Applications of the Premack Principle: A Review of the Literature

Abstract

The Premack principle states that any Response A can reinforce any other Response B if the independent rate of A is greater than the independent rate of B. This theory demonstrates reinforcer relativity, where the relative probabilities of responses can be more impactful than preference. Applying the Premack principle involves arranging the environment to restrict access to certain responses based on relative probabilities of a set of given responses. Though the Premack principle is described in modern behavior analytic texts, Konarski et al. identified a lack of empirical evidence to support its application. The purpose of the current paper is to systematically review the extant literature using the Premack principle and evaluate how and if researchers have applied reinforcer relativity as described by Premack and the subsequent effectiveness of these procedures. Additionally, we make recommendations for practitioners and future researchers based on our findings.

Keywords

Premack principle response deprivation hypothesis disequilibrium theory

Premack’s probability-differential provided behavior analysts an alternative perspective to program for reinforcement. Structural descriptions of positive reinforcement have traditionally described it as access to a stimulus in temporal relation to a response (Skinner, 1938), whereas Premack made the distinction that reinforcement could be achieved through access to engaging in a response (Premack, 1959). This led to the Premack principle, which involves arranging the environment to restrict access to certain responses based on relative probabilities of a set of given responses (Timberlake & Farmer-Dougan, 1991). This theory capitalizes on reinforcer relativity, which suggests the relative probabilities of responses can be more impactful than preference. The Premack principle states that any Response A can reinforce any other Response B if the independent rate of A is greater than the independent rate of B, which is greater than the independent rate of response C (Premack, 1959). Thus, a response can serve as a reinforcer in some environmental circumstances, but not others. For example, Response A will Reinforce response B and Response C. Response B will reinforce Response C, but Response C will not reinforce Responses A or B. Therefore, Response B both is and is not a reinforcer depending on the other available responses available in a situation (Premack, 1959). The Premack principle focuses on two separate responses. The first response, the one that serves as the reinforcer, is referred to as the “contingent response” and the second response, the one that grants access to the contingent response, is referred to as the “instrumental response.” In the example above, Response A is a contingent response to reinforce the instrumental Response B.

The introduction of the Premack principle was widely popular, as it made identification of reinforcers convenient and unintrusive (Timberlake & Farmer-Dougan, 1991). Furthermore, arranging contingencies in this way provides a variety of conveniences to interventionists in applied settings. For example, interventionists can arrange naturally occurring activities and/or responses already in an individual’s repertoire based on the Premack principle to increase desired behavior without making substantial changes to their setting. Additionally, practitioners would not need to introduce reinforcers not already existing in the environment (e.g., edible items).

Klatt and Morris (2001) cite activity as a reinforcer dating back to 1922 in work by Richter examining rat behavior. Premack (1962) demonstrated that the reinforcement relation between two activities could be “reversed” depending on how he arranged exercise and drinking. Premack and Premack (1963) conceptualized the restriction of eating more as a loss of an activity than the physiological state of the animal after demonstrating that restriction and free access to an exercise wheel covaried with food intake (i.e., when the wheel was available, the animals ate less; when the wheel was unavailable, they ate more). Nevin (2019) described this interchangeability in response-reinforcer relations reported by Premack as changing the way reinforcement is viewed. The 50 years of work since Premack’s seminal work have seen an evolution of this understanding. Therefore, a review and synthesis on the extant literature can provide guidance on implementation variation, parameters, and contexts in which this has been effective.

The basic work of Allison and Timberlake who began to demonstrate further how response deprivation can drive instrumental responding to Timberlake and Farmer-Dougan who illustrated how to translate this into applied settings. Premack principle is often applied in practical scenarios through first - then statements, which describe a behavioral contingency where the “first’’ component specifies the targeted behavior and the “then” component specifies the consequence contingent upon the targeted behavior occurring (Mechner, 2008). Practitioners use first - then statements prior to placing a demand that specifies the response requirement to gain access to a reinforcer (Trump et al., 2018). This contingency arrangement may be presented using a visual support, such as a first-then board or a visual schedule (e.g., Warren et al., 2021).

Though a prominent development in the field, the Premack principle has some limitations. Premack described that the only way to determine response probability was collecting duration data on unrestricted responding, but this creates challenges when determining probability of discrete behaviors (Konarski et al., 1981). For example, some practitioners may want to evaluate response probability of behaviors with restricted opportunities to occur, such as responding to teacher directed academic instruction. Additionally, the responses possible to serve as reinforcers are limited in that the contingent response must always be of higher probability than the instrumental response. If not, Premack suggested that the subject being forced to engage in the lower probability response would theoretically serve as a punisher (Premack, 1959).

