Single Neurons in M1 and Premotor Cortex Directly Reflect Behavioral Interference

Some motor tasks, if learned together, interfere with each other's consolidation and subsequent retention, whereas other tasks do not. Interfering tasks are said to employ the same internal model whereas noninterfering tasks use different models. The division of function among internal models, as well as their possible neural substrates, are not well understood. To investigate these questions, we compared responses of single cells in the primary motor cortex and premotor cortex of primates to interfering and noninterfering tasks. The interfering tasks were visuomotor rotation followed by opposing visuomotor rotation. The noninterfering tasks were visuomotor rotation followed by an arbitrary association task. Learning two noninterfering tasks led to the simultaneous formation of neural activity typical of both tasks, at the level of single neurons. In contrast, and in accordance with behavioral results, after learning two interfering tasks, only the second task was successfully reflected in motor cortical single cell activity. These results support the hypothesis that the representational capacity of motor cortical cells is the basis of behavioral interference and division between internal models

Understanding the factors affecting teachers' burnout during the COVID-19 pandemic: A cross-sectional study.

BackgroundDuring the COVID-19 pandemic, which enforced social distancing and isolation, teachers were required to handle multiple challenges related to their work, including dealing with remote teaching, in addition to personal, medical and financial challenges. The goal of the current research was to examine factors that contributed to professional burnout and commitment to work among teachers during the first and second waves of the COVID-19 pandemic.MethodsA total of 344 elementary school teachers in Israel completed online self-report questionnaires, including assessments of stressors, anxiety, resilience, self-efficacy beliefs, and coping strategies. Structured Equation Modeling [SEM] was used to examine the contribution of these factors to professional burnout and commitment.ResultsThe gaps between needed and received support had a direct effect on teachers' burnout and commitment, and an indirect effect through anxiety and self-efficacy beliefs. Stress relating to remote teaching and support-gaps regarding remote teaching were the most significant of all the stressors and sources of support.ConclusionsCollectively, these findings highlight the significance of remote teaching as the main cause of stress and professional burnout and suggest that proper preparation of teachers-before and during times of crisis, may have a significant impact on their mental and professional well-being

Simultaneous representation of multiple tasks after learning rotation and arbitrary association.

<p>(a, c) An increase in discrimination ratio for target colors (i.e. color sensitivity) after learning an arbitrary association task, either alone (n = 140 cells; part a) or after learning both rotation and arbitrary association tasks (n = 317 cells; part c). Black lines represent activity before learning, red lines after learning. Error bars indicate ±1 SEM. Stars indicate a significant discrimination ratio. (b,d) Changes in the SNR for different movement directions for a rotation task either alone (n = 194 cells; part b) or followed by an arbitrary association task (n = 317; part d). SNR changes were normalized to be between −1 to 1 (0</p

The lack of simultaneous representation after learning rotation followed by opposing rotation.

<p>(a) SNR changes for rotation followed by opposite rotation tasks (n = 140 cells). Notation same as <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032986#pone-0032986-g002" target="_blank">Figure 2c</a>. Note that SNR elevation is restricted to the directions used during the second rotation. (b) Percentage of neurons significantly increasing or decreasing their SNR after learning (during the TO epoch). Red marks directions where a significant portion of the neurons changed their SNR. (c–e) Examples of single neurons, notation same as <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032986#pone-0032986-g004" target="_blank">Figure 4</a>. (c) Neuron with a pre-learning PD that was close to the directions used during the second rotation. (d–e) Two neurons whose pre-learning PDs were close to the directions used during the first rotations.</p

Examples of simultaneous representation in single neurons.

<p>Examples of single neuronal responses during the center-out task before and after learning of (a) rotation alone, (b) arbitrary association alone or (c–d) rotation followed by arbitrary association. Red indicates center-out responses to target colors used during the learning blocks, black indicates responses to target colors not used. Solid lines represent activity before learning; dashed lines represent activity after learning. Directions are aligned according to distance from the direction used during the learning blocks.</p

Additional analysis of color sensitivity.

<p>Change in the SNR for different target colors during the TO epoch (upper panel) and MO (lower panel), where positive values indicate an increase in SNR after learning: (a) an arbitrary association task alone; (b) arbitrary association following rotation; (c) rotation alone or (d) rotation followed by the opposite rotation. Grey lines indicate the mean of the post-learning distribution, black lines represent zero change. Note that learning an arbitrary association, but not a rotation, resulted in increased SNR.</p