A SHAP-based controversy analysis through communities on Twitter

Inputs from GABAergic and glutamatergic neurons are suspected to play an important role in regulating the activity of the gonadotropin-releasing hormone (GnRH) neurons. The GnRH neurons exhibit marked plasticity to control the ovarian cycle with circulating estradiol concentrations having profound “feedback” effects on their activity. This includes “negative feedback” responsible for suppressing GnRH neuron activity and “positive feedback” that occurs at mid-cycle to activate the GnRH neurons to generate the preovulatory luteinizing hormone surge. In the present study, we employed brain slice electrophysiology to question whether synaptic ionotropic GABA and glutamate receptor signaling at the GnRH neuron changed at times of negative and positive feedback. We used a well characterized estradiol (E)–treated ovariectomized (OVX) mouse model to replicate negative and positive feedback. Miniature and spontaneous postsynaptic currents (mPSCs and sPSCs) attributable to GABAA and glutamatergic receptor signaling were recorded from GnRH neurons obtained from intact diestrous, OVX, OVX + E (negative feedback), and OVX + E+E (positive feedback) female mice. Approximately 90% of GnRH neurons exhibited spontaneous GABAA-mPSCs in all groups but no significant differences in the frequency or kinetics of mPSCs were found at the times of negative or positive feedback. Approximately 50% of GnRH neurons exhibited spontaneous glutamate mPSCs but again no differences were detected. The same was true for spontaneous PSCs in all cases. These observations indicate that the kinetics of ionotropic GABA and glutamate receptor synaptic transmission to GnRH neurons remain stable across the different estrogen feedback states.

How children learn from positive and negative performance feedback lies at the foundation of successful learning and is therefore of great importance for educational practice. In this study, we used functional magnetic resonance imaging (fMRI) to examine the neural developmental changes related to feedback-based learning when performing a rule search and application task. Behavioral results from three age groups (8-9, 11-13, and 18-25 years of age) demonstrated that, compared with adults, 8- to 9-year-old children performed disproportionally more inaccurately after receiving negative feedback relative to positive feedback. Additionally, imaging data pointed toward a qualitative difference in how children and adults use performance feedback. That is, dorsolateral prefrontal cortex and superior parietal cortex were more active after negative feedback for adults, but after positive feedback for children (8-9 years of age). For 11- to 13-year-olds, these regions did not show differential feedback sensitivity, suggesting that the transition occurs around this age. Pre-supplementary motor area/anterior cingulate cortex, in contrast, was more active after negative feedback in both 11- to 13-year-olds and adults, but not 8- to 9-year-olds. Together, the current data show that cognitive control areas are differentially engaged during feedback-based learning across development. Adults engage these regions after signals of response adjustment (i.e., negative feedback). Young children engage these regions after signals of response continuation (i.e., positive feedback). The neural activation patterns found in 11- to 13-year-olds indicate a transition around this age toward an increased influence of negative feedback on performance adjustment. This is the first developmental fMRI study to compare qualitative changes in brain activation during feedback learning across distinct stages of development.

One barrier to interpreting past studies of cognition and major depressive disorder (MDD) has been the failure in many studies to adequately dissociate the effects of MDD from the potential cognitive side effects of selective serotonin reuptake inhibitors (SSRIs) use. To better understand how remediation of depressive symptoms affects cognitive function in MDD, we evaluated three groups of subjects: medication-naïve patients with MDD, medicated patients with MDD receiving the SSRI paroxetine, and healthy control (HC) subjects. All were administered a category-learning task that allows for dissociation between learning from positive feedback (reward) vs. learning from negative feedback (punishment). Healthy subjects learned significantly better from positive feedback than medication-naïve and medicated MDD groups, whose learning accuracy did not differ significantly. In contrast, medicated patients with MDD learned significantly less from negative feedback than medication-naïve patients with MDD and healthy subjects, whose learning accuracy was comparable. A comparison of subject’s relative sensitivity to positive vs. negative feedback showed that both the medicated MDD and HC groups conform to Kahneman and Tversky’s (1979) Prospect Theory, which expects losses (negative feedback) to loom psychologically slightly larger than gains (positive feedback). However, medicated MDD and HC profiles are not similar, which indicates that the state of medicated MDD is not “normal” when compared to HC, but rather balanced with less learning from both positive and negative feedback. On the other hand, medication-naïve patients with MDD violate Prospect Theory by having significantly exaggerated learning from negative feedback. This suggests that SSRI antidepressants impair learning from negative feedback, while having negligible effect on learning from positive feedback. Overall, these findings shed light on the importance of dissociating the cognitive consequences of MDD from those of SSRI treatment, and from cognitive evaluation of MDD subjects in a medication-naïve state before the administration of antidepressants. Future research is needed to correlate the mood-elevating effects and the cognitive balance between reward- and punishment-based learning related to SSRIs.

