Significance. Choking under pressure is a frustrating phenomenon experienced sometimes by skilled performers as well as during everyday life. The phenomenon has been extensively studied in humans, but it has not been previously shown whether animals also choke under pressure. Here we report that rhesus monkeys also choke under pressure. This indicates that there may be shared neural mechanisms that underlie the behavior in both humans and monkeys. Introducing an animal model for choking under pressure allows for opportunities to study the neural causes of this paradoxical behavior.
Abstract. In high-stakes situations, people sometimes exhibit a frustrating phenomenon known as “choking under pressure.” Usually, we perform better when the potential payoff is larger. However, once potential rewards get too high, performance paradoxically decreases—we “choke.” Why do we choke under pressure? An animal model of choking would facilitate the investigation of its neural basis. However, it could be that choking is a uniquely human occurrence. To determine whether animals also choke, we trained three rhesus monkeys to perform a difficult reaching task in which they knew in advance the amount of reward to be given upon successful completion. Like humans, monkeys performed worse when potential rewards were exceptionally valuable. Failures that occurred at the highest level of reward were due to overly cautious reaching, in line with the psychological theory that explicit monitoring of behavior leads to choking. Our results demonstrate that choking under pressure is not unique to humans, and thus, its neural basis might be conserved across species.
Contrats to all of the authors: Adam Smoulder, Nick Pavlosky, Patrick Marino, Nicole McClain, Aaron Batista, and Steve Chase.
Associated press/news:
Full citation: Smoulder, A.L.*, Pavlosky, N.P.*, Marino, P.J.*, Degenhart, A.D., McClain, N.T., Batista, A.P.^, Chase, S.M.^ Monkeys exhibit a paradoxical decrease in performance in high-stakes scenarios. Proceedings of the National Academy of Sciences (2021). (*,^: denotes equal contributions) https://doi.org/10.1073/pnas.2109643118
]]>Some recent examples of work coming out of the Allen Institute:
]]>To address this problem, we developed a manifold-based stabilizer that extracts a stable representation of neural activity from neural recordings where instabilities may be present. Our technique leverages the concept of a “neural manifold”: a low-dimensional space that captures specific correlation patterns across neurons. By aligning estimates of the neural manifold made at different points in time, we can stabilize the inputs to the BCI system, thus allowing control to proceed unimpeded. We hope that this work will help to improve the clinical viability of BCIs.
This work was the result of a collaboration between researchers at Carnegie Mellon University and the University of Pittsburgh, including myself, William Bishop, Emily Oby, Elizabeth Tyler-Kabara, MD, PhD, Steven Chase, Aaron Batista, and Byron Yu.
Link to full article: https://www.nature.com/articles/s41551-020-0542-9
]]>Consider a skill you would like to learn, like playing the piano. How do you progress from “Chopsticks” to Chopin? As you learn to do something new with your hands, does the brain also do something new? We found that monkeys learned new skilled behavior by generating new neural activity patterns. We used a brain–computer interface (BCI), which directly links neural activity to movement of a computer cursor, to encourage animals to generate new neural activity patterns. Over several days, the animals began to exhibit new patterns of neural activity that enabled them to control the BCI cursor. This suggests that learning to play the piano and other skills might also involve the generation of new neural activity patterns.
These experiments were incredibly to conduct, involving training animals to perform a difficult brain-computer interface task across multiple days. Kudos to her for all of the hard work!
The full article is available here:
https://www.pnas.org/content/early/2019/06/06/1820296116
Oby, E. R., Golub, M. D., Hennig, J. A., Degenhart, A. D., Tyler-Kabara, E. C., Yu, B. M., Chase, S. M., Batista, A. B. New neural activity patterns emerge with long-term learning. Proceedings of the National Academy of Sciences (PNAS), (2019).
]]>Degenhart, AD et al., 2017. “Remapping Cortical Modulation for Electrocorticographic Brain-Computer Interfaces: a Somatotopy-Based Approach in Individuals with Upper-Limb Paralysis..” Journal of Neural Engineering 15 (2). IOP Publishing: 026021. doi:10.1088/1741-2552/aa9bfb. http://iopscience.iop.org/article/10.1088/1741-2552/aa9bfb
]]>Objective. Brain–computer interface (BCI) technology aims to provide individuals with paralysis a means to restore function. Electrocorticography (ECoG) uses disc electrodes placed on either the surface of the dura or the cortex to record field potential activity. ECoG has been proposed as a viable neural recording modality for BCI systems, potentially providing stable, long-term recordings of cortical activity with high spatial and temporal resolution. Previously we have demonstrated that a subject with spinal cord injury (SCI) could control an ECoG-based BCI system with up to three degrees of freedom (Wang et al. 2013 PLoS One). Here, we expand upon these findings by including brain-control results from two additional subjects with upper-limb paralysis due to amyotrophic lateral sclerosis and brachial plexus injury, and investigate the potential of motor and somatosensory cortical areas to enable BCI control. Approach. Individuals were implanted with high-density ECoG electrode grids over sensorimotor cortical areas for less than 30 d. Subjects were trained to control a BCI by employing a somatotopic control strategy where high-gamma activity from attempted arm and hand movements drove the velocity of a cursor. Main results. Participants were capable of generating robust cortical modulation that was differentiable across attempted arm and hand movements of their paralyzed limb. Furthermore, all subjects were capable of voluntarily modulating this activity to control movement of a computer cursor with up to three degrees of freedom using the somatotopic control strategy. Additionally, for those subjects with electrode coverage of somatosensory cortex, we found that somatosensory cortex was capable of supporting ECoG-based BCI control. Significance. These results demonstrate the feasibility of ECoG-based BCI systems for individuals with paralysis as well as highlight some of the key challenges that must be overcome before such systems are translated to the clinical realm. ClinicalTrials.gov Identifier: NCT01393444.