humans. Since learning seems to occur when a reinforcer is present, Guthrie will argue that the role of the so-called reinforcer is really to minimize the possibility of response competition, making it more likely that we see the same response repeatedly. We will return to this shortly.
The third principle, the principle of response probability, states that the probability of a response in a given context is dependent on the proportion of stimuli in that context that are associated with the response. This, too, has had a major impact on later stimulus sampling theories of learning and memory. Stimulus sampling theories (Estes's theory of learning is a major example of these, although Bower's sampling theory of memory also deserves mention) claim that we sample some of the many stimuli (or their features) in our environment, so that learning and recall are best described as probabilistic, depending on the samples. But for now, concentrate on the fact that a response may become conditioned to a large number of stimuli. It won't become conditioned to all or most of them on the same trial, of course. But, if we can prevent competing responses from occurring, then over the course of a number of trials, more and more stimuli in the environment will become connected with the response, resulting in a greater and greater probability that the response will be excited. Thus, you now see a second reason why learning can appear to be gradual: The probability of exciting a response increases over trials because of an increase in the number of associations with the response, even though the critical association that the experimenter tends to be interested in may have formed at full strength on one of these trials.
So how does this relate to reinforcement? We, the experimenters, have defined a certain response we want to see, and whose increased probability of expression we will take to be evidence of learning. Let us assume this response is lever pressing. When the animal makes that response, we provide it with a novel stimulus that we have termed a reinforcer. Any response it makes following the lever press is thus, by the principle of association, likely to be associated with the novel stimulus, and not the previous stimulus complex that triggered the response. Thus, our animal will not overwrite the lever press response with a potentially competing response, because subsequent responses are not connected to the same stimulus as the lever press. Competing responses require connection to the same stimulus; the provision of a so-called reinforcer has prevented that.
What about punishment? We can extend the analysis to aversive stimulation by pointing out that such stimulation inevitably increases the animal's behavioral level, resulting in a number of responses. As most of these are likely to compete with the original response (running away is incompatible with a number of responses, for example), we stop seeing the original response. Moreover, higher levels of punishers result in greater behavioral expression, increasing the probability of acquiring a competing response early.
Why aren't punishers like reinforcers? That is, why don't punishers create novel stimuli to which a potentially competing response conditions? If you analyze what typically happens with punishers and reinforcers, the latter involve an external stimulus (food; drink; etc.) which results in appetitive or consummatory activity, but the former typically do not involve a change in the external stimuli. Shock administered through the floor grid of a Skinner box doesn't involve changing what the floor grid looks like. And also, aversive stimuli tend to trigger immediate reflexive defense mechanisms, as you know from our discussion in a previous chapter of Bolles and SSDRs. The presence of food or drink leads to appetitive behavior which, once consummation starts, may result in reflexive behaviors such as drooling. But the presence of punishment doesn't have this delay built in between the onset of the outcome and the reflexive behavior. In that sense, they ought not to act the same way.
Thus, given this overview of Guthrie's theory, let us look at a few predictions. An advantage of the theory, of course, is that it can potentially handle learning situations (like Tolman and Honzik) in which there is no reinforcer. Voeks set up some experiments to test the theory more explicitly, however. Based on the principle of response probability, she argued that an environment in which the stimuli were kept identical trial to trial would result in faster expression of a response than one in which the stimuli were allowed to vary. When stimuli vary, of course, the probability that most of them are associated with the response grows much more slowly, because of the constant introduction of new stimuli. Consistent with the theory, she found faster expression in the constant environment. (Note that use of the word expression: The results appear to suggest faster learning, but since learning is all-or-none, that is really not the case.) Moreover, when she examined the individual learning curves, they did appear to be all-or-none, rather than incremental.
Another study that fits in with Guthrie's notion of response competition was introduced in the last chapter: This is the study by Fowler and Miller. To remind you, they shocked rats on their rear paws after entering a goal box, or on their front paws while entering the goal box. The latter event should have caused backwards motion, which is incompatible with the forward motion of running to the goal. The animals shocked on their front paws extinguished more rapidly, as Guthrie would have predicted.
