Noncontingent Reinforcement (NCR): A Practitioner's Guide to Schedule Design, Function Matching, and Thinning

The founding logic: abolishing operations via satiation

NCR suppresses problem behavior through two complementary mechanisms. The primary one is the abolishing operation: continuous or near-continuous access to the maintaining reinforcer reduces the current value of that reinforcer, which in turn reduces the probability of any operant maintained by it Phillips et al. (2017). A 27-consecutive-application case series from Kennedy Krieger's inpatient unit provides the strongest single aggregate dataset for this mechanism in clinical practice: Phillips, Iannaccone, Rooker, and Hagopian (2017) found that NCR plus extinction produced clinically significant reductions — defined as ≥90% reduction from baseline — in 89% of socially maintained cases across a mixed population of children and adults with intellectual disability Phillips et al. (2017). Importantly, that same study demonstrated that automatic-reinforcement cases — where NCR cannot easily deliver the maintaining reinforcer — needed supplemental procedures in 5 of 9 cases, confirming that the AO mechanism is only as strong as the functional match Phillips et al. (2017).

The secondary mechanism is response competition: dense noncontingent delivery partially competes with whatever reinforcement contingency was previously maintaining problem behavior, creating a direct contest between the scheduled delivery and the behavior-contingent one. Translational work by Kelley and colleagues (2017) complicated the clinical picture here — NCR delivered at high densities (up to 100% of session time) also suppressed adaptive responding, specifically manding in a child with developmental disabilities and keypecking in pigeons Kelley et al. (2017). The mechanism is the same: an abundance of freely available reinforcement reduces the relative value of engaging in any operant, good or bad. This is the direct empirical basis for the clinical rule that NCR schedules should be thinned as quickly as data permit.

Fixed-time versus variable-time: which schedule and why it matters

Fixed-time (FT) schedules deliver the reinforcer at exactly the same interval across every delivery — FT 60 s means the reinforcer arrives at 0:60, 2:00, 3:00, and so on. Variable-time (VT) schedules deliver the reinforcer at an average interval with variability around that mean — VT 60 s might deliver at 47 s, then 71 s, then 63 s. The distinction has practical consequences. López-Tolsa and Pellón (2025) demonstrated in a well-controlled series of rat experiments that FT schedules produce temporally patterned adjunctive behaviors in the inter-reinforcer interval: organisms begin to anticipate the delivery and organize behavior around the timing López-Tolsa & Pellón (2025). In classrooms or therapy rooms, this translates to the observation that clients on FT-NCR schedules may engage in pre-reinforcer anticipatory behaviors — seeking attention, standing up, approaching the therapist — in the final seconds before scheduled delivery, which can inadvertently pattern behavior toward the reinforcer even without a contingency López-Tolsa & Pellón (2025). VT schedules remove that predictability. For most clinical implementations, start with an FT schedule during treatment initiation (easier to implement reliably) and transition to VT as the schedule is thinned, a progression well-documented in the perseverative speech case study by Migan-Gandonou Horr and Michael (2021): treatment began at FT 90 s and thinned to VT 10 min over sessions while extinction remained in effect throughout Migan-Gandonou Horr & Michael (2021).

Deriving initial schedule density from baseline data

The most common clinical error in NCR implementation is setting the initial schedule too thin, which means the reinforcer arrives less frequently than it was available when problem behavior was working. The standard derivation method is to compute the mean inter-response time (IRT) for problem behavior from baseline data and then set the initial NCR interval at or slightly below that IRT. If a child's baseline data show attention-maintained aggression occurring at approximately one episode every 3 minutes (mean IRT = 180 s), the initial NCR schedule should deliver attention at roughly FT 90–120 s — dense enough that the reinforcer is arriving before the motivation to aggress typically builds to action. Migan-Gandonou Horr and Michael (2021) demonstrated this derivation explicitly, beginning NCR intervals near the client's observed inter-response time and thinning only after behavior fell to ≤20% of baseline rates across five consecutive sessions Migan-Gandonou Horr & Michael (2021).