It is well known that noise is inevitable in gene regulatory networks due to the low-copy numbers of molecules and local environmental fluctuations. The prediction of noise effects is a key issue in ensuring reliable transmission of information. Interlinked positive and negative feedback loops are essential signal transduction motifs in biological networks. Positive feedback loops are generally believed to induce a switch-like behavior, whereas negative feedback loops are thought to suppress noise effects. Here, by using the signal sensitivity (susceptibility) and noise amplification to quantify noise propagation, we analyze an abstract model of the Myc/E2F/MiR-17-92 network that is composed of a coupling between the E2F/Myc positive feedback loop and the E2F/Myc/miR-17-92 negative feedback loop. The role of the feedback loop on noise effects is found to depend on the dynamic properties of the system. When the system is in monostability or bistability with high protein concentrations, noise is consistently suppressed. However, the negative feedback loop reduces this suppression ability (or improves the noise propagation) and enhances signal sensitivity. In the case of excitability, bistability, or monostability, noise is enhanced at low protein concentrations. The negative feedback loop reduces this noise enhancement as well as the signal sensitivity. In all cases, the positive feedback loop acts contrary to the negative feedback loop. We also found that increasing the time scale of the protein module or decreasing the noise autocorrelation time can enhance noise suppression; however, the systems sensitivity remains unchanged. Taken together, our results suggest that the negative/positive feedback mechanisms in coupled feedback loop dynamically buffer noise effects rather than only suppressing or amplifying the noise.

Performance feedback signals to people where they stand in their goal progress. Positive feedback suggests that one is on a good track to achieve a goal. Negative feedback indicates that another strategy or greater effort is required. However, not everyone reacts equally to such feedback. Through four studies using a variety of methods, we examined how individuals’ experience of power affects motivation and performance depending on whether they receive negative or positive performance feedback. While results were not completely uniform, a general pattern could be observed. Specifically, a meta‐analysis across all studies showed that negative feedback had an overall detrimental effect on motivation and performance of low‐power individuals but not of high‐power individuals. Positive feedback neither impacted low‐ nor high‐power individuals' motivation but increased low‐power individuals’ performance relative to their high‐power counterparts. Our findings imply that performance feedback needs to be tailored to the recipient's experience of power.

687: The relationship of type of feedback to learning during simulation: a randomized clinical trial

A wide range of cellular processes require molecular regulatory pathways to convert a graded signal into a discrete response. One prevalent switching mechanism relies on the coexistence of two stable states (bistability) caused by positive feedback regulations. Intriguingly, positive feedback is often supplemented with negative feedback, raising the question of whether and how these two types of feedback can cooperate to control discrete cellular responses. To address this issue, we formulate a canonical model of a protein–protein interaction network and analyze the dynamics of a prototypical two-component circuit. The appropriate combination of negative and positive feedback loops can bring a bistable circuit close to the oscillatory regime. Notably, sharply activated negative feedback can give rise to a bistable regime wherein two stable fixed points coexist and may collide pairwise with two saddle points. This specific type of bistability is found to allow for separate and flexible control of switch-on and switch-off events, for example (i) to combine fast and reversible transitions, (ii) to enable transient switching responses and (iii) to display tunable noise-induced transition rates. Finally, we discuss the relevance of such bistable switching behavior, and the circuit topologies considered, to specific biological processes such as adaptive metabolic responses, stochastic fate decisions and cell-cycle transitions. Taken together, our results suggest an efficient mechanism by which positive and negative feedback loops cooperate to drive the flexible and multifaceted switching behaviors arising in biological systems.

