Pothos & Busemeyer (P&B) argue how key concepts of quantum probability, for example, order/context, interference, superposition, and entanglement, can be used in cognitive modeling. Here, we suggest that these concepts can be extended to analyze neurophysiological measurements of cognitive tasks in humans, especially in functional neuroimaging investigations of large-scale brain networks.

This commentary focuses on the importance of auditory object processing for producing and comprehending human language, the relative lack of development of this capability in nonhuman primates, and the consequent need for hominid neurobiological evolution to enhance this capability in making the transition from protosign to protospeech to language.

Gold & Stoljar argue that there are two (often confused) neuron doctrines, one trivial and the other radical, with only the latter having the consequence that non-neuroscientific sciences of the mind will be discarded. They also attempt to show that there is no evidence supporting the radical doctrine. It is argued here that their dichotomy is artificial and misrepresents modern approaches to understanding the neuroscientific correlates of cognition and behavior.

The subtraction and covariance paradigms are two analytic techniques used with functional neuroimaging data. The first assumes that a brain region participating in a task should show altered neural activity (relative to a control task). The second assumes that tasks are mediated by networks of interacting regions.Images of mindattempts to link results from the subtraction paradigm with a network interpretation that could have been more explicitly done using the covariance paradigm.

Although interesting, the hypotheses proposed by Phillips & Silverstein lack unifying structure both in specific mechanisms and in cited evidence. They provide little to support the notion that low-level sensory processing and high-level cognitive coordination share dynamic grouping by synchrony as a common processing mechanism. We suggest that more realistic large-scale modeling at multiple levels is needed to address these issues.