13. when behaviors are being maintained, reinforcement schedules move from:

Rehearsal and Performance Feedback

Provide examples of various schedules and have the trainees determine which schedule is in effect with each of the examples. Provide feedback regarding which answer is correct and why. Give the trainees feedback on their score individually. Continue providing instruction, assigning readings, and presenting more examples until the trainees achieve the previously established criterion with this activity. Appendix E includes examples for you to use within your instruction.

Ethics Related to Schedules of Reinforcement

Emphasize with your trainees that we have an ethical obligation to thin schedules of reinforcement to the natural reinforcers available in the environment. It is also critical for us to refrain from using reinforcers that may be harmful for our clients even when they may be effective (Bailey & Burch, 2016, p. 135).

6.1 Schedules of reinforcement

The schedule of reinforcement used to maintain the baseline has received limited study. More studies use fixed ratio (FR) schedules of food reinforcement than any other schedule. Overton (1979) showed that fixed ratio (FR) and differential reinforcement of low rate schedules engendered stronger stimulus control than a tandem variable-interval fixed-ratio schedule, which in turn engendered stronger stimulus control than a variable interval (VI) schedule. The simplicity of the FR schedule combined with its capacity to support strong stimulus control by many different drugs has ensured its widespread use. It is clearly satisfactory for many purposes. The main limitation of using simple FR schedules is in the nature of the dependent variable that can be employed. In essence, the ease with which subjects can distinguish between reinforced test sessions and different contingencies during generalisation tests means that test data can be obtained only from very short periods of responding. These data are usually quantal in nature, whereas some workers prefer to work with a graded dependent variable. However, a graded dependent variable obtained under such conditions can only be calculated on a single FR run of responses that is typically emitted as a unit that is unlikely to be divided between alternatives. Furthermore, when drugs used for training decrease rates of responding, differential densities of reinforcement for drug- and vehicle-appropriate responding occur in direct proportion to response rates, with a potential for introducing unwanted biases in response tendencies that may influence dose-response determinations. Such biases have been studied systematically by Koek and Slangen (1982) and by McMillan and Wenger (1984).

Simple VI schedules are not subject to the same problems but the relatively weak stimulus control is a serious disadvantage. Tandem VI-FR schedules seem to combine the advantages of both simple FR and VI schedules by supporting strong stimulus control, by minimising bias due to differential reinforcement of responses occurring at different rates and by allowing the use of both quantal and graded measures of stimulus control. On these grounds, tandem schedules appear optimal for many studies. They are used less widely used than FR schedules because the inherent simplicity of the latter has a large intrinsic appeal. However, despite their additional complexity, tandem schedules have a minimal impact on the overall training time and demands of drug discrimination procedures generally and they should, therefore, be considered seriously when projects are planned. The impact of schedules on the quantitative or quantal nature of stimulus control was also discussed above in the section on the dependent variables in drug discrimination.

Second-Order Schedules of Reinforcement

Second-order schedules of reinforcement involve training animals to work for a previously neutral stimulus that ultimately predicts drug availability. These schedules maintain high rates of responding (e.g., up to thousands of responses per session in monkeys) and can motivate the animal to emit extended sequences of behavior before any drug is administered. Such extended schedules minimize potentially disruptive, nonspecific, acute drug and treatment effects on response rates. High response rates are maintained even for doses that decrease rates of responding on a regular fixed-ratio schedule, indicating that performance on the second-order schedule is unaffected by the acute effects of the drug that would otherwise disrupt operant responding. The maintenance of performance under second-order schedules with drug-paired stimuli appears to be analogous to the maintenance and reinstatement of drug seeking behavior in humans who are also presented with drug-paired stimuli.

Second-order Schedules of Reinforcement

In second-order schedules of reinforcement, animals can be trained to work for a previously neutral stimulus that ultimately predicts drug availability (Katz and Goldberg, 1991). In such a paradigm, completion of the first component or unit of the schedule usually results in the presentation of a brief stimulus (often a light), and completion of the overall schedule produces the stimulus and the primary reinforcer. For example, each nth response produces a brief visual stimulus, and the first fixed-ratio completed after a fixed-interval produces the visual stimulus and the primary reinforcer (a drug injection). A second-order schedule can be repeated several times during a session, which would result in multiple drug administrations. Responses occurring before any drug administration can be used as measures of the conditioned reinforcing properties of drugs.

