Simple Recurrent Networks are Interactive.

There is disagreement among cognitive scientists as to whether a key computational framework - the Simple Recurrent Network (SRN; Elman, Machine Learning, 7(2), 195-225, 1991; Elman, Cognitive Science, 14(2), 179-211, 1990) - 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).

Do They Know It's Christmash? Lexical Knowledge Directly Impacts Speech Perception.

We recently reported strong, replicable (i.e., replicated) evidence for lexically mediated compensation for coarticulation (LCfC; Luthra et al.

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

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.

Resolving competing predictions in speech: How qualitatively different cues and cue reliability contribute to phoneme identification.

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 simultaneously consider multiple cues, especially those that differ qualitatively (i.e.

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.

Right Posterior Temporal Cortex Supports Integration of Phonetic and Talker Information.

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.

Investigating the Extent to which Distributional Semantic Models Capture a Broad Range of Semantic Relations.

Distributional semantic models (DSMs) are a primary method for distilling semantic information from corpora. However, a key question remains: What types of semantic relations among words do DSMs detect? Prior work typically has addressed this question using limited human data that are restricted to semantic similarity and/or general semantic relatedness. We tested eight DSMs that are popular in current cognitive and psycholinguistic research (positive pointwise mutual information; global vectors; and three variations each of Skip-gram and continuous bag of words (CBOW) using word, context, and mean embeddings) on a theoretically motivated, rich set of semantic relations involving words from multiple syntactic classes and spanning the abstract-concrete continuum (19 sets of ratings).

Using TMS to evaluate a causal role for right posterior temporal cortex in talker-specific phonetic processing.

Theories suggest that speech perception is informed by listeners' beliefs of what phonetic variation is typical of a talker. A previous fMRI study found right middle temporal gyrus (RMTG) sensitivity to whether a phonetic variant was typical of a talker, consistent with literature suggesting that the right hemisphere may play a key role in conditioning phonetic identity on talker information. The current work used transcranial magnetic stimulation (TMS) to test whether the RMTG plays a causal role in processing talker-specific phonetic variation.

Attention, task demands, and multitalker processing costs in speech perception.

Determining how human listeners achieve phonetic constancy despite a variable mapping between the acoustics of speech and phonemic categories is the longest standing challenge in speech perception. A clue comes from studies where the talker changes randomly between stimuli, which slows processing compared with a single-talker baseline. These multitalker processing costs have been observed most often in speeded monitoring paradigms, where participants respond whenever a specific item occurs.

LexFindR: A fast, simple, and extensible R package for finding similar words in a lexicon.

Language scientists often need to generate lists of related words, such as potential competitors. They may do this for purposes of experimental control (e.g.

Boosting lexical support does not enhance lexically guided perceptual learning.

A challenge for listeners is to learn the appropriate mapping between acoustics and phonetic categories for an individual talker. Lexically guided perceptual learning (LGPL) studies have shown that listeners can leverage lexical knowledge to guide this process. For instance, listeners learn to interpret ambiguous /s/-/∫/ blends as /s/ if they have previously encountered them in /s/-biased contexts like Here, we examined whether the degree of preceding lexical support might modulate the extent of perceptual learning.

Listener expectations and the perceptual accommodation of talker variability: A pre-registered replication.

Researchers have hypothesized that in order to accommodate variability in how talkers produce their speech sounds, listeners must perform a process of talker normalization. Consistent with this proposal, several studies have shown that spoken word recognition is slowed when speech is produced by multiple talkers compared with when all speech is produced by one talker (a multitalker processing cost). Nusbaum and colleagues have argued that talker normalization is modulated by attention (e.

Robust Lexically Mediated Compensation for Coarticulation: Christmash Time Is Here Again.

A long-standing question in cognitive science is how high-level knowledge is integrated with sensory input. For example, listeners can leverage lexical knowledge to interpret an ambiguous speech sound, but do such effects reflect direct top-down influences on perception or merely postperceptual biases? A critical test case in the domain of spoken word recognition is lexically mediated compensation for coarticulation (LCfC). Previous LCfC studies have shown that a lexically restored context phoneme (e.

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

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.

Talker familiarity and the accommodation of talker variability.

A fundamental problem in speech perception is how (or whether) listeners accommodate variability in the way talkers produce speech. One view of the way listeners cope with this variability is that talker differences are normalized - a mapping between talker-specific characteristics and phonetic categories is computed such that speech is recognized in the context of the talker's vocal characteristics. Consistent with this view, listeners process speech more slowly when the talker changes randomly than when the talker remains constant.