Timberlake and Allison (1974) expanded upon Premack’s work with the response deprivation hypothesis, which subsequently introduced the disequilibrium model (Timberlake & Farmer-Dougan, 1991) for effective use of the Premack principle. This suggests that an instrumental response, even if it is the lower probability behavior, will still serve as the contingent response if the schedule of reinforcement satisfies response-deprivation below independent levels of responding (Timberlake & Allison, 1974). This differs from Premack’s initial work in which he described that for a response to be a reinforcer, independent levels of responding of the contingent response had to be higher than the instrumental response. Timberlake and Allison (1974) present their approach using the equation I/C > $O_{I} / O_{C}$ . In this equation, I represents the scheduled amount of instrumental responding required to obtain C amount of the contingent response. $O_{I}$ and $O c$ represent the operant levels of instrumental and contingent responding during an independent baseline with no contingencies in place. Similarly, they present the equation I/C > $O_{I} / O_{C}$ to demonstrate that the environment can be arranged so that, through restricting access to the lower probability behavior, the lower probability can serve as a reinforcing, contingent response with the high probability behavior as the instrumental response. Essentially, this suggests that a response of any probability can serve as a reinforcer of another response if the practitioner is able to restrict access to the response. With this essential component, researchers can manipulate the schedule of reinforcement and adapt to motivation that changes based on the imposed contingency schedule (Timberlake & Allison, 1974).

Timberlake and Farmer-Dougan (1991) reported that the Premack principle was described in a variety of textbooks used by the behavior analytic community since the 1970s (e.g., Donnellan et al., 1988; Kazdin, 1980; Sulzer-Azeroff & Mayer, 1977). However, Konarski et al. (1981) cited a lack of empirical support for the Premack principle at that time. Furthermore, the extant data often selected the contingent response to be a reinforcer based on anecdotal reports, not on the probability of a response (Konarski et al., 1981). The gap in the literature that Konarski et al. identified is relevant to both practitioners and researchers and warrants further investigation given that the Premack principle is still described in many contemporary behavior analytic texts, such as Cooper et al. (2020) and Alberto and Troutman (2013). The purpose of the current review of the literature is to assess the extent to which researchers have evaluated Premack principle and re-examine the data supporting its applied use in the 40 years since Konarski et al.’s reported the lack of published, empirical evidence. Further, this review weighs the rigor of the extant literature in an effort to identify best practices for future evaluation by researchers and practitioners.

Method

Search Procedures

The first author conducted a search of the databases PsychINFO, ERIC, PsychARTICLES, and PubMed in March 2020. They used the search terms “Premack principle” in quotation marks and “response deprivation” in two separate searches. Neither truncation nor wildcards were used in the search. Studies were included in the review based on the following criteria: (a) human participants, (b) contains the word “Premack,” (c) written in English. After duplicates were removed, this initial search yielded 79 studies. The first author evaluated the 79 studies using the additional inclusion criteria of (d) applied research (evaluating behaviors of importance to humans or society as opposed to theory; Baer et al., 1968; Cooper et al., 2020) and (e) experimental (comparison of a phenomenon of interest under two or more conditions; Cooper et al., 2020). The additional inclusion criteria reduced the included studies to a total of 33. These studies included both published articles as well as unpublished theses and dissertations to reduce the likelihood of publication bias influencing review findings (King et al., 2020).

The first author then conducted an ancestral search on the references of the 33 studies from the initial search that added 12 more studies in the review. Next, the first author conducted a forward search of Premack’s (1959) article on PsychINFO. This yielded 76 studies; of these 7 studies met all inclusion criteria and were added to the review. Additionally, the authors completed a hand search of the two journals most represented in the 52 included studies, Journal of Applied Behavior Analysis and Behavior Therapy. After reviewing the tables of contents of these journals from the years 2000 to 2020, no additional articles were added to the review. This resulted in a total of 52 studies with 61 separate experiments to be evaluated in the current review of the literature. These search procedures are described in a PRISMA Flowchart in Figure 1 (Page et al., 2021).

Figure 1.

PRISMA flowchart.

Coding Procedures

The reviewers coded descriptive participant and experiment characteristics for all experiments. Participant characteristics included gender, age, diagnosis, setting, and intellectual ability. Experiment characteristics included experimental design, dependent variable, independent, and contingent responses, evidence of independent responding, and potential of a causal relation. Additionally, coders evaluated the appropriateness of each article for inclusion considering the following criteria: (a) human participants, (b) contains the word “Premack,” (c) written in English, (d) applied research, and (e) experimental. All coders were trained on procedures and provided with operational definitions for each coding category.

