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The Individual Brain Charting (IBC) is a multi-task functional Magnetic Resonance Imaging dataset acquired at high spatial-resolution and dedicated to the cognitive mapping of the human brain. It consists in the deep phenotyping of twelve individuals, covering a broad range of psychological domains suitable for functional-atlasing applications. Here, we present the inclusion of task data from both naturalistic stimuli and trial-based designs, to uncover structures of brain activation. We rely on the Fast Shared Response Model (FastSRM) to provide a data-driven solution for modelling naturalistic stimuli, typically containing many features. We show that data from left-out runs can be reconstructed using FastSRM, enabling the extraction of networks from the visual, auditory and language systems. We also present the topographic organization of the visual system through retinotopy. In total, six new tasks were added to IBC, wherein four trial-based retinotopic tasks contributed with a mapping of the visual field to the cortex. IBC is open access: source plus derivatives imaging data and meta-data are available in public repositories.

The neural basis of choice behavior is commonly investigated with tasks in which a subject analyzes a stimulus and reports his or her perceptual experience with an appropriate motor action. We recently developed a novel task, the compelled-saccade task, with which the influence of the sensory information on the subject's choice can be tracked through time with millisecond resolution, thus providing a new tool for correlating neuronal activity and behavior. This paradigm has a crucial feature: the signal that instructs the subject to make an eye movement is given before the cue that indicates which of two possible choices is the correct one. Previously, we found that psychophysical performance in this task could be accurately replicated by a model in which two developing oculomotor plans race to a threshold and the incoming perceptual information differentially accelerates their trajectories toward it. However, the task design suggests an alternative mechanism: instead of modifying an ongoing oculomotor plan on the fly as the sensory information becomes available, the subject could try to wait, withholding the oculomotor response until the sensory cue is revealed. Here, we use computer simulations to explore and compare the performance of these two types of model. We find that both reproduce the main features of the psychophysical data in the compelled-saccade task, but they give rise to distinct behavioral and neurophysiological predictions. Although, superficially, the waiting model is intuitively appealing, it is ultimately inconsistent with experimental results from this and other tasks.

It is hard to dissociate the time taken for purely perceptual processes from motor reaction times when making responses to stimuli. Using a combination of a novel task design and computational modeling, this study dissociates these two processes and finds that monkeys can discriminate perceptual color information in as little time as 30 ms. In perceptual discrimination tasks, a subject's response time is determined by both sensory and motor processes. Measuring the time consumed by the perceptual evaluation step alone is therefore complicated by factors such as motor preparation, task difficulty and speed-accuracy tradeoffs. Here we present a task design that minimizes these confounding factors and allows us to track a subject's perceptual performance with unprecedented temporal resolution. We find that monkeys can make accurate color discriminations in less than 30 ms. Furthermore, our simple task design provides a tool for elucidating how neuronal activity relates to sensory as opposed to motor processing, as demonstrated with neural data from cortical oculomotor neurons. In these cells, perceptual information acts by accelerating and decelerating the ongoing motor plans associated with correct and incorrect choices, as predicted by a race-to-threshold model, and the time course of these neural events parallels the time course of the subject's choice accuracy.

The study explores the effect of demographic variables such as age and level of experience on the level of stress experienced by non-gazette officers of the Central Reserve Police Force (CRPF). A purposive sample of 163 CRPF personnel was chosen. The Police Stress Inventory developed for use among CRPF personnel was administered. Various statistical parameters such as mean, standard deviation, standard error, mean difference and single-factor ANOVA were used to analyze the data. The study strongly indicates the relationship between stress and demographic variables such as age and level of experience.

The Individual Brain Charting (IBC) is a multi-task functional Magnetic Resonance Imaging dataset acquired at high spatial-resolution and dedicated to the cognitive mapping of the human brain. It consists in the deep phenotyping of twelve individuals, covering a broad range of psychological domains suitable for functional-atlasing applications. Here, we present the inclusion of task data from both naturalistic stimuli and trial-based designs, to uncover structures of brain activation. We rely on the Fast Shared Response Model (FastSRM) to provide a data-driven solution for modelling naturalistic stimuli, typically containing many features. We show that data from left-out runs can be reconstructed using FastSRM, enabling the extraction of networks from the visual, auditory and language systems. We also present the topographic organization of the visual system through retinotopy. In total, six new tasks were added to IBC, wherein four trial-based retinotopic tasks contributed with a mapping of the visual field to the cortex. IBC is open access: source plus derivatives imaging data and meta-data are available in public repositories.

Perceptual decisions are extremely fast and take on the order of milliseconds. The time taken to view the stimuli during these choices, termed processing time (PT), is an important quantity because it defines how informed, and thereby accurate, the choice is. Typically, the PT has been estimated from the reaction time (RT), which is the total amount of time taken to report a choice, starting from stimulus onset. The issue here is that the RT reflects not only the PT, but also related motor and other non-decision delays involved in a choice. To resolve this, the compelled-saccade (CS) task was developed. The CS task is a two-alternative forced-choice task with a crucial difference: the instruction to initiate a saccade (GO signal) precedes cue presentation, which is provided a variable delay later. This design achieves a separation between the perceptual and non-perceptual processes that constitute a choice, which then provides a means to extract PTs and study the speed of perceptual processing. To aid in this effort, the tachometric curve - a psychophysical metric that plots accuracy as a function of PT - was generated. Three features of the tachometric curve, namely, center-point, slope and maximum height help quantify the onset, speed and efficiency of perceptual processing, respectively. Prior to using the tachometric curve to characterize perceptual capacity, it was ensured that it reflected perceptual processing alone. The validation process was two-fold. First, motor behavior was altered using a directional bias instituted by differentially rewarding correct saccades to the two target locations. While this produced dramatic differences in the saccade metrics to the two locations, the tachometric curves in the two conditions were virtually identical, thus proving that the tachometric curve was not influenced by non-perceptual factors. Second, stimulus characteristics were altered in three different ways to show that this did, indeed, change the tachometric curve. First, a motivational bias was induced by differentially rewarding target color. While the change in saccade metrics was small and subtle, the tachometric curves showed a more robust change reflecting altered perception as a result of the motivational bias. Next, physical features of the stimulus were altered. This was done by (1) lowering the saturation of the color stimuli and (2) changing the relevant discrimination feature from color to shape. With both these manipulations, overall RTs did not change substantially but the tachometric curve parameters indicated significantly different perceptual processing between conditions. Finally, the tachometric curve was used to examine the perceptual effects of learning, which is known to lead to higher accuracy and shorter RTs. As the subject learned the task, they not only became faster and more efficient at it, but also started stimulus processing earlier. All of these factors went into improving the subject's overall performance. The tachometric curve has thus been used to study perceptual processing and the ways it influences behavior. The separation of perceptual and non-perceptual processes that the CS task enables will help parse out the neural basis of decision-making and the specific contributions of brain areas to different aspects of the phenomenon.

The technique of turning function is a powerful method for measuring similarity between two dimensional shapes. The method works well when the boundary of the shape does not contain noise edges. We propose an algorithm for smoothing noise edges by decomposing the boundary into monotone components and smoothing the noise edges in each component. We also present an implementation of the proposed smoothing algorithm.