Neural organization achieves its stated goal to “show how theory and experiment can supplement each other in an integrated, evolving account of structure, function, and dynamics” (p. ix), showing in a variety of contexts – from olfactory processing to spatial navigation, motor learning and more – how function may be realized in the neural tissue, with explanatory and predictive neural network models providing a cornerstone in this approach.

This commentary analyzes the target article to determine whether shared-intention development could be implemented and tested in robotic systems. The analysis indicates that such an implementation should be feasible and will likely rely on a construction-based approach similar to that employed in the construction grammar framework.

In arguing for a rules-similarity continuum, Pothos should demonstrate that a single process or mechanism (a neural network model, for example) can handle the entire continuum. Pothos deliberately avoids this exercise as beyond the scope of the current research. In this context, I will present simulation, neuropsychological, neurophysiological, and experimental psychological results, arguing against the continuity hypothesis.

Pickering & Garrod (P&G) describe a mechanism by which the situation models of dialog participants become progressively aligned via priming at different levels. This commentary attempts to characterize how alignment and routinization can be extended into the language acquisition domain by establishing links between alignment and joint attention, and between routinization and grammatical construction learning.

In the context of Hurford's claim that “some feature of language structure maps onto a feature of primitive mental representations,” I will argue that Hurford's focus on 1-place predicates as the basis of the “mental representations of situations in the world” is problematic, particularly with respect to spatiotemporal events. A solution is proposed.

In Carruthers’ formulation, cross-domain thinking requires translation of domain specific data into a common format, and linguistic LF thus plays the role of the common medium of exchange. Alternatively, I propose a process-oriented characterization, in which there is no common representation and cross-domain thinking is rather the process of establishing mappings across domains, as in the process of analogical reasoning.

Grodzinsky's characterization of the syntactic function of Broca's area is convincing, but his argument that this transformation processing capability is specific to language is less so. Based on predictions from simulation studies of sequence learning, we report a correlation between agrammatic patients' impairments in (a) syntactic comprehension, and (b) nonlinguistic sequence transformation processing, indicating the existence of a nonlinguistic correlate of agrammatic aphasia.

Flexible categorization clearly requires an adaptive component, but at what level of representation? We have investigated categorization in sequence learning that requires the extraction of abstract rules, but no modification of sensory primitives. This motivates the need to make explicit the distinction between sensory-level “atomic” features as opposed to concept-level “abstract” features, and the proposal that flexible categorization probably relies on learning at the abstract feature level.

Deictic pointers allow the nervous system to exploit information in a frame that is centered on the object of interest. This processing may take place in visual or haptic space, but the information processing advantages of deictic pointing can also be applied in abstract spaces, providing the basis for analogical transfer. Simulation and behavioral results illustrating this progression from embodiment to abstraction are discussed.

Analogical transfer in sequence learning is presented as an example of how the type-2 problem of learning an unbounded number of isomorphic sequences is reduced to the type-1 problem of learning a small finite set of sequences. The commentary illustrates how the difficult problem of appropriate analogical filter creation and selection is addressed while avoiding the trap of strong nativism, and it provides theoretical and experimental evidence for the existence of dissociable mechanisms for type-1 learning and type-2 recoding.