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. Author manuscript; available in PMC: 2025 Jul 1.
Published in final edited form as: J Appl Behav Anal. 2024 May 29;57(3):742–750. doi: 10.1002/jaba.1091

Examination of clinical variables affecting resurgence: A reanalysis of 46 applications

Brianna Laureano 1,2, Joel Ringdahl 3, John M Falligant 1,2
PMCID: PMC11260239  NIHMSID: NIHMS1996757  PMID: 38808478

Abstract

Despite the efficacy of behavioral interventions, resurgence of challenging behavior (e.g., aggression, self-injury) following successful treatment can still occur. Applied work has focused on identifying treatment-related variables thought to affect the occurrence and magnitude of resurgence. The current study describes the relation between several variables (i.e., phase duration, response rates in baseline and treatment, obtained rates of reinforcement, downshift in reinforcement step size) and resurgence in a retrospective consecutive controlled case series of 46 treatment applications for challenging behavior conducted in an inpatient setting. Only the downshift in reinforcement (e.g., schedule-thinning) step size was correlated with the magnitude of resurgence. These results are consistent with recent findings suggesting that treatment duration and other factors have inconsistent effects on resurgence of challenging behavior.

Keywords: challenging behavior, consecutive controlled case series, resurgence, retrospective analysis

Broadly defined, resurgence is the reemergence of previously eliminated or reduced responding when the reinforcement conditions for an alternative response worsen (e.g., Epstein, 1985; Perrin et al., 2021; Podlesnik et al., 2023). Resurgence is a clinically relevant concern, as many behavioral interventions that are directed at reducing challenging behavior entail reinforcing an alternative behavior (e.g., a functional communicative response) while challenging behavior is concurrently placed on extinction. During initial treatment implementation, the alternative response is typically reinforced on a very dense schedule (e.g., a fixed-ratio 1 schedule). Following clinically significant reductions in challenging behavior, the density (i.e., availability, rate, magnitude) of reinforcement for the alternative response is progressively decreased during schedule thinning (e.g., Greer et al., 2016). Unfortunately, decreases (sometimes referred to as “downshifts”) in the density of alternative reinforcement during schedule thinning or following procedural-fidelity omission errors create the conditions for challenging behavior to reemerge.

In research preparations, resurgence is often evaluated in a three-phase sequence that mirrors the clinical situations in which resurgence has been shown to occur. Phase 1 simulates baseline conditions of the intervention in which the target response is reinforced and no consequences are provided for the alternative response. Phase 2 simulates a differential-reinforcement-of-alternative-behavior (DRA) program (e.g., functional communication training [FCT]) in which the target response is placed on extinction and an alternative response is reinforced. Phase 3 simulates a decrement in alternative reinforcement in which the target response remains on extinction and the alternative response is either placed on extinction or there is a downshift in alternative reinforcement (analogous to schedule thinning; Shahan & Greer, 2021). Resurgence is said to occur if the target response reemerges at higher levels in Phase 3 relative to levels observed at the end of Phase 2. When resurgence occurs, the reemergence of challenging behavior may lead to decrements in procedural fidelity, (e.g., Ringdahl & St. Peter, 2017), cause injury to the client or others, and ultimately result in a more intensive intervention (e.g., Schindler & Horner, 2005). Clinicians are likely to encounter resurgence, as a relatively high prevalence of resurgence has been reported among treatment applications from both inpatient and outpatient settings (e.g., Briggs et al., 2018; Haney et al., 2022; Mitteer et al., 2022).

For example, practitioners are likely to encounter resurgence during schedule thinning (e.g., Muething et al., 2021). Schedule thinning typically commences after clinically significant reductions in challenging behavior have been achieved with dense schedules of reinforcement. The purpose of schedule thinning is to make the schedule of reinforcement for the alterative response more feasible and acceptable for community implementation. During situations in which reinforcement, such as attention, for the functional communicative response (FCR) is not available, the client may revert to the same forms of behavior that previously served to recruit attention. In this example, the increase in attention-maintained challenging behavior when reinforcement for the FCR is thinned is considered resurgence.

