At least 3 different types of computational model have been shown to account for various facets of both normal and impaired single word reading: (a) the connectionist triangle model, (b) the dual-route cascaded model, and (c) the connectionist dual process model. Major strengths and weaknesses of these models are identified. In the spirit of nested incremental modeling, a new connectionist dual process model (the CDPϩ model) is presented. This model builds on the strengths of 2 of the previous models while eliminating their weaknesses. Contrary to the dual-route cascaded model, CDPϩ is able to learn and produce graded consistency effects. Contrary to the triangle and the connectionist dual process models, CDPϩ accounts for serial effects and has more accurate nonword reading performance. CDPϩ also beats all previous models by an order of magnitude when predicting individual item-level variance on large databases. Thus, the authors show that building on existing theories by combining the best features of previous models-a nested modeling strategy that is commonly used in other areas of science but often neglected in psychology-results in better and more powerful computational models.

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A popular metaphor for the representation of numbers in the brain is the 'mental number line', in which numbers are represented in a continuous, quantity-based analogical format. Here we show that patients with hemispatial neglect misplace the midpoint of a numerical interval when asked to bisect it (for example, stating that five is halfway between two and six), with an error pattern that closely resembles the bisection of physical lines. This new form of representational neglect constitutes strong evidence that the mental number line is more than simply a metaphor, and that its spatial nature renders it functionally isomorphic to physical lines.

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