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Eye movements to pictures of four objects on a screen were monitored as participants followed a spoken instruction to move one of the objects, e.g., 'Pick up the beaker: now put it below the diamond' (Experiment 1) or heard progressively larger gates and tried to identify the referent (Experiment 2). The distractor objects included a cohort competitor with a name that began with the same onset and vowel as the name of the target object (e.g., beetle), a rhyme competitor (e.g. speaker), and an unrelated competitor (e.g., carriage). In Experiment 1, there was clear evidence for both cohort and rhyme activation as predicted by continuous mapping models such as TRACE (McClelland and Elman, 1986) and Shortlist (Norris, 1994). Additionally, the time course and probabilities of eye movements closely corresponded to response probabilities derived from TRACE simulations using the Luce choice rule (Luce, 1959). In the gating task, which emphasizes word-initial information, there was clear evidence for multiple activation of cohort members, as measured by judgments and eye movements, but no suggestion of rhyme effects. Given that the same sets of pictures were present during the gating task as in Experiment 1, we conclude that the rhyme effects in Experiment 1 were not an artifact of using a small set of visible alternatives. (c) 1998 Academic Press [ABSTRACT FROM AUTHOR]

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). However, the complexities of deep learning architectures and training regimens make it difficult to use them to provide direct insights into mechanisms that may support HSR. In this brief article, we report preliminary results from a two-layer network that borrows one element from ASR, long short-term memory nodes, which provide dynamic memory for a range of temporal spans. This allows the model to learn to map real speech from multiple talkers to semantic targets with high accuracy, with human-like timecourse of lexical access and phonological competition. Internal representations emerge that resemble phonetically organized responses in human superior temporal gyrus, suggesting that the model develops a distributed phonological code despite no explicit training on phonetic or phonemic targets. The ability to work with real speech is a major advance for cognitive models of HSR., (© 2020 Cognitive Science Society, Inc.)

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). We study PNNs for five languages: English, Spanish, French, Dutch, and German. Consistent with previous work, we find that the PNNs share a consistent set of topological features. Using an approach that generates random lexicons with increasing levels of phonological realism, we show that even random forms with minimal relationship to any real language, combined with only the empirical distribution of language-specific phonological form lengths, are sufficient to produce the topological properties observed in the real language PNNs. The resulting pseudo-PNNs are insensitive to the level of lingustic realism in the random lexicons but quite sensitive to the shape of the form length distribution. We therefore conclude that "universal" features seen across multiple languages are really string universals, not language universals, and arise primarily due to limitations in the kinds of networks generated by the one-step neighbor definition. Taken together, our results indicate that caution is warranted when linking the dynamics of human spoken word recognition to the topological properties of PNNs, and that the investigation of alternative similarity metrics for phonological forms should be a priority.

Three experiments investigated how knowledge influences concept formation and representation in a standard concept acquisition task. The primary comparison was among arbitrary concepts, which had meaningless features; meaningful concepts, which had meaningful features from different domains; and integrated concepts, which had meaningful features interconnected by common knowledge. Experiment 1 found that learning was superior for the integrated concepts but that there was little difference as a function of feature meaningfulness. Experiment 2 suggested that the integrated Ss were learning to form a knowledge-based schema as their concept representation because they did not distinguish the typicality of features that differed in frequency. Experiment 3 introduced a category whose features were from the same domain but were not otherwise related. This concept was as difficult to learn and use as the meaningful concepts were. These comparisons help specify the ways in which knowledge does and does not influence concept formation.