Fritz and colleagues (2017) studied NCR without extinction in five children with ASD whose problem behavior was socially maintained, finding that continuous NCR (dense enough to approximate free access) produced immediate reductions in all five, and schedule thinning to FT 5 min maintained low rates in three participants Fritz et al. (2017). For the two in whom behavior rebounded during thinning, adding differential reinforcement of alternative behavior (DRA) without introducing extinction was sufficient to suppress resurgence — a clinically important finding because it means practitioners can avoid extinction bursts in cases where extinction is contraindicated (e.g., aggression in a group setting) Fritz et al. (2017).

Function-matched NCR: delivering the maintaining reinforcer

The canonical NCR procedure delivers the specific reinforcer that maintains problem behavior — attention for attention-maintained behavior, brief breaks from demands for escape-maintained behavior, access to preferred items for tangible-maintained behavior. The logic is clean: if aggression is maintained by attention, delivering attention on a dense time-based schedule should abolish the motivation to aggress for attention because attention is already plentiful.

The empirical picture is slightly more nuanced. Newman, Roscoe, Errera, and Davis (2021) ran a direct comparison for four children with escape-maintained problem behavior, pitting function-matched NCR (time-based delivery of brief escape from demands) against arbitrary NCR (time-based delivery of edibles, not the maintaining reinforcer), both implemented without extinction Newman et al. (2021). Two of the four participants responded equally well to arbitrary NCR, suggesting that for some individuals an alternative reinforcer can produce equivalent suppression without the logistical difficulty of delivering continuous escape in an instructional setting Newman et al. (2021). Critically, individual response patterns were not predictable from any pre-treatment variable in this dataset, making individual empirical testing — rather than universal reliance on function matching — the defensible approach Newman et al. (2021). The practical recommendation: start with the function-matched reinforcer; if logistical constraints make that impossible, probe an arbitrary alternative under reversal or multielement conditions before committing to it.

The NCR + extinction package

In clinical practice, NCR is rarely implemented without extinction. The most common package — the one Phillips et al. (2017) applied across 27 consecutive inpatient cases — delivers the maintaining reinforcer on a time-based schedule and simultaneously places problem behavior on extinction, meaning the behavior no longer produces the reinforcer Phillips et al. (2017). This package combines the motivational effect of NCR (abolishing the EO) with the direct contingency effect of extinction (breaking the operant link between behavior and reinforcer). Together they create a situation in which the reinforcer is freely available and problem behavior no longer works to produce it — two separate reasons to stop.

The trade-off is the extinction burst: when problem behavior is placed on extinction, response rates often temporarily increase before decreasing. For severe self-injury or aggression, a burst can represent significant injury risk. Fritz et al.'s (2017) finding that NCR without extinction plus DRA can maintain low rates after thinning provides the empirical foundation for extinction-free protocols in high-risk cases Fritz et al. (2017). The decision between including or omitting extinction is a risk-benefit clinical judgment — not a default.

Schedule thinning: the plan you must write before you start

Schedule thinning is the systematic reduction of NCR delivery frequency over time, from the dense initial schedule toward a rate that is sustainable in the natural environment. Without a written thinning plan, NCR creates dependency: the behavior remains low only as long as the dense schedule is maintained, and the problem resurfaces when implementation lapses.

The Migan-Gandonou Horr and Michael (2021) case study provides the most operationally specified thinning protocol in the corpus: thin each step only when the target behavior remains at or below 20% of baseline across five consecutive sessions, move from FT to VT at intermediate steps, and document each thinning decision in the behavior plan Migan-Gandonou Horr & Michael (2021). Fritz et al. (2017) thinned to FT 5 min successfully in three of five participants; for the two who showed resurgence, the data-decision rule was clear — resurgence at any thinning step triggers DRA addition rather than returning to a denser NCR schedule Fritz et al. (2017). Both protocols share the same architecture: small thinning steps, objective data-decision rules, and a pre-specified fallback procedure.

