Mark R. Dixon, PhDRuth Anne Rehfeldt, PhD
Rehabilitation Institute, Southern Illinois University
The purpose of this chapter is to summarize principles that explain the operation of direct contingencies on behavior, in the form of habituation, operant conditioning, and classical conditioning. We will also explore their impact on the processes of stimulus control and generalization and will briefly mention habituation and the extension of direct contingencies into issues of language and cognition.
Direct contingencies are ancient processes of behavioral regulation. Habituation is present even in slime molds (Boisseau, Vogel, & Dussutour, 2016), nonneural single-cell organisms that evolved about 1.7 billion years ago. Contingency learning—operant and classical conditioning—appears to be about 0.5 billion years old since virtually all complex species that have evolved since the Cambrian Period show these processes, while earlier life-forms do not (Ginsburg & Jablonka, 2010).
Despite the age of these regulatory processes, clinically relevant behavior is often the result, at least in part, of direct-acting contingencies found within the environment. Such conditions either elicit or evoke behavior from the subject of interest and encompass the core principles of classical and operant conditioning. Although operant and classical conditioning principles are typically described in isolation, these learning processes overlap and interact to a degree (Rescorla & Solomon, 1967). In order to gain a basic understanding of them, however, it is most effective to first describe them separately.
One of the oldest and most basic forms of learning (Pierce & Cheney, 2013) is habituation (and its less studied opposite, sensitization): when an unconditioned stimulus elicits an unconditioned response, and that stimulus is presented over and over, the response may decline in magnitude to the point that it no longer occurs at all. For example, Bradley, Lang, and Cuthbert (1993) recorded heart rate, electrodermal, and facial corrugator-muscle responses as measures of the startle reflex, finding that the startle responses decreased dramatically with repeated presentations of stimuli that induced them. Researchers often use habituation paradigms to study the physiological bases of different neurological disorders. For instance, Penders and Delwaide (1971) found that patients with Parkinson’s disease showed no habituation of the eye-blink response with electromyography relative to individuals without the disease, but they did display normal habituation responses when treated with either L-dopa or amantadine medication.
Human and nonhuman organisms display many types of reflexive behaviors, many of which are unlearned and may help the organism survive. For example, placing food in one’s mouth elicits salivation, and a puff of air into one’s eye may elicit a blink. Because such behavior-environment relations are unlearned and of innate origin, the eliciting stimuli are referred to as unconditioned stimuli, while the response is described as an unconditioned response. Classical conditioning occurs when a once-neutral stimulus (NS) is paired temporally with an unconditioned stimulus (US) to produce the unconditioned response (UR). Over repeated pairings the US becomes unnecessary and the NS begins to produce an elicited response on its own. This new “automatic” response to a once-neutral stimulus is termed a conditioned response (CR). An example that is commonly provided to illustrate this basic form of classical conditioning consists of a dog that initially has no response to the sound of a bell, yet when the bell (NS) is paired with food (US), which produces a salivation response (UR), the dog salivates at the sound of the bell. After the food (US) is no longer provided with the sound of the bell, the animal still salivates (CR) at the sound of the bell (CS).
In classical conditioning, the eliciting functions of one stimulus transfer to another stimulus due to their contiguity, or pairing. When the neutral stimulus has acquired the eliciting functions of the unconditioned stimulus, it’s referred to as a conditioned stimulus, and the response is referred to as a conditioned response. For example, certain poisonous foods may induce nausea as an automatic, reflexive response. A neutral stimulus, such as an odor or sound that has no such effect on behavior, may similarly come to elicit that nausea response after repeated pairings of the unconditioned and neutral stimuli. This “taste aversion” effect can produce havoc with cancer patients, who need to avoid eating unfamiliar food before chemotherapy in order to avoid conditioned nausea with that food. In a more positive example, the smell of coffee alone wakes up coffee drinkers in the morning (Domjan, 2013). Coffee is a stimulant drug, and its taste and smell precede its stimulant effects. The temporal contiguity of stimuli is critical for conditioning to occur; in other words, the two stimuli must be presented close in time to one another in order to establish the conditioned response.
Importantly, in second-order conditioning, additional previously neutral stimuli can acquire eliciting functions based on their temporal contiguity with other conditioned stimuli. This means that an organism doesn’t always need to have repeated contact with an unconditioned stimulus in order for conditioned responses to new stimuli to develop. Second-order conditioning helps explain how, in the clinical environment, classical conditioning can lead to a client reacting to a stimulus that is only distally related to directly impactful events.
Most general forms of classical conditioning appear to require close proximity in stimulus pairings (generally less than a second), although with taste aversion the delay between the unconditioned stimulus and conditioned stimulus can be as long as a day (Bureš, Bermúdez-Rattoni, & Yamamoto, 1998). Though typically the conditioned stimulus and unconditioned stimulus need to be paired close together in time, they can occur in different temporal arrangements. In forward conditioning, the previously described paradigm, the conditioned stimulus is presented first, and the unconditioned stimulus is presented while the conditioned stimulus remains present. In backward conditioning, the conditioned stimulus is presented after the unconditioned stimulus has been presented. There has long been a debate as to whether backward conditioning can actually occur, in part due to Pavlov’s skepticism about it, but the body of evidence suggests that it does (Spetch, Wilkie, & Pinel, 1981).
