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The influence of a native language on learning new speech sounds in adulthood is addressed using a network model in which speech categories are attractors implemented through interactive activation and Hebbian learning. The network has a representation layer that receives topographic projections from an input layer and has reciprocal excitatory connections with deeper layers. When applied to an experiment in which Japanese adults were trained to distinguish the English /r/-/l/ contrast (McCandliss, Fiez, Protopapas, Conway, &amp; McClelland, 2002), the model can account for many aspects of the experimental results, such as the time course and outcome of the learning, how it varies as a function of feedback, the relative efficacy of adaptive and initially easy training stimuli versus nonadaptive and difficult stimuli, and the development of a discrimination peak at the acquired category boundary. The model is also able to capture some aspects of the individual differences in learning.

Success and failure of new speech category learning in adulthood: Consequences of learned Hebbian attractors in topographic maps