Similarly, Baum examined the extinction of fear in animals who were prevented from making a learned avoidance response to a danger situation. One group of animals was simply not allowed to avoid or escape, so that they were exposed to the conditioned danger signals, but without the accompanying shock. This type of extinction procedure (presence of the conditioned aversive CS, but absence of the aversive UCS) is referred to as flooding. Rats flooded in a chamber they can't escape from tend to become motionless. A second group was also flooded with danger signals, but they were forced to move about. Within a Guthrian framework, forcing animals to make responses should increase the probability of a competing response, resulting in quicker extinction of avoidance. That is what happened.
Subsequent work claimed that incremental learning was sometimes obtained at the individual level (and that incremental theories could be modified to predict something that looked very like all-or-none learning). In addition, problems with the single mechanism of response competition were found (although response competition will indeed prove to have an important role in extinction: see the next chapter). Thus, the Seward and Levy study on latent extinction, for example, is difficult to reconcile with the notion of response competition, since the latent extinction appears to occur in a different stimulus complex (the goal box) than the one conditioned to initiating the response of running.
And finally, some of the findings regarding the nature of reinforcement don't seem to fit Guthrie's theory elegantly. How are we to account for the fact that different concentrations of sucrose or saccharine appear to have different effects on the response, for example? Will a higher concentration of saccharine count as an internal stimulus that makes acquiring a competing response less likely? Or to take the Miller and Kessen study, how are we to account for the fact that different fluids pumped directly into the stomach have different effects? Are there noticeable stimulus properties by which these fluids differ when not tasted or ingested in the usual manner, so that they act as different internal stimuli?
As these caveats demonstrate,
Guthrie's theory itself did not survive unscathed. Nevertheless, the ideas
he expressed were original enough that they influenced or became part of
many later theories.
I have already mentioned that Bandura in some sense represents one of Tolman's intellectual heirs. He is perhaps most widely known for his work on whether children learn aggression from watching violent shows on television. In a famous study by Bandura, Ross, and Ross, several groups of kids in a nursery school were matched for level of aggression. One group saw an adult play aggressively with some toys. Among the toys was a "BoBo" doll: a weighted punching bag doll that could be knocked down, and would return to upright position to be knocked down again. The adult knocked the doll down repeatedly; threw it in the air several times; and hit it with a hammer. A second group saw a film of this behavior. A third group, the control group, saw none of the above. Then, the kids were sent in to a room with some really neat toys (much neater than the BoBo doll), played with them for a bit, and then were stopped from playing with them any further, in order to experimentally induce frustration.
We will see in the next chapter that Amsel's theory of extinction claims in part that frustration causes an increase in behavioral level, thus enabling the possibility of learning responses that compete with the original, learned response (as per Guthrie). Among the consequences of frustration that we often see is an increase in aggression. This is what Bandura et al. were interested in. When the kids had to play with the other toys (including the BoBo doll), would we see any evidence that they had learned how to play aggressively? In fact, all three groups displayed an increase in aggressive behavior, but the increase was much larger for the two groups who saw the adult's aggressive play. In fact, it was about double the level of the control group's aggressive play. Moreover, the two groups who saw the adult play with the BoBo doll displayed the same type of aggressive behavior (hitting it with a hammer; throwing it in the air) as the model, in contrast to the control group. Thus, the children had learned to emit certain behaviors by simply observing another person perform those acts.
In a follow-up study by Bandura, another three groups of children watched a movie of a clown playing in a room full of toys. In this film, the clown also spends a lot of time playing with the BoBo doll. Besides punching it down and throwing it up in the air, he straddles it once it is down, takes a hammer, hits it on the nose with the hammer, and utters comments such as "knock 'em, sock 'em, wowee!" (I am going on memory for the comments, by the way: I saw this tape a long time ago, and those are the ones -- perhaps falsely -- that stick in my mind.) The film is identical for the three groups of children up to this point: a lot of aggressive play of this sort. However, each group views a different ending.