Trace conditioning involves presenting the unconditioned stimulus and then, after it stops, the conditioned stimulus (conditioning is said to occur because the unconditioned stimulus left a “trace” in the organism’s nervous system or memory). Simultaneous conditioning involves presenting two stimuli at the same time.
Researchers have proposed that respondent conditioning is the learning process underlying the development of any number of conditioned fear and phobic responses. For example, John B. Watson, the founder of behaviorism, conducted the famous “Little Albert” experiment. In this experiment, a young child was shown a small, furry white animal, the display of which was paired with the sound of a steel bar being struck, which caused a startle response in the child. In a process known as respondent generalization, stimuli that physically resembled the small, furry animal came to elicit the same startle and emotional response. Öhman and Mineka (2001) suggest that the acquisition of such conditioned fear responses has an evolutionary basis, noting that there are typically cues or warning stimuli that signal to an organism that some pending disaster may threaten its survival. The acquisition of such conditioned fear responses, the authors elaborate, may allow an organism to escape or avoid stimuli that could be harmful. These researchers, as well as others, have focused their work on the neural circuitry involved in the acquisition of responses, implicating, for example, the role of the amygdala in classical conditioning.
Behavior therapists have long appealed to respondent conditioning as an explanation for the genesis of anxiety disorders (e.g., Wolpe & Rowan, 1988). In recent years, research of this kind has focused especially on the neural mechanisms involved in fear conditioning. It appears, however, that much of the fear conditioning in humans is based on symbolic and cognitive generalization, not just the formal similarities between aversive experiences and the current situation (Dymond, Dunsmoor, Vervliet, Roche, & Hermans, 2015). We will touch on this issue at the end of this chapter, and the point is expanded on in chapter 7.
Most nonreflexive forms of learning fall into the operant category of conditioning, a class of response topographies that operate in a similar way upon the environment to produce a consequence. Consider the many different ways one can pass through a doorway: a person can walk, dance, run, roll, summersault, or be dragged by another through the entrance. All these response forms, or topographies, operate in a similar fashion upon the environment: they get the person through the doorway. A focus on responses that have common effects, or classes, has proved useful to researchers and therapists in their understanding of how various conditioning processes strengthen or weaken behavior over time.
The three-term contingency (Skinner, 1953; Sidman, 2009) is the unit of analysis most researchers use to investigate operant conditioning. This contingency of conditioning, often denoted as A-B-C, specifies the contextual conditions that surround and involve the behavior of interest being studied. The A represents the “antecedent,” or precursors, that sets the occasion for a behavior; the B represents the “behavior” engaged in by the subject of interest; and the C indicates the “consequences” that follow the behavior (additional terms can be added to this three-term formulation, as we note later). This three-term contingency provides the analyst with information about why an individual exhibits a behavior, as well as how to produce similar behavior in the future.
Given particular antecedent conditions, when the behavior is emitted, the consequence that follows may alter the probability of similar behaviors occurring in the future. If a class of behaviors of interest is followed by a consequence that increases the probability of those behaviors happening in the future, reinforcement is said to have occurred (Skinner, 1969); if the consequence that follows suppresses the probability of the behaviors happening again in the future, then punishment is said to have occurred (Dinsmoor, 1998).
A real-world example may help illustrate these processes (see also chapters 11–14). Consider a child engaging in a tantrum. In isolation, emotional displays provide us little insight into the why of the tantrum or the conditions that may increase or decrease the probability of a tantrum in the future. However, once we examine the antecedents and consequences surrounding this behavior, we can obtain needed information that may help us alter it. Suppose we learn that tantrums happen whenever the child’s father makes reasonable task demands (e.g., “It is time to set the table. Remember, you have to do your chores to get your allowance.”) but not her mother. We have information needed to deduce the probability of the behavior but still lack information on why it’s happening. When examining the consequences of such tantrums, suppose we discover that the father withdraws the task request and goes to the living room to watch TV as soon as a tantrum occurs, but the mother stays with the request and records the tantrum in order to implement the allowance contingency. Together the antecedents and consequences provide us a complete account of why the tantrums occur and the conditions under which they increase in probability. The three-term contingency is complete.
The basic notions of antecedents and consequences become exponentially intricate rather quickly. For example, it matters whether consequences are delayed (Madden, Begotka, Raiff, & Kastern, 2003); are not highly preferred by the subject (DeLeon & Iwata, 1996); stay identical over too long of a period of time (Podlesnik & Shahan, 2009); or require behavior that was too effortful, demanding, or complex (Heyman & Monaghan, 1987). Similar issues exist in antecedent stimulus control (see chapter 12).
