We process information from the world through multiple senses, and the brain must decide what information belongs together and what information should be segregated. One challenge in studying such multisensory integration is how to quantify the multisensory interactions, a challenge that is amplified by the host of methods that are now used to measure neural, behavioral, and perceptual responses. Many of the measures that have been developed to quantify multisensory integration (and which have been derived from single unit analyses), have been applied to these different measures without much consideration for the nature of the process being studied. Here, we provide a review focused on the means with which experimenters quantify multisensory processes and integration across a range of commonly used experimental methodologies. We emphasize the most commonly employed measures, including single- and multiunit responses, local field potentials, functional magnetic resonance imaging, and electroencephalography, along with behavioral measures of detection, accuracy, and response times. In each section, we will discuss the different metrics commonly used to quantify multisensory interactions, including the rationale for their use, their advantages, and the drawbacks and caveats associated with them. Also discussed are possible alternatives to the most commonly used metrics.

The temporal synchrony of auditory and visual signals is known to affect the perception of an external event, yet it is unclear what neural mechanisms underlie the influence of temporal synchrony on perception. Using parametrically varied levels of stimulus asynchrony in combinations with BOLD fMRI, we identified two anatomically distinct subregions of multisensory superior temporal cortex (mSTC) that showed qualitatively distinct BOLD activation patterns. A synchrony-defined subregion of mSTC (synchronous > asynchronous) responded only when auditory and visual stimuli were synchronous, whereas a bimodal subregion of mSTC (auditory > baseline and visual > baseline) showed significant activation to all presentations, but showed monotonically increasing activation with increasing levels of asynchrony. The presence of two distinct activation patterns suggests that the two subregions of mSTC may rely on different neural mechanisms to integrate audiovisual sensory signals. An additional whole-brain analysis revealed a network of regions responding more with synchronous than asynchronous speech, including right mSTC, and bilateral superior colliculus, fusiform gyrus, lateral occipital cortex, and extrastriate visual cortex. The spatial location of individual mSTC ROIs was much more variable in the left than right hemisphere, suggesting that individual differences may contribute to the right lateralization of mSTC in a group SPM. These findings suggest that bilateral mSTC is composed of distinct multisensory subregions that integrate audiovisual speech signals through qualitatively different mechanisms, and may be differentially sensitive to stimulus properties including, but not limited to, temporal synchrony.

Systems Factorial Technology is a powerful framework for investigating the fundamental properties of human information processing such as architecture (i.e., serial or parallel processing) and capacity (how processing efficiency is affected by increased workload). The Survivor Interaction Contrast (SIC) and the Capacity Coefficient are effective measures in determining these underlying properties, based on response-time data. Each of the different architectures, under the assumption of independent processing, predicts a specific form of the SIC along with some range of capacity. In this study, we explored SIC predictions of discrete-state (Markov process) and continuous-state (Linear Dynamic) models that allow for certain types of cross-channel interaction. The interaction can be facilitatory or inhibitory: one channel can either facilitate, or slow down processing in its counterpart. Despite the relative generality of these models, the combination of the architecture-oriented plus the capacity oriented analyses provide for precise identification of the underlying system.

To date, few studies have characterized the influence of energy deprivation on direct measures of skeletal muscle protein turnover. In this investigation, we characterized the effect of an acute, moderate energy deficit (10 d) on mixed muscle fractional synthetic rate (FSR) and associated intracellular signaling proteins in physically active adults. Eight men and 4 women participated in a 20-d, 2-phase diet intervention study: weight maintenance (WM) and energy deficient (ED; approximately 80% of estimated energy requirements). Dietary protein (1.5 g x kg(-1) x d(-1)) and fat (approximately 30% of total energy) were constant for WM and ED. FSR and intracellular signaling proteins were measured on d 10 of both interventions using a primed, constant infusion of [(2)H(5)]-phenylalanine and Western blotting techniques, respectively. Participants lost approximately 1 kg body weight during ED (P < 0.0001). FSR was reduced approximately 19% (P < 0.05) for ED (0.06 +/- 0.01%/h) compared with WM (0.074 +/- 0.01%/h). Protein kinase B and eukaryotic initiation factor 4E binding protein 1 phosphorylation were lower (P < 0.05) during ED compared with WM. AMP activated protein kinase phosphorylation decreased (P < 0.05) over time regardless of energy status. These findings show that FSR and associated synthetic intracellular signaling proteins are downregulated in response to an acute, moderate energy deficit in physically active adults and provide a basis for future studies assessing the impact of prolonged, and perhaps more severe, energy restriction on skeletal muscle protein turnover.

Measures of human efficiency under increases in mental workload or attentional limitations are vital in studying human perception, cognition, and action. Assays of efficiency as workload changes have typically been confined to either reaction times (RTs) or accuracy alone. Within the realm of RTs, a nonparametric measure called the workload capacity coefficient has been employed in many studies (Townsend & Nozawa, 1995). However, the contribution of correct versus incorrect responses has been unavailable in that context. A nonparametric statistic that is capable of simultaneously taking into account accuracy as well as RTs would be highly useful. This theoretical study develops such a tool for two important decisional stopping rules. Preliminary data from a simple visual identification study illustrate one potential application.