CUMULATIVE EFFECTS MODEL - RESPONSE

The cumulative effects (CE) model explains free-operant choice by the ratio of total numbers of responses and reinforcements, a probability- like variable. Williams (1994) argues that the model is vulnerable to experiments that disprove melioration, a local probability model. The authors note critical differences between the nonlocal CE model and lo cal probability models that allow the CE model to handle some data wit h which they are incompatible. All models are simplifications of reali ty; hence, a model's failures are as revealing as its successes. Willi ams suggests that simple models may need to be abandoned in favor of a ''representational'' account. The authors point out that representati ons must be both acquired and acted on. Acquisition requires processin g of responses and reinforcers; action requires decision rules. Models are simply testable suggestions for what these rules and processes mi ght be.