Interaction in Spoken Word Recognition Models: Feedback Helps

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.

Successfully treating 90 patients with obsessive compulsive disorder in eight days: the Bergen 4-day treatment

“…While OUH employed a protocol for individual exposure and response prevention (ERP) [ 2 ] where patients typically are treated over 12–16 weeks, the OCD-team at Haukeland University Hospital, Bergen, has developed a novel treatment format where ERP is delivered during just four consecutive days. Despite the relatively brief treatment, the Bergen 4-day treatment (B4DT) has been shown to yield a good outcome [ 3 – 5 ]. At post-treatment assessment the proportion of responders varied from 83% [ 4 ] to 93.8% [ 5 ] with a weighted mean of 89.4%.…”

“…Despite the relatively brief treatment, the Bergen 4-day treatment (B4DT) has been shown to yield a good outcome [ 3 – 5 ]. At post-treatment assessment the proportion of responders varied from 83% [ 4 ] to 93.8% [ 5 ] with a weighted mean of 89.4%. At 6- or 12-month follow-up the mean response rate was 82.4%.…”

Quantifying Expectation Modulation in Human Speech Processing

“…The regression model was fitted on 80% of the data and tested on 20% unseen data. Based on the results of the unseen data, we computed the R 2 CV (cross validated). Similar R 2 and R 2 CV values indicate that the model generalizes well to unseen data and was not overfitted to the current sample.…”

“…The details of the cognitive processes underlying human speech processing are still contentious. A long-standing debate revolves around the importance and timing of top-down versus bottom-up influence for word recognition during speech comprehension [2,3]. Certain autonomous models (e.g.…”

Commentary on “Interaction in Spoken Word Recognition Models”

“…Magnuson et al ( 2018 : MMLSH), responding to Norris et al ( 2016 : NMC16), postulate that feedback of activation from words to pre-lexical representations is helpful in spoken-word recognition. Their argument (1) is flawed by being bound to a particular class of model, (2) misses the central point about parsimony in recognition models, and (3) ignores crucial data.…”