Participant and setting characteristics

Reviewers coded participant and setting characteristics to examine the variety of circumstances in which researchers applied the Premack principle. Participant and setting characteristics included gender, age, diagnosis, setting, and intellectual ability. Reviewers coded any age description of the participants or if the specific ages were not reported. Participant diagnosis codes included autism spectrum disorder (ASD), intellectual disability (ID), developmental delays (DD), multiple diagnoses (coded separately from other diagnoses that might have been part of their multiple diagnoses), other diagnoses, or none (i.e., participants were typically developing). Experimental settings included hospital rooms, therapy rooms, classrooms, vocational settings, or living spaces.

Experimental characteristics

Experimental characteristics included experimental design, dependent variable, evidence of response probability, and ability to demonstrate a causal relation. Reviewers coded the experimental designs as multiple probe/multiple baseline, alternating treatment design, group design, or other design. Dependent variables were coded in the categories of problem behavior, academic responses, communicative response, vocational skills, activity allocation, and “other” variables that did not fit the definitions of the other, specific dependent variable categories. Reviewers coded for evidence of response probability assessment to determine if researchers presented baseline data to support the identified high and low probability responses. To accomplish this, researchers had to conduct a free operant paired baseline where the responses evaluated were simultaneously available without restriction (Timberlake & Allison, 1974). Experiments were coded as “with evidence” if the researchers presented data demonstrating independent levels of responding and coded “without evidence” if researchers did not have data on independent levels of responding or assumed probability based on preference or anecdotes.

Because our aim was to explicitly focus on studies evaluating Premack as outlined in 1959, we isolated those studies that evaluated baseline levels of response allocation. These studies would have established higher and lower probability allocation and could therefore, based on evidence, arrange contingencies based on Premack. This allowed us to concentrate our analysis on those studies that could truly be said to evaluate Premack. Including those studies that did not present data related to baseline level evaluation of response allocation would have risked incorporating data that did not represent Premack and thus obscured our conclusions. For this group of studies that could be determined to have truly used Premack, reviewers coded for the potential of a causal relation demonstration based on criteria described by Gast and Ledford (2018), which involves replication of experimental effects at least three times. If experiments met this criteria, they were scored as potentially demonstrating a causal relation. If not, the study was coded as not having the potential to demonstrate a causal relation (e.g., some studies used an A-B-C-B arrangement which does not meet contemporary standards for direct replication within a study). If an experiment met the criteria for the probability of a causal relation, reviewers used the Single Case Analysis and Review Framework (SCARF; Ledford et al., 2016) to evaluate the rigor of experiments. The SCARF data collection template specifies that its use is to evaluate rigor of studies designed with at least three potential demonstrations of effect, similarly to the qualifications for a causal relation as described by Gast and Ledford (2018). For those familiar with What Works Clearinghouse (WWC) standards for single case design (Kratochwill et al., 2010), SCARF was designed to build on the strengths of those standards and address some of the WWC omissions (e.g., requirement of procedural fidelity). The SCARF template uses response options of yes/no/not applicable or scaled responses from 0 to 4 to systematically code information about each individual experiment in a study. The information collected from SCARF is displayed graphically to present a scatter plot of the included studies’ quality and rigor. For experiments that demonstrated evidence of response probability and evaluated Premack principle using a group design, authors evaluated rigor using the WWC standards.

Interrater Reliability

Interrater reliability (IRR) was conducted by the second, third, fourth authors on all components of the review’s search and coding procedures. All coders were graduate students who received training on the search procedures, coding procedures, and coding definitions. The second author conducted an independent initial search following the search procedures described above. The third author then analyzed the articles retrieved from the initial search and used the inclusion criteria to determine if they were appropriate for review. The second, third, fourth authors coded 100% of the included studies for inclusion criteria, participant descriptions, and experiment characteristics. Additionally, the second author conducted an independent SCARF coding of 100% of the articles which met the criteria to be coded in SCARF.

IRR results

IRR was calculated by dividing agreements by the number of agreements plus disagreements and multiplying by 100. The independent literature search, conducted by the second author, resulted in 100% agreement (i.e., identical 79 studies to the first author’s initial search). Additionally, the third author’s evaluation of these 79 studies meeting inclusion criteria removed 43 studies, which was identical to the first author’s evaluation resulting in 100% IRR. The second, third, fourth authors took IRR data on the studies included from the initial, ancestral, and forward search results meeting inclusion criteria (a) to (e) with 100% agreement compared to the first author’s assessment. Participant and experiment characteristics IRR, also conducted by the second through fourth authors, resulted in 97.42% agreement when averaged across articles and 96.55% agreement as well when averaged across coding categories. The second author’s SCARF scoring resulted in 98.21% IRR when compared to the first author’s scoring.