One group may be referred to as the neutral group. The film fades out on the clown's play. A second group is what Bandura called the vicarious reinforcement group. They see some additional footage in which an adult authority figure enters the toy room and rewards the clown for the way in which he was playing. A third group, the vicarious punishment group, sees the adult punish the clown for how he played. And the question Bandura asked, of course, was what would happen when each of the children was placed in the toy room. How would they play with the BoBo doll?
In brief, the neutral group and the vicarious reinforcement group displayed the same behaviors as the clown, including hitting the BoBo doll with the hammer, and in some cases, uttering some of the same comments. In contrast, the vicarious punishment group played in a very different fashion. However, once the experimenter asked the children in this group to show him what the clown had done, they also performed the same aggressive responses. Thus, all groups had learned the responses by observing, but whether the responses were emitted or withheld depended on what happened to the clown.
These two studies exhibit several major features of Bandura's observational learning approach. First, observational learning can include imitative behavior: We can acquire responses by watching others perform them. Second, there is a learning-performance distinction in this approach, as there is in many others: What is learned need not be what is expressed: The outcomes associated with different behaviors will determine their probability of expression. Third, the child need not first perform the response for learning to occur, nor need the child directly experience a reinforcing or punishing outcome associated with the response. Observing these events in others is sufficient: Observing them in others provides the child with the cognitive knowledge or information about the likely consequences of an outcome.
Bandura's study reminds me of a visit I had with my sister and her kids years ago, when they were 3, 5, and 7. As an uncle, I had a reputation with the niblings (my term for nieces and nephews; it goes along with siblings) for doing magic tricks. One day, as a joke, I did the following 'magic trick:' I said, "Watch closely!" I then lowered a towel to cover my legs, slipped one foot out of its shoe, raised that leg behind me, and then slowly raised the towel up to slightly below the knee, to show two shoes and one leg. "Ta da!" I said. I then lowered the towel, and re-raised it to show I had gotten my leg back. The older kids got the joke, but the three-year-old simply stared, apparently uncomprehendingly, at this somewhat 'sophisticated' performance. Sure enough, several hours later, at dinner, she trooped in with a towel and proceeded to give the same performance.
Imitative behavior as an aspect of observational learning actually has a very venerable tradition. Both Thorndike and Watson asked whether this type of behavior would be found in animals, and answered the question in the negative. Thorndike, for example, allowed cats to watch other cats learn to escape from his puzzleboxes, but found no evidence of faster learning in the observers. Thus, imitation through observational learning was essentially ignored for many years. Dollard and Miller brought the possibility back into play by arguing that humans and animals could be reinforced for imitating others, and that this would essentially be no different than reinforcing them for making any other sort of a response. But note that this approach required the reinforcement-based assumption that a certain type of response (imitation) would be immediately followed by a consequence (reinforcement or punishment). We now know that animals may indeed learn from observing others (recall the experiments by Menzel and by Kohn and Dennis, for example), and that Dollard and Miller were simply wrong in claiming that such observation relied upon the presence of an immediate reinforcer. In any case, Tolman claimed that learning could occur through simple observation, and Bandura seconds that notion.
More formally, Bandura notes that observational learning is not simply imitation. Rather, it involves the acquisition of information about consequences. Thus, the vicarious punishment group in the study above failed to imitate because of what they had learned. But there is also more to it than this. In addition to the possibility of acquiring new responses or behaviors by seeing others perform them, and in addition to the inhibition or disinhibition of those responses depending on knowledge of likely outcomes, there is also a type of priming effect or facilitation whereby seeing someone do something that is already in your behavioral repertoire makes it more likely that you will engage in the same response. Regarding inhibition and disinhibition, a negative outcome associated with an observed response will tend to inhibit that response (as in the vicarious punishment group), whereas a positive or non-negative outcome will tend to disinhibit the response (as in the vicarious reinforcement group). The notion of disinhibition here in part reflects Bandura's concern with learning aggression: Seeing someone else act aggressively may tend to disinhibit our aggressive behaviors.