Translational work on contingency discrimination training by Shahan and colleagues (2026) offers an additional thinning principle from the basic literature: brief, predictable periods without scheduled reinforcement during the thinning phase may actually sharpen the learner's discrimination that problem behavior also goes unreinforced during these periods, reducing the risk of resurgence (Shahan et al., 2026). The applied translation is still preliminary, but it supports the practice of using VT rather than FT at thinned steps — unpredictability reduces anticipatory behavioral patterns that can become stepping stones toward relapse.

NCR across settings

Clinic. The strongest NCR evidence base comes from inpatient and outpatient clinical settings, anchored by Phillips et al.'s 27-consecutive-application retrospective case series demonstrating ≥90% reduction in 89% of socially maintained cases Phillips et al. (2017). Clinic-based NCR has also been evaluated in specialty contexts: Nguyen and colleagues (2025) implemented NCR as the opening phase of a telehealth-guided dental desensitization protocol for adults with IDD, using continuous access to RAISD-identified preferred items from the moment of check-in to reduce dental avoidance behavior, with implementation supervised remotely by a behavior analyst working with dental staff (Nguyen et al., 2025). The dental desensitization case illustrates a practical NCR principle: low-effort continuous access to preferred items can be the first antecedent layer in any novel or aversive procedure, not just in formal behavior reduction programs.

School. Attention-maintained disruptive behavior is among the most prevalent targets in school settings, and NCR is well-positioned for it: teachers can deliver brief attention comments on a VT schedule without disrupting instruction. Noel and Getch (2016) demonstrated an afterschool delivery model in which two preservice staff implemented fixed-time NCR for two elementary-aged students with ASD during an afterschool program, producing marked reductions in disruptive behavior in the classroom the next school day — without any changes to classroom contingencies Noel & Getch (2016). This is a clinically significant finding because it means NCR can be delivered in a lower-stakes setting (afterschool) to produce generalized benefit in a higher-demand one (classroom), lowering implementation burden on general education teachers Noel & Getch (2016). For in-class implementation, the practical approach is a brief attention delivery every 3–5 minutes delivered to the whole class or individually while instruction continues, timed with a vibrating watch rather than requiring teacher attention to a timer.

Home. NCR in home settings typically takes the form of pre-session access to preferred activities before transitions (e.g., free access to preferred tablet time before bath time that typically evokes escape behavior), or scheduled brief access to parental attention during dinner preparation before attention-seeking escalates. Parent implementation requires careful procedural training: parents must learn to deliver reinforcement independent of behavior — not contingent on compliance or good behavior — and to avoid inadvertently reinforcing problem behavior by delivering the reinforcer within seconds of an episode.

Residential and intensive care. NCR in residential settings for severe problem behavior is supported most extensively by Phillips et al. (2017), whose 27-case series across an inpatient unit found the NCR + extinction package sufficient for socially maintained cases but requiring supplemental procedures for the majority of automatically reinforced cases Phillips et al. (2017).

Pediatric feeding. Van Arsdale, Ibáñez, and Vollmer (2024) conducted a concise systematic review of NCR in pediatric feeding intervention across 15 single-case studies published 2018–2022, finding that NCR appeared in the literature most often as continuous access to a preferred food or toy during mealtimes and was consistently associated with decreases in inappropriate mealtime behavior and increases in food acceptance Van Arsdale et al. (2024). The review flagged wide procedural variation and limited parametric data on optimal NCR schedules in feeding contexts, indicating this is a setting where standard NCR parameter guidance from the problem behavior literature needs empirical calibration Van Arsdale et al. (2024).