Clinical researchers evaluating resurgence have largely focused on understanding variables that moderate its occurrence and magnitude to improve the long-term durability of interventions for challenging behavior. These applied, prospective studies often focus on differential-reinforcement-based interventions that program downshifts in the availability of alternative reinforcement via multiple schedules (e.g., Betz et al., 2013; Fisher et al., 2020; Fuhrman et al., 2016). For example, one emerging finding is that the magnitude of resurgence increases as an exponential function of the size of the downshift in alternative reinforcement during schedule thinning (e.g., Falligant, Hagopian, et al., 2022; Greer et al., in press; Shahan et al., 2020; Shahan & Greer, 2021). In other words, the larger the downshift in reinforcement (e.g., schedule-thinning) step size, the greater the magnitude of resurgence. For instance, in Experiment 2 of Greer et al. (in press), resurgence was observed more often and at a higher magnitude when the schedule-thinning step size was relatively large for five children who engaged in destructive behavior. Increases in challenging behavior were more significant during schedule thinning when the total availability of alternative reinforcement decreased by 74% (i.e., 300 s out of a 600-s session) as opposed to 14% (i.e., 540 s of a 600-s session).

Apart from the relative downshift in the availability of alternative reinforcement, preliminary research also suggests that resurgence may be affected by other variables such as the duration of baseline and treatment phases (e.g., Smith & Greer, 2022). That is, higher magnitude resurgence may be associated with longer Phase 1 durations and shorter Phase 2 durations (e.g., Bruzek et al., 2009; Smith & Greer, 2022; Winterbauer et al., 2013). In other words, the longer the period that challenging behavior is reinforced during baseline, the greater the resurgence. This implies that an increase in challenging behavior may be larger during schedule thinning if challenging behavior was reinforced during 100 min of baseline (e.g., ten 10-min baseline sessions) rather than 30 min of baseline (e.g., three 10-min baseline sessions). The same relation is also observed under shorter exposures to treatment contingencies: The shorter the period that alternative behavior is reinforced during treatment, the greater the degree of resurgence (cf. Greer et al., 2020, 2023). This outcome means that an increase in challenging behavior may be more substantial during schedule thinning if the FCR was reinforced during 30 min of treatment (e.g., three 10-min treatment sessions) rather than 100 min of treatment (e.g., ten 10-min treatment sessions).

There is also an apparent relation between response rates of the target behavior during baseline and resurgence. Higher rates of challenging behavior during baseline are associated with higher magnitudes of resurgence (e.g., Fisher et al., 2019; Podlesnik & Shahan, 2009). Meaning, an increase in challenging behavior (resurgence) would be greater during schedule thinning if challenging behavior was occurring at higher levels during baseline than if challenging behavior was occurring at a lower levels during baseline (e.g., magnitude of resurgence may be higher if challenging behavior was occurring at six responses per minute during baseline than at two responses per minute). Not surprisingly, beyond programmed schedules of reinforcement, resurgence is also affected by the history of reinforcement vis-à-vis the obtained rates of reinforcement. Again, higher magnitude resurgence may be associated with higher obtained rates of reinforcement for alternative behavior (e.g., Fisher et al., 2018; Romani et al., 2016).

Although such preliminary findings may explain some of the sources of control over resurgence, additional research in this area is needed. Apart from the limited prospective experimental data in these areas, a number of findings are mixed or otherwise difficult to replicate in clinical settings. For example, Greer et al. (2023) recently found that increases in treatment duration did not correspond with decreases in resurgence magnitude in an applied context. Irwin Helvey et al. (2023) also recently demonstrated that programming leaner rates of reinforcement during treatment did not mitigate resurgence to a greater degree than programming denser rates, running counter to some prior work in this area (e.g., Fisher et al., 2018). More research is needed to bridge the basic–applied research continuum and investigate the degree to which the relations between these variables and resurgence are evident in clinical settings.

The retrospective analysis of clinical data following the implementation of DRA and schedule thinning is an established method for evaluating the extent to which environmental variables affect resurgence is (e.g., Shahan & Greer, 2021). For example, Falligant, Hagopian, et al. (2022) obtained the data of 19 patients in an outpatient clinic whose prescribed treatment included FCT and schedule thinning. They evaluated whether there was a correlation between the magnitude of resurgence during schedule thinning and the downshift in reinforcement step size (i.e., downshift). In their reanalysis, Falligant, Hagopian, et al. observed a statistically significant positive relation between the rate of resurgence and the size of the downshift in reinforcement (e.g., schedule-thinning) step. These findings are similar to those described by Shahan and Greer (2021) and to the results of some basic experimental analyses (e.g., Shahan et al., 2020).