Although Bandura does use phrases such as vicarious reinforcement and vicarious punishment, you should not think that punishment or reinforcement are necessary for learning in his theory. They indeed constitute an additional element of learning, but do so by providing information. Learning occurs regardless of vicarious punishment or reinforcement. So, the theory is a non-reinforcement theory, and moreover, a non-associational, informational approach (a representation-level theory). The presence of outcomes associated with observed behaviors simply provides the observer with information about the contingencies between a response and an outcome. Evaluating those contingencies and the relative desirability of the outcome is the additional process that determines performance.
Finally, we will end our discussion of Bandura by pointing out that he establishes four requirements for observational learning to occur (especially to the extent that it results in imitation). First, the observer must be paying attention to the relevant behaviors of the model, and to the outcomes. Second, these must be successfully stored in the observer's memory system. In humans, they may be stored as images, or they may be stored as verbal descriptions. Third, the observer must be capable of producing the response. Observing a gifted concern pianist play Mozart will not help a non-musician perform the same piece the next day. Behavior that is too complicated and that relies on too high a level of skill of the many component parts will likely not be acquired or successfully imitated. Rather, we imitate things we are potentially capable of doing; and in so doing, we use our memories to compare what we do with what we saw earlier. Hence, observational learning often involves a component of refining an imitative response over time through practice and comparison with a remembered performance observed perhaps much earlier. And fourth, there has to be some motivation to perform the behavior. Some aspect of that motivation is given in part, of course, by whether the behavior is associated with vicarious reinforcement or vicarious punishment.
We have discussed just instrumental
behavior, but Bandura extends the notion of observational learning to classical
conditioning, as well. In any case, since Bandura's is an admittedly expectancy-based
approach, let us consider some other work on expectancy.
We will start with some studies
involving appetitive outcomes.
S1 ---> R1
S2 ---> R2
What would happen, Trapold asked, if we used different reinforcers for these two situations, instead of the same reinforcer, as was often done up until that point? (Use of the same reinforcer is referred to as the common outcomes procedure.) From a non-expectancy, traditional approach, two reinforcers of otherwise equal value ought to have effects identical to use of the same reinforcer, as the reinforcer in a traditional associationist approach simply moderates the S-R link (the strength of the habit). But, from an expectancy-based approach in which an outcome is an integral part of what an animal learns about a situation, Trapold reasoned that having different outcomes ought to have an effect of a certain sort. Before describing what that effect might be, let us first schematically diagram the expectancies. Using a system like that introduced in discussing Tolman's theory in the previous chapter, the situation in which the same reinforcer is used (the common outcomes procedure) involves the following two expectancies (which we will call E1 and E2):
E1: S1 R1 ---> O1
E2: S2 R2 ---> O1
But now let us systematically use different reinforcers (the differential outcomes procedure). In this latter case, the expectancies would be:
E1: S1 R1 ---> O1
E2: S2 R2 ---> O2
Note that if the outcome is represented along with the stimulus-response link in the animal's memory, then the differential outcomes procedure ought to result in faster learning: The expectancies are more different, and thus less likely to be confused with one another. And that is what Trapold found. Contrary to traditional claims that a reinforcement perhaps operates on (but does not become a part of) the association that constitutes learning, there was some clear evidence here that the reinforcer was also represented as part of the learned complex. Such a claim, of course, comports perfectly with Bandura's and Tolman's approaches.
A number of studies have now found this effect of reinforcement differentiation. It occurs not only when the reinforcers are physically different, but as Carlson and Wielkiewicz show in their studies, when the reinforcers differ only in amount, or in how long they are delayed following a response. Very precise information about the nature of reinforcement thus appears to be acquired in the course of learning.