Results

The review included 52 studies containing 61 experiments spanning the years 1959 to 2017. Tables 1 and 2 present participant information, dependent variables evaluated, setting, and research design per study. To address Konarski et al.’s (1981) concern regarding the lack of baseline responding prior to intervention in the Premack literature, we divided studies based on whether they established response probability in prior to implementation of Premack. The reviewed studies were separated into tables by those with evidence of response probability in Table 1 and those without evidence of response probability in Table 2. Figure 2 displays the percentage of each dependent variable evaluated in studies with and without evidence of response probability. Figure 3 displays the assessment of rigor of the studies with evidence of response probability that had the potential to demonstrate causal relation as generated by SCARF. Those that did not have the potential to demonstrate a causal relation because they were not designed with at least three opportunities to observe experimental effects were excluded from SCARF as they lacked sufficient rigor to evaluate internal validity further.

Table 1.

Studies With Evidence of Response Probability.

Citation	N	Diagnosis	DV	Setting	Research Design
Aeschleman and Williams (1989)	2 males 1 female Ages 17–19 years	ID	Duration engagement in low probability tasks	Therapy room	MB/MP
Allen and Iwata (1980)	6 males 4 females Mean age 42 years	ID	Exercise completion	Classroom	Other
* Amari et al. (1995)	1 female Age 15 years	ID, Epilepsy	Ketogenic food consumption	Not reported	ATD
Bateman (1975)	1 male 1 female Ages 28–3 years	ID, Downs Syndrome	% of time in less preferred activity	Vocational	W/R
Birch et al. (1982)	5 males 7 females Preschool age	Not reported	Juice preference	Therapy room	Other
Dougher (1983)	2 males Ages 41 years	Schizophrenia	Frequency of social responses	Hospital room	W/R
Eddy (1975)	1 male 1 female Ages 6–9 years	ID	% of intervals with communication	Therapy room	W/R
Gupton and LeBow (1971)	2 males Ages not reported	Not reported	% of sales	Vocational	W/R
* Hanley et al. (2003)	4 males Ages 23, 26, 42, 50 years	ID, ASD	% of interaction	Therapy room	W/R
Holburn and Dougher (1986)	1 female Age 21 years	ID	Frequency of problem behavior	Therapy room	Other
Hosie et al. (1974)	Experiment 1 14 participants Sixth grade Experiment 2 20 participants Fifth–sixth grade	Not reported	Time to complete written report	Classroom	Group design
Konarski et al. (1982)	4 males Ages 7–10 years	ID	Duration of on task behavior	Classroom	W/R
Konarski et al. (1980)	Experiment 1 & 2 2 participants First grade	Typically developing	Duration of activity engagement	Classroom	W/R
* Levin and Carr (2001)	Experiment 1& 2 2 males 1 female Ages 5, 6, 6 years	ID, ASD	Grams of nonpreferred food consumed	Classroom	MB/MP
Love (1977)	8 participants Ages 7–8 years	Not reported	Academic responding	Classroom	W/R
* Mitchell and Stoffelmayr (1973)	2 participants Ages 55–60 years	Schizophrenia	Frequency of intervals working	Therapy room	W/R
* Noell et al. (2003)	2 males 3 females Ages 3, 4, 4, 5 years	ASD, Language delay	% academic tasks correct	Classroom	ATD
Premack (1959)	33 participants Average age 6 years	Not reported	Response allocation	Classroom	Other
Roberts (1969)	1 male Age 40 years	Schizophrenia	Rate of positive and negative statements	Hospital room	Other
Robinson and Lewinsohn (1973)	20 females Undergraduate college students	Depression	Rate of low frequency verbal behavior	Therapy room	Group design
Roemmich et al. (2004)	11 males 7 females Ages 10–11 years	Not reported	Rates of physical activity	Living space	Group design
Whitehurst (1972)	6 males 2 females Ages 6–7 years	Not reported	Number of correct academic responses	Classroom	W/R
Whitmarsh (2002)	Experiment 1 & 2 4 males Ages 5–18 years	ID, ASD	% of accurate academic responses	Therapy room	W/R
* Williamson (1984)	1 female Age 73 years	Depression	% of time engaging in relevant topics	Hospital room	W/R

Note. N = number of participants, ID = intellectual disability, ASD = autism spectrum disorder, ADHD = attention deficit hyperactivity disorder, DV = dependent variable, ATD = alternating treatment design/ W/R = withdrawal/reversal to MB/MP = multiple baseline/multiple probe.

Potential to demonstrate experimental control.

Table 2.

Studies Without Evidence of Response Probability.

