Description of a practitioner model for identifying preferred stimuli with individuals with autism spectrum disorders.

Amore et al. (2011) walked 20 autistic clients through a decision tree. The tree picks the fastest preference test that still finds strong reinforcers.

They showed each step with real cases. No numbers on how well it worked—just the model.

The paper gives you a flow chart, not data. You answer yes/no questions like “Can the client approach items?” and land on one of four quick assessments.

The 20 stories show how the choices look in clinic rooms.

Kahng et al. (1999) and Cohen-Almeida et al. (2000) ran head-to-head tests first. S found real items beat pictures. D found asking beats showing for most clients and saves minutes. M’s tree folds both lessons in: it steers you to tangible items unless the client can talk, then it lets you ask.

Herbek et al. (2026) adds a new twist. They show free-operant with response restriction cuts problem behavior during the test. M’s older tree does not list that option, so you might add it when behavior is rough.

Simonian et al. (2020) widens the lens. Their review shows preference tests already work with adult employees. M’s decision model was built for kids with autism, but the same flow could travel to workplace settings.

You no longer guess which preference test to run. Follow the flow chart, pick the quickest route, and start teaching faster. Add Herbek’s free-operant twist if problem behavior pops up. Save time, reduce stress, and still find reinforcers that work.