Another study in which a similar finding occurs involves the area of acquired stimulus equivalence. Here, a bit like sensory preconditioning, there will be an attempt to equate two stimuli to see whether they act as functional equivalents in setting the occasion for a response. As this is rather abstract, let's look at a specific study by Peterson.
Peterson looked at discrimination learning in pigeons. These animals were faced with three keys. The two keys on the outside differed in that one had a horizontal line over it, whereas the other had a vertical line over it. The center key first lit up in a white color. That was the signal for the pigeon to peck it. The center key subsequently changed color to either red or green. If it changed to red, then the pigeon had to peck the vertical-line key for an outcome (e.g., food). But if it changed to green, then the pigeon had to peck the other key (the horizontal line) for an outcome. In one group, different outcomes were used (food versus a tone), but a mixed outcomes procedure was used with another group. (In the mixed outcomes procedure, either reinforcer can occur after either response; which reinforcer actually appears is determined randomly.) There were actually more groups, but we will concentrate on these. Thus, schematically, the expectancies for the differential outcomes group should have been:
E1: Sred Rvertical ---> Ofood
E2: Sgreen Rhorizontal ---> Otone
Next, Peterson established a training phase in which a peck at the white key would change it either to a projected circle, or a projected triangle, following which the animal would receive the food or the tone. In this phase, the pigeon did not peck at one of the two side keys (the horizontal or vertical keys); they remained unlit. Again, some pigeons received a differential outcomes procedure (in which the circle was always followed by food, and the triangle by the tone), and others received a mixed outcomes procedure (in which the pairing was random). Thus, looking at all these possibilities, we have the following schematic representation of learning for our various training combinations:
Group Color Task 2nd Task
E1: Sred Rvertical ---> Ofood
E3: Scircle ---> Ofood
E2: Sgreen Rhorizontal ---> Otone E4: Striangle ---> Otone
E1: Sred Rvertical ---> Ofood
E3: Scircle ---> Ofood/Otone
E2: Sgreen Rhorizontal ---> Otone E4: Striangle ---> Ofood/Otone
E1: Sred Rvertical ---> Ofood/Otone
E3: Scircle ---> Ofood
E2: Sgreen Rhorizontal ---> Ofood/Otone E4: Striangle ---> Otone
E1: Sred Rvertical ---> Ofood/Otone
E3: Scircle ---> Ofood/Otone
E2: Sgreen Rhorizontal ---> Ofood/Otone E4: Striangle ---> Ofood/Otone
To help you digest this somewhat complicated experiment, note that Groups 1 and 2 had reinforcement differentiation on the first task, but Groups 3 and 4 did not. Note also that the expectancies for the second task were different in Groups 1 and 3, but not 2 and 4. And of course, the question that Peterson asked was what would happen in these four groups when pecking the white key would change it to a circle or a triangle, and the two side keys would come on, requiring a further peck for the outcome?
If you look at Group 1, you will see that both the circle and the red light had a single food outcome in their expectancies, whereas both the triangle and the green light had a single tone outcome. Peterson found that this procedure resulted in Group 1 showing near perfect first-session learning in this new task. They had apparently learned that the circle and red light were equivalent, so that they needed relatively little additional training to peck the vertical key in the presence of the circle. Similarly, they needed little additional training to peck the horizontal key when the triangle came on. In other words, they were able to coordinate their expectancies to derive the following new expectancies:
E5: Scircle Rvertical ---> Ofood
E6: Striangle Rhorizontal ---> Otone
The other groups required
more extensive training to acquire these expectancies. Or to put this in
a nutshell, coordinating two stimuli with the same outcome led to the
pigeon making a response in the presence of one stimulus that it had earlier
made in the presence of another. What is novel about this study on
acquired stimulus equivalence is that the equivalence was based on the
common outcome, thus again providing evidence that information about the
outcome constitutes part of what is learned and stored.
