Predicting Novel Leisure Reinforcers Based on Preference Category

Lachance et al. (2026) tested three kids with autism. Each child first did an 8-item MSWO preference test. Items were sorted by sensory feel: light-up, soft, noisy, or moving.

Next the team picked three new items from each kid’s top sensory group. They ran a quick reinforcer test to see if these unknown items would still make the child work for them.

All three kids worked hardest for the new items that matched their favorite sensory group. Items from other groups rarely worked as reinforcers.

In plain words: choose new leisure toys from the same sensory family the learner already likes and you will probably keep their motivation high.

Kelley et al. (2016) showed that preference rankings stay stable for days. Lachance builds on that idea by using one quick MSWO to predict future reinforcer power of never-seen items.

Gilroy et al. (2021) added an elasticity probe to see which highly preferred items keep working under heavier work demands. You can combine the two methods: first use Lachance’s sensory trick to pick new items, then run Gilroy’s elasticity check to be sure they will survive tougher schedules.

Critchfield et al. (2003) proved that rotating several preferred leisure items prevents satiation during long NCR sessions. Lachance gives you an easy way to find more items for that rotation without extra preference tests.

You no longer need to test every new toy. Just note the sensory class your learner chooses most often, then pick unseen items from that class. This single step can save minutes of session time and keeps motivation fresh when old favorites lose their shine.