The purpose of the current analysis was to examine the generality of additional experimental findings from the resurgence literature to clinical samples. Specifically, we conducted a retrospective consecutive controlled case series (Hagopian, 2020) by reanalyzing clinical data sets from Kranak and Falligant (2021) and Falligant, Chin, and Kurtz (2022). The objective of the current study was to evaluate the relation between resurgence and several variables including (a) phase duration, (b) baseline and treatment response rate, (c) obtained rates of reinforcement, and (d) downshift in reinforcement step size of DRA-based treatments for challenging behavior.

METHOD

Participants and setting

Data were obtained for reanalysis from two previously published retrospective analyses on the prevalence of resurgence for inpatient (Kranak & Falligant, 2021) and intensive outpatient (Falligant, Chin, & Kurtz, 2022) cases. Therefore, participants included in this analysis were 40 individuals admitted to a hospital-based inpatient or outpatient unit for the assessment and treatment of challenging behavior and who were discharged between 2000 and 2019 (see Table 1 in Kranak & Falligant, 2021, and Falligant, Chin, & Kurtz, 2022, for participant demographic information). Across the 40 participants, we evaluated 461 applications of FCT with extinction and schedule thinning in which there was a demonstration of experimental control with a single-case experimental design. All treatment sessions took place in therapy rooms within the inpatient and outpatient clinics, and they were conducted by trained therapists under the supervision of doctoral-level psychologists and Board Certified Behavior Analysts.

Data preparation and analysis

Resurgence in the current analysis was calculated using the procedures described by Shahan and Greer (2021). Specifically, resurgence was examined by first converting the rate of challenging behavior during treatment and schedule-thinning sessions to a proportion of baseline. Proportion of baseline was calculated by dividing the rate of challenging behavior in a single treatment or schedule-thinning session by the mean rate of challenging behavior during baseline. The prechange period was considered the last treatment session before schedule thinning was implemented. The postchange period was considered the first three sessions that occurred following the initial schedule-thinning step change. If fewer than three sessions were conducted in the initial schedule-thinning step, then we used all available data (i.e., data from only one or two sessions). Then, resurgence was said to occur if the rate of challenging behavior (as proportion of baseline) in the postchange period exceeded that in the prechange period (e.g., Shahan & Greer, 2021). If resurgence occurred, then we also calculated the magnitude of resurgence. The magnitude of resurgence was calculated as the mean rate of challenging behavior of the entire postchange period (i.e., the first three sessions following the initial schedule-thinning step change) as a proportion of baseline.

We examined the relation between the magnitude of resurgence and multiple variables including phase duration, response rate, obtained duration of reinforcement, and downshift in reinforcement step size.2 To evaluate phase duration, we examined the number of sessions and the total duration (minutes) of each condition (i.e., baseline, treatment, and schedule thinning). To evaluate response rates, we calculated the rate (responses/minute) of (a) challenging behavior during baseline and treatment and (b) FCRs during baseline, treatment, and schedule thinning. To evaluate reinforcement duration, we calculated the total duration (minutes) that programmed reinforcers were accessed during baseline and treatment. To examine downshifts in the availability of alternative reinforcement, we evaluated the absolute and relative decrease in reinforcement that was available (i.e., the relative duration of the discriminative stimulus and S-delta components of the multiple schedule) during schedule thinning. The absolute decrease in reinforcement was evaluated as the total number of seconds in which reinforcement for FCRs was unavailable in the first schedule-thinning session. The relative decrease in reinforcement was evaluated as the proportional downshift in available reinforcement, which was calculated by dividing the duration (seconds) that reinforcement for FCRs was unavailable in the first schedule-thinning session by the duration (seconds) that reinforcement for FCRs was available in the last treatment session (Shahan & Greer, 2021; see also Davis et al., 2023). Finally, we calculated Pearson’s correlation coefficients to quantify the relation between the magnitude of resurgence and each of our test variables (i.e., phase duration, response rates, obtained duration of reinforcement, and downshift in reinforcement step size).