Studies in nonhuman primates (e.g., macaque monkeys, baboons) and dogs indicated that subjects will readily and reliably perform long behavioral sequences in second-order schedules, which are several minutes to hours in duration, in order to receive a variety of drugs, including psychomotor stimulants, opiates, nicotine and barbiturates, even when the drug is administered only at the end of the schedule (Goldberg, 1973; 86 et al., 1975; Kelleher and Goldberg, 1977; Goldberg and Gardner, 1981). Rats also have been successfully trained to work on second-order schedules for drugs (12 et al., 1998) (Fig. 2.21). Performance in second-order schedules is maintained by injections (intravenous, intramuscular, or oral) of a variety of drugs that are abused by humans, with the animals exhibiting similar behavioral patterns in second-order schedules that terminate in drug injections (87 et al., 1976; Goldberg and Gardner, 1981).

Alteration of the stimuli that maintain a second-order schedule can alter acquisition, maintenance, resistance to extinction, and recovery from extinction in second-order schedules (Goldberg and Gardner, 1981). For example, replacement of drug-paired stimuli with nondrug-paired stimuli decreases response rates (Spear and Katz, 1991). These findings suggest that drug-paired stimuli function as conditioned reinforcers, and as such they are essential in maintaining performance in these long schedules. This maintenance of performance in second-order schedules with drug-paired stimuli appears to be analogous to the maintenance and reinstatement of drug-seeking behavior in humans with the presentation of drug-paired stimuli (150 et al., 1986; 36 et al., 1988).

Second-order schedules maintain high rates of responding (e.g., thousands of responses per session in monkeys) and extended sequences of behavior before any drug administration. Thus, potentially disruptive, nonspecific, acute drug and treatment effects on response rates are minimized. High response rates are maintained even for doses that decrease rates during a session on a regular fixed-ratio schedule, indicating that performance on the second-order schedule is unaffected by those acute effects of the drug which disrupt operant responding (Katz and Goldberg, 1987).

7 Behavioral context, behavioral and pharmacological history

Factors other than those associated with the schedule of reinforcement and baseline response rates may also influence the effects of pharmacological challenge on behavior maintained by schedules of positive reinforcement. Behavioral context may play an important role, albeit one which has not yet received very much attention in behavioral pharmacology. The importance of behavioral context in determining the behavioral effects of drug administration has mostly been examined in experiments in which behavior was maintained by negative reinforcement (for review see Barrett and Witkin, 1986). Evidence to support the notion that behavioral context may play a role in determining the effects of drugs on behavior maintained by positive reinforcement may be found in the work of Branch and his colleagues (Hoffman, et al., 1987; Schama and Branch, 1989; Branch, 1990). When cocaine's rate altering effects were examined on three different FR schedules presented in a multiple schedule arrangement, Hoffman et al. (1987) showed that the behavior of pigeons maintained by schedules with a larger FR requirement was more easily disrupted than behavior maintained by schedules with a smaller ratio requirement. Similar observations can be derived from other data presented by Branch (1990) who trained pigeons to respond on differently valued RR schedules. When differently valued FI and RI schedules were presented in a multiple schedule context acute cocaine administration decreased response rates, but the decrease in response rate after a given dose of cocaine did not differ as a function of schedule parameter (Schama and Branch, 1989; Branch, 1990).

That pharmacological history may be an important determinant of the behavioral effects of drug administration is evident from the extensive body of literature in which the effects of long-term drug administration on subsequent pharmacological challenge has been described (see Branch, Chapter 13).