The NCR evidence base is strongest at the single-subject experimental design (SCED) level, with well-controlled reversal, multiple-baseline, and multielement designs across clinic, school, and residential populations Phillips et al. (2017) Fritz et al. (2017) Newman et al. (2021). The best aggregate evidence is Phillips et al.'s (2017) 27-consecutive-application case series, which is the closest the field has to epidemiological data on NCR outcomes for severe behavior Phillips et al. (2017). Translational work from basic operant laboratories — particularly Kelley et al. (2017) on response competition Kelley et al. (2017) and López-Tolsa and Pellón (2025) on FT schedule behavioral patterning López-Tolsa & Pellón (2025) — provides the mechanistic underpinning for clinical decision rules. Narrative reviews (Gover, Hanley, & Ruppel, 2022) document a cross-species preference for contingent over noncontingent reinforcement that practitioners should factor into long-term planning Gover et al. (2022). The field lacks large-scale RCTs comparing NCR packages head-to-head with FCT or DRO for the same population, which limits quantitative effect size comparisons across procedures.

NCR alone vs. NCR + extinction vs. differential reinforcement vs. FCT

The choice of which procedure to use — or which combination — hinges on the function, the severity, the setting, and what the client needs to learn.

Use NCR + extinction as the default package for socially maintained problem behavior of moderate-to-severe intensity in clinic or intensive settings. The 27-case series provides the strongest empirical support for this combination Phillips et al. (2017). Extinction is the active ingredient that prevents problem behavior from accidentally recruiting the reinforcer during NCR delivery; NCR is the ingredient that removes motivation to try.

Use NCR without extinction when extinction is contraindicated — for example, when placing aggression on extinction would create injury risk to staff or peers in a group setting, or when institutional constraints prevent full extinction. Fritz et al. (2017) demonstrated this path is viable with a DRA backup for resurgence management Fritz et al. (2017).

Prefer FCT over NCR when the treatment goal includes teaching a functional communication response. FCT is the most behavior-analytically elegant solution because it replaces problem behavior with an adaptive alternative that contacts the same maintaining reinforcer; NCR does not teach anything. Gover, Hanley, and Ruppel's (2022) review of preference for contingent reinforcement argues that organisms — including children with developmental disabilities — reliably prefer contingent access to noncontingent access when given the choice Gover et al. (2022). That preference should inform long-term planning: NCR creates satiation, but it does not build the communicative or adaptive repertoire that will sustain low problem behavior rates once the dense schedule is thinned.

Prefer DRO over NCR when the target is specific topographies that occur at a manageable rate and staff can reliably observe the reset interval. DRO is response-contingent: it delivers the reinforcer after a specified interval only if the target behavior has not occurred. This means the contingency is maintained, which avoids the response-competition problem Kelley et al. (2017) identified for adaptive responding Kelley et al. (2017). DRO is harder to implement fidelity because it requires monitoring behavior during the interval; NCR is easier because delivery is time-based regardless of behavior.

Consider NCR as a first layer in novel or high-aversion contexts (dental, medical procedures, new settings) before introducing contingent reinforcement or demand fading. Nguyen et al. (2025) provide the telehealth-guided dental example (Nguyen et al., 2025). The logic is that NCR reduces state motivation before the aversive stimulus is introduced, lowering the ceiling for problem behavior at the outset.

The multi-function case: when functional analysis identifies both attention and escape as maintaining functions, NCR for both simultaneously requires delivering two different reinforcers on two separate schedules — logistically demanding but documented in single cases. In these situations, FCT with a single communication response that accesses a combined escape-and-attention consequence (the Hanley-style synthesized contingency) is typically more efficient.

See the What the Research Says section for setting-specific breakdowns. The short-form decision matrix:

• Clinic / inpatient severe behavior: NCR + extinction first; supplement for automatic-reinforcement cases Phillips et al. (2017)
• School / classroom: attention-maintained disruption — VT attention delivery every 3–5 min; train after-school staff for no-classroom-change implementation Noel & Getch (2016)
• Home: pre-session access before aversive transitions; parent training must include delivery-independent-of-behavior criterion
• Feeding: continuous access to preferred item or small edible during mealtimes; monitor satiation to target food Van Arsdale et al. (2024)
• Dental / medical / novel aversive: NCR as first antecedent layer, then add contingent reinforcement or graduated exposure as tolerated (Nguyen et al., 2025)

1. Initial schedule too thin. If the NCR interval is longer than the IRT of problem behavior at baseline, the client is still being reinforced at a higher rate by problem behavior than by the NCR schedule. The result is that problem behavior persists or even increases because the function-matched reinforcer is still contacting the behavior-contingent pathway. Derivation of the initial schedule from baseline IRT data is non-optional.