We'll first dispense with what the two factors are. These are classical conditioning and operant conditioning. According to Mowrer, a good behaviorist believing in a positivist approach would have a major problem with avoidance learning. This problem, the problem of foresight, appears to implicate an organism being aware of future consequences, and taking action to avoid those consequences. The problem for a good behaviorist, of course, is that foresight involves prediction -- cognitive states referring to knowledge rather than reactive associations that get triggered by the presence of the proper stimuli. So the relevant issue for a positivist behaviorist is how to explain avoidance learning without having to rely on foresight.
It turns out that there is a clever way of doing this. Mower pointed out that avoidance learning situations typically include a component of classical conditioning. Specifically, in the initial trials when the animal has not yet acquired the avoidance response, the various stimulus cues that are present become associated with the aversive stimulus. Thus, through classical conditioning, the general environment associated with the shock (or whatever aversive stimulus is being used) becomes a secondary punisher. For example, a light presented 5 seconds before the shock comes on serves as the CS for the UCS of the shock, and as such, takes on some of the negative qualities of the shock. Among these may be fear (recall Miller's study of acquired fear). According to Mowrer, so-called avoidance learning is then due to acquisition of an instrumental response that reduces the animal's acquired fear. Put another way, the animal learns a response that gets it away from the nasty conditioned stimulus.
On this account, the second factor (instrumental conditioning) involves use of negative reinforcement: Escaping from the newly conditioned secondary punisher acts as a reinforcer, so the animal learns to escape the CS. So, on this account, avoidance learning is really escape learning: But, it is escape from the CS, rather than the UCS. And as such, it need involve no appeal to foresight. We need not think the animal capable of predicting the oncoming shock; we need only think the animal desirous of getting away from some situation involving stimulus cues that increase the animal's fear.
One more point, while we are on the subject of Mowrer. Mowrer's general approach arose from Hull's theory. So, some aspects of that theory such as drive level were assumed to apply here, as well. Specifically, the performance of the avoidance response was assumed to be dependent on the presence of a non-zero fear drive: So long as the fear-of-the-CS drive was non-negative, excitation would also be non-negative.
Other theories have adopted differing accounts of avoidance learning. Bolles's SSDR theory, for example, claims that much avoidance involves animals performing species-specific, preprogrammed responses in the presence of danger signals, and so ought not to count as learning at all. Rather, the animal acquires information about danger signals that will trigger these SSDRs, and safety signals in which 'normal' learning may occur. Several studies by Blanchard and Blanchard, for example, show that rats will freeze in situations where running cannot get them out of danger, but will run in other situations, if that response is an option. Certainly, as we have mentioned earlier, avoidance responses that are similar to SSDRs may be acquired quite easily and rapidly, in contrast to other responses that conflict with SSDRs.
Yet other theories adopt a contingency-based or expectancy-based approach. Thus, Seligman and Johnston have claimed that avoidance learning requires animals to conjoin two expectancies into a single contingency. One expectancy is that performing a response of a certain sort results in no shock. The other is that not performing that response results in obtaining a shock. Thus, by stumbling on the response that will successfully escape (and later avoid) the shock, the animal comes to build these expectancies, and coordinate them. Once they have been coordinated, then the animal is once more in control of its environment, insofar as experiencing an unpleasant outcome is concerned.
The cognitive expectancy theory of avoidance learning, in particular, makes some very different claims about what an animal experiences during training than does the two-factor theory. In particular, so long as the animal has failed to coordinate the two expectancies into a contingency, the animal ought to experience fear because of the expectancy that tells it shock is coming in the environment in which it now finds itself. But once that coordination occurs, the fear ought to extinguish. The shock is now predictable on the basis of whether the animal performs the response, rather than just the stimulus cues present before the shock. Thus, those cues become occasion setters signaling when the animal ought to execute the avoidance response. In contrast, because of the close tie-in of Mowrer's theory to the Hullian notion of drive level, animals in the latter are assumed to be fearful so long as they are still performing an avoidance response.