One of the most commonly explored modifications to the general process of reinforcement is its delivery cycle. Often termed a “schedule of reinforcement” (Skinner, 1969), this delivery of a consequence can have an important impact on the probability of a behavior occurring. Schedules of reinforcement abound, with perhaps the most common variants using ratio and interval parameters. When a ratio schedule is in place, only a certain number of responses will yield the programmed consequence. The amount can be fixed, as in after every five responses (a fixed ratio–5, or FR-5 schedule) there is a consequence, or it can be variable, as in on average there will be a consequence following every five responses (a variable ratio–5, or VR-5 schedule). When an interval schedule is in place, only the first response will produce the consequence after a period of time has elapsed, and like the ratio schedule, it too can contain a fixed (FI) or variable (VI) period of time that must elapse. Seeing Old Faithful erupt is an example of an FI schedule: no amount of looking will hasten or delay it. Seeing an unoccupied taxi cab to hail is a VI schedule: regular looking will not make the cab arrive, but it could come by at any moment. Logical deductions and empirical data allow us to conclude how these various schedules can produce different behavior patterns. A ratio schedule will yield consequences much quicker if the response is emitted more frequently, and thus it tends to encourage higher rates of responding than an interval schedule.
A great deal of research and analysis has been performed and predictions made regarding these basic schedules of reinforcement (e.g., Zuriff, 1970), and this work has laid a foundation for the clinical application of contingency processes (see chapter 11). One important discovery within the domain of schedules of reinforcement and punishment is that all complex species tend to show very similar patterns of responding under identical schedule contingencies, at least until the arrival of verbal behavior (Lowe & Horne, 1985).
Behavior that is controlled by positive consequence appears to be different from behavior controlled by an aversive consequence being removed following the emission of a response (what is termed escape conditioning), or when a consequence is postponed or prevented by responding (avoidance conditioning; see Dinsmoor, 1977, for more on this distinction); this is a key area of concern for applied workers in clinical psychology. Avoidance learning can be especially troublesome in applied contexts, because it prevents further contact with the environment, which can allow avoidance to continue long after its reasons for being have disappeared.
A clinical example of avoidance conditioning is the avoidance of physiological conditions that typically accompany fear. Classical conditioning may have had a role in establishing these physiological conditions, but operant contingencies can lead to active escape or avoidance, reinforcing the overt behavior. There is a long history of such “two factor” reasoning (e.g., Dinsmoor, 1954) in behavioral and cognitive therapies.
Negative reinforcement procedures involve the removal or prevention of a stimulus, whereas positive reinforcement procedures involve the presentation of a stimulus. The terms “positive” and “negative” should be thought of more in their additive or subtractive senses than in their good or bad evaluative senses. There are still theoretical arguments about the fundamental nature of this distinction, but as an applied matter it is an important one both practically and ethically. For example, the deliberate utilization of aversive stimuli as part of a negative reinforcement procedure may introduce ethical considerations, especially when procedures based on a more positive consequence may yield very similar outcomes (Bailey & Burch, 2013).
One of the most crucial factors that should not be overlooked when implementing behavior change procedures using direct contingencies, regardless of the schedule or type of reinforcement, is the passage of time. Time between the emission of the behavior and the delivery of the consequence has a radical impact on the future probability of behavior emission (Ainslie & Herrnstein, 1981). To produce optimal effects, delays should be kept to a minimum. As time increases from behavior emission to consequence delivery, the ability to influence future behavior weakens (Mazur, 2000). If a child stops a tantrum at 1 p.m. and special privileges are delivered at 3 p.m., there are many other behaviors that may have occurred during this two-hour interval of time. As such, the delayed consequence may inadvertently strengthen the behavior occurring at 2:59 p.m., whatever that may be. Many cultural practices are based on the idea that delayed consequence linked to temporally distant prior behavior will be effective. Examples include a yearly bonus at work or report card grades. These delayed consequences are more likely to be operational, if at all, through verbal rules than through direct contingency control.
The perversely weak effect of delayed consequences can be seen in the many clinically significant self-control problems people face. Behavior surrounding obesity, for example, is difficult to address because of the long delay between eating or proper exercise and the actual consequences of weight gain or weight loss.
Although delayed consequences are inherently weak for controlling behavior, therapists can improve on their effectiveness through a variety of contingency manipulation techniques (see chapter 14). First, the therapist can initially make the delayed consequences available immediately and then gradually delay them over time, resulting in much higher rates of sustained behavior (Logue & Peña-Correal, 1984). Second, therapists can provide clients with a concurrent activity to engage in during a delay to reinforce delivery, leading to more-sustained behavior than when no activities are present (Grosch & Neuringer, 1981). People who are asked to speak about the eventual delivery of delayed consequences perform better at tasks requiring a delayed consequence compared with those who do not make such verbalizations (Binder, Dixon, & Ghezzi, 2000). Delay to consequence delivery is an inherent challenge when attempting to increase or decrease a behavior of interest. When clinical situations necessitate delays, therapists should take concrete steps to improve the effectiveness of delayed consequences.
When consequences that previously maintained a behavior are no longer provided, the principle of extinction is considered to be in place. Extinction is the elimination of the previously delivered consequence in the A-B-C contingency, and it has a somewhat predictable effect on behavior over time. Eliminating positive consequences will eventually suppress a response until it’s terminated completely, and the elimination of aversive consequences will reinstate the response. A variety of other effects are commonly seen in extinction: previously reinforced and then extinguished behavior is likely to show resurgence (Shahan & Sweeney, 2011); the rates of a particular behavior are likely to temporarily increase in an “extinction burst” (Lerman & Iwata, 1995); and aggression or other potentially problematic behaviors, such as self-harm, may occur (Lerman, Iwata, & Wallace, 1999). In part, to reduce these negative side effects, when attempting to eliminate an undesired behavior with extinction, typically therapists concurrently reinforce an alternative behavior that is incompatible or simply more appropriate (for a review, see Petscher, Rey, & Bailey, 2009). Sometimes therapists pair extinction with time-based reinforcement schedules that deliver noncontingent consequences—irrespective of alternative behavior—in an attempt to eliminate an undesirable contingency without also instigating the emotional or aggressive results of a sudden decrease in reinforcement (Lalli, Casey, & Kates, 1997). Over the past few decades, these combinations have considerably increased the ability of applied psychologists to use extinction to promote more socially appropriate behaviors in clinical settings.
