Gender Differences in Sleep Deprivation Effects on Risk and Inequality Aversion: Evidence from an Economic Experiment

Excessive working hours—even at night—are becoming increasingly common in our modern 24/7 society. The prefrontal cortex (PFC) is particularly vulnerable to the effects of sleep loss and, consequently, the specific behaviors subserved by the functional integrity of the PFC, such as risk-taking and pro-social behavior, may be affected significantly. This paper seeks to assess the effects of one night of sleep deprivation on subjects’ risk and social preferences, which are probably the most explored behavioral domains in the tradition of Experimental Economics. This novel cross-over study employs thirty-two university students (gender-balanced) participating to 2 counterbalanced laboratory sessions in which they perform standard risk and social preference elicitation protocols. One session was after one night of undisturbed sleep at home, and the other was after one night of sleep deprivation in the laboratory. Sleep deprivation causes increased sleepiness and decreased alertness in all subjects. After sleep loss males make riskier decisions compared to the rested condition, while females do the opposite. Females likewise show decreased inequity aversion after sleep deprivation. As for the relationship between cognitive ability and economic decisions, sleep deprived individuals with higher cognitive reflection show lower risk aversion and more altruistic behavior. These results show that one night of sleep deprivation alters economic behavior in a gender-sensitive way. Females’ reaction to sleep deprivation, characterized by reduced risky choices and increased egoism compared to males, may be related to intrinsic psychological gender differences, such as in the way men and women weigh up probabilities in their decision-making, and/or to the different neurofunctional substrate of their decision-making.The authors acknowledge financial support from the Spanish Ministry of Economic Competititveness (ECO2012-34928), Italian Ministry of University and Research MIUR (PRIN 20103S5RN3_002), Generalitat Valenciana (Research Projects Gruposo3/086), the Instituto Valenciano de Investigaciones Económicas (IVIE), and the Ministero della Salute (RF-2009-1528677)

Manipulation of Pre-Target Activity on the Right Frontal Eye Field Enhances Conscious Visual Perception in Humans

The right Frontal Eye Field (FEF) is a region of the human brain, which has been consistently involved in visuo-spatial attention and access to consciousness. Nonetheless, the extent of this cortical site’s ability to influence specific aspects of visual performance remains debated. We hereby manipulated pre-target activity on the right FEF and explored its influence on the detection and categorization of low-contrast near-threshold visual stimuli. Our data show that pre-target frontal neurostimulation has the potential when used alone to induce enhancements of conscious visual detection. More interestingly, when FEF stimulation was combined with visuo-spatial cues, improvements remained present only for trials in which the cue correctly predicted the location of the subsequent target. Our data provide evidence for the causal role of the right FEF pre-target activity in the modulation of human conscious vision and reveal the dependence of such neurostimulatory effects on the state of activity set up by cue validity in the dorsal attentional orienting network

Perceptual and physiological evidence for a role for early visual areas in motion-induced blindness

Visual disappearance illusions, such as motion-induced blindness, are commonly used to study the neural correlates of visual perception. In such illusions a salient visual target becomes perceptually invisible. Previous studies are inconsistent regarding the role of early visual areas in these illusions. Here we provide physiological and psychophysical evidence suggesting a role for early visual areas in generating motion-induced blindness, and we provide a conceptual model by which different brain areas might contribute to the perceptual disappearance in this illusion

Minimally dependent activity subspaces for working memory and motor preparation in the lateral prefrontal cortex

10.7554/ELIFE.58154eLife91-2

Clustering Neural Patterns in Kernel Reinforcement Learning Assists Fast Brain Control in Brain-Machine Interfaces

Neuroprosthesis enables the brain control on the external devices purely using neural activity for paralyzed people. Supervised learning decoders recalibrate or re-fit the discrepancy between the desired target and decoder's output, where the correction may over-dominate the user's intention. Reinforcement learning decoder allows users to actively adjust their brain patterns through trial and error, which better represents the subject's motive. The computational challenge is to quickly establish new state-action mapping before the subject becomes frustrated. Recently proposed quantized attention-gated kernel reinforcement learning (QAGKRL) explores the optimal nonlinear neural-action mapping in the Reproducing Kernel Hilbert Space (RKHS). However, considering all past data in RKHS is less efficient and sensitive to detect the new neural patterns emerging in brain control. In this paper, we propose a clustering-based kernel RL algorithm. New neural patterns emerge and are clustered to represent the novel knowledge in brain control. The current neural data only activate the nearest subspace in RKHS for more efficient decoding. The dynamic clustering makes our algorithm more sensitive to new brain patterns. We test our algorithm on both the synthetic and real-world spike data. Compared with QAGKRL, our algorithm can achieve a quicker knowledge adaptation in brain control with less computational complexity

Sleep deprivation alters valuation signals in the ventromedial prefrontal cortex

10.3389/fnbeh.2011.00070Frontiers in Behavioral NeuroscienceOCTOBER