RESULTS

Table 1 includes descriptive and correlational statistics for each variable. First, we evaluated whether there was a relation between the magnitude of resurgence and phase duration. Again, phase duration was calculated separately as the number of sessions and the total number of minutes of each phase. Figure 1 depicts the relation between the magnitude of resurgence and the number of sessions in baseline, treatment, or schedule thinning. The mean number of sessions conducted in baseline equaled 8.24 (range: 3–24, SD = 4.19), in treatment equaled 12.30 (range: 3–51, SD = 9.08), and in schedule thinning equaled 3.83 (range: 1–15, SD = 2.88). There was no statistically significant relation between the magnitude of resurgence and the number of sessions of baseline, r(46) = −.02, treatment r(46) = .02, or schedule thinning r(46) = .11. Figure 2 depicts the relation between the magnitude of resurgence and the total duration (minutes) of baseline, treatment, or schedule thinning. The mean time (minutes) during baseline equaled 81.74 (range: 30.00–235.00, SD = 42.53), during treatment equaled 127.75 (range: 30.00–510.00, SD = 95.54), during schedule thinning equaled 42.69 (range: 10.00–225.00, SD = 39.91). Thus, when session duration was examined as a function of time spent in session, as opposed to the number of sessions, there was a weak, nonsignificant positive relation between the magnitude of resurgence and the duration (minutes) of baseline, treatment, and schedule thinning, r(46) = .16, r(46) = .10, and r(46) = .26, respectively.

TABLE 1.

Descriptive statistics and correlations for study variables.

Measure Range Median Mean SD Correlation
Min Max r p
Number of sessions
 BL 3.00 24.00 7.50 8.24 4.19 −0.02 .90
 TX 3.00 51.00 9.50 12.30 9.08 0.02 .90
 ST 1.00 15.00 3.00 3.83 2.88 0.11 .47
Duration (min)
 BL 30.00 235.00 70.00 81.74 42.53 0.16 .29
 TX 30.00 510.00 95.00 127.75 95.54 0.10 .51
 ST 10.00 225.00 30.00 42.69 39.91 0.26 .08
Mean rate of CB
 BL 0.08 34.05 1.84 3.53 5.45 −0.17 .08
 TX 0.00 4.37 0.17 0.55 0.85 −0.11 .26
 ST 0.00 4.50 0.18 0.68 1.06 - -
Mean rate of FCR
 BL 0.00 0.37 0.00 0.04 0.10 0.06 .72
 TX 0.12 6.98 1.24 1.57 1.45 −0.14 .36
 ST 0.02 10.00 1.10 1.46 1.65 −0.19 .22
Total duration of reinforcement (min)
 BL 0.14 86.04 30.69 33.06 22.92 −0.02 .92
 TX 3.07 223.12 44.46 59.04 50.40 0.20 .24
Schedule thinning step
 Downshift (s) 2.00 1,200.00 30.00 106.78 222.32 0.50* <.001*
 Downshift (prop) 0.004 1.00 0.05 0.14 0.39 0.64* <.001*
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Note: BL = baseline; TX = treatment; ST = schedule thinning; CB = challenging behavior; FCR = functional communication response; Downshift (s) = absolute size of the downshift in duration of reinforcer availability; Downshift (prop) = proportional size of the downshift in duration of reinforcer availability. A separate Pearson’s correlational coefficient (r) was computed between the magnitude of resurgence and each individual test variable.

*

p < .05.

FIGURE 1.

FIGURE 1

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The relation between the magnitude of resurgence and the number of sessions in baseline, treatment, or schedule thinning. Each data point depicts the magnitude of resurgence and the number of sessions in the respective condition (i.e., baseline, treatment, or schedule thinning). The dashed line is a trend line fit to the data via simple linear regression.

FIGURE 2.

FIGURE 2

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The relation between the magnitude of resurgence and the total duration (minutes) of baseline, treatment, or schedule thinning. Each data point depicts the magnitude of resurgence and the total duration (minutes) of the respective condition (i.e., baseline, treatment, or schedule thinning). The dashed line is a trend line fit to the data via simple linear regression.

Second, we evaluated whether there was a relation between the magnitude of resurgence and the rate of challenging behavior that occurred prior to schedule thinning, which is depicted in Figure 3. The mean rate (responses per minute) of challenging behavior in baseline equaled 3.53 (range: 0.08–34.05, SD = 5.45) and treatment equaled 0.55 (range: 0.00–4.37, SD = 0.85). The results of a Pearson’s correlation indicated that there was a weak, nonsignificant negative relation between the magnitude of resurgence and the mean rate of challenging behavior during baseline, r(46) = −.17 and treatment r(46) = −.11.

FIGURE 3.