Some basic forms of social learning occur by just observing others. Observational learning exists across the animal kingdom: in very young children, nonhuman animals, and fully developed adult humans (Zentall, 1996). Consider this example from animal cognition research: A food-deprived target subject is allowed to observe a rival model obtain food consequences when it engages in a behavior for which the target subject has not been trained. Following a few observations, when the antecedents are presented to the target animal it presents accurate emissions of the behavior. Researchers have observed learning of this kind in a wide variety of animals (Fiorito & Scotto, 1992; McKinley & Young, 2003), suggesting that many complex organisms come into the world evolutionarily prepared to learn from the actions, successes, and failures of others.
Other learning processes then build upon basic observational learning. For example, normal human neonates will imitate a small number of specific behaviors, such as smiling or tongue thrusting (Meltzoff & Moore, 1977), but later they will use these gestures to regulate others socially (Nagy & Molnar, 2004), leading to a self-sustaining learning process and the acquisition of imitation as a generalized class of behavior (Poulson, Kymissis, Reeve, Andreatos, & Reeve, 1991).
The social nature of human beings makes observational learning especially important in applied programs. It can be a force for good or ill. For example, research has shown that group therapy in the area of youth addiction has iatrogenic effects due to social learning within the group (Dishion, McCord, & Poulin, 1999). Properly managed, however, learning in a social context can have profound and even lifelong effects. The “good behavior game,” in which classes compete to show good behavior, provides an example of these effects. Even brief exposure to this game in elementary school affects violence, drug use, and other outcomes over many years (Embry, 2002).
As practitioners develop optimal responding using principles of direct contingency learning, they should place emphasis on refining the precision with which actions are elicited or evoked. For example, clients may fail to respond because they do not detect antecedent conditions that signal the availability of reinforcement. Conversely, they may respond even though stimuli indicating that reinforcement could occur are not present, and the predictable but unexpected subsequent absence of reinforcement may weaken the operant response over time. Similar issues can occur with classical conditioning processes when conditioned stimuli are weak in salience or vague across a variety of stimulus dimensions (volume, tone, color, temperature), such that conditioned responses are not elicited.
Not only is it important for people to learn when reinforcement will be available and what pattern of responding will produce it, it is also important to learn the contextual conditions under which responding will be reinforced (see chapter 12). A discriminative stimulus, or Sd, is a stimulus event that predicts reinforcement is likely if a behavior occurs; an event that predicts that reinforcement is not likely even if a behavior occurs is called S-delta, or SΔ. It is often clinically important to ensure that responding occurs only in some contexts but not others; when responding is regulated in that way it is said to be under stimulus control. Generally, alternate contingencies are used to train such discriminations. A multiple schedule (MULT for short) consists of a reinforcement-dense schedule for specific action when an Sd is present, and a reinforcement-lean schedule (or even extinction) when an SΔ is present. Differential reinforcement is the difference in access to preferred consequences, and it is the foundation for the development of stimulus control.
By simply bringing needed actions under good stimulus control, people can sometimes make appropriate behavior more likely. For example, Fisher, Greer, Fuhrman, and Querim (2015) used a multiple schedule that alternated a reinforcement schedule with extinction (EXT) to teach individuals with severe, challenging behaviors simple requests. The schedule resulted in rapid stimulus control over requests and decreases in challenging behaviors as the environment itself became more predictable to the individuals.
Discrimination training of this kind can be used in another way; for example, it can be used to help an existing consequence become more effective. In one study, a MULT VI-VI schedule was changed to a MULT VI-EXT schedule. As a result, responding during the unchanged component of the schedule increased substantially, a phenomenon known as behavioral contrast (Pierce & Cheney, 2013).
In everyday behavior, much of discrimination learning involves learning to do the right thing in the right time and place. For example, children learn that certain jokes may be reinforced in the presence of peers but not adults, or that quiet, still behavior is expected in the school classroom but loud behavior may be differentially reinforced on the school playground. Osborne, Rudrud, and Zezoney (1990) used a creative example of discrimination teaching to enhance the ability of collegiate baseball players to hit curve balls. In an alternating fashion, in some periods balls were unmarked, while in others the seams of the balls were marked with ¼-inch or ⅛-inch orange stripes. Players hit a greater percentage of the balls that included the visually discriminative stimuli. Discrimination learning is also involved when individuals are taught functional communication skills. The Picture Exchange Communication System, for example, is a widely used alternative and augmentative communication system for individuals with severe language impairments due to autism or other developmental disabilities (e.g., Bondy & Frost, 2001). When an individual selects the picture of a preferred item in an array of pictures and exchanges it with a caregiver, the individual is granted access to that preferred item, differentially reinforcing the picture presentation with the real item.
