Modular processes in mind and brain

Can quantum probability help analyze the behavior of functional brain networks?

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.

Interplay of emotional valence and concreteness in word processing: an event-related potential study with verbs

The functional locus of emotional valence in word processing remains an open question. In event-related potentials, emotion has been found to elicit an early posterior negativity (EPN), which is assumed to reflect attention catching by the words' meaning. Previously, the EPN was modulated by word category with verbs exhibiting longer EPN latencies compared with other word categories. Here we examined whether concreteness, a semantic variable, influences emotion processing. Within a lexical decision task for verbs, emotional valence (positive, negative, and neutral) was orthogonally combined with concreteness (concrete and abstract). EPN onset was found already at 250 ms post-stimulus for concrete verbs, whereas it started 50 ms later for abstract verbs. Concreteness effects occurred after the start of main effects of emotion. Thus, the elicitation of the EPN seems to be based on semantic processes, with emotional valence being accessed before other semantic aspects such as concreteness of verbs.

Macrocircuits: decision networks

Decision-making requires stimulus categorization and localization to guide accurate responses that can be produced through multiple effectors. The success of actions is monitored so that performance can be adjusted to achieve goals. This review will survey recent empirical and theoretical developments very selectively with an emphasis on neurophysiological data from nonhuman primates that provide the clearest information about neural mechanisms.

Verbal and novel multisensory associative learning in adults

To date, few studies have focused on the behavioural differences between the learning of multisensory auditory-visual and intra-modal associations. More specifically, the relative benefits of novel auditory-visual and verbal-visual associations for learning have not been directly compared. In Experiment 1, 20 adult volunteers completed three paired associate learning tasks: non-verbal novel auditory-visual (novel-AV), verbal-visual (verbal-AV; using pseudowords), and visual-visual (shape-VV). Participants were directed to make a motor response to matching novel and arbitrarily related stimulus pairs. Feedback was provided to facilitate trial and error learning. The results of Signal Detection Theory analyses suggested a multisensory enhancement of learning, with significantly higher discriminability measures (d-prime) in both the novel-AV and verbal-AV tasks than the shape-VV task. Motor reaction times were also significantly faster during the verbal-AV task than during the non-verbal learning tasks.  Experiment 2 (n = 12) used a forced-choice discrimination paradigm to assess whether a difference in unisensory stimulus discriminability could account for the learning trends in Experiment 1. Participants were significantly slower at discriminating unisensory pseudowords than the novel sounds and visual shapes, which was notable given that these stimuli produced superior learning. Together the findings suggest that verbal information has an added enhancing effect on multisensory associative learning in adults.

Network attributes for segregation and integration in the human brain

Network studies of large-scale brain connectivity have begun to reveal attributes that promote the segregation and integration of neural information: communities and hubs. Network communities are sets of regions that are strongly interconnected among each other while connections between members of different communities are less dense. The clustered connectivity of network communities supports functional segregation and specialization. Network hubs link communities to one another and ensure efficient communication and information integration. This review surveys a number of recent reports on network communities and hubs, and their role in integrative processes. An emerging focus is the shifting balance between segregation and integration over time, which manifest in continuously changing patterns of functional interactions between regions, circuits and systems.

How to discover modules in mind and brain: the curse of nonlinearity, and blessing of neuroimaging. A comment on Sternberg (2011)

Sternberg (2011) elegantly formalizes how certain sets of hypotheses, specifically modularity and pure or composite measures, imply certain patterns of behavioural and neuroimaging data. Experimentalists are often interested in the converse, however: whether certain patterns of data distinguish certain hypotheses, specifically whether more than one module is involved. In this case, there is a striking reversal of the relative value of the data patterns that Sternberg considers. Foremost, the example of additive effects of two factors on one composite measure becomes noninformative for this converse question. Indeed, as soon as one allows for nonlinear measurement functions and nonlinear module processes, even a cross-over interaction between two factors is noninformative in this respect. Rather, one requires more than one measure, from which certain data patterns do provide strong evidence for multiple modules, assuming only that the measurement functions are monotonic. If two measures are not monotonically related to each other across the levels of one or more experimental factors, then one has evidence for more than one module (i.e., more than one nonmonotonic transform). Two special cases of this are illustrated here: a “reversed association” between two measures across three levels of a single factor, and Sternberg's example of selective effects of two factors on two measures. Fortunately, functional neuroimaging methods normally do provide multiple measures over space (e.g., functional magnetic resonance imaging, fMRI) and/or time (e.g., electroencephalography, EEG). Thus to the extent that brain modules imply mind modules (i.e., separate processors imply separate processes), the performance data offered by functional neuroimaging are likely to be more powerful in revealing modules than are the single behavioural measures (such as accuracy or reaction time, RT) traditionally considered in psychology.

Modules and brain mapping

This review highlights the key role of modularity and the additive factors method in functional neuroimaging. Our focus is on structure–function mappings in the human brain and how these are disclosed by brain mapping. We describe how modularity of processing (and possibly processes) was a key point of reference for establishing functional segregation as a principle of brain organization. Furthermore, modularity plays a crucial role when trying to characterize distributed brain responses in terms of functional integration or coupling among brain areas. We consider additive factors logic and how it helped to shape the design and interpretation of studies at the inception of brain mapping, with a special focus on factorial designs. We look at factorial designs in activation experiments and in the context of lesion–deficit mapping. In both cases, the presence or absence of interactions among various experimental factors has proven essential in understanding the context-sensitive nature of distributed but modular processing and discerning the nature of (potentially degenerate) structure–function relationships in cognitive neuroscience.

The specialization of function: cognitive and neural perspectives

A unifying theme that cuts across all research areas and techniques in the cognitive and brain sciences is whether there is specialization of function at levels of processing that are "abstracted away" from sensory inputs and motor outputs. Any theory that articulates claims about specialization of function in the mind/brain confronts the following types of interrelated questions, each of which carries with it certain theoretical commitments. What methods are appropriate for decomposing complex cognitive and neural processes into their constituent parts? How do cognitive processes map onto neural processes, and at what resolution are they related? What types of conclusions can be drawn about the structure of mind from dissociations observed at the neural level, and vice versa? The contributions that form this Special Issue of Cognitive Neuropsychology represent recent reflections on these and other issues from leading researchers in different areas of the cognitive and brain sciences.