Effects of video self‐modeling on the preference and reinforcer value of toys for children diagnosed with autism spectrum disorder

Livingston et al. (2023) plan to test video self-modeling to make toys more fun for children with autism.

Kids will watch short clips of themselves happily playing with target toys.

The team will then measure if the children choose those toys more often and work harder to get them.

The abstract gives zero data.

We do not know if the trick worked, how many kids took part, or how big the change was.

Wait for the full paper before you count on this method.

Earlier single-case work is upbeat. Marcus et al. (2009) showed self-modeling beat peer modeling for teaching letter skills to three children with autism.

Mulder et al. (2020) also found self-modeling faster than peer clips for social tasks in high-functioning learners.

Yet two meta-analyses paint a wider picture. Hong et al. (2016) and Storch et al. (2012) show any video modeling—self or other—lifts daily and social skills.

So the new toy-preference test sits inside a solid toolbox, but we still need its own numbers to judge size and ease.

If future data mirror earlier self-modeling wins, you could film a child happily stacking blocks, then watch free-play choices shift toward blocks without extra tokens or prompts. That would give you a low-cost way to widen reinforcer pools and reduce problem behavior tied to toy scarcity. Keep the study on your watch list and prep a camera—just wait for real outcomes before you promise parents it works.