We critique five points that impede the target article's far-reaching efforts toward formulating a neurobiological theory of language. Neurolinguistics amounts to no more than neurology in linguistics in this account, because it assumes “perceptual representational isomorphism,” processing autonomy and “meaning,” thereby aiming primarily at justifying modular concepts in terms of associative principles.

Within the Hebbian paradigm the mechanism for integrating cell assemblies oscillating with different frequencies remains unclear. We hypothesize that such an integration may occur in cortical “interaction foci” that unite synchronously oscillated assemblies through hard-wired connections, synthesizing the information from various functional systems of the brain.

Pulvermüller's work in extending Hebb's theory into the realm of language is exciting. However, we feel that what he characterizes as a single cell assembly is actually a set of cooperating cell assemblies that form parts of larger cognitive structures. These larger structures account more easily for a variety of phenomena, including the psycholinguistic.

Pulvermüller identifies two major flaws of the subtraction method of neuroimaging studies and proposes remedies. We argue that these remedies are themselves flawed and that the cognitive science community badly needs to take initial steps toward a cross-fertilization between mind mappers and cognitive modelers. Such steps could include the development of computational task models that transparently and falsifiably link the input (stimuli) and output (changes in blood flow or brain waves) of neuroimaging studies to changes in information processing activity that is the stuff of cognitive models.

A Hebbian model for speech generation opens a number of paths. A cross-linguistic scheme of functional relationships (inspired by Aristotle) dispenses with distraction by the “parts of speech” distinctions, while bridging the gap between “content” and “structure” words. A gradient model identifies emotional and rational dynamics and shows speech generation as a process where a speaker's dissatisfaction gets minimized.

Hebb's theory of cell assemblies is a precursor of the neural network approach used as an implicit hypothesis by most contemporary neuroscientists. Applying this model to language representation leads to demanding predictions about the organization of semantic categories. Other implications of a Hebbian approach to language representation, however, may prove problematic with respect to both neurolinguistic concepts and the results of neuroimaging studies.

One of the main aspects of a neurobiological theory of language is the problem of meaning (or semantic content) in the brain. A full explanation of meaning requires a combined approach to semantic typing and the semantic success of cerebral states or processes. Pulvermüller presents his Hebbian model of language in the brain (HML) as an account of semantic success. If his proposal turns out to be viable, however, it may also promote a theory of semantic typing.

Empirical evidence suggests that high frequency electrographic activity is involved in active representation of meaningful entities in the cortex. Theoretical work suggests that distributed cell assemblies also represent meaningful entities. However, we are still some way from understanding how these two are related. This commentary also makes suggestions for further investigation of the neural basis of language at the level of both words and sentence planning.

Pulvermüller advocates an experimental design in which well-matched tokens from various word categories are presented in list form to subjects making rapid lexical decisions about these stimuli. Implicit in this paradigm are several problematic assumptions. We conclude that such experiments are not likely to tell us much about the biological bases of human language.

We raise the possibility that the roles words typically play in sentence context, rather than their motor or visual associations, determine the organization of language in the brain. We also discuss time-sensitive functional imaging approaches that allow experimentation both on isolated words and connected language.

ERP studies have shown modulation of activation in left frontal and posterior cortical language areas, as well as recruitment of right hemisphere homologues, based on task demands. Furthermore, blood-flow studies have demonstrated changes in the neural circuitry of word processing based on experience. The neural areas and time course of language processing are plastic depending on task demands and experience.

Pulvermüller's account of lexical representation has implications for visual word recognition, given the claim we make that a foveally presented word is precisely split and contralaterally projected to the two hemispheres, and that this splitting conditions the whole process of visual word recognition. This elaboration of Pulvermüller's account raises issues of hemispheric differences and collaboration.

Pulvermüller restricts himself to an unnecessarily narrow range of evidence to support his claims. Evidence from neural modeling and behavioral experiments provides further support for an account of words encoded as transcortical cell assemblies. A cognitive neuroscience of language must include a range of methodologies (e.g., neural, computational, and behavioral) and will need to focus on the on-line processes of real-time language processing in more natural contexts.

When words are read, the visual cortex is activated, independent of whether visual or motor associations are elicited. This word-evoked brain activity is significantly influenced by semantic meaning. Such effects occur very early after stimulus presentation (at latencies between 80 and 130 msec), indicating that semantic meaning activates different neuronal assemblies in the human visual cortex when words are processed.

Recent investigations have explored how large-scale systems in the brain operate in the processes of retrieving knowledge for words and concepts. Much of the crucial evidence derives from lesion studies, because word retrieval and concept retrieval can be clearly dissociated in brain-damaged individuals. We discuss these findings from the perspective of our neurobiological framework, which is cited in Pulvermüller's target article.

The important Hebbian architecture for language may not be the phonological networks of perisylvian cortex, but rather the semantic networks of limbic cortex. Although the high-frequency EEG findings are intriguing, the results may not yet warrant a confident theory of neural assemblies. Nonetheless, Pulvermüller succeeds in framing a comprehensive theory of language function in the literal terms of neuroanatomy and neurophysiology.

Pulvermüller's attempt to link language with brain activity appears to depend on the assumption that words have context-independent meanings. An examination of everyday talk contradicts this assumption. The meaning that speakers convey depends not only on word content, but also, and importantly, on the location of a “word” in an ongoing sequence of turns in talk.

The EEG and MEG studies cited in the target article found reduced gamma band power following pseudowords in comparison with words. Pulvermüller interprets this power difference in terms of reverberating lexical cell assemblies. An alternative interpretation in terms of latency jitter in the gamma band following pseudowords is proposed that does not appeal to lexical cell assemblies.

The idea of representing words with cell assemblies is very appealing. However, syntactic sequences need to be represented as well. This cannot be done by using the activity levels of assemblies. Instead, structural relations and operations between assemblies are needed to achieve serial order in syntactic word strings.

In this response to multidisciplinary commentaries on the target article, “Words in the brain's language,” additional features of the cell-assembly model are reviewed, as demanded by some of the commentators. Subsequently, methodological considerations on how to perform additional tests of neurobiological language models as well as a discussion of recent data from neuroimaging, neuropsychological, and other behavioral studies in speakers of spoken and sign languages follow. Special emphasis is put on the explanatory power of the cell-assembly model regarding neuropsychological double dissociations. Future perspectives on neural network simulations, neuronal mechanisms of syntax and semantics, and the interaction of attention mechanisms and cell assemblies are pointed out in the final paragraphs.