Interactive Activation and Mutual Constraint Satisfaction in Perception and Cognition

• Published in: Cognitive science Vol. 38; no. 6; pp. 1139 - 1189
• Main Authors: McClelland, James L., Mirman, Daniel, Bolger, Donald J., Khaitan, Pranav
• Format: Journal Article
• Language: English
• Published: United States Wiley-Blackwell 01.08.2014; Wiley Subscription Services, Inc
• Subjects: Alphabets; Bayesian Statistics; Behavior; Brain; Brain - physiology; Cognition & reasoning; Cognition - physiology; Cognitive Processes; Comprehension; Computation; Connectionist models; Humans; Interactive activation; Language Processing; Models, Neurological; Neural networks; Neurosciences; Optimal perceptual inference; Parallel distributed processing; Perception; Perception - physiology; Sensory perception; Speech; Speech Perception - physiology; Statistical Inference; Connectionist models; Interactive activation; Perception; Neural networks; Optimal perceptual inference; Parallel distributed processing
• ISSN: 0364-0213; 1551-6709
• DOI: 10.1111/cogs.12146

In a seminal 1977 article, Rumelhart argued that perception required the simultaneous use of multiple sources of information, allowing perceivers to optimally interpret sensory information at many levels of representation in real time as information arrives. Building on Rumelhart's arguments, we present the Interactive Activation hypothesis—the idea that the mechanism used in perception and comprehension to achieve these feats exploits an interactive activation process implemented through the bidirectional propagation of activation among simple processing units. We then examine the interactive activation model of letter and word perception and the TRACE model of speech perception, as early attempts to explore this hypothesis, and review the experimental evidence relevant to their assumptions and predictions. We consider how well these models address the computational challenge posed by the problem of perception, and we consider how consistent they are with evidence from behavioral experiments. We examine empirical and theoretical controversies surrounding the idea of interactive processing, including a controversy that swirls around the relationship between interactive computation and optimal Bayesian inference. Some of the implementation details of early versions of interactive activation models caused deviation from optimality and from aspects of human performance data. More recent versions of these models, however, overcome these deficiencies. Among these is a model called the multinomial interactive activation model, which explicitly links interactive activation and Bayesian computations. We also review evidence from neurophysiological and neuroimaging studies supporting the view that interactive processing is a characteristic of the perceptual processing machinery in the brain. In sum, we argue that a computational analysis, as well as behavioral and neuroscience evidence, all support the Interactive Activation hypothesis. The evidence suggests that contemporary versions of models based on the idea of interactive activation continue to provide a basis for efforts to achieve a fuller understanding of the process of perception.