FIGURE 3

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The relation between the magnitude of resurgence and the mean rate of challenging behavior in baseline and treatment. Each data point depicts the magnitude of resurgence and the mean rate of challenging behavior in the respective condition (i.e., baseline or treatment). The dashed line is a trend line fit to the data via simple linear regression.

Third, we evaluated whether there was relation between the magnitude of resurgence and the rate of FCRs that occurred across each phase, which is depicted in Figure 4. The mean rate of the FCRs in baseline was 0.04 (range: 0.00–0.37, SD = 0.10), in treatment was 1.57 (range: 0.21–6.98, SD = 1.45), and in schedule-thinning was 1.46 (range: 0.02–10.00, SD = 1.65). There was a weak, nonsignificant relation between the magnitude of resurgence and the mean rate of FCRs during baseline, r(46) = .06, treatment r(46) = −.14, and schedule thinning r(46) = −.19.

FIGURE 4.

FIGURE 4

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The relation between the magnitude of resurgence and the mean rate of functional communication responses in baseline, treatment, or schedule thinning. Each data point depicts the magnitude of resurgence and mean rate of functional communication responses in the respective condition (i.e., baseline, treatment, or schedule thinning). The dashed line is a trend line fit to the data via simple linear regression. FCRs = functional communication responses.

Fourth, we evaluated whether there was a relation between the magnitude of resurgence and the duration of reinforcement for challenging behavior (during baseline) or for FCRs (during treatment). Figure 5 depicts the relation between the magnitude of resurgence and the total duration (minutes) of reinforcement in baseline and treatment. The mean total duration (minutes) of reinforcement for challenging behavior during baseline was 33.06 (range: 0.14–86.04, SD = 22.92). There was a weak, nonsignificant negative relation between the magnitude of resurgence and the total duration of reinforcement of challenging behavior, r(30) = −.02. Comparatively, the mean total duration (minutes) of reinforcement for FCRs during treatment was 59.06 (range: 3.07–223.12, SD = 50.40). There was a weak, nonsignificant positive relation between the magnitude of resurgence and the total duration of reinforcement of FCRs during treatment, r(36) = .20. We also analyzed this relation using obtained rates of reinforcement (in lieu of duration of reinforcement) among the subset of participants where this could be calculated but similarly found no statistically significant relation between obtained rate of reinforcement and resurgence magnitude, r(10) = −.43, p = .215.

FIGURE 5.

FIGURE 5

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The relation between the magnitude of resurgence and the total duration of reinforcement (minutes) in baseline and treatment. Each data point depicts the magnitude of resurgence and the total duration of reinforcement in the respective condition (i.e., baseline or treatment). The dashed line is a trend line fit to the data via simple linear regression.

Finally, we evaluated whether there was a relation between the magnitude of resurgence and size of the downshift in reinforcement. The downshift in reinforcement step size was calculated as either the number of seconds that reinforcement for the alternative response was unavailable or the proportional downshift in reinforcement. Figure 6 depicts the relation between the magnitude of resurgence and proportional schedule-thinning downshift step size. The mean decrease in the duration of reinforcement (seconds) in the first schedule-thinning step was 106.78 (range: 2.00–1,200.00, SD = 222.32). The mean proportional downshift from treatment was 0.1 (range: 0.004–1.0, SD = 0.1). There was a strong, statistically significant positive relation between the magnitude of resurgence and the durational decrease in reinforcement, r(46) = 0.50, p < .001, as well as the proportional downshift in reinforcement, r(46) = 0.64, p < .001.

FIGURE 6.

FIGURE 6

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The relation between the magnitude of resurgence and the schedule-thinning downshift step size. Each data point depicts the magnitude of resurgence and the schedule-thinning downshift step size. The top panel depicts the absolute (s) downshift step size. The bottom panel depicts the relative (proportional) downshift step size. The dashed line is a trend line fit to the data via simple linear regression.

DISCUSSION

We examined variables that were specifically chosen because of their putative (but sometimes inconsistent; Greer et al., 2023; Irwin Helvey et al., 2023) relation to resurgence found in basic, translational, and/or applied analyses of resurgence (e.g., Fisher et al., 2019; Podlesnik & Shahan, 2009; Shahan et al., 2020; Smith & Greer, 2022; Winterbauer et al., 2013). Our results are consistent with findings from Greer et al. (2020, 2023) and Irwin Helvey et al. (2023), suggesting that treatment duration may not affect resurgence of challenging behavior. In addition, there was no apparent relation between resurgence magnitude and the other variables analyzed (i.e., phase duration, response rate, and obtained reinforcement).

