A connectionist modeling study of the neural mechanisms underlying pain's ability to reorient attention.

Connectionist modeling was used to investigate the brain mechanisms responsible for pain's ability to shift attention away from another stimulus modality and toward itself. Different connectionist model architectures were used to simulate the different possible brain mechanisms underlying this attentional bias, where nodes in the model simulated the brain areas thought to mediate the attentional bias, and the connections between the nodes simulated the interactions between the brain areas. Mathematical optimization techniques were used to find the model parameters, such as connection strengths, that produced the best quantitative fits of reaction time and event-related potential data obtained in our previous work.

Neural mechanisms underlying pain's ability to reorient attention: evidence for sensitization of somatic threat detectors.

Pain typically signals damage to the body, and as such can be perceived as threatening and can elicit a strong emotional response. This ecological significance undoubtedly underlies pain's well-known ability to demand attention. However, the neural mechanisms underlying this ability are poorly understood.

The role of somatic threat feature detectors in the attentional bias toward pain: effects of spatial attention.

Our previous work suggests that somatic threat feature detectors indexed by a pain-evoked midlatency negative scalp potential play an important role in the attentional bias toward pain. In these studies the somatic threat feature detectors facilitated the shift in attention to a somatic threat when attention was focused on another stimulus modality but not when it was focused on another spatial location. This experiment used the Posner cuing paradigm to investigate possible explanations for this discrepancy.

EEG indices of tonic pain-related activity in the somatosensory cortices.

Objective: To identify EEG features that index pain-related cortical activity, and to identify factors that can mask the pain-related EEG features and/or produce features that can be misinterpreted as pain-specific.

Methods: The EEG was recorded during three conditions presented in counterbalanced order: a tonic cold pain condition, and pain anticipation and arithmetic control conditions. The EEG was also recorded while the subjects made a wincing facial expression to estimate the contribution of scalp EMG artifacts to the pain-related EEG features.

An artificial neural network model of orienting attention toward threatening somatosensory stimuli.

An artificial neural network model was designed to test the threat detection hypothesis developed in our experimental studies, where threat detector activity in the somatosensory association areas is monitored by the medial prefrontal cortex, which signals the lateral prefrontal cortex to redirect attention to the threat. As in our experimental studies, simulated threat-evoked activations of all three brain areas were larger when the somatosensory target stimulus was unattended than attended, and the increase in behavioral reaction times when the target stimulus was unattended was smaller for threatening than nonthreatening stimuli. The model also generated a number of novel predictions, for example, the effect of threat on reaction time only occurs when the target stimulus is unattended, and the P3a indexes prefrontal cortex activity involved in redirecting attention toward response processes on that trial and sensory processes on subsequent trials.