Friends in Low-Entropy Places: Orthographic Neighbor Effects on Visual Word Identification Differ Across Letter Positions.

Visual word recognition is facilitated by the presence of orthographic neighbors that mismatch the target word by a single letter substitution. However, researchers typically do not consider where neighbors mismatch the target. In light of evidence that some letter positions are more informative than others, we investigate whether the influence of orthographic neighbors differs across letter positions.

Is that a pibu or a pibo? Children with reading and language deficits show difficulties in learning and overnight consolidation of phonologically similar pseudowords.

Word learning is critical for the development of reading and language comprehension skills. Although previous studies have indicated that word learning is compromised in children with reading disability (RD) or developmental language disorder (DLD), it is less clear how word learning difficulties manifest in children with comorbid RD and DLD. Furthermore, it is unclear whether word learning deficits in RD or DLD include difficulties with offline consolidation of newly learned words.

Cohesion of Cortical Language Networks During Word Processing Is Predicted by a Common Polymorphism in the SETBP1 Gene.

The etiological mechanisms of the genetic underpinnings of developmental language disorder (DLD) are unknown, in part due to the behavioral heterogeneity of the disorder's manifestations. In this study, we explored an association between the SETBP1 gene (18q21.1), revealed in a genome-wide association study of DLD in a geographically isolated population, and brain network-based endophenotypes of functional intracortical coherence between major language-related brain areas.

EARSHOT: A Minimal Neural Network Model of Incremental Human Speech Recognition.

Despite the lack of invariance problem (the many-to-many mapping between acoustics and percepts), human listeners experience phonetic constancy and typically perceive what a speaker intends. Most models of human speech recognition (HSR) have side-stepped this problem, working with abstract, idealized inputs and deferring the challenge of working with real speech. In contrast, carefully engineered deep learning networks allow robust, real-world automatic speech recognition (ASR).

Individual differences in subphonemic sensitivity and phonological skills.

Many studies have established a link between phonological abilities (indexed by phonological awareness and phonological memory tasks) and typical and atypical reading development. Individuals who perform poorly on phonological assessments have been mostly assumed to have (or "fuzzy") phonological representations, with typical phonemic categories, but with greater category overlap due to imprecise encoding. An alternative posits that poor readers have phonological representations, with speech sounds perceived allophonically (phonetically distinct variants of a single phonemic category).

Individual differences in decoding skill, print exposure, and cortical structure in young adults.

This exploratory study investigated relations between individual differences in cortical grey matter structure and young adult readers' cognitive profiles. Whole-brain analyses revealed neuroanatomical correlations with word and nonword reading ability (decoding), and experience with printed matter. Decoding was positively correlated with grey matter volume (GMV) in left superior temporal sulcus, and thickness (GMT) in right superior temporal gyrus.

Universal Features in Phonological Neighbor Networks.

Human speech perception involves transforming a countinuous acoustic signal into discrete linguistically meaningful units (phonemes) while simultaneously causing a listener to activate words that are similar to the spoken utterance and to each other. The Neighborhood Activation Model posits that phonological neighbors (two forms [words] that differ by one phoneme) compete significantly for recognition as a spoken word is heard. This definition of phonological similarity can be extended to an entire corpus of forms to produce a phonological neighbor network (PNN).

TISK 1.0: An easy-to-use Python implementation of the time-invariant string kernel model of spoken word recognition.

This article describes a new Python distribution of TISK, the time-invariant string kernel model of spoken word recognition (Hannagan et al. in Frontiers in Psychology, 4, 563, 2013). TISK is an interactive-activation model similar to the TRACE model (McClelland & Elman in Cognitive Psychology, 18, 1-86, 1986), but TISK replaces most of TRACE's reduplicated, time-specific nodes with theoretically motivated time-invariant, open-diphone nodes.

Interaction in Spoken Word Recognition Models: Feedback Helps.

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 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.

Neural representations for newly learned words are modulated by overnight consolidation, reading skill, and age.

Word learning depends not only on efficient online binding of phonological, orthographic and lexical information, but also on consolidation of new word representations into permanent lexical memory. Work on word learning under a variety of contexts indicates that reading and language skill impact facility of word learning in both print and speech. In addition, recent research finds that individuals with language impairments show deficits in both initial word form learning and in maintaining newly learned representations over time, implicating mechanisms associated with maintenance that may be driven by deficits in overnight consolidation.