There are several potential explanations for this discrepancy. For example, in the laboratory, target and alternative responses have known reinforcement histories that can be established and equated. In clinical application, that rarely, if ever, is the case. Behavioral histories for challenging behavior are, by definition, relatively well established for individuals referred for behavioral assessment and treatment services. For these individuals, shorter baseline phases at the clinic (or baseline phases with minimal reinforcement for challenging behavior) may not override the extended history of reinforcement for challenging behavior from the natural environment. Thus, there may be differences between what is predicted and obtained with respect to the magnitude of reinforcement based on variables like phase duration and obtained reinforcement.

Another factor that may affect the generality of laboratory resurgence findings to the clinic relates to presence or absence of appropriate alternative responses and when those alternatives were established in the client’s repertoire. The behavioral histories of the alternative response may vary from individual to individual. In some cases, established repertoires of alternative responses are present when the assessment and intervention process begins (Tiger et al., 2008). These alternative responses may be part of the response class with challenging behavior. In other cases, alternative responses must be established in the repertoire before intervention can begin. This difference in response class membership and history may affect the resurgence of challenging behavior as well as the predictions of quantitative models of resurgence (e.g., Laureano & Falligant, 2023).

Variables unrelated to the individuals’ behavioral histories may also have affected outcomes. Several studies have highlighted the fact that retrospective investigations of resurgence in clinical contexts necessarily sample a measure of clinicians’ decision making as part of these analyses (e.g., Falligant, Hagopian, et al., 2022). For example, clinicians may be more likely to conduct baseline phases for longer periods for patients at less risk for resurgence (e.g., milder, less persistent challenging behavior) and abbreviate baseline for patients at greater risk for resurgence (e.g., more severe and persistent challenging behavior).

That said, despite inherent limitations of the retrospective study of resurgence, the exponential relation between the downshift in alternative reinforcement and resurgence magnitude has been replicated across studies and research groups (e.g., Shahan & Greer, 2021). As Falligant, Hagopian, et al. (2022) already reported this relation using the same data set, our results by themselves do not provide any new support. However, these findings indicate the utility of the analytic approach used for this reanalysis given that we did replicate these known findings. Although there may be situations where it is advantageous to probe or initiate schedule thinning at leaner, terminal schedules (Hagopian et al., 2004), practitioners must be aware of the increased likelihood of challenging behavior in these situations.

Overall, resurgence, and relapse in general, continues to be an important area of clinical investigation related to intervention for challenging behavior. Although this area of research is ripe in opportunities for experimental analyses, clinically derived data offer a potentially more externally valid method of investigating the phenomenon than laboratory or other highly controlled investigations. Emerging findings using a retrospective approach have produced some uniformity. For example, the findings of the current analysis provided support for the relation between resurgence and the downshift in reinforcement during schedule thinning. However, a retrospective approach to relapse may also produce some inconsistency. For example, we did not observe a strong relation between resurgence and any other test variables tested. Thus, the inconsistencies identified between retrospective analysis of clinically derived data and experimental data should define a path for more research directed at understanding and mitigating resurgence.

ACKNOWLEDGMENTS

Brianna Laureano is now at the University of South Florida. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

CONFLICT OF INTEREST STATEMENT

All authors declare that they have no conflicts of interest to disclose.

ETHICS APPROVAL

These data were obtained during the course of providing clinical services that were sought by the individual and their guardian to address the clinical target described. The relevant party provided ongoing consent for treatment and approved all the procedures described. Institutional review board approval was obtained to disseminate these data.

1

There was a total of 47 combined treatment evaluations between Kranak and Falligant (2021) and Falligant, Chin, et al. (2022). However, portions of the data set from Application 17 in Kranak & Falligant were not available to permit several calculations. Therefore, the final application total in the current study is 46.

2

Downshifts in reinforcement during schedule thinning were previously analyzed by Falligant, Hagopian, et al. (2022). We reanalyzed the downshifts in reinforcement conducted by Falligant, Hagopian, et al. as an integrity check and because there is an empirically established relation between the size of the downshift in alternative reinforcement and resurgence magnitude of challenging behavior.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author, Dr. Brianna Laureano, upon reasonable request.