Challenging behaviors among people with developmental or psychiatric disabilities often occur in the presence of particular stimuli, and knowledge of stimulus control processes can help undermine the detrimental regulation of behavior. Touchette, MacDonald, and Langer (1985) used a tool known as a scatter plot to help identify temporal periods throughout the day during which a severe challenging behavior never occurs or occurs with near certainty. This tool is especially appropriate for severe problem behavior, for which there may be only two practically important rates: zero and unacceptable. If a practitioner finds that challenging behavior occurs most frequently when certain work tasks or chores are presented to an individual, or when particular staff members are present, these stimulus situations can be targeted for change.
Many academic tasks involve discrimination learning. For example, teaching a child to receptively identify letters is an example of a discrimination task: a child’s selection of the letter b is occasioned by the presentation of the letter b. Advanced reading is also considered a form of discrimination learning, as reading aloud comes under the discriminative control of the print stimuli and eventually recedes to the covert level (i.e., not reading aloud). Many individuals with autism spectrum disorder and other developmental disabilities display a phenomenon known as stimulus overselectivity, which occurs when restricted properties of stimuli control responding (Ploog, 2010). In the case of the letter-labeling task mentioned before, stimulus overselectivity occurs when an individual inaccurately identifies every letter with a closed loop as the letter b. Dube and colleagues (2010) suggest that when a reinforcement contingency is in place for the emission of an observing response to all of the relevant features of a stimulus (i.e., not only the closed loop but the stem on the letter), difficulties with overselectivity can be remedied. In other words, if attending to all of the important features of a stimulus is reinforced, all of the relevant properties of a stimulus are likely to occasion correct responses.
While discrimination learning is regarded as an example of a three-term contingency, a fourth term, a conditional stimulus, may come to control the three-term contingency. For example, Catania (1998) notes that an individual stating “apple” in the presence of an apple is only differentially reinforced if another person has asked “What is that?” while pointing at the apple. In this scenario, the question (“What is that?”) is regarded as a conditional stimulus. The apple serves as a discriminative stimulus, meaning that labeling it “apple” in its presence will only be reinforced on the condition that the question “What is that?” is asked.
Some practitioners view stimulus generalization as the opposite process of discrimination. In stimulus generalization, responding occurs in the presence of stimuli that have not been directly reinforced but are physically similar (e.g., color, shape, and so on) to an original conditioned or discriminative stimulus. A generalization gradient shows the relationship between the probability of a response occurring and the value of a stimulus along that physical dimension. For example, if a child learns to say “it’s blue” in the presence of a specific wavelength of light, the probability of that response will steadily decrease when the child is presented with lights of more and more dissimilar wavelengths.
Practitioners typically view stimulus generalization as a desirable intervention outcome in applied settings. They often implement behavioral interventions in very structured, tightly controlled settings, only to find that intervention effects may not generalize to novel but important contexts. Stokes and Baer (1977) proposed a technology for promoting stimulus generalization, including the following strategies: teaching with sufficient examples, teaching loosely, using indiscriminable stimuli between the teaching and generalization settings, programming common stimuli between the teaching and generalization environments, and sequentially modifying the teaching environment until it more closely resembles generalization settings. Teaching with multiple examples involves using different stimuli so that an individual is likely to respond correctly in the presence of stimuli that may be dissimilar from the one used during instruction. For example, a child may be likely to correctly label all dogs as “dog” if he or she has been taught to label many varieties, sizes, breeds, and colors of dogs as “dog.”
Response generalization involves the spread of the effects of reinforcement to other responses not correlated with reinforcement. For example, if the target behavior of smiling at peers is differentially reinforced, making eye contact with and initiating conversation with peers may also begin to increase in probability even though these actions were not directly reinforced. When this occurs, the behaviors are said to compose a response class or functional class (Catania, 1998).
The implementation of the basic principles of learning in applied settings needs to be tempered by the known interaction between them and human symbolic processes. Basic behavioral and cognitive approaches to the study of human cognition will be explored in the next chapter, but it is worth noting that when language abilities emerge in human beings, more than direct contingencies and simple forms of observational learning regulate behavior. For example, we have all been told to not touch a hot stove, but not all of us have had a history of being burned by a stove. Our ability to avoid the stove when it’s hot seems to be under a different sort of stimulus control than the stove itself. Cognitive perspectives have long claimed this to be the case, but in the context of this chapter (and the theme of this volume) it seems worth the effort to briefly note that the behavioral wings of the behavioral and cognitive therapy traditions have studied this phenomenon for several decades in an attempt to understand it.
More than thirty years ago behavioral psychologists concluded that, at times, verbal stimuli in the form of instructions, commands, or rules stated by an individual or another person come to control responding in ways that alter the operation of direct contingencies (Catania, Matthews, & Shimoff, 1982). Describing contingencies (Catania, Shimoff, & Matthews, 1989), or motivating behavior, verbally (Schlinger & Blakely, 1987) can alter how direct contingencies operate. A number of laboratory studies have shown that when experimenter-provided rules conflict with programmed contingencies, the responding of normal adult participants tends to remain under instructional control rather than adapt to changing contingencies, even when doing so has a cost (e.g., Catania, Lowe, & Horne, 1990); and when adaption to the environment does take place, that effect too can be due to the presence of verbal rules, which can alter sensitivity to subsequent environmental changes (e.g., Hayes, Brownstein, Haas, & Greenway, 1986).