A number of studies have examined these differing claims. In one famous study, Black monitored the heart rates of dogs during extinction of avoidance learning. The Mowrer theory would seem to predict a close correlation between heart rate and the process of extinction: As heart rate returns to less elevated, normal levels, so should the avoidance response become weaker. Such an approach assumes, of course, that elevated heart rates provide an adequate measure of how fearful an animal is. But contrary to what Mowrer would predict (and consistent with Seligman and Johnston), heart rate returned to normal much, much sooner than avoidance extinguished. In fact, avoidance took about five times longer to cease.
In another study, Kamin, Brimer, and Black attempted to measure fear by use of the suppression ratio (see the chapters on classical conditioning). If the stimulus cues present during avoidance training become conditioned to fear as Mowrer would predict, then they ought to interfere with on-going instrumental appetitive responding in a very different context (i.e., a version of the summation test we used in classical conditioning to determine whether true inhibition occurred). Kamin et al. found that the CS signaling the approaching shock did successfully suppress in a summation test when animals had undergone a small number of avoidance learning trials, but had no suppressive effects when animals had undergone a large number of trials. Such a finding, of course, is exactly what Seligman and Johnston's theory predicts.
Finally, consider an experiment by Herrnstein and Hineline using rats. In their study, animals received a number of shocks at fairly quick intervals. If the animal pressed a bar, then the next shock would occur after a longer interval of time, thus effectively reducing the total number of shocks the rat was receiving. After that delayed shock, however, the shocks would revert to the rapid rate unless the rat continued pressing the bar. Avoidance in this case involved making a series of responses to keep on a reduced rate of punishment.
Seligman and Johnston, of course, can easily handle this in terms of learning a contingency involving bar pressing and rate of shock. But a problem this study poses for Mowrer is the following: What is the CS in this experiment that enables classical conditioning of fear (Mowrer's first factor)? And how does the animal get away from that CS to experience negative reinforcement when it bar presses (Mowrer's second factor)?
In contrast to Mowrer's theory,
Seligman and Johnston also include the various outcomes (shock; a place
of safety) in their account of what an animal learns. Predicting consequences
is of central importance in their account of avoidance learning. They thus
embrace foresight, rather than view it as a problem. Foresight (the
ability to predict) is one of the critical characteristics of an
expectancy theory. Evaluating that knowledge and determining a course of
action contingent upon it is another critical characteristic. The
work of Bandura, Tolman, and Seligman and Johnston encompasses both of
Let us briefly return to the Peterson study to see how a central associative model might predict the learned stimulus equivalence result Peterson obtained. To remind you, two stimuli (vertical and circle, for example) were both associated with the same outcome. Previously, pigeons had learned to peck at a lit vertical key when a red key was lit to get some grain. On other trials, pecking at a white key caused a circle to light up briefly (but not the vertical key), followed by the grain reinforcement. Later, the pigeons pecked the lit vertical key when the circle lit up. (Similar results occurred with a tone: You may wish to review the training for Group 1 above). Using Group 1's training, we can now proceed to identify the associative links these animals acquired as being something like the following:
(1) Sred ------
(2) Sgreen ------
In each of the above, the top associative links represent the learning from the first phase, and the bottom link represents the learning from the second phase. Because a common outcome was used in both, these two phases may be connected through that common outcome in memory. Thus, presence of a circle should cause activation to travel through its links, and at least some of that activation should also reach the response. In this way, an associational model including outcome links can explain the apparently 'rapid' learning of the Group 1 pigeons in the third phase of the experiment.
But in addition, there is another feature of classical associationist models that might be used to distinguish between expectancy and habit-based approaches to behavior. As Hull's term habit implies, associations in traditional stimulus-response theories suggest fairly rigid, inflexible behavior. In contrast, theories such as Bandura's appear to rely on a more dynamic, flexible cognitive system in which evaluations based on current knowledge guide behavior. Is there any way to distinguish among these?
