Priming is characterized by a sensitivity of reaction times to the sequence of stimuli in psychophysical experiments. The reduction of the reaction time observed in positive priming is well-known and experimentally understood [Scarborough et al., 1977]. Negative priming – the opposite effect – is experimentally less tangible [Fox, 1995]. The dependence on subtle parameter changes (such as response-stimulus interval) usually varies. The sensitivity of the negative priming effect bears great potential for applications in research in fields such as memory, selective attention, and aging effects. We develop and analyze a computational realization, CISAM, of a recent psychological model for action decision making, the ISAM [Kabisch, 2003], which is sensitive to priming conditions. With the dynamical systems approach of the CISAM, we show that a single adaptive threshold mechanism is sufficient to explain both positive and negative priming effects. This is achieved by comparing results obtained by the computational modeling with experimental data from our lab. The implementation provides a rich base from which testable predictions can be derived, e.g. with respect to hitherto untested stimulus-combinations (e.g. single-object trials).