A relatively large number of experiments has provided evidence to attest to the importance of behavioral histories in determining the effects of drug administration on behavior maintained by schedules of negative reinforcement (Barrett and Witkin, 1986). Only a few studies have examined the way in which prior exposure to schedules of positive reinforcement influences the way in which drug administration affects behavior maintained by other schedules of positive reinforcement. This lack of information may be due to the difficulties encountered in studying the influence of different behavioral histories as these are likely to produce variations in the frequency and pattern of behavior maintained by current experimental contingencies. As the importance of the current experimental contingencies in determining the effects of behaviorally active drug administration has been duly emphasized in this chapter, it is understandably difficult to evaluate the relative contribution of past and present contingencies under those circumstances. However, the contribution of different behavioral histories to the effects of drug administration on behavior maintained by a common behavioral baseline has been investigated in a few different studies. Urbain et al. (1978) assessed the effects of three doses of d-amphetamine on the FI behavior of rats with a history of responding under either an FR or an IRT > 12-s schedule, using food as a reinforcer. Typically, while behavior was maintained by the FI schedule, d-amphetamine decreased rates after the FR history and increased rates after the IRT > t history. Nader and Thompson (1989) confirmed some of these effects when they showed that methadone administration decreased high FI rates of pigeons without an FR history, but not high FI rates of pigeons with an FR history. On the other hand, when the effects of different histories were assessed on a variable-interval (VI) schedule of reinforcement (Poling et al., 1980) previous exposure to a FR 20 or an IRT > 12-s schedule of reinforcement did not differentiate between groups of subjects. Similar observations were reported by Nader and Thompson (1987) who first trained pigeons to respond on a DRL or an FR schedule and then assessed the effects of methadone administration on behavior maintained by a VI schedule. These observations suggest that behavioral histories may only affect drug effects on current behavior when present experimental contingencies do not exercise powerful and direct control over behavior. Such observation fits in well with the previously suggested role for direct and indirect schedule variables in the determination of other, behavioral effects of drug administration.

2.2 Conditioned Place Preference

In contrast to the behavior-outcome schedules of reinforcement that maintain responding for the drug in self-administration experiments, the place conditioning task relies on the outcome of stimulus–stimulus associations that come to control behavior. In the place conditioning task, one environment or discrete cue (conditioned stimulus, CS) is paired with a biologically and motivationally rewarding stimulus (unconditioned stimulus, US; e.g., cocaine, novelty, water, conspecific), whereas another environment remains unpaired or paired with only a neutral stimulus such as saline administration. On the test day, the animal is given a choice between the two environments. In most place conditioning preparations, exteroceptive cues of the paired environment (CS) become associated with the rewarding aspects of the stimuli of interest (i.e., the US). Accordingly, the paired environment acquires an appetitive value, thus, eliciting approach behaviors (Bardo & Bevins, 2000; Panksepp, Nocjar, Burgdorf, Panksepp, & Huber, 2004; Reichel, Wilkinson, & Bevins, 2010). This approach behavior is expressed as an increase in the amount of time spent in the paired environment on the choice test day.

Compartment preferences motivated by associations with drug reward are also subject to extinction and reinstatement procedures (Mueller & Stewart, 2000). In this case, extinction procedures refer to re-exposure to the drug-associated chamber without the US (i.e., drug effects) (Rescorla, 2004). Accordingly, conditioned responding (i.e., time spent on the cocaine-paired side) decreases on subsequent test days. After conditioned responding is no longer evident, re-exposure US can reinstate conditioned responding (Rescorla, 2004; Shaham, Shalev, Lu, de Wit, & Stewart, 2003). With regards to place conditioning, cocaine-associated cues maintain effectiveness over a considerable length of time (Mueller & Stewart, 2000; Reichel & Bevins, 2008). Notably, this extinguished preference can be reinstated by a priming dose of the drug (Mueller & Stewart, 2000; Reichel & Bevins, 2008).

2.4.2 Use of complex schedules of reinforcement

Several investigators have suggested that more complex schedules of reinforcement that measure reinforcing strength (efficacy) are a better model of the human condition than simple schedules of reinforcement (Ahmed, 2010; Ahmed et al., 2013; Badiani, 2013; Banks and Negus, 2012; Banks et al., 2015). The two most frequently used models of reinforcing strength are the progressive-ratio (PR) schedule and drug choice procedures (either drug vs. drug or food vs. drug). For responding maintained under PR schedules, the number of responses required for a drug injection increase with each injection; this may occur within the same session (e.g., Czoty et al., 2010a; Kimmel et al., 2008) or across sessions (e.g., Griffiths et al., 1978; see also Rowlett et al., 1996). For these studies, the primary dependent variable is the final ratio completed, termed the break point (BP), when no injections have been received after a specified period of time (termed the limited hold) or at the end of the session. As with all schedules of drug self-administration involving reinforcement, the shape of the dose–response curve is an inverted U-shaped function; for PR studies, BPs for different drugs can be compared statistically (see Stafford et al., 1998 for review).

