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1. Contra assertions, feedback improves word recognition: How feedback and lateral inhibition sharpen signals over noise

   https://doi.org/10.1016/j.cognition.2023.105661  

   Magnuson, James S; Crinnion, Anne Marie; Luthra, Sahil; Gaston, Phoebe; Grubb, Samantha (January 2024, Cognition)

   Whether top-down feedback modulates perception has deep implications for cognitive theories. Debate has been vigorous in the domain of spoken word recognition, where competing computational models and agreement on at least one diagnostic experimental paradigm suggest that the debate may eventually be resolvable. Norris and Cutler (2021) revisit arguments against lexical feedback in spoken word recognition models. They also incorrectly claim that recent computational demonstrations that feedback promotes accuracy and speed under noise (Magnuson et al., 2018) were due to the use of the Luce choice rule rather than adding noise to inputs (noise was in fact added directly to inputs). They also claim that feedback cannot improve word recognition because feedback cannot distinguish signal from noise. We have two goals in this paper. First, we correct the record about the simulations of Magnuson et al. (2018). Second, we explain how interactive activation models selectively sharpen signals via joint effects of feedback and lateral inhibition that boost lexically-coherent sublexical patterns over noise. We also review a growing body of behavioral and neural results consistent with feedback and inconsistent with autonomous (non-feedback) architectures, and conclude that parsimony supports feedback. We close by discussing the potential for synergy between autonomous and interactive approaches. 

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2. Lexical Feedback in the Time-Invariant String Kernel (TISK) Model of Spoken Word Recognition

   https://doi.org/10.5334/joc.362  

   Magnuson, James S; You, Heejo; Hannagan, Thomas (January 2024, Journal of Cognition)

   The Time-Invariant String Kernel (TISK) model of spoken word recognition (Hannagan, Magnuson & Grainger, 2013; You & Magnuson, 2018) is an interactive activation model with many similarities to TRACE (McClelland & Elman, 1986). However, by replacing most time-specific nodes in TRACE with time-invariant open-diphone nodes, TISK uses orders of magnitude fewer nodes and connections than TRACE. Although TISK performed remarkably similarly to TRACE in simulations reported by Hannagan et al., the original TISK implementation did not include lexical feedback, precluding simulation of top-down effects, and leaving open the possibility that adding feedback to TISK might fundamentally alter its performance. Here, we demonstrate that when lexical feedback is added to TISK, it gains the ability to simulate top-down effects without losing the ability to simulate the fundamental phenomena tested by Hannagan et al. Furthermore, with feedback, TISK demonstrates graceful degradation when noise is added to input, although parameters can be found that also promote (less) graceful degradation without feedback. We review arguments for and against feedback in cognitive architectures, and conclude that feedback provides a computationally efficient basis for robust constraint-based processing. 

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3. Feedback in the Time-Invariant String Kernel model of spoken word recognition

   Magnuson, J.; You, H. (July 2018, Proceedings of the Cognitive Science Society)

   The Time-Invariant String Kernel (TISK) model of spoken word recognition (Hanngan et al., 2013) is an interactive activation model like TRACE (McClelland & Elman, 1986). However, it uses orders of magnitude fewer nodes and connections because it replaces TRACE's time-specific duplicates of phoneme and word nodes with time-invariant nodes based on a string kernel representation (essentially a phoneme-by-phoneme matrix, where a word is encoded as by all ordered open diphones it contains; e.g., cat has /kæ/, /æt/, and /kt/). Hannagan et al. (2013) showed that TISK behaves similarly to TRACE in the time course of phonological competition and even word-specific recognition times. However, the original implementation did not include feedback from words to diphone nodes, precluding simulation of top-down effects. Here, we demonstrate that TISK can be easily adapted to lexical feedback, affording simulation of top-down effects as well as allowing the model to demonstrate graceful degradation given noisy inputs. 

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4. Does signal reduction imply predictive coding in models of spoken word recognition?

   https://doi.org/10.3758/s13423-021-01924-x  

   Luthra, Sahil; Li, Monica Y.; You, Heejo; Brodbeck, Christian; Magnuson, James S. (January 2021, Psychonomic Bulletin & Review)

   null (Ed.)

   Abstract Pervasive behavioral and neural evidence for predictive processing has led to claims that language processing depends upon predictive coding. Formally, predictive coding is a computational mechanism where only deviations from top-down expectations are passed between levels of representation. In many cognitive neuroscience studies, a reduction of signal for expected inputs is taken as being diagnostic of predictive coding. In the present work, we show that despite not explicitly implementing prediction, the TRACE model of speech perception exhibits this putative hallmark of predictive coding, with reductions in total lexical activation, total lexical feedback, and total phoneme activation when the input conforms to expectations. These findings may indicate that interactive activation is functionally equivalent or approximant to predictive coding or that caution is warranted in interpreting neural signal reduction as diagnostic of predictive coding. 

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5. Multiple traces and altered signal-to-noise in systems consolidation: Evidence from the 7T fMRI Natural Scenes Dataset

   https://doi.org/10.1073/pnas.2123426119  

   Vanasse, Thomas J.; Boly, Melanie; Allen, Emily J.; Wu, Yihan; Naselaris, Thomas; Kay, Kendrick; Cirelli, Chiara; Tononi, Giulio (November 2022, Proceedings of the National Academy of Sciences)

   The brain mechanisms of memory consolidation remain elusive. Here, we examine blood-oxygen-level-dependent (BOLD) correlates of image recognition through the scope of multiple influential systems consolidation theories. We utilize the longitudinal Natural Scenes Dataset, a 7-Tesla functional magnetic resonance imaging human study in which ∼135,000 trials of image recognition were conducted over the span of a year among eight subjects. We find that early- and late-stage image recognition associates with both medial temporal lobe (MTL) and visual cortex when evaluating regional activations and a multivariate classifier. Supporting multiple-trace theory (MTT), parts of the MTL activation time course show remarkable fit to a 20-y-old MTT time-dynamical model predicting early trace intensity increases and slight subsequent interference ( R 2 > 0.90). These findings contrast a simplistic, yet common, view that memory traces are transferred from MTL to cortex. Next, we test the hypothesis that the MTL trace signature of memory consolidation should also reflect synaptic “desaturation,” as evidenced by an increased signal-to-noise ratio. We find that the magnitude of relative BOLD enhancement among surviving memories is positively linked to the rate of removal (i.e., forgetting) of competing traces. Moreover, an image-feature and time interaction of MTL and visual cortex functional connectivity suggests that consolidation mechanisms improve the specificity of a distributed trace. These neurobiological effects do not replicate on a shorter timescale (within a session), implicating a prolonged, offline process. While recognition can potentially involve cognitive processes outside of memory retrieval (e.g., re-encoding), our work largely favors MTT and desaturation as perhaps complementary consolidative memory mechanisms. 

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