Citation	N	Diagnosis	DV	Setting	Research Design
Andrews (1970)	Experiment 1 1 male Age 13 years Experiment 2 Unspecified participant number seventh grade	Not reported	Experiment 1 % correct Experiment 2 Frequency of problem behavior	Classroom of residential facility	Other
Azrin et al. (2007)	2 males Ages 13 years	ADHD, ID	% of activity calm	Classroom	Other
Blinder et al. (1970)	4 females Ages 15, 17, 20, 22 years	Anorexia nervosa	Weight gain	Hospital room	Other
* Browder et al. (1984)	8 participants Ages 24, 25, 29, 30, 36, 36, 40, 43 years	ID	Sight word and functional skill acquisition	Vocational	MB/MP
* Brown et al. (2002)	1 male Age 7 years	Learning disability	Food consumed per session	Living space	MB/MP
Charlop et al. (1990)	Experiment 1 4 males Ages 6, 7, 7, 8 years Experiment 2 3 males Ages 8, 8, 9 years Experiment 3 3 males Ages 6, 6, 9 years	ASD	% correct task performance	Therapy room	ATD
Geiger (1996)	67 participants sixth, seventh, eighth grade	Not reported	Work completion	Classroom	Group design
Guidry (1974)	1 male Age 20 years	Typically developing	Stealing behavior	Not reported	Other
Hartje (1973)	108 participants fifth grade	Not reported	Task preference	Classroom	Group design
Homme et al. (1963)	3 participants Age 3 years	Not reported	Problem behavior	Classroom	Other
Horan and Gilmore Johnson (1971)	80 females Undergraduate college students	Not reported	Weight lost	Therapy room	Group design
Houtz and Feldhusen (1976)	Approximately 240 participants fourth grade	Not reported	Test scores	Classroom	Group design
Kumchy and Kores (1981)	1 participant Age not reported	ID	Duration of problem and appropriate behavior	Hospital room	Other
Lattal (1969)	8 males Ages 10–12 years	Not reported	% of subjects brushing teeth	Living space	Other
* Lyon (1976)	1 female Age 25 years	ID	Duration spent in classroom	Classroom	W/R
Makin and Hoyle (1993)	4 males Age not reported	Not reported	Engineer performance	Vocational	Other
McCullough and Southard (1972)	56 participants Ages 12–18 years	Not reported	Frequency of problem behavior	Living space	Other
McMorrow et al. (1978)	2 males 1 female Ages 22, 28, 30 years	ID, Schizophrenia	Frequency of activities attended	Living space	MB/MP
McNamee-McGrory and Cipani (1995)	1 female Age 4 years	Not reported	Occurrence of problem behavior	Classroom	Other
* Mithaug and Mar (1980)	1 male 1 female Ages 19–20 years	ID, Downs Syndrome	% choice of task	Classroom	W/R
Osborne (1969)	6 females Ages 11, 12, 12, 13, 13, 13 years	ID, Deaf	Mean out to seat responses per class segment	Classroom	W/R
Ramer (1972)	Experiment 1 12 participants Ages 17–40 years Experiment 2 105 participants Ages not reported Experiment 3 30 participants Ages not reported	Not reported	Oral hygiene	Therapy room	W/R
Slate and Jones (2003)	30 males Sixth grade	Not reported	Subject grades and disciplinary referrals	Classroom	Group design
Turcios et al. (2017)	11 males 4 females Mean age 9 years	ASD	Completion of familiarization protocol	Not reported	Other
Van Hevel and Hawkins (1974)	12 participants seventh, eighth, ninth grade	Not reported	Correct responses	Classroom	W/R
Wasik (1970)	11 males 9 females second grade	Typically developing	% of time engaging in desirable behavior	Classroom	W/R
* Welsh et al. (1993)	4 males 4 females Average age 20 years	Not reported	Frequency of food preparation errors	Vocational	Other
Yawkey and Le Penna Griffith (1974)	2 females Ages 5 years	Not reported	Operant crying behaviors	Classroom	W/R

Potential to demonstrate experimental control.

Figure 2.

Dependent variable percentages.

Figure 3.

SCARF scatterplot.

With Evidence of Response Probability

Of the evaluated studies, 46% (n = 24) met the criteria of providing evidence of response probability from participants before intervention. This means that researchers reported data demonstrating one response as high probability and another as low probability before arranging intervention based on the Premack principle. Many researchers collected these data through a free operant evaluation of response allocation (e.g., Aeschleman & Williams, 1989) but others measured response outcomes, such as grams of food consumed in Levin and Carr (2001). For all 24 studies, interventions involved restricted access to the high probability response and contingent access to it based on participant exhibition of the low probability response.