REFERENCES

  1. Betz AM, Fisher WW, Roane HS, Mintz JC, & Owen TM (2013). A component analysis of schedule thinning during functional communication training. Journal of Applied Behavior Analysis, 46(1), 219–241. 10.1002/jaba.23 [DOI] [PubMed] [Google Scholar]
  2. Briggs AM, Fisher WW, Greer BD, & Kimball RT (2018). Prevalence of resurgence of destructive behavior when thinning reinforcement schedules during functional communication training. Journal of Applied Behavior Analysis, 51(3), 620–633. 10.1002/jaba.472 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Bruzek JL, Thompson RH, & Peters LC (2009). Resurgence of infant caregiving responses. Journal of the Experimental Analysis of Behavior, 92(3), 327–343. 10.1901/jeab.2009-92-327 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Davis TN, Akers JS, Exline E, Avery SK, & Wicker M (2023). Reinforcement schedule thinning following functional communication training: A systematic review. Behavioral Interventions, 38(4), 1–26. 10.1002/bin.1959 [DOI] [Google Scholar]
  5. Epstein R (1985). Extinction-induced resurgence: Preliminary investigations and possible applications. Psychological Record, 35, 143–153. 10.1007/BF03394918 [DOI] [Google Scholar]
  6. Falligant JM, Chin MD, & Kurtz PF (2022). Renewal and resurgence of severe problem behavior in an intensive outpatient setting: Prevalence, magnitude, and implications for practice. Behavioral Interventions, 37(3), 909–924. 10.1002/bin.1878 [DOI] [Google Scholar]
  7. Falligant JM, Hagopian LP, Kranak MP, & Kurtz PF (2022). Quantifying increases in problem behavior following downshifts in reinforcement: A retrospective analysis and replication. Journal of the Experimental Analysis of Behavior, 118(1), 148–155. 10.1002/jeab.769 [DOI] [PubMed] [Google Scholar]
  8. Fisher WW, Fuhrman AM, Greer BD, Mitteer DR, & Piazza CC (2020). Mitigating resurgence of destructive behavior using the discriminative stimuli of a multiple schedule. Journal of the Experimental Analysis of Behavior, 113(1), 263–277. 10.1002/jeab.552 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Fisher WW, Greer BD, Fuhrman AM, Saini V, & Simmons CA (2018). Minimizing resurgence of destructive behavior using behavioral momentum theory. Journal of Applied Behavior Analysis, 51(4), 831–853. 10.1002/jaba.499 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Fisher WW, Saini V, Greer BD, Sullivan WE, Roane HS, Fuhrman AM, Craig AR, & Kimball RT (2019). Baseline reinforcement rate and resurgence of destructive behavior. Journal of the Experimental Analysis of Behavior, 111(1), 75–93. 10.1002/jeab.488 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Fuhrman AM, Fisher WW, & Greer BD (2016). A preliminary investigation on improving functional communication training by mitigating resurgence of destructive behavior. Journal of Applied Behavior Analysis, 49(4), 884–899. 10.1002/jaba.338 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Greer BD, Fisher WW, Retzlaff BJ, & Fuhrman AM (2020). A preliminary evaluation of treatment duration on the resurgence of destructive behavior. Journal of the Experimental Analysis of Behavior, 113(1), 251–262. 10.1002/jeab.567 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Greer BD, Fisher WW, Romani PW, & Saini V (2016). Behavioral momentum theory: A tutorial on response persistence. The Behavior Analyst, 39(2), 269–291. 10.1007/s40614-016-0050-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Greer BD, Shahan TA, Fisher WW, Mitteer DR, & Fuhrman AM (2023). Further evaluation of treatment duration on the resurgence of destructive behavior. Journal of Applied Behavior Analysis, 56(1), 166–180. 10.1002/jaba.956 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Greer BD, Shahan TA, Irwin Helvey C, Fisher WW, Mitteer DR, & Fuhrman AM (in press). Resurgence of destructive behavior following decreases in alternative reinforcement: A prospective analysis. Journal of Applied Behavior Analysis. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Hagopian LP (2020). The consecutive controlled case series: Design, data‐analytics, and reporting methods supporting the study of generality. Journal of Applied Behavior Analysis, 53(2), 596–619. 10.1002/jaba.691 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Hagopian LP, Toole LM, Long ES, Bowman LG, & Lieving GA (2004). A comparison of dense‐to‐lean and fixed lean schedules of alternative reinforcement and extinction. Journal of Applied Behavior Analysis, 37(3), 323–338. 