2. Initial schedule too rich (creating dependency without thinning). The opposite error: setting a continuous or near-continuous NCR schedule and leaving it there indefinitely. Kelley et al. (2017) demonstrated that dense NCR suppresses not only problem behavior but also adaptive responding — mands, skill-acquisition targets, and academic engagement Kelley et al. (2017). A dense schedule is a treatment-initiation tool, not a maintenance tool. Every NCR behavior plan must include a thinning schedule with written data-decision rules.

3. No thinning plan. The most common real-world failure mode. The BCBA sets NCR, sees dramatic behavior reduction, and then moves on to other cases without documenting a thinning protocol. When the implementing therapist or teacher inevitably becomes inconsistent with delivery, behavior resurges and the team interprets this as "NCR stopped working" rather than "NCR was never designed to work at this density indefinitely."

4. Adventitious reinforcement. Because NCR delivery is time-based, a delivery will occasionally coincide temporally with an instance of problem behavior. If that coincidence happens repeatedly — especially at the start of treatment when the schedule is dense — the delivery may begin to function as a contingent consequence and accidentally reinforce the behavior. Practitioners should monitor for systematic timing patterns (does delivery consistently follow within a few seconds of problem behavior?) and adjust delivery timing or involve a second staff member to confirm that delivery is not contingent on behavior.

5. Ignoring extinction. Implementing NCR without extinction and without a DRA plan leaves a pathway through which problem behavior can still contact the reinforcer. A child who receives NCR attention every 2 minutes but whose aggression still produces immediate attention has not experienced an actual abolishing operation for aggression-contingent attention. NCR without extinction requires active monitoring and DRA as a backup, not just the hope that schedule density will be sufficient Fritz et al. (2017).

6. Function mismatch. Delivering food on a time-based schedule to reduce escape-maintained problem behavior is unlikely to produce suppression via the AO mechanism because food is not the maintaining reinforcer. The mechanism for any meaningful behavior change in that scenario would be response competition (the client is eating or attending to food rather than engaging in problem behavior), which is a weaker and more context-dependent effect. Always verify function before selecting the NCR reinforcer. When function-matched delivery is logistically impractical, probe alternatives empirically as Newman et al. (2021) did Newman et al. (2021).

7. No monitoring of adaptive responding. Because dense NCR can suppress the adaptive responses you want to increase Kelley et al. (2017), behavior plans that include NCR should also include data sheets or probes for skill targets — mands, independent task completion, on-task behavior — so that suppression of adaptive responding is detected early and the NCR density is adjusted.

1. Confirm function before selecting the NCR reinforcer. NCR works through an abolishing operation on the maintaining reinforcer. A function mismatch means you are adding free reinforcement without eliminating the motivation for problem behavior.

2. Derive initial interval from baseline IRT. Set the first NCR interval at or below the mean inter-response time of problem behavior in baseline data. A schedule less dense than baseline problem behavior contact is not an AO — it is background reinforcement.

3. Write a thinning plan before you start treatment. Document the data-decision rule (e.g., ≤20% of baseline for five consecutive sessions), the step size, and the fallback procedure (e.g., return to previous step or add DRA) — following the protocol demonstrated in Migan-Gandonou Horr and Michael (2021) Migan-Gandonou Horr & Michael (2021).

4. Use FT initially, transition to VT during thinning. FT is easier to implement with high procedural integrity at treatment initiation; VT removes anticipatory behavioral patterning that FT schedules can induce López-Tolsa & Pellón (2025).

5. Include extinction when severity and setting permit. NCR + extinction is the standard clinical package for socially maintained severe problem behavior; the 27-case series documents 89% success rates with this combination Phillips et al. (2017). Reserve NCR-without-extinction for cases where extinction is contraindicated, and add DRA as a backup Fritz et al. (2017).