PR schedules are quite amenable to examining the effects of treatments on drug self-administration, including cocaine self-administration (e.g., Czoty et al., 2006, 2010b, 2013). As an example, the effects of d-amphetamine on cocaine BP will be described. Amphetamine has been shown to have efficacy as a cocaine pharmacotherapy (Grabowski et al., 2001; Negus and Mello, 2003a,b). In one study, Czoty et al. (2011) had monkeys self-administering cocaine under a PR schedule; the dose of cocaine was on the ascending limb of the dose–response curve. Monkeys received a continuous infusion of d-amphetamine at a rate of 0.4 ml/h and every 7 days they were given access to cocaine. If the amphetamine treatment decreased the cocaine BP, they were retested 1 week later to examine for tolerance to these effects; if tolerance developed or if the initial amphetamine dose had no effect on cocaine BP, the daily amphetamine dose was increased. In this study, d-amphetamine decreased the BP for cocaine and, importantly from a clinical perspective, tolerance did not develop to these effects. Also of relevance is that different amphetamine doses produced optimal effects in monkeys, so if all animals had been tested with the same doses and mean data presented, the effects would not have been statistically significant. Studies of this type highlight the importance of individual subject variability in drug responses.

For studies involving drug choice, the primary dependent variable is percentage of trials the drug is chosen. There are two general variations of the choice procedure: drug versus drug choice and food versus drug choice. In one sense, if an investigator wanted to directly compare the reinforcing strength of a novel drug with a known drug of abuse, the drug–drug choice procedure is ideal (e.g., Johanson and Schuster, 1975). For these studies, animals are implanted with double-lumen catheters in which drug A is available through one lumen and drug B through the other. For example, Lile et al. (2002) compared the reinforcing strength of a novel DA transporter (DAT) blocker, PTT, with cocaine. When first studied under a PR schedule, the BP for PTT was significantly lower than that for cocaine (Lile et al., 2002). However, when monkeys were given the opportunity to choose between cocaine and PTT, at the highest dose of each, PTT and cocaine were chosen on 50% of the completed trials. Interestingly though, cocaine intake was reduced by nearly 90% relative to when choice was between cocaine and saline. That is, the monkeys did not complete many trials when both drugs were available (although half the trials resulted in cocaine and the other half PTT), suggesting that perhaps a long-acting DAT blocker would be an effective treatment for cocaine addiction in the context in which cocaine is still being used (see Nader et al., 2015 for additional discussion).

The second variation of drug choice involves comparing self-administration in the context of alternative nondrug reinforcers. However, the food–drug choice procedure is too labor intensive to use to directly compare novel drugs in terms of measures of reinforcing strength. That is, how different drugs dose–response curves appear in the context of a nondrug alternative are difficult studies to interpret. For example, Nader and Woolverton (1991) had different groups of monkeys, one choosing between cocaine and food the other between procaine and food. Under baseline conditions, the shapes of the dose–response curves for both drugs appeared similar. However, when the magnitude of the alternative was manipulated (i.e., increases in the number of food pellets available as an alternative to drug), the procaine dose–response curve became much flatter than the cocaine curve, suggesting that procaine had weaker reinforcing strength than cocaine.

When only one drug is studied (e.g., cocaine), investigators can utilize a food–drug choice procedure to compare different groups of subjects in terms of sensitivity to environmental context and alternative reinforcers. For example, when monkeys are placed in social groups, they form a linear hierarchy from most dominant to most subordinate (see Nader et al., 2012a) and the formation of these hierarchies results in changes in brain DA receptors and initial vulnerability to cocaine abuse (Morgan et al., 2002). However, if monkeys are permitted to continue self-administering cocaine under FR schedules, the differences between dominant and subordinate monkeys dissipate (Czoty et al., 2005). When the conditions are changed to a concurrent FR schedule of food and cocaine presentation, subordinate monkeys are more sensitive to cocaine reinforcement, choosing lower doses of cocaine relative to food compared to dominant monkeys (Czoty et al., 2005). Importantly, the ability of drugs to alter cocaine–food choice also varies depending on the social rank of the monkey (Czoty and Nader, 2013, 2015).

More frequently, food–drug choice studies are used to identify potential treatment drugs. As described by Banks and Negus (2012), if addiction is conceptualized as a choice (Heyman, 2009), then drug versus nondrug choice behavior may have the greatest face validity to the human condition (see Haney and Spealman, 2008; Hutsell et al., 2015). The primary objective of these studies is to examine novel treatment drugs on percent drug choice and a positive outcome would be represented by a shift in preference from drug to the food alternative. This reallocation of behavior would model the human condition in which the drug user chooses an alternative reinforcer (e.g., job) over continued drug use. Some recent examples are described by Nader and Banks (2014) and Banks et al. (2015).