The increasing dominance of symbolic processes over the processes of direct contingency learning has a developmental trajectory. For example, on similar reinforcement schedules, young, preverbal children show patterns of responding that mirror those of nonhumans, but as verbal repertoires develop, the patterns of reinforcement schedule performance in older children and adults differ from those commonly seen in textbooks (Bentall & Lowe, 1987). In particular, the literature on derived relational responding (Hayes, Barnes-Holmes, & Roche, 2001) has provided behavioral psychologists with a way to forge common ground with the traditional concerns of cognitive therapists and theorists, and it has done so in ways that appear to be empowering practitioners to develop new methods to facilitate flexible cognitive repertoires (see Rehfeldt & Barnes-Holmes, 2009; Rehfeldt & Root, 2005; Rosales & Rehfeldt, 2007).
A study by Dougher, Hamilton, Fink, and Harrington (2007) provides a basic example of how symbolic processes interact with operant and classical conditioning. One group of subjects learned that three arbitrary events (squiggles on a screen) were related comparatively, such that X < Y < Z. Another group learned nothing about how X, Y, and Z were related. Both groups were then shocked repeatedly in the presence of Y until that graphic form elicited anxiety as measured by a galvanic skin response. Participants in both groups were not much aroused by the stimulus X, and in the group that had not been trained how to relate X, Y, and Z, participants showed little arousal to Z. In the relationally trained group, however, participants were more aroused by Z than they were by Y. This response cannot be stimulus generalization, because the stimuli were arbitrary. Instead the symbolic relation of “Z is bigger than Y” created more arousal to a stimulus that had never been paired with shock than one that had repeatedly been paired.
These same basic findings extend to self-rules as well. For example, Taylor and O’Reilly (1997) and Faloon and Rehfeldt (2008) found that the stating of overt self-rules by participants with developmental disabilities facilitated the acquisition of a chained task, and the participants maintained their performance when they were taught to state such self-rules at the covert level. When participants in both studies were required to recite random numbers backward, blocking the emission of self-rules, performance declined, thus showing a functional relationship between the emission of overt and covert self-rules and the performing of a task.
In these cases, self-verbalization had a facilitative effect, but in many clinical situations the opposite is true. For example, a person having an anxiety attack in one situation may respond even more powerfully to another situation merely because it is thought to be “bigger” regardless of its actual physical properties, such as in the study by Dougher and colleagues (2007). This is a problem empirical clinicians often find themselves trying to solve with clients, as will be explored in section 3 of this volume. However, such effects do not eliminate the relevance of the principles of direct contingency learning; rather, they draw the field into a more process-oriented focus in which older and more recently acquired processes interact to produce behavior.
Core behavioral processes provide practitioners with precise principles to generate treatment options for individuals with behavioral, emotional, or physical concerns. Regardless of the appearance of the behavior, treatment needs to be individualized based on the processes impacting it. Selecting an inaccurate cause of behavior will most typically prevent the client from experiencing positive change. The principles of direct contingency learning are among the best established in all of psychology and have the great benefit of orienting the practitioner toward contextual events that can be changed. Empirical clinicians need to rest their actions on core processes that have the most proven scientific merit, because people have placed their lives in our hands.
Ainslie, G., & Herrnstein, R. J. (1981). Preference reversal and delayed reinforcement. Animal Learning and Behavior, 9(4), 476–482.
Bailey, J. S., & Burch, M. R. (2013). Ethics for behavior analysts (2nd expanded ed.). Abingdon, UK: Taylor and Francis.
Bentall, R. P., & Lowe, C. F. (1987). The role of verbal behavior in human learning: III. Instructional effects in children. Journal of the Experimental Analysis of Behavior, 47(2), 177–190.
Binder, L. M., Dixon, M. R., & Ghezzi, P. M. (2000). A procedure to teach self-control to children with attention deficit hyperactivity disorder. Journal of Applied Behavior Analysis, 33(2), 233–237.
Boisseau, R. P., Vogel, D., & Dussutour, A. (2016). Habituation in non-neural organisms: Evidence from slime moulds. Proceedings of the Royal Society B, 283(1829), n.p.
Bondy, A. S., & Frost, L. A. (2001). The Picture Exchange Communication System. Behavior Modification, 25(5), 725–744.
Bradley, M. M., Lang, P. J., & Cuthbert, B. N. (1993). Emotion, novelty, and the startle reflex: Habituation in humans. Behavioral Neuroscience, 107(6), 970–980.
Bureš, J., Bermúdez-Rattoni, F., & Yamamoto, T. (1998). Conditioned taste aversion: Memory of a special kind. Oxford: Oxford University Press.
Catania, A. C. (1998). Learning (4th ed.). Upper Saddle River, NJ: Prentice Hall.
Catania, A. C., Lowe, C. F., & Horne, P. (1990). Nonverbal behavior correlated with the shaped verbal behavior of children. Analysis of Verbal Behavior, 8, 43–55.