Feedback modules, which appear ubiquitously in biological regulations, are often subject to disturbances from the input, leading to fluctuations in the output. Thus, the question becomes how a feedback system can produce a faithful response with a noisy input. We employed multiple time scale analysis, Fluctuation Dissipation Theorem, linear stability, and numerical simulations to investigate a module with one positive feedback loop driven by an external stimulus, and we obtained a critical quantity in noise attenuation, termed as “signed activation time”. We then studied the signed activation time for a system of two positive feedback loops, a system of one positive feedback loop and one negative feedback loop, and six other existing biological models consisting of multiple components along with positive and negative feedback loops. An inverse relationship is found between the noise amplification rate and the signed activation time, defined as the difference between the deactivation and activation time scales of the noise-free system, normalized by the frequency of noises presented in the input. Thus, the combination of fast activation and slow deactivation provides the best noise attenuation, and it can be attained in a single positive feedback loop system. An additional positive feedback loop often leads to a marked decrease in activation time, decrease or slight increase of deactivation time and allows larger kinetic rate variations for slow deactivation and fast activation. On the other hand, a negative feedback loop may increase the activation and deactivation times. The negative relationship between the noise amplification rate and the signed activation time also holds for the six other biological models with multiple components and feedback loops. This principle may be applicable to other feedback systems.

Previous studies have suggested that negative feedback is more effective in driving learning than positive feedback. We investigated the effect on learning of providing varying amounts of negative and positive feedback while listeners attempted to discriminate between three identical tones; an impossible task that nevertheless produces robust learning. Four feedback conditions were compared during training: 90% positive feedback or 10% negative feedback informed the participants that they were doing equally well, while 10% positive or 90% negative feedback informed them they were doing equally badly. In all conditions the feedback was random in relation to the listeners’ responses (because the task was to discriminate three identical tones), yet both the valence (negative vs. positive) and the probability of feedback (10% vs. 90%) affected learning. Feedback that informed listeners they were doing badly resulted in better post-training performance than feedback that informed them they were doing well, independent of valence. In addition, positive feedback during training resulted in better post-training performance than negative feedback, but only positive feedback indicating listeners were doing badly on the task resulted in learning. As we have previously speculated, feedback that better reflected the difficulty of the task was more effective in driving learning than feedback that suggested performance was better than it should have been given perceived task difficulty. But contrary to expectations, positive feedback was more effective than negative feedback in driving learning. Feedback thus had two separable effects on learning: feedback valence affected motivation on a subjectively difficult task, and learning occurred only when feedback probability reflected the subjective difficulty. To optimize learning, training programs need to take into consideration both feedback valence and probability.

The direct effects of climate change are increasing the frequency of high-intensity fire events in many ecosystems across the globe, including wet Eucalypt forests of south-eastern (SE) Australia. Recurrent high-intensity fire can alter vegetation structure and composition, and the resultant alternative vegetation states may be more likely (positive feedback) or less likely (negative feedback) to burn again than the vegetation community replaced. These indirect effects of climate change have been reported for a range of different ecosystems across the globe. However, a common limitation to many empirical studies is the narrow temporal range of observations, often limited to a single fire season. In turn, this limits our understanding of the potential for vegetation-mediated indirect effects of climate change to generate positive or negative fire feedbacks across the range of climate conditions common to the region. In wet Eucalypt forests of SE Australia, dead fuel moisture content (FMC) is a key determinant of fire activity, and therefore a useful metric on which to quantify the potential for feedbacks across alternative forest states. To quantify potential for indirect effects of climate change to alter future fire activity, FMC was modelled in the open and at seven alternative forest states to wet Eucalypt forest using a process-based FMC model. The model was run using a long-term climate dataset spanning 1973 – 2020, which were transferred from macro- to microclimate values using forest structural properties derived from lidar. Hourly FMC outputs were summarised to fuel availability (FMC < 16% for one hour each day) to understand the potential for positive, negative of no feedbacks on potential fire activity. Mean annual FMC was significantly different between each alternative forest state across each of the 48 years of climate data – which act as a replicate for different climate conditions in our experimental design. By quantifying these differences using the metric of fuel availability, we have demonstrated that statistically significant differences in FMC translate into meaningful differences in the context of potential fire activity. Overall, the results show strong positive and negative feedbacks across the alternative forest states compared to the mature wet Eucalypt forest that they replaced, which were greater than age-related differences within the wet Eucalypt forest sites. Overall, our results support the hypothesis that indirect effects of climate change, acting through vegetation conversion to alternative forest states, have a substantial impact on the potential for future fire activity, with important implications for land and fire managers in this region.