6. Monitor and plan for adventitious reinforcement. Observe whether time-based deliveries are systematically coinciding with problem behavior, especially during dense early-treatment phases. If the timing is correlated, adjust delivery offset or add a brief pause after problem behavior before delivering the scheduled reinforcer.

7. Track adaptive responding alongside problem behavior. Dense NCR competes with adaptive responding as well as problem behavior Kelley et al. (2017). Data on skill targets, mands, or task engagement give you early warning if NCR density is suppressing the repertoire you want to build.

8. Prefer FCT as the long-term destination. NCR creates satiation-based suppression but does not build adaptive communication or self-regulatory skills. For most clients, NCR is a short- to medium-term bridge that buys time for FCT, DRA, or skill instruction to take effect Gover et al. (2022).

9. Plan automatic-reinforcement cases differently from the start. The Phillips et al. (2017) data show that automatically reinforced cases needed supplemental procedures in more than half of cases Phillips et al. (2017). If your FA identifies automatic reinforcement, budget time and personnel for a more complex treatment package — do not assume standard NCR + extinction will be sufficient.

10. For escape-maintained behavior, consider whether functional escape can be delivered noncontingently. Delivering brief breaks from demands on a time-based schedule is often logistically disruptive in instructional settings. Newman et al. (2021) show that arbitrary edibles sometimes work equally well for escape-maintained cases — probe empirically rather than defaulting to either option Newman et al. (2021).

11. In schools, consider cross-setting delivery. Noel and Getch (2016) showed that afterschool NCR can produce next-day classroom reductions without requiring teacher implementation during instruction Noel & Getch (2016). This is an underutilized implementation strategy.

12. Use NCR as the opening layer in novel or aversive contexts. Before introducing demands or procedures that are likely to evoke problem behavior in a new environment, establish a saturating baseline of the preferred reinforcer via NCR. It lowers the evocative threshold before the aversive context begins (Nguyen et al., 2025).

Primary research synthesized in this guide.

