Quantitative prediction of perceptual decisions during near-threshold fear detection

Abstract

A fundamental goal of cognitive neuroscience is to explain how mental decisions originate from basic neural mechanisms. The goal of the present study was to investigate the neural correlates of perceptual decisions in the context of emotional perception. To probe this question, we investigated how fluctuations in functional MRI (fMRI) signals were correlated with behavioral choice during a near-threshold fear detection task. fMRI signals predicted behavioral choice independently of stimulus properties and task accuracy in a network of brain regions linked to emotional processing: posterior cingulate cortex, medial prefrontal cortex, right inferior frontal gyrus, and left insula. We quantified the link between fMRI signals and behavioral choice in a whole-brain analysis by determining choice probabilities by means of signaldetection theory methods. Our results demonstrate that voxelwise fMRI signals can reliably predict behavioral choice in a quantitative fashion (choice probabilities ranged from 0.63 to 0.78) at levels comparable to neuronal data. We suggest that the conscious decision that a fearful face has been seen is represented across a network of interconnected brain regions that prepare the organism to appropriately handle emotionally challenging stimuli and that regulate the associated emotional response.decision making ͉ emotion ͉ functional MRI T he brain uses sensory information to make decisions that guide behavior. Currently, there is considerable knowledge, for example, about how contour, depth, and motion information is processed by early visual areas. In contrast, relatively little is known about how perceptual decisions are made. Even simple perceptual decisions, such as determining the presence or absence of a sensory stimulus, are not well understood. A fundamental goal of cognitive neuroscience is to explain how mental decisions originate from basic neural mechanisms.In non-human primates, Newsome, Movshon, and colleagues (1, 2) have pioneered the investigation of the neural correlates of perceptual decisions. Their experiments have probed the mechanisms associated with deciding the direction of motion of an array of moving dots, only a fraction of which move coherently at a time. Newsome et al. (1,2) showed that the activity of directionally selective neurons in middle temporal visual area (MT) could account for the psychophysical performance in a forced-choice direction discrimination task (1, 2). Related studies have quantified the link between performance and cell activity during depth perception (3) and the perception of structure from motion (4). Little is known, however, about the involvement of nonsensory regions in perceptual decisions. Previous neurophysiology studies typically have investigated one, and in some cases two [e.g., visual area 1 (V1) and MT], sensory processing regions (but see ref. 5).Decision-making processes have been investigated with neuroimaging, too. Binder et al. (6) operationally defined decisions in an auditory object identification task in terms of reaction...

Brain signals for spatial attention predict performance in a motion discrimination task

“…Similar values for choice probability using fMRI have been recently found during a perceptual decision involving facial expressions (17). One explanation for this surprising degree of reliability is that, whereas the BOLD signal is degraded by a number of noise sources (50), it represents the cumulative effects of synaptic activity of large populations of afferent and intra-cortical fibers (51,52), similar to local field potentials, which have been shown to be more accurate than single-unit spike rates in predicting aspects of behavior (53).…”

Interpreting fMRI data: maps, modules and dimensions

“…Evidence for fine-grained selectivity for other object properties, such as orientation and size, or for specific exemplars within a category is sparse. Some reports suggest, based on activity in the ventral visual pathway, that brainreading algorithms have weak but above-chance classification performance on withincategory discriminations (for example, for pigeons versus seagulls or for fearful versus happy faces) [32][33][34] . Conversely, even high-resolution scans have so far failed to find abovechance classification performance for discriminating the identity of faces based on activity in the FFA 35 , or for discriminating different body parts (for example, hands versus legs) from activity in the EBA (R. F. Schwarzlose and N.G.K., unpublished observations).…”

Real‐time fMRI using brain‐state classification

“…Consequently, there has been a remarkable surge in cognitive neuroscientific interest and inventive experimental designs focused on classification of brain states from fMRI data. The applications have been broad and include lie detection [Davatzikos et al, 2005], unconsciously perceived sensory stimuli [Haynes and Rees, 2005], behavioral choices in the context of emotional perception [Pessoa and Padmala, 2005], early visual areas [Kamitani and Tong, 2005], information-based mapping [Kriegeskorte et al, 2006], and memory recall [Polyn et al, 2005].…”