The participants in these studies ranged in age from 3 to 60 years. Participant diagnoses included a range of intellectual functioning from severe intellectual disability to typically developing. Studies also applied the Premack principle to individuals with a variety of other diagnoses, such as ASD, epilepsy, schizophrenia, language delay, depression, and Down’s Syndrome. The Premack principle was applied on a wide variety of behaviors in diverse settings: classrooms (37.5%, n = 9), therapy rooms (33.33%, n = 8), hospital rooms (12.5%, n = 3), vocational settings (8.33%, n = 2), living areas (4.17%, n = 1), and one did not report the setting (4.17%, n = 1).

As depicted in Figure 2, dependent variables evaluated in these studies included communication (20.83%, n = 5), allocation between activities (20.83%, n = 5), vocational skills (12.5%, n = 3), academic skills (20.83%, n = 5), problem behavior (8.33%, n = 2), and other (16.67%, n = 4). The “other” category included variables such as food consumption (Amari et al., 1995) or participation in exercise (Allen & Iwata, 1980). In addition to the dependent variables, the authors coded specifically for instrumental and contingent response evaluated in each experiment. Instrumental responses included appropriate behavior (7.14%, n = 2), academic behaviors (26.57%, n = 8), physical activity (7.14%, n = 2), vocational skills (10.71%, n = 3), leisure activities (17.85%, n = 5), consumption of edible items (14.28%, n = 4) and other (14.28%, n = 4). Examples of “other” responses used as the instrumental responses included positive statements (Roberts, 1969) or positive thoughts (Robinson & Lewinsohn, 1973). Contingent responses included academic behaviors (7.14%, n = 2), physical activity (3.5%, n = 1), vocational skills (7.14%, n = 2), leisure activities (60.71%, n = 17), consumption of edible items (14.28%, n = 4), and other (7.14%, n = 2). An example of an “other” response is a high probability vocalization (Robinson & Lewinsohn, 1973). These variables were evaluated using withdrawal/reversal designs (50%, n = 12), group designs (12.5%, n = 3), multiple probe/baseline designs (8.33%, n = 2), alternating treatment designs (8.33%, n = 5), and other designs (20.83%, n = 5). Examples of “other” designs included having AB design, such as Premack (1959) and Roberts (1969).

Analysis of Rigor

Six of the single-case deign studies with evidence of independent responding met criteria to be coded in SCARF (i.e., they were designed in such a way so as to permit evaluation of effects of the independent variable at three points in time). The studies included 15 separate experiments that were evaluated individually. The SCARF graphic display is separated into four quadrants. The top left quadrant represents experiments with low rigor and positive effects, the top right quadrant represents experiments with high rigor and positive effects, the bottom left quadrant represents experiments with low rigor and negative or minimal effects, and the bottom right quadrant represents experiments with high rigor and negative or minimal effects (Ledford et al., 2016).

Figure 3 displays the experimental outcomes of studies included in the SCARF coding (n = 15). One will note, if counting data points, only 11 are visible. This is because of overlap of data points: three points for Hanley et al. (2003) at 1.93, 5, two data points for Mitchell and Stoffelmayr (1973) at 1.36, 5, and finally, two data points from Noell et al. (2003) at 2.12 and 5. Overlap within a study reflects the similarity in rigor and outcome across participants. Though all researchers indicate an increase in the instrumental response after application of the Premack principle, the SCARF scatterplot allows for further analysis of the evidence of these results. The majority of the data points are in the top left quadrant, representing experiments with low quality evidence of positive effects. Additionally, some data points are in the bottom left quadrant, representing low quality evidence of negative or minimal effects, and there are three data points in the top right quadrant, which represents the experiments with high quality evidence of positive effects. Overall, the evaluation of studies included in SCARF suggest that the majority of studies do demonstrate positive effects of the Premack principle, however they lack quality evidence of findings.

Authors evaluated rigor of the three group design studies with evidence of independent responding using the WWC criteria including group determination by randomization, combination of overall and differential attrition high, and equivalence established at baseline for the groups (WWC, 2020). All three studies (identified in Table 1) were deemed eligible to meet WWC group design standards without reservations.

Without Evidence of Response Probability

The remaining studies (54%, n = 28) included in the review did not provide evidence of participant response probability prior to beginning intervention. Though researchers described that they used the Premack principle in their evaluation, these studies did not demonstrate baseline, independent performance to establish high probability versus low probability behaviors. This was often demonstrated by the assumption of a high probability behavior without data to support it. Geiger, for example, used recess as the contingent response but did not provide baseline responding that would empirically establish this as a high probability response (1996). Other studies, such as Browder et al. (1984), selected the contingent response based on anecdotal participant preference, but did not collect data to establish the preferred activity as a high probability response.