The answer is that there might be. In one of the major current theories of human learning, ACT*, John Anderson discusses two different types of memory systems. One is Declarative Memory, and the other is Procedural Memory. Declarative Memory is the type of memory humans have for information that can be declared. It includes factual knowledge such as "Halloween comes on October 31," and perhaps mistaken beliefs such as "Aliens regularly visit Roswell, New Mexico." Such contents are often referred to as know-that knowledge, because you are aware of a certain content, and can express it. Thus, if you can say that "I know that P" where P is any proposition or statement, then you are expressing declarative knowledge. Procedural Memory, on the other hand, includes what Anderson terms production rules: If-Then rules that execute an action (the then part) whenever a certain condition (the if part) occurs in working memory. These rules are sometimes called know-how knowledge: You know how to ride a bike, but expressing that knowledge declaratively will prove enormously difficult.
As you might gather from this very brief introduction to Anderson's work, production rules are relatively inflexible, and operate automatically. They thus seem good candidates for the types of automatic actions captured by the term habit. Some external or internal stimulus configurations active in working memory automatically cause a production to fire. Contrary to stimulus-response theories, the productions need not involve overt responses. But, productions may be regarded as condition-action associations that are triggered strictly by activating the condition part of the association (much as a stimulus-response association is supposed to be triggered by presenting the stimulus).
Declarative Memory, in contrast, includes all sorts of associations amongst different facts, events, and beliefs. It is more flexible in that various processes can operate on these associations to yield behavior. The associations in some sense serve as a database or repository of knowledge, and one of the questions Anderson and his colleagues ask is what processes operate over that database under which circumstances?
The basic types of structures Anderson posits in Declarative Memory include images, lists, and propositions. Images, of course, ought to remind you of Tolman's cognitive maps. But propositions are also of interest to us here. These are like elementary ideas that involve a topic about which something may be asserted. In a very real sense, an expectancy could be expressed as a proposition, although it need not be. Thus, an animal that expects to get fed when it presses a bar in the presence of a tone could have this knowledge represented declaratively as a proposition, or procedurally as a condition-action production.
The issue I am raising here, of course, is whether animals (besides our own species) have declarative knowledge. It is possible that they have just procedural knowledge. But, it seems to me that many of the claims and theories we have looked at regarding expectancies imply a declarative component: The animal knows-that, rather than knows-how. And the question this raises is whether we can come up with a proper test to determine what type of knowledge animals really acquire.
As of this writing, one of the graduate students in the University of Southwestern Louisiana's Center for Advanced Computer Studies, Chris Prince, is collecting data relevant to this question. Prince's rats are learning to navigate a radial maze according to certain principles. Some rats have to learn, for example, the following rule:
When there is food in the south arm, then there will be food in the east arm.
If this is stored as a production rule, then it should be non-reversible: Being in the south arm is the condition that will activate the production, but being in the east arm will not. If the rule is stored as a declarative rule, however, then any part of the rule could be activated, and activation should then travel to and bring up the other parts of the rule. So, the question Prince's data is relevant to is whether rats who have learned this rule will run to the south arm when they are first placed in the east arm. Will they be able to reverse their paths? (Note the similarity to the cognitive map studies of Tolman and Honzik.)
As of this writing, the answer appears to be "no." Many additional studies will be required to verify this result and its explanation, of course. But in any case, the results we've looked at in this and the last chapter now strongly suggest that we need to evaluate learning in terms of an animal's memory systems, and the type of information likely to be stored in them.
Learning need not depend
on reinforcement or punishment, but when those events occur, they become
part of the memory, and thus subsequently affect behavior. Whether such
knowledge in animals can be characterized as declarative is perhaps
one of the most interesting and important questions learning theorists
can now explore.
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1. Chapter © 1998 by Claude G. Cech