Unified principle of reinforcement in a neural-network model: Reply to N. T. Calvin and J. J. McDowell

•We reply to Calvin and McDowell’s critique of our neural-network model.•They claim partially connected networks in this model cannot simulate blocking.•They claim networks that simulate blocking cannot simulate successive conditioning.•We describe a simulation that proves otherwise.•We also argue that, contrary to their claim, the model is falsifiable. An article published in Behavioural Processes (Calvin and McDowell, 2015) contemplated that the approach to neural networks developed by the present authors cannot simulate certain behavioral findings, notably the Kamin blocking effect and successive conditioning. Here we demonstrate that these concerns are unwarranted as an overall characterization of the approach. In addition, several other more general issues identified in the target article are addressed as well. These include the determination of network architectures, the assignment-of-credit problem, the potential for catastrophic interference, and the falsifiability of the model.