As with the studies with evidence of independence of responding, this group of studies also report to evaluate the Premack principle in diverse circumstances. The age range of participants in these studies was 3 to 43 years. Participant diagnoses included a range of intellectual functioning from severe intellectual disability to typically developing as well as a variety of other diagnoses, such as attention deficit hyperactivity disorder, anorexia nervosa, learning disability, deafness, and Down’s Syndrome. These studies took place in classrooms (53.57%, n = 15), living spaces (14.28%, n = 4), vocational settings (7.14%, n = 2), hospital rooms (7.14%, n = 2), therapy rooms (10.71%, n = 3), and some did not report the study setting (7.14%, n = 2).

As depicted in Figure 2, the dependent variables evaluated included: academic skills (41.67%, n = 10), problem behavior (33.33%, n = 8), vocational skills (7.14%, n = 2), activity allocation (3.57%, n = 1), and “other” (29.17%, n = 7), such as weight loss (Blinder et al., 1970), stealing (Guidry, 1974), and tooth brushing (Lattal, 1969). These dependent variables in contingency arrangements with instrumental responses including appropriate behavior (39.39%, n = 13), academic behavior (24.24%, n = 8), vocational skills, (9.1%, n = 3), consumption of edible items (3.03%, n = 1), and other (24.24%, n = 8). Contingent responses included academic behavior (6.06%, n = 20), physical activity (3.03%, n = 1), vocational skills (9.09%, n = 3), leisure activities (54.54 to n = 18), consumption of edible items (3.03%, n = 1), and other (24.24%, n = 8). Experimenters evaluated these dependent variables and contingency arrangements using withdrawal/reversal designs (29.17%, n = 7), group designs (17.86%, n = 5), multiple baseline/probe designs (14.28%, n = 4), and “other” designs (45.83%, n = 11). Studies coded as “other” for design included AB designs, such as McNamee-McGrory and Cipani (1995) or no experimental design at all, such as Homme et al. (1963). Of the studies using single case designs, five were designed to evaluate demonstrations of effect by the independent variable at three points in time. Of those, three studies appear to document experimental control (i.e., Three demonstrations of experimental effect at three points in time).

Discussion

Many of the most comprehensive texts used to train behavior analysts include the Premack principle. For example, Cooper et al. (2020) discuss how Premack principle describes that the opportunity to engage in a behavior that occurs at a high free operant rate contingent on a lower frequency behavior will function as a reinforcer. They continue to explain how the Premack principle is informally referred to as “Grandma’s Law” to help practitioners understand how to arrange contingencies, such as “when you finish your vegetables, you can have desert.” Though this kind of contingency arrangement follows the premise of Premack principle, this example from Cooper et al. (2020) reflects concerns raised by Konarski et al. (1981) review. Konarski et al. (1981) described that published data on Premack relied heavily on anecdotes about perceived preferences of participants. They suggested further research was needed with comprehensive data establishing probabilities of responding before implementing a contingency based on the Premack principle. Yet, approximately 40 years later, little has changed, and this review draws a similar conclusion. To the end that practitioners may use Premack, they may benefit from a more thorough explication of the process. However, to provide a description of the most efficient process may require further study.

This review of the literature identified a variety of strengths in the extant applied research. Most notably, the Premack principle has been examined with a variety of participants in a range of circumstances. This finding suggests that Premack’s advancement in the construction of schedules of reinforcement has utility in diverse situations, which supports the idea that the Premack principle is a practitioner friendly manipulation of reinforcement schedules and can be conveniently utilized with existent resources in one’s environment. One of the advantages of structuring intervention with Premack rather than programing other contrived reinforcement (introduction of edibles etc.) is that it takes advantage of activities that already exist in the environment (i.e., the therapist would have needed to assess baseline level free operant response allocation to the activity). Further, this can help segment therapy sessions and classroom schedules with naturally occurring breaks that encourage active engagement rather than relying to provision of other reinforcers outside of the instructional arrangement.

The current review also identified some weaknesses in the current literature. However, these weaknesses are not an indictment of the researchers. We purposefully evaluated research according to contemporary standards. While no single standard exists on which all researchers agree, in general the quality and rigor have evolved over time. The overwhelming majority of research on Premack occurred before 1990. Holding those single case design studies to standards that began to emerge following Horner et al.’s (2005) recommendations may seem overly stringent, but it still represents the most current expectations of rigor.

