Individual differences, intelligence, and behavior analysis.

Williams et al. (2008) wrote a think-piece. They asked why behavior analysts ignore IQ scores.

The paper maps intelligence onto speed of learning, memory for rules, and how fast kids notice contingencies.

There is no finding to graph. The paper argues we should treat 'smart' and 'slow' as learnable response patterns, not fixed traits.

If we measure processing speed and working memory in real tasks, we can predict how fast each learner will master new skills.

Hobson (1984) and Parrott (1984) beat the same drum earlier. Both said behavior analysis must study complex stuff like remembering or problem-solving to stay relevant. Ben et al. answer by aiming the lens at intelligence itself.

Langthorne et al. (2007) swap the mental word 'sensitivity' for the behavioral term 'motivating operation.' Ben et al. do the same trick: swap 'IQ' for functional relations among speed, memory, and contingency learning.

Moore (2016) stretches behavior-analytic thinking across the lifespan. Ben et al. stretch it across individual differences. Same method, new turf.

Next time you label a client 'low functioning,' try writing speed and memory goals instead. Measure how many seconds the kid needs to find the correct card, or how many rules he can hold in a sequence. Chart those behaviors and watch them grow. You will stop talking about fixed ability and start talking about teachable variables that your intervention can move.