II. Theoretical Bases

Contingency of Feedback

I have already discussed briefly some of the schedules of reinforcement that had been employed for nearly 100 years to promote learning. The contingency between the event and operant to be reinforced needs to be considered in more detail. In the simplest case of continuous reinforcement (CRF schedule), the reward should be discreet and rapidly follow the physiological event which is to be either enhanced or inhibited. Some physiological events change very slowly, e.g., peripheral skin temperature which is related to blood flow. In this case, a proportional type of feedback such as a sound which changes as the temperature changes has been used for many years quite successfully. One could set a threshold such as 92°F for digital skin temperature which could result in a discreet second tone or visual feedback stimulus when the threshold line is crossed for a specific period of time. The tonic EDR also changes slowly and can be trained with similar feedback procedures. Interestingly, feedback based on the BOLD response (blood oxygen level dependent response) leads to very rapid learning often in a matter of a small number of sessions (De Charms et al., 2005). They reported decreased sensitivity to two painful noxious stimuli with training in the anterior cingulate. Even though the bold response is slow, the accuracy for localization in subcortical structures is even better than it is for LORETA, S LORETA, or even E LORETA, the latter claiming zero localization error.

In the case of EEG, changes occur very rapidly. A burst of SMR rarely lasts for more than 0.75 s, gamma bursts are usually shorter, whereas beta spindles may occur for 1–2 s and alpha bursts may last for many seconds. One of the features of Neuroguide’s training paradigm is that the required duration of the trained event can now be set from 0.25 to 3.0 s. If one is training an event such as a beta burst, which is known to last for 1.0 s, then the clinician can start where the required time duration window can be slowly increased in stages up to 1.0 s before a discreet stimulus is provided. Specifically, the clinician can begin with 0.25 s and progress over time to 0.5 and 0.75 and finally up to 1.0 s. If the goal is to either increase or decrease Z-score of long alpha bursts, then longer durations up to 3 s can be trained.

Different Reinforcers for Different Behaviors

Different schedules of reinforcement will affect how organisms allocate responding between two available response options. Organisms tend to allocate more behavior to the response option that results in greater amounts of reinforcement. Conversely, organisms tend to allocate less behavior to the response option that results in lesser amounts of reinforcement.

Consider an example of a BCBA with multiple clients on her caseload. That BCBA’s time is compensated in the form of money by hours billed to an insurance company. At any point in time, they can spend time analyzing data, updating skill acquisition or behavior reduction programs, or supervising staff—for one client. The amount of time spent working each week is a finite resource and they cannot bill their time (i.e., services) for two different clients at the same time. Therefore a BCBA’s allocation of time to the clients on their caseload is a daily decision. The matching law suggests the length of time a behavior analyst spends on each client’s case will be influenced by the amount of reinforcement gained from working on each client’s case. More specifically, the matching law predicts the ratio of time spent on one client compared to all other clients will equal, or match, the amount of reinforcement gained from working on that client’s program compared to the amount of reinforcement gained from working on all other clients’ programs (McDowell, 1989).

It is important to note the money that results from hours billed are not the only reinforcers that may affect how a BCBA allocates his or her time. Social interactions occur with the unique set of employees and caregivers associated with each individual with autism. Also, different individuals with autism likely require different amounts of effort based on the skill set of the BCBA and the problems presented by the individual with autism. As a result, the amount of time and effort spent will differ across individuals with autism even though the same amount of money may be earned for each individual (e.g., 2 h of billable indirect time per month).

