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Christopher T Kello

Publications and source records attributed to Christopher T Kello.

4 recordsLinked to original sources
PubMed2004

Characterizing the evolutionary dynamics of language.

In a recent article Mitchener and Nowak present a model of the evolutionary dynamics of language. The model exhibits regular and chaotic oscillations in changes to the proportions of grammars spoken in a population over the course of evolution. These oscillations are within the purview of evolutionary game theory, but they suggest the lack of an evolutionarily stable strategy. Implications for self-organization across scales of adaptation are discussed.

Adaptation, Psychological↗
PubMed2004

Control over the time course of cognition in the tempo-naming task.

Five experiments are reported in which standard naming and tempo-naming tasks were used to investigate mechanisms of control over the time course of lexical processing. The time course of processing was manipulated by asking participants to time their responses with an audiovisual metronome. As the tempo of the metronome increased, results showed that (a) the rate of lexical errors increased, whereas the rate of regularization errors remained constant; (b) onset errors increased at a faster rate than body errors; (c) stimulus effects weakened on latencies, whereas they strengthened on durations and errors; and (d) naming durations decreased more slowly when stimuli were presented prior to the response cue. These results constitute evidence that time pressure in the tempo-naming task caused a compression in the time course of lexical processing. Compression is discussed in terms of threshold mechanisms and rate mechanisms of control.

Adolescent↗
PubMed2004

A neural network model of the articulatory-acoustic forward mapping trained on recordings of articulatory parameters.

Three neural network models were trained on the forward mapping from articulatory positions to acoustic outputs for a single speaker of the Edinburgh multi-channel articulatory speech database. The model parameters (i.e., connection weights) were learned via the backpropagation of error signals generated by the difference between acoustic outputs of the models, and their acoustic targets. Efficacy of the trained models was assessed by subjecting the models' acoustic outputs to speech intelligibility tests. The results of these tests showed that enough phonetic information was captured by the models to support rates of word identification as high as 84%, approaching an identification rate of 92% for the actual target stimuli. These forward models could serve as one component of a data-driven articulatory synthesizer. The models also provide the first step toward building a model of spoken word acquisition and phonological development trained on real speech.

Adult↗