Although van der Velde's de Kamps's (vdV&dK) attempt to put syntactic processing into a broader context of combinatorial cognition is promising, their coverage of neuroscientific evidence is disappointing. Neither their case against binding by temporal coherence nor their arguments against recurrent neural networks are compelling. As an alternative, vdV&dK propose a blackboard model that is based on the assumption of special processors (e.g., lexical versus grammatical), but evidence from the cognitive neuroscience of language, which is, overall, less than supportive of such special processors, is not considered. As a consequence, vdV&dK's may be a clever model of syntactic processing, but it remains unclear how much we can learn from it with regard to biologically based human language.

As Striedter explores the concerted principles that drive brain evolution and the departures that create uniqueness, he scrutinizes and ultimately supports the (unnecessarily controversial) Finlay/Darlington model in which mathematical relationships across mammalian neural development are identified (Finlay & Darlington 1995). Pragmatic impact includes the ability to make novel comparisons across developing species, including humans.

The target article does not provide insight into how the proposed neural blackboard architecture can be mapped to known neural structures in the brain. There are theories suggesting that the thalamus may be a good candidate. However, the experimental evidence suggests that the cortex may be involved (if in fact the blackboard is implemented in the brain). Issues arising from such a mapping will be discussed.

Principles of Brain Evolution (Striedter 2005) places little emphasis on natural selection. However, one cannot fully appreciate the diversity of brains across species, nor the evolutionary processes driving such diversity, without an understanding of the effects of natural selection. Had Striedter included more extensive discussions about natural selection, his text would have been more balanced and comprehensive.

The attempt to derive mental function from brain structure is highly constrained by study of the allometric changes among brain components with evolution. In particular, even if homologous structures in different species produce similar computations, they may be constituents of larger systems (e.g., cortical-subcortical loops) that exhibit different composite operations as a function of relative size and connectivity in different-sized brains. The resulting evolutionary constraints set useful and specific conditions on candidate hypotheses of brain circuit computation.

It is argued that van der Velde and de Kamps employ binding circuitry that effectively constitutes a form of conjunctive binding. Analogies with prior systems are discussed and hypothetical origins of binding circuitry are examined for credibility.

The target article demonstrates how neurocognitive modellers should not be intimidated by challenges such as Jackendoff's and should explore neurally plausible implementations of linguistic constructs. The next step is to take seriously insights offlered by neuroscience, including the robustness allowed by analogue computation with distributed representations and the power of attractor dynamics in turning analogue into nearly discrete operations.

The neural blackboard architecture is a localist structured connectionist model that employs a novel connection matrix to implement dynamic bindings without requiring propagation of temporal synchrony. Here I note the apparent need for many distinct matrices and the effect this might have for scale-up to semantic processing. I also comment on the authors' initial foray into the symbol grounding problem.

Using neoPiagetian theory of mental attention (or working memory), I task-analyze two complex performances of great apes and one symbolic performance (funeral burials) of early Homo sapiens. Relating results to brain size growth data, I derive estimates of mental attention for great apes, Homo erectus, Neanderthals, and modern Homo sapiens, and use children's cognitive development as reference. This heuristic model seems consistent with research.

Contrary to the assertions made in the target article, temporal synchrony, coupled with an appropriate choice of representational primitives, leads to a functionally adequate and neurally plausible architecture that addresses the massiveness of the binding problem, the problem of 2, the problem of variables, and the transformation of activity-based transient representations of events and situations into structure-based persistent encodings of the same.

Human cognition is unique in the way in which it relies on combinatorial (or compositional) structures. Language provides ample evidence for the existence of combinatorial structures, but they can also be found in visual cognition. To understand the neural basis of human cognition, it is therefore essential to understand how combinatorial structures can be instantiated in neural terms. In his recent book on the foundations of language, Jackendoff described four fundamental problems for a neural instantiation of combinatorial structures: the massiveness of the binding problem, the problem of 2, the problem of variables, and the transformation of combinatorial structures from working memory to long-term memory. This paper aims to show that these problems can be solved by means of neural “blackboard” architectures. For this purpose, a neural blackboard architecture for sentence structure is presented. In this architecture, neural structures that encode for words are temporarily bound in a manner that preserves the structure of the sentence. It is shown that the architecture solves the four problems presented by Jackendoff. The ability of the architecture to instantiate sentence structures is illustrated with examples of sentence complexity observed in human language performance. Similarities exist between the architecture for sentence structure and blackboard architectures for combinatorial structures in visual cognition, derived from the structure of the visual cortex. These architectures are briefly discussed, together with an example of a combinatorial structure in which the blackboard architectures for language and vision are combined. In this way, the architecture for language is grounded in perception. Perspectives and potential developments of the architectures are discussed.

van der Velde & de Kamps argue for the importance of considering the binding problem in accounts of human mental representation. However, their proposed solution fails as a complete account because it represents the bindings between roles and their fillers through associations (or connections). In addition, many criticisms leveled by the authors towards synchrony-based bindings models do not hold.

van der Velde & de Kamps (dvV&dK) present a response to Jackendoff's four challenges in terms of a computational model. This commentary supports the position that neural assemblies mediated by recurrence and delay indeed have sufficient theoretical power to deal with all four challenges. However, we question the specifics of the model proposed, in terms of both neurophysiological plausibility and computational complexity.

Striedter's book offers precious insight into the comparative neuroanatomy of vertebrate brains, but it stops short of addressing what their evolution is all about: how effectively neural networks process information important for survival. To understand the principles of brain evolution, neuroanatomy needs to be combined not only with genetics, neurophysiology, and ethology, but also with quantitative network analyses.