Catania, A. C., Matthews, B. A., & Shimoff, E. (1982). Instructed versus shaped human verbal behavior: Interactions with nonverbal responding. Journal of the Experimental Analysis of Behavior, 38(3), 233–248.
Catania, A. C., Shimoff, E., & Matthews, B. A. (1989). An experimental analysis of rule-governed behavior. In S. C. Hayes (Ed.), Rule-governed behavior: Cognition, contingencies, and instructional control (pp. 119–150). New York: Springer.
DeLeon, I. G., & Iwata, B. A. (1996). Evaluation of a multiple-stimulus presentation format for assessing reinforcer preferences. Journal of Applied Behavior Analysis, 29(4), 519–533.
Dinsmoor, J. A. (1954). Punishment: I. The avoidance hypothesis. Psychological Review, 61(1), 34–46.
Dinsmoor, J. A. (1977). Escape, avoidance, punishment: Where do we stand? Journal of the Experimental Analysis of Behavior, 28(1), 83–95.
Dinsmoor, J. A. (1998). Punishment. In W. T. O’Donohue (Ed.), Learning and behavior therapy (pp. 188–204). Needham Heights, MA: Allyn and Bacon
Dishion, T. J., McCord, J., & Poulin, F. (1999). When interventions harm: Peer groups and problem behavior. American Psychologist, 54(9), 755–764.
Domjan, M. (2013). Pavlovian conditioning. In A. L. C. Runehov & L. Oviedo (Eds.), Encyclopedia of sciences and religions (pp. 1608–1608). Netherlands: Springer.
Dougher, M. J., Hamilton, D. A., Fink, B. C., & Harrington, J. (2007). Transformation of the discriminative and eliciting functions of generalized relational stimuli. Journal of the Experimental Analysis of Behavior, 88(2), 179–197.
Dube, W. V., Dickson, C. A., Balsamo, L. M., O’Donnell, K. L., Tomanari, G. Y., Farren, K. M., et al. (2010). Observing behavior and atypically restricted stimulus control. Journal of the Experimental Analysis of Behavior, 94(3), 297–313.
Dymond, S., Dunsmoor, J. E., Vervliet, B., Roche, B., & Hermans, D. (2015). Fear generalization in humans: Systematic review and implications for anxiety disorder research. Behavior Therapy, 46(5), 561–582.
Embry, D. D. (2002). The good behavior game: A best practice candidate as a universal behavioral vaccine. Clinical Child and Family Psychology Review, 5(4), 273–297.
Faloon, B. J., & Rehfeldt, R. A. (2008). The role of overt and covert self-rules in establishing a daily living skill in adults with mild developmental disabilities. Journal of Applied Behavior Analysis, 41(3), 393–404.
Fiorito, G., & Scotto, P. (1992). Observational learning in Octopus vulgaris. Science, 256(5056), 545–547.
Fisher, W. W., Greer, B. D., Fuhrman, A. M., & Querim, A. C. (2015). Using multiple schedules during functional communication training to promote rapid transfer of treatment effects. Journal of Applied Behavior Analysis, 48(4), 713–733.
Ginsburg, S., & Jablonka, E. (2010). The evolution of associative learning: A factor in the Cambrian explosion. Journal of Theoretical Biology, 266(1), 11–20.
Grosch, J., & Neuringer, A. (1981). Self-control in pigeons under the Mischel paradigm. Journal of the Experimental Analysis of Behavior, 35(1), 3–21.
Hayes, S. C., Barnes-Holmes, D., & Roche, B. (Eds.). (2001). Relational frame theory: A post-Skinnerian account of human language and cognition. New York: Kluwer Academic/Plenum Publishers.
Hayes, S. C., Brownstein, A. J., Haas, J. R., & Greenway, D. E. (1986). Instructions, multiple schedules, and extinction: Distinguishing rule-governed from schedule-controlled behavior. Journal of the Experimental Analysis of Behavior, 46(2), 137–147.
Heyman, G. M., & Monaghan, M. M. (1987). Effects of changes in response requirement and deprivation on the parameters of the matching law equation: New data and review. Journal of Experimental Psychology: Animal Behavior Processes, 13(4), 384–394.
Lalli, J. S., Casey, S. D., & Kates, K. (1997). Noncontingent reinforcement as treatment for severe problem behavior: Some procedural variations. Journal of Applied Behavior Analysis, 30(1), 127–137.
Lerman, D. C., & Iwata, B. A. (1995). Prevalence of the extinction burst and its attenuation during treatment. Journal of Applied Behavior Analysis, 28(1), 93–94.
Lerman, D. C., Iwata, B. A., & Wallace, M. D. (1999). Side effects of extinction: Prevalence of bursting and aggression during the treatment of self-injurious behavior. Journal of Applied Behavior Analysis, 32(1), 1–8.
Logue, A. W., & Peña-Correal, T. E. (1984). Responding during reinforcement delay in a self-control paradigm. Journal of the Experimental Analysis of Behavior, 41(3), 267–277.
Lowe, C. F., & Horne, P. J. (1985). On the generality of behavioural principles: Human choice and the matching law. In C. F. Lowe (Ed.), Behaviour analysis and contemporary psychology (pp. 97–115). London: Lawrence Erlbaum.
