Abstract
Human perception, cognition, and action requires fast integration of bottom-up signals with top-down knowledge and context. A key theoretical perspective in cognitive science is the interactive activation hypothesis: forward and backward flow in bidirectionally connected neural networks allows humans and other biological systems to approximate optimal integration of bottom-up and top-down information under real-world constraints. An alternative view is that online feedback is neither necessary nor helpful; purely feed forward alternatives can be constructed for any feedback system, and online feedback could not improve processing and would preclude veridical perception. In the domain of spoken word recognition, the latter view was apparently supported by simulations using the interactive activation model, TRACE, with and without feedback: as many words were recognized more quickly without feedback as were recognized faster with feedback, However, these simulations used only a small set of words and did not address a primary motivation for interaction: making a model robust in noise. We conducted simulations using hundreds of words, and found that the majority were recognized more quickly with feedback than without. More importantly, as we added noise to inputs, accuracy and recognition times were better with feedback than without. We follow these simulations with a critical review of recent arguments that online feedback in interactive activation models like TRACE is distinct from other potentially helpful forms of feedback. We conclude that in addition to providing the benefits demonstrated in our simulations, online feedback provides a plausible means of implementing putatively distinct forms of feedback, supporting the interactive activation hypothesis.
Highlights
A central question in cognitive science is how sensory data should be integrated with prior knowledge
A benefit of feedback in TRACE simulations, especially given increasing levels of noise, would support interactivity as a viable candidate mechanism for approximating optimal inference under uncertainty. Such a finding by itself would not suffice to address claims made by Norris et al (2016) that activation feedback in interactive activation models (IAMs) is qualitatively different from other forms of feedback; we address this issue in the section “General Discussion.”
The theorem specifies a straightforward way to optimally estimate the probability of an outcome given some evidence based upon the probability of the evidence given the outcome and the independent probabilities of the outcome and evidence
Summary
A central question in cognitive science is how sensory data should be integrated with prior knowledge. Bi-directional (i.e., bottom-up and top-down) information flow is one solution. Such “interactive activation” allows early and continuous access to prior knowledge, which can tune perceptual systems to prior and conditional probabilities based on experience (MacDonald et al, 1994; Knill and Richards, 1996; McClelland et al, 2006). Implementation of perceptual and other cognitive processes within bidirectionally connected neural networks in the brain provides the mechanism that addresses the key computational challenges facing perceptual systems, and it gives rise to the approximate conformity of human performance to optimal perceptual inference in real time Implementation of perceptual and other cognitive processes within bidirectionally connected neural networks in the brain provides the mechanism that addresses the key computational challenges facing perceptual systems, and it gives rise to the approximate conformity of human performance to optimal perceptual inference in real time (p. 6)
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