A method for evaluating parameters of reinforcement during parent-child interactions.

Borrero et al. (2005) watched parents and children with developmental delay play at home. They timed every reinforcer: how soon it came, how long it lasted, how often it happened, and how likely it was.

The team used lag-sequential coding to see which reinforcer details best predicted where the child put effort next.

Delay, duration, rate, and probability all mattered. Change one and the whole picture shifted.

Looking at only rate or probability missed the action. You need all four numbers to guess where the child will play.

Laugeson et al. (2014) later tested rate, delay, quality, and magnitude in a lab with adults who had intellectual disabilities. Quality and magnitude ruled response-class hierarchies; delay mattered least. The lab result seems to clash with the home result, but the settings differ: tight lab schedules versus loose family life.

Leon et al. (2016) also singled out delay. They showed that food kept kids working better than tokens when both were delayed, backing the idea that delay must be measured.

Vos et al. (2013) used pigeons on fixed-ratio schedules and saw the same blunt fact: longer delay means fewer responses. Animal or child, the delay effect holds.

Castillo et al. (2018) added a real-world angle. They saw more problem behavior when the next activity had thin reinforcer density. Like W et al., they proved that descriptive data about reinforcer density predict behavior without any fancy experiment.

Next time you do a descriptive assessment, bring a stopwatch. Mark when the reinforcer starts, when it ends, and when the next one might come. Those extra seconds explain why a child stays with one toy or leaves it. Rate alone is not enough; add delay and duration columns to your data sheet and your intervention choices will sharpen.