Search for: All records

I review several alternative linking hypotheses for relating eye tracking data from the visual world paradigm (VWP) to cognitive theories and models. While some models are able to simulate VWP data surprisingly well (such as the TRACE model), there is still ample ambiguity to resolve in the meaning of fixation proportions over time, despite decades of work with the VWP. I also present a simple fixation model based on probabilistic sampling from an underlying lexical activation that allows simulation of individual trials. Unsurprisingly, a properly-parameterized sampling procedure approximates the underlying activation patterns when sufficient trials are averaged together. However, the utility of simulating trial-level behavior is not in reconstructing central tendencies (which can be derived directly without simulating fixations), but in addressing, for example, individual differences. I also discuss critiques and misunderstandings of linking models to the VWP, and analogies to a simpler paradigm – lexical decision – to illuminate the logic of linking hypotheses in the VWP. 

Statistical learning (SL) is typically assumed to be a core mechanism by which organisms learn covarying structures and recurrent patterns in the environment, with the main purpose of facilitating processing of expected events. Within this theoretical framework, the environment is viewed as relatively stable, and SL ‘captures’ the regularities therein through implicit unsupervised learning by mere exposure. Focusing primarily on language— the domain in which SL theory has been most influential—we review evidence that the environment is far from fixed: it is dynamic, in continual flux, and learners are far from passive absorbers of regularities; they interact with their environments, thereby selecting and even altering the patterns they learn from. We therefore argue for an alternative cognitive architecture, where SL serves as a subcomponent of an information foraging (IF) system. IF aims to detect and assimilate novel recurrent patterns in the input that deviate from randomness, for which SL supplies a baseline. The broad implications of this viewpoint and their relevance to recent debates in cognitive neuroscience are discussed. 

There is disagreement among cognitive scientists as to whether a key computational framework – the Simple Recurrent Network (SRN; Elman, 1990, 1991) – is a feedforward system. SRNs have been essential tools in advancing theories of learning, development, and processing in cognitive science for more than three decades. If SRNs were feedforward systems, there would be pervasive theoretical implications: Anything an SRN can do would therefore be explainable without interaction (feedback). However, despite claims that SRNs (and by extension recurrent neural networks more generally) are feedforward (Norris, 1993), this is not the case. Feedforward networks by definition are acyclic graphs; they contain no loops. SRNs contain loops – from hidden units back to hidden units with a time delay – and are therefore cyclic graphs. As we demonstrate, they are interactive in the sense normally implied for networks with feedback connections between layers: In an SRN, bottom-up inputs are inextricably mixed with previous model-internal computations. Inputs are transmitted to hidden units by multiplying them by input-to-hidden weights. However, hidden units simultaneously receive their own previous activations as input via hidden-to-hidden connections with a 1-step time delay (typically via context units). These are added to the input-to-hidden values, and the sums are transformed by an activation function. Thus, bottom-up inputs are mixed with the products of potentially many preceding transformations of inputs and model-internal states. We discuss theoretical implications through a key example from psycholinguistics where the status of SRNs as feedforward or interactive has crucial ramifications. 

Abstract We recently reported strong, replicable (i.e., replicated) evidence forlexically mediated compensation for coarticulation(LCfC; Luthra et al., 2021), whereby lexical knowledge influences a prelexical process. Critically, evidence for LCfC provides robust support forinteractivemodels of cognition that includetop‐down feedbackand is inconsistent withautonomousmodels that allow only feedforward processing. McQueen, Jesse, and Mitterer (2023) offer five counter‐arguments against our interpretation; we respond to each of those arguments here and conclude that top‐down feedback provides the most parsimonious explanation of extant data. 

Listeners have many sources of information available in interpreting speech. Numerous theoretical frameworks and paradigms have established that various constraints impact the processing of speech sounds, but it remains unclear how listeners might simultane-ously consider multiple cues, especially those that differ qualitatively (i.e., with respect to timing and/or modality) or quantita-tively (i.e., with respect to cue reliability). Here, we establish that cross-modal identity priming can influence the interpretation of ambiguous phonemes (Exp. 1, N = 40) and show that two qualitatively distinct cues – namely, cross-modal identity priming and auditory co-articulatory context – have additive effects on phoneme identification (Exp. 2, N = 40). However, we find no effect of quantitative variation in a cue – specifically, changes in the reliability of the priming cue did not influence phoneme identification (Exp. 3a, N = 40; Exp. 3b, N = 40). Overall, we find that qualitatively distinct cues can additively influence phoneme identifica-tion. While many existing theoretical frameworks address constraint integration to some degree, our results provide a step towards understanding how information that differs in both timing and modality is integrated in online speech perception. 

Psycholinguists define spoken word recognition (SWR) as, roughly, the processes intervening between speech perception and sentence processing, whereby a sequence of speech elements is mapped to a phonological wordform. After reviewing points of consensus and contention in SWR, we turn to the focus of this review: considering the limitations of theoretical views that implicitly assume an idealized (neurotypical, monolingual adult) and static perceiver. In contrast to this assumption, we review evidence that SWR is plastic throughout the life span and changes as a function of cognitive and sensory changes, modulated by the language(s) someone knows. In highlighting instances of plasticity at multiple timescales, we are confronted with the question of whether these effects reflect changes in content or in processes, and we consider the possibility that the two are inseparable. We close with a brief discussion of the challenges that plasticity poses for developing comprehensive theories of spoken language processing. 

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. 

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. 

Abstract Though the right hemisphere has been implicated in talker processing, it is thought to play a minimal role in phonetic processing, at least relative to the left hemisphere. Recent evidence suggests that the right posterior temporal cortex may support learning of phonetic variation associated with a specific talker. In the current study, listeners heard a male talker and a female talker, one of whom produced an ambiguous fricative in /s/-biased lexical contexts (e.g., epi?ode) and one who produced it in /∫/-biased contexts (e.g., friend?ip). Listeners in a behavioral experiment (Experiment 1) showed evidence of lexically guided perceptual learning, categorizing ambiguous fricatives in line with their previous experience. Listeners in an fMRI experiment (Experiment 2) showed differential phonetic categorization as a function of talker, allowing for an investigation of the neural basis of talker-specific phonetic processing, though they did not exhibit perceptual learning (likely due to characteristics of our in-scanner headphones). Searchlight analyses revealed that the patterns of activation in the right superior temporal sulcus (STS) contained information about who was talking and what phoneme they produced. We take this as evidence that talker information and phonetic information are integrated in the right STS. Functional connectivity analyses suggested that the process of conditioning phonetic identity on talker information depends on the coordinated activity of a left-lateralized phonetic processing system and a right-lateralized talker processing system. Overall, these results clarify the mechanisms through which the right hemisphere supports talker-specific phonetic processing.