Assessment & Research

Using stimulus preference assessments to identify preferred break environments

Castelluccio et al. (2019) · Journal of Applied Behavior Analysis 2019
★ The Verdict

A one-minute pictorial choice predicts which break room will actually reinforce work for kids with autism.

✓ Read this if BCBAs running clinic or school sessions where break rooms are an option.
✗ Skip if Practitioners who only have one break area or who already use edible reinforcers effectively.

01Research in Context

01

What this study did

Castelluccio et al. (2019) asked a simple question: can pictures tell us which break room a child with autism will work for? They showed two kids photos of three break spots: a gym, a computer corner, and a quiet reading nook. Each child tapped the picture they liked best. The team then ran a quick reversal design. Kids earned tokens for math work. They could trade tokens for time in the break spot they picked or a control spot they never chose.

02

What they found

Both kids spent almost all their tokens on the high-preference break. They barely touched the low-preference or control spots. The pictorial assessment predicted real-world reinforcing power. When the preferred break was removed, work dropped. When it came back, work rose again. The photos worked like a crystal ball for break value.

03

How this fits with other research

Heinicke et al. (2016) had warned that pictures alone can lie. Some kids picked photos but did not work for the real item. Their fix was to let kids briefly experience the real item first. Castelluccio skipped that step yet still got clean results. The difference may be the domain: Heinicke tested toys and snacks; Castelluccio tested whole break rooms. Rooms may be easier to imagine than novel toys.

Chebli et al. (2016) used a tablet to let kids pick videos. Like Castelluccio, they saw picture choices match later reinforcer use. Both studies show that screen-based or paper-based photos can work when the stimulus is familiar and easy to picture.

Isenhower et al. (2025) broke leisure into parts: social, electronic, movement. They found adults worked best when activities matched their part-profile. Castelluccio kept the break whole. Together the papers suggest: start with a quick whole-room photo test; if results are shaky, drill down to parts like Isenhower did.

04

Why it matters

You can save session time. Snap three photos of your available break areas. Ask the child to point to the one they want. Run a three-minute reversal to be sure. Once verified, use that room as the reinforcer for the rest of the month. No need to drag out toys or set up fancy stations. A picture really can be worth a thousand trials.

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Take three photos of your break spaces, let your client pick one, and test if tokens earned for that picture buy more work than a no-break control.

02At a glance

Intervention
preference assessment
Design
reversal abab
Sample size
2
Population
autism spectrum disorder
Finding
positive
Magnitude
large

03Original abstract

A pictorial preference assessment was conducted for 2 individuals with autism who had programmed breaks in their behavior plans. Assessed break environments were individualized, based on indirect assessments and direct observations. The most highly (HP) and least preferred (LP) environments and a control with no associated break were included in a subsequent reinforcer assessment using a concurrent-chains arrangement within a reversal design. Participants selected a multitask sequence (initial link) associated with one of the break environments. Phase A evaluated the reinforcing properties of all three breaks; the HP was removed in Phase B. Both participants allocated more responding to HP than LP, and to LP than control, suggesting that breaks functioned as reinforcers. The results indicated that preference assessment technology can be used to identify highly preferred breaks that function as reinforcers. Social validity measures indicated that the individuals' clinicians found the results useful for future clinical programming.

Journal of Applied Behavior Analysis, 2019 · doi:10.1002/jaba.567