Integration versus interactive activation: The joint influence of stimulus and context in perception

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Empirical results from both reading and speech perception indicate that stimulus and context information have independent influences on perceptual recognition. Massaro (1989) argued that these data are inconsistent with an interactive activation and competition (IAC) model ( McClelland & Rumelhart, 1981), and consistent with the fuzzy logical model of perception (FLMP) (Massaro, 1979; 1989). McClelland (1991) then modified the interactive activation model to be stochastic rather than deterministic and to use a best one wins (BOW) decision rule, allowing it to predict independent influences of stimulus and context. When tested against real data, however, the network proposed by McClelland and extended by us gives a poorer description of actual empirical results than the FLMP. To account for the dynamics of information processing, the SIAC model, an interactive model based on the Boltzmann machine, and the FLMP are formulated to make quantitative predictions of performance as a function of processing time. It is shown that the dynamic FLMP provides a better description of the time course of perceptual processing than does interactive activation. The SIAC and Boltzmann models have difficulty predicting 1) context effects given little processing time and 2) a strong stimulus influence given substantial processing time. Finally, we demonstrate that the FLMP predicts that context can improve the accuracy of performance, in addition to providing a bias to respond with the alternative supported by context. In summary, there is now both empirical and theoretical evidence in favor of the FLMP over SIAC models of pattern recognition. We therefore argue that interactive activation is both less consistent with empirical results and not necessary to describe the joint influence of stimulus and context in language perception.