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We follow up on recent work demonstrating clear advantages of lexical-to-sublexical feedback in the TRACE model of spoken word recognition. The prior work compared accuracy and recognition times in TRACE with feedback on or off as progressively more noise was added to inputs. Recognition times were faster with feedback at every level of noise, and there was an accuracy advantage for feedback with noise added to inputs. However, a recent article claims that those results must be an artifact of converting activations to response probabilities, because feedback could only reinforce the “status quo.” That is, the claim is that given noisy inputs, feedback must reinforce all inputs equally, whether driven by signal or noise. We demonstrate that the feedback advantage replicates with raw activations. We also demonstrate that lexical feedback selectively reinforces lexically-coherent input patterns – that is, signal over noise – and explain how that behavior emerges naturally in interactive activation.
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Contra assertions, feedback improves word recognition: How feedback and lateral inhibition sharpen signals over noise
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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- Award ID(s):
- 2043903
- PAR ID:
- 10509567
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Cognition
- Volume:
- 242
- Issue:
- C
- ISSN:
- 0010-0277
- Page Range / eLocation ID:
- 105661
- Subject(s) / Keyword(s):
- spoken word recognition computational models feedback interaction
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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.more » « less
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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.more » « less
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Abstract There is a general consensus in theories of human speech recognition that humans engage in predictive processing during online speech processing. There are also claims that predictive processing is indicative of the operation of a predictive coding (PC) mechanism. PC is a generative, hierarchical feedback framework where feedback signals consist of input predictions, while feedforward signals consist primarily of prediction errors (PE). Some researchers have taken decreased neural signals when inputs conform to expectations as evidence for PC and claim that other possible explanatory frameworks (e.g., interactive activation) are incompatible with reported reductions in PE. However, these claims have been advanced using narrow-scope computational implementations of PC without known abilities to adequately (i.e., plausibly) simulate broader human spoken word recognition (SWR) behavioral phenomena. Here, we present the first known neurally and behaviorally adequate, mathematically formal neural network PC model of time-dependent human SWR. After demonstrating that the new model is able to emulate a fundamental, empirical signature of human SWR behavior (time course of lexical activation and competition), we compare model dynamics to neural performance targets that have been touted as hallmarks of PC in SWR. While the new model readily exhibits predictive processing (anticipatory activation of phonemes consistent with lexical knowledge) and reduced neural activity when inputs match expectations (a necessary component of PC) more subtle patterns of neural activity under priming and noise conditions, previously proposed as diagnostic of PC do not emerge appreciably. This suggests that such patterns may not be hallmarks of PC. We discuss implications for PC-based theories of SWR.more » « less
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This study investigates whether short-term perceptual training can enhance Seoul-Korean listeners’ use of English lexical stress in spoken word recognition. Unlike English, Seoul Korean does not have lexical stress (or lexical pitch accents/tones). Seoul-Korean speakers at a high-intermediate English proficiency completed a visual-world eye-tracking experiment adapted from Connell et al. (2018) (pre-/post-test). The experiment tested whether pitch in the target stimulus (accented versus unaccented first syllable) and vowel quality in the lexical competitor (reduced versus full first vowel) modulated fixations to the target word (e.g., PARrot; ARson) over the competitor word (e.g., paRADE or PARish; arCHIVE or ARcade). In the training (eight 30-min sessions over eight days), participants heard English lexical-stress minimal pairs uttered by four talkers (high variability) or one talker (low variability), categorized them as noun (first-syllable stress) or verb (second-syllable stress), and received accuracy feedback. The results showed that neither training increased target-over-competitor fixation proportions. Crucially, the same training had been found to improve Seoul- Korean listeners’ recall of English words differing in lexical stress (Tremblay et al., 2022) and their weighting of acoustic cues to English lexical stress (Tremblay et al., 2023). These results suggest that short-term perceptual training has a limited effect on target-over-competitor word activation.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1016/j.cognition.2023.105661