10.1901/jaba.2004.37-323 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Haney SD, Greer BD, Mitteer DR, & Randall KR (2022). Relapse during the treatment of pediatric feeding disorders. Journal of Applied Behavior Analysis, 55(3), 704–726. 10.1002/jaba.913 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Irwin Helvey C, Fisher WW, Greer BD, Fuhrman AM, & Mitteer DR (2023). Resurgence of destructive behavior following differential rates of alternative reinforcement. Journal of Applied Behavior Analysis, 56(4), 804–815. 10.1002/jaba.1010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Kranak MP, & Falligant JM (2021). Further investigation of resurgence following schedule thinning: Extension to an inpatient setting. Behavioral Interventions, 36(4), 1003–1012. 10.1002/bin.1831 [DOI] [Google Scholar]
  21. Laureano B, & Falligant JM (2023). Modeling behavioral persistence with resurgence as choice in context (RaC2): A tutorial. Behavior Analysis in Practice, 16(2), 640–651. 10.1007/s40617-023-00796-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Mitteer DR, Greer BD, Randall KR, & Haney SD (2022). On the scope and characteristics of relapse when treating severe destructive behavior. Journal of Applied Behavior Analysis, 55(3), 688–703. 10.1002/jaba.912 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Muething C, Pavlov A, Call N, Ringdahl J, & Gillespie S (2021). Prevalence of resurgence during thinning of multiple schedules of reinforcement following functional communication training. Journal of Applied Behavior Analysis, 54(2), 813–823. 10.1002/jaba.791 [DOI] [PubMed] [Google Scholar]
  24. Perrin J, Morris C, & Kestner K (2021). Resurgence of clinically relevant behavior: A systematic review. Education and Treatment of Children, 45(1), 85–104. 10.1007/s43494-021-00054-2 [DOI] [Google Scholar]
  25. Podlesnik CA, Ritchey CM, Waits J, & Gilroy SP (2023). A comprehensive systematic review of procedures and analyses used in basic and preclinical studies of resurgence, 1970–2020. Perspectives on Behavior Science, 46(1), 137–184. 10.1007/s40614-022-00361-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Podlesnik CA, & Shahan TA (2009). Behavioral momentum and relapse of extinguished operant responding. Learning & Behavior, 37(4), 357–364. 10.3758/LB.37.4.357 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Ringdahl JE, & St. Peter C (2017). Resurgence: The unintended maintenance of problem behavior. Education and Treatment of Children, 40(1), 7–26. 10.1353/etc.2017.0002 [DOI] [Google Scholar]
  28. Romani PW, Ringdahl JE, Wacker DP, Lustig NH, Vinquist KM, Northup J, Kane AM, & Carrion DP (2016). Relations between rate of negative reinforcement and the persistence of task completion. Journal of Applied Behavior Analysis, 49(1), 122–137. 10.1002/jaba.252 [DOI] [PubMed] [Google Scholar]
  29. Schindler HR, & Horner RH (2005). Generalized reduction of problem behavior of young children with autism: Building trans-situational interventions. American Journal on Mental Retardation, 110(1), 36–47. 10.1352/0895-8017(2005)110<36:GROPBO>2.0.CO;2 [DOI] [PubMed] [Google Scholar]
  30. Shahan TA, & Greer BD (2021). Destructive behavior increases as a function of reductions in alternative reinforcement during schedule thinning: A retrospective quantitative analysis. Journal of the Experimental Analysis of Behavior, 116(2), 243–248. 10.1002/jeab.708 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Shahan TA, Browning KO, Nist AN, & Sutton GM (2020). Resurgence and downshifts in alternative reinforcement rate. Journal of the Experimental Analysis of Behavior, 114(2), 163–178. 10.1002/jeab.625 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Smith SW, & Greer BD (2022). Phase duration and resurgence. Journal of the Experimental Analysis of Behavior, 117(1), 91–104. 10.1002/jeab.725 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Tiger JH, Hanley GP, & Bruzek J (2008). Functional communication training: A review and practical guide. Behavior Analysis in Practice, 1(1), 16–23. 10.1007/BF03391716 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Winterbauer NE, Lucke S, & Bouton ME (2013). Some factors modulating the strength of resurgence after extinction of an instrumental behavior. Learning and Motivation, 44(1), 60–71. 10.1016/j.lmot.2012.03.003 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, Dr. Brianna Laureano, upon reasonable request.

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