Allocation of billable time is analogous to responding to different schedules of reinforcement that are present at the same time. For some individuals with autism, only 2 h of work per week and behavior-analytic skills already within a BCBA’s repertoire are needed to make the changes to improve a client’s programs. These clients require little relative effort. For other clients, the same BCBA may need 3–4 h of work per week (even though they may be able to bill for only 2 h), and they may have to learn new skills (e.g., approaches to implementing preference assessments) or review the research literature to make necessary changes to improve a client’s programs. In the former situation the BCBA is on a denser schedule of reinforcement because low effort is put into only 2 h of work. In the latter the BCBA is on a leaner schedule of reinforcement because more effort is put into 3–4 h of work. If each situation represents a different individual with autism on the BCBA’s caseload, the matching law predicts the BCBA will spend more time on the first case (i.e., the one with low response effort but pays the same as the one with high response effort). However, arguably, the BCBA should be spending more time on the latter case (i.e., the one needing more work to result in the same quality of programming). Such differences may result in unethical allocation of time and resources to various individuals with autism on one’s caseload (see Chapter 1, Introduction to ABA, Ethics, and Core Ethical Principles, for ethical arguments on allocation of scarce resources; see Behavior Analyst Certification Board [BACB], 2014 Section 2.0 for responsibility to clients).1

A second example of how different schedules of reinforcement may affect ethical behavior involves client intake. It is hardly a secret that different funding agencies pay different hourly rates for BCBA and Registered Behavior Technician (RBT) services. Basic research on choice behavior suggests organizations will likely accept more individuals with autism from funding sources that pay the best (e.g., the highest) rates. From a business standpoint, this may seem like commonsense practice. However, individuals with autism whose insurance companies reimburse at rates lower than other providers have no less need of services than individuals with autism whose insurance companies reimburse at high funding rates (see Chapter 1, Introduction to ABA, Ethics, and Core Ethical Principles, and principle of justice).

As an overly simplified example, Medicaid often pays less per hour of service than private insurance companies (Accelify, 2016). Relatedly, approximately 19% of the current US population is covered by Medicaid, 67% are covered by private insurance, and the remaining 14% are covered by other mechanisms or are uninsured (CDC, 2017). If an organization serves clients funded by only Medicaid and private insurance, selecting clients based on a random draw from the population would suggest 78% of their clients would come from private insurance and 22% would come from Medicaid.2 Research on choice with concurrent ratio schedules indicates people will allocate most-to-all of their responses to the ratio schedule with lower requirements (Bailey & Mazur, 1990; Herrnstein & Loveland, 1975). This would suggest the organization would exclusively accept and serve clients funded by private insurance because they pay more. This would leave clients with Medicaid disproportionately underserved.

The current BACB Professional and Ethical Compliance Code (hereafter referred to as the BACB Code) does not directly address the ethics of establishing caseloads based on the reimbursement rates you receive. As a result, BCBAs and organizational leaders can choose clients in whatever manner they prefer. If organizational leaders are unaware of how different schedules of reinforcement impact their behavior, basic behavioral processes suggest intake allocation will lean toward exclusive preference for higher paying clients. It seems difficult to justify failing to provide services to certain clients because their funding rates are lower than other clients with private insurance.

This is a highly simplified analysis. We recognize there is a difference between not-preferring a funding agency because it pays less, and being unable to afford using a funding agency because an organization loses money through the contract as reimbursement rates are too low. We also recognize the amount of work required to submit billable time to some funding agencies is another influential factor (e.g., paperwork, clinical processes). Relatedly, this does not include ongoing changes in reimbursement rates and other variables that influence interactions between organizations and funders. But, see Djulbegovic, Hozo, and Ioannidis (2015) who show how insurance companies and providers can reduce overall cost and maximize profits by approaching healthcare contracts from a game theory framework. Thus the same point of this section holds even when the complexity of everyday settings is considered—understanding basic research on choice behavior is helpful for understanding and modifying ethical decisions.

Understanding how different schedules of reinforcement influence ethical behavior will allow BCBAs and organizational leaders to actively ensure they can ethically justify the choices they make. Continuing the client intake example, it is easy to justify serving a client distribution of 78% private insurance and 22% Medicaid. That is the distribution you would expect when serving all people equally (i.e., just allocation of services to those in need). Deviations from this distribution would mean one group was receiving more services than expected. Organizational leaders could then determine why. Perhaps more of one group seek services from the organization, or the proportion of people from different funding streams differ from national distributions in the areas served by an organization. These would be fair reasons to serve a different distribution of clients because the organization would be serving all people in their area or that seek their services equally. However, if a reason could not be found, it is likely some other basic behavioral process is impacting choice that may not be ethically justifiable.

What is when target behaviors are broken down into steps?

Which of the following is the term that describes starting a new and complex behavior with small steps and progressing slowly toward the desired result?

Which procedure is best to use when a behavioral response is not already in a learner's repertoire?

What is an advantage of backward chaining?