Looking more broadly, over half of the studies identified as applied usages of the Premack principle did not collect data to establish the probability of behaviors prior to intervention. The convenience of applying Premack principal in “first – then” or “if – then,” scenarios may contribute to the regularity in which the Premack principle is implemented without this critical component. Without evaluating free operant rates of responding experimenters cannot substantiate that their arrangement of responses as instrumental or contingent truly reflect they Premack principle. Therefore, treatment outcomes based on these arrangements may lead to conclusions about Premack that do not actually reflect Premack’s arrangement. This is analogous to running a study without sound procedural fidelity. An author may report they evaluated a specific treatment but if they lack evidence of faithful implementation the interpretation of the data becomes untenable. This represents the greatest weakness in the literature because, fundamentally, there are no assurances that the researchers in much of the “Premack” literature actually evaluated Premack.

Additionally, the SCARF assessment identified that even in the studies that established behavior probability prior to intervention, the majority experiments with positive outcomes have low quality evidence of findings. If one were to weigh the use of Premack as an evidence-based practice, consider the quality of these findings. With such a parsimonious procedure, researchers could easily construct more robust investigations that would allow for more convincing conclusions.

Despite the findings that failed to fully implement Premack principle with baseline assessment of unrestricted responding, the data are largely promising and suggest that Premack principle, if implemented as first described, should result in behavior changes. Advances on Premack’s underlying idea (i.e., disequilibrium theory) may provide the quantitative model that would allow more careful high-fidelity implementation. Ultimately, much of the research that reflects Konarski et al.’s original concern suffers from a lack of procedural fidelity.

Future Directions

Researchers should continue to evaluate applications of the Premack principle with humans to expand the literature base of studies with probability of responding established as well as improve the rigor or the evaluations. Establishing a strong evidence base adds credibility to recommendations for using the Premack principle in practice. Further, those investigations should consider providing clear documentation of unrestricted baseline responding to communicate that they are in fact evaluating Premack (or disequilibrium theory). Doing so would represent an advancement in what fundamentally amounts to improving procedural fidelity and communicating that to readers. When practitioners program contingencies using Premack principle, they should first demonstrate baseline probability of responding prior to selecting the instrumental and contingent responses. Authors contributing to behavior analytic text books or research should emphasize the importance of this pre-intervention data collection and provide guidance on strategies for collecting free operant data that would permit appropriate assignment of responses to contingent and instrumental. This can be accomplished by conducting a concurrent schedule arrangement or allowing unrestrained access to both responses and collecting data on an individual’s response allocation between the two responses (Cooper et al., 2020). By doing this, authors would ensure that practitioners have the requisite tools and knowledge for implementing an evidenced based practice. This may help practitioner’s implementation failures because of their over-simplified application “Premack” (i.e., not appropriately assigning responses).

We recommend consulting Jacobs et al. (2017) prior to arranging environmental contingencies using the Premack principle or response deprivation hypothesis Jacobs et al. provide operationalized practitioner friendly procedures for utilizing the disequilibrium theory model, which combines the probability-differential hypothesis and response deprivation hypothesis. Additionally, they provide resources to help practitioners or researchers utilize Heth and Warren’s (1978) model to predict the effectiveness of a contingency arrangement. This extends the utility of Premack in such a way that can allow for a most systematic and faithful application. Adding more work in this area would allow for a more nuanced understanding of the benefits and limits of response deprivation-based models.

Footnotes

Availability of Data

Compliance with Ethical Standards

On behalf of all authors, the corresponding author states that there is no conflict of interest. Furthermore, as this is a literature review, this manuscript did not require informed consent.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Jessica L. Herrod

Author Biographies

Jessica L. Herrod is a doctoral candidate at the University of Georgia in the Department of Communication Sciences and Special Education and Behavior Analyst at the University of Georgia Center for Autism and Behavioral Education Research. Her research interests include the evaluation of evidence-based practices in applied settings for students with developmental and intellectual disabilities.

Sara Kathryn Snyder is a doctoral student in Special Education with an emphasis in Applied Behavior Analysis at the University of Georgia as well as a Behavior Analyst at the University of Georgia Center for Autism and Behavioral Education Research. Her research interests include function-based assessment and classroom-based treatment for challenging behavior for students with autism and intellectual disabilities.

Joseph B. Hart is pursuing his Ph.D. in special education at the University of Georgia. His research interests include behavior analysis in schools and the assessment and treatment of severe problem behavior. He is also a Behavior Analyst at the University of Georgia Center for Autism and Behavioral Education Research.

Sarah J. Frantz was a student and behavior specialist at the University of Georgia. She currently is pursuing operational excellence for a private organization specializing in Applied Behavior Analysis for children with autism.

Kevin M. Ayres is a professor at the University of Georgia in the Department of Communication Sciences and Special Education and the Co-director of the University of Georgia Center for Autism and Behavioral Education Research.

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