• Nguyen, J. T., Tsami, L., Lerman, D. C., Harrison, T. C., & Walker, E. J. (2025). An interdisciplinary telehealth model to increase the comfort and cooperation of adults with intellectual and developmental disabilities during routine dental exams. Behavior Analysis in Practice. https://doi.org/10.1007/s40617-025-01139-9 https://doi.org/10.1007/s40617-025-01139-9
• Kelley, M. E., Nadler, C. B., Rey, C., Cowie, S., & Podlesnik, C. A. (2017). Noncontingent reinforcement competes with response performance. Journal of the Experimental Analysis of Behavior, 107(3), 343-353. https://doi.org/10.1002/jeab.255 https://doi.org/10.1002/jeab.255
• Phillips, C. L., Iannaccone, J. A., Rooker, G. W., & Hagopian, L. P. (2017). Noncontingent reinforcement for the treatment of severe problem behavior: An analysis of 27 consecutive applications. Journal of Applied Behavior Analysis, 50(2), 357-376. https://doi.org/10.1002/jaba.376 https://doi.org/10.1002/jaba.376
• Gover, H. C., Hanley, G. P., & Ruppel, K. W. (2022). On the generality of preference for contingent reinforcement. Journal of Applied Behavior Analysis, 55(2), 318-336. https://doi.org/10.1002/jaba.892 https://doi.org/10.1002/jaba.892
• López-Tolsa, G. E. & Pellón, R. (2025). Dynamic Interactions between Induction and Reinforcement in the Organization of Behavior. Perspectives on Behavior Science, 48(2), 315-339. https://doi.org/10.1007/s40614-025-00453-5 https://doi.org/10.1007/s40614-025-00453-5
• Frampton, S. E., Davis, C. R., Meleshkevich, O., & Axe, J. B. (2024). A clinical tutorial on methods to capture and contrive establishing operations to teach mands. Behavior Analysis in Practice, 17, 1270–1282. https://doi.org/10.1007/s40617-024-00985-3 https://doi.org/10.1007/s40617-024-00985-3
• Fritz, J. N., Jackson, L. M., Stiefler, N. A., Wimberly, B. S., & Richardson, A. R. (2017). Noncontingent reinforcement without extinction plus differential reinforcement of alternative behavior during treatment of problem behavior. Journal of Applied Behavior Analysis, 50(3), 590-599. https://doi.org/10.1002/jaba.395 https://doi.org/10.1002/jaba.395
• Migan-Gandonou Horr, J. A. & Michael, A. V. (2021). Functional Analysis and Noncontingent Reinforcement With Extinction in the Treatment of Perseverative Speech. Behavior Analysis in Practice, 14(1), 208-213. https://doi.org/10.1007/s40617-020-00523-x https://doi.org/10.1007/s40617-020-00523-x
• Newman, Z. A., Roscoe, E. M., Errera, N. P., & Davis, C. R. (2021). Noncontingent reinforcement: Arbitrary versus maintaining reinforcers for escape‐maintained problem behavior. Journal of Applied Behavior Analysis, 54(3), 984-1000. https://doi.org/10.1002/jaba.821 https://doi.org/10.1002/jaba.821
• Van Arsdale, A., Ibañez, V. F., & Vollmer, T. R. (2024). Noncontingent reinforcement in the treatment of pediatric feeding disorder: A concise review. Journal of Applied Behavior Analysis, 57(3), 798-802. https://doi.org/10.1002/jaba.1073 https://doi.org/10.1002/jaba.1073
• Noel, C. R. & Getch, Y. Q. (2016). Noncontingent Reinforcement in After-School Settings to Decrease Classroom Disruptive Behavior for Students with Autism Spectrum Disorder. Behavior Analysis in Practice, 9(3), 261-265. https://doi.org/10.1007/s40617-016-0117-0 https://doi.org/10.1007/s40617-016-0117-0
• Shahan, T. A., Hiltz, J. B., Avellaneda, M., & Greer, B. D. (2026). Contingency discrimination training and resurgence: effects of reduced extinction session durations. Journal of the Experimental Analysis of Behavior, 125(1), e70072. https://doi.org/10.1002/jeab.70072 https://doi.org/10.1002/jeab.70072
• Thakore, A. H., & Kettering, T. L. (2025). Functional analysis and treatment of repetitive verbal behavior in children diagnosed with autism spectrum disorder. The Analysis of Verbal Behavior, 41, 68–83. https://doi.org/10.1007/s40616-024-00208-4 https://doi.org/10.1007/s40616-024-00208-4
• Allen, A. E., Bridges, K. G., Pizarro, E. M., & Morris, S. L. (2026). Comparing methods of evaluating sensitivity to common establishing operations and bias toward challenging behavior. Journal of Applied Behavior Analysis, 59(1), e70046. https://doi.org/10.1002/jaba.70046 https://doi.org/10.1002/jaba.70046
• Jessel, J., Fruchtman, T., Raghunauth‑Zaman, N., Leyman, A., Lemos, F. M., Costa Val, H., Howard, M., & Hanley, G. P. (2024). A two step validation of the performance‑based IISCA: A trauma‑ informed functional analysis model. Behavior Analysis in Practice, 17, 727–745. https://doi.org/10.1007/s40617-023-00792-2 https://doi.org/10.1007/s40617-023-00792-2
• King, H. C., Spink, C., & Falligant, J. M. (2025). Discriminative properties of reinforcers modulate resurgence: A human‐operant demonstration. Journal of the Experimental Analysis of Behavior, 123(3), 526-538. https://doi.org/10.1002/jeab.70010 https://doi.org/10.1002/jeab.70010
• Gallistel, C. R. (2025). Reconceptualized Associative Learning. Perspectives on Behavior Science, 48(2), 203-239. https://doi.org/10.1007/s40614-025-00442-8 https://doi.org/10.1007/s40614-025-00442-8