Madden, G. J., Begotka, A. M., Raiff, B. R., & Kastern, L. L. (2003). Delay discounting of real and hypothetical rewards. Experimental and Clinical Psychopharmacology, 11(2), 139–145.
Mazur, J. E. (2000). Tradeoffs among delay, rate, and amount of reinforcement. Behavioural Processes, 49(1), 1–10.
McKinley, S., & Young, R. J. (2003). The efficacy of the model-rival method when compared with operant conditioning for training domestic dogs to perform a retrieval-selection task. Applied Animal Behaviour Science, 81(4), 357–365.
Meltzoff, A. N., & Moore, M. K. (1977). Imitation of facial and manual gestures by human neonates. Science, 198(4312), 75–78.
Nagy, E., & Molnar, P. (2004). Homo imitans or homo provocans? Human imprinting model of neonatal imitation. Infant Behavior and Development, 27(1), 54–63.
Öhman, A., & Mineka, S. (2001). Fears, phobias, and preparedness: Toward an evolved module of fear and fear learning. Psychological Review, 108(3), 483–522.
Osborne, K., Rudrud, E., & Zezoney, F. (1990). Improved curveball hitting through the enhancement of visual cues. Journal of Applied Behavior Analysis, 23(3), 371–377.
Penders, C. A., & Delwaide, P. J. (1971). Blink reflex studies in patients with Parkinsonism before and during therapy. Journal of Neurology, Neurosurgery and Psychiatry, 34(6), 674–678.
Petscher, E. S., Rey, C., & Bailey, J. S. (2009). A review of empirical support for differential reinforcement of alternative behavior. Research in Developmental Disabilities, 30(3), 409–425.
Pierce, W. D., & Cheney, C. D. (2013). Behavior analysis and learning (5th ed.). Oxon, UK: Psychology Press.
Ploog, B. O. (2010). Stimulus overselectivity four decades later: A review of the literature and its implications for current research in autism spectrum disorder. Journal of Autism and Developmental Disorders, 40(11), 1332–1349.
Podlesnik, C. A., & Shahan, T. A. (2009). Behavioral momentum and relapse of extinguished operant responding. Learning and Behavior, 37(4), 357–364.
Poulson, C. L., Kymissis, E., Reeve, K. F., Andreatos, M., & Reeve, L. (1991). Generalized vocal imitation in infants. Journal of Experimental Child Psychology, 51(2), 267–279.
Rehfeldt, R. A., & Barnes-Holmes, Y. (2009). Derived relational responding: Applications for learners with autism and other developmental disabilities: A progressive guide to change. Oakland, CA: New Harbinger Publications.
Rehfeldt, R. A., & Root, S. L. (2005). Establishing derived requesting skills in adults with severe developmental disabilities. Journal of Applied Behavior Analysis, 38(1), 101–105.
Rescorla, R. A., & Solomon, R. L. (1967). Two-process learning theory: Relationships between Pavlovian conditioning and instrumental learning. Psychological Review, 74(3), 151–182.
Rosales, R. R., & Rehfeldt, R. A. (2007). Contriving transitive conditioned establishing operations to establish derived manding skills in adults with severe developmental disabilities. Journal of Applied Behavior Analysis, 40(1), 105–121.
Schlinger, H., & Blakely, E. (1987). Function-altering effects of contingency-specifying stimuli. Behavior Analyst, 10(1), 41–45.
Shahan, T. A., & Sweeney, M. M. (2011). A model of resurgence based on behavioral momentum theory. Journal of the Experimental Analysis of Behavior, 95(1), 91–108.
Sidman, M. (2009). The measurement of behavioral development. In N. A. Krasnegor, D. B. Gray, & T. Thompson (Eds.), Advances in behavioral pharmacology (vol. 5, pp. 43–52). Abingdon, UK: Routledge.
Skinner, B. F. (1953). Science and human behavior. New York: Free Press.
Skinner, B. F. (1969). Contingencies of reinforcement: A theoretical analysis. Englewood Cliffs, NJ: Prentice Hall.
Spetch, M. L., Wilkie, D. M., & Pinel, J. P. J. (1981). Backward conditioning: A reevaluation of the empirical evidence. Psychological Bulletin, 89(1), 163–175.
Stokes, T. F., & Baer, D. M. (1977). An implicit technology of generalization. Journal of Applied Behavior Analysis, 10(2), 349–367.
Taylor, I., & O’Reilly, M. F. (1997). Toward a functional analysis of private verbal self-regulation. Journal of Applied Behavior Analysis, 30(1), 43–58.
Touchette, P. E., MacDonald, R. F., & Langer, S. N. (1985). A scatter plot for identifying stimulus control of problem behavior. Journal of Applied Behavior Analysis, 18(4), 343–351.
Wolpe, J., & Rowan, V. C. (1988). Panic disorder: A product of classical conditioning. Behaviour Research and Therapy, 26(6), 441–450.
Zentall, T. R. (1996). An analysis of imitative learning in animals. In C. M. Heyes & B. G. Galef Jr. (Eds.), Social learning in animals: The roots of culture (pp. 221–243). San Diego: Academic Press.
Zuriff, G. E. (1970). A comparison of variable-ratio and variable-interval schedules of reinforcement. Journal of the Experimental Analysis of Behavior, 13(3), 369–374.