Failure to retreat: Blunted sensitivity to negative feedback supports risky behavior in adolescents

With the development of online healthcare social media, a large volume of user generated content are available in different health discussion forums or groups. Healthcare social media sites could empower patients to play a substantial role in their treatment by acquiring knowledge and support through actively involving in online discussions and interactions. However, users may have difficulty to find relevant topics or peers in these online health forums with a large amount of unstructured information. Most recommendation systems rely on content-based approach to recommend peers or discussions to their participants. However, in healthcare social media sites, content-based approach is not sufficient because health consumers may have different intentions of participation or may be interest in different types of support even if the content matches their interest. Based on previous studies, we utilize Naïve Bayes methods and propose two tasks for classifying posts and comments on Quit Stop forum, an online community for smoking cessation intervention, respectively: (1) classification of intentions and (2) classification of social support types. Different text feature sets and user health feature sets are selected to develop classifiers. Taking different evaluation indicators as optimizing goals, we develop genetic algorithms to combine classifiers with different feature sets and optimize the classification results. It is found that for post classification, integrating text and health features could achieve the highest precision, recall and F1 measure. For comment classification, combining different text features could reach the best result. In the future, the classification result could be applied to developing recommender systems for topic recommendation and user prediction of online health forums.

. Positive feedback is one of the core strategies used to make educational environment and students’ learning outcomes better. This review aims at revealing the role of positive feedback in improving students’ psychological and physical learning outcomes. Meanwhile, it questions the efficiency of combining positive and negative feedbacks in order to provide better educational experience for students. The findings of the analysis reveal that there is positive relationship between positive feedback and students’ physical and psychological learning outcomes. More specifically, positive feedback is advantageous in increasing students’ motivation, confidence, self-efficacy and academic skills. Furthermore, the study also confirms the benefits of combining positive and negative feedbacks in order to improve students’ learning outcomes and advance their academic skills.

Interferons (IFN) play a pivotal role in innate immunity, orchestrating a cell-intrinsic anti-pathogenic state and stimulating adaptive immune responses. The complex interplay between the primary response to IFNs and its modulation by positive and negative feedback loops is incompletely understood. Here, we implement the combination of high-resolution gene-expression profiling of nascent RNA with translational inhibition of secondary feedback by cycloheximide. Unexpectedly, this approach revealed a prominent role of negative feedback mechanisms during the immediate (≤60 min) IFNα response. In contrast, a more complex picture involving both negative and positive feedback loops was observed on IFNγ treatment. IFNγ-induced repression of genes associated with regulation of gene expression, cellular development, apoptosis and cell growth resulted from cycloheximide-resistant primary IFNγ signalling. In silico promoter analysis revealed significant overrepresentation of SP1/SP3-binding sites and/or GC-rich stretches. Although signal transducer and activator of transcription 1 (STAT1)-binding sites were not overrepresented, repression was lost in absence of STAT1. Interestingly, basal expression of the majority of these IFNγ-repressed genes was dependent on STAT1 in IFN-naïve fibroblasts. Finally, IFNγ-mediated repression was also found to be evident in primary murine macrophages. IFN-repressed genes include negative regulators of innate and stress response, and their decrease may thus aid the establishment of a signalling perceptive milieu.

Negative social feedback often generates aggressive feelings and behavior. Prior studies have investigated the neural basis of negative social feedback, but the underlying neural mechanisms of aggression regulation following negative social feedback remain largely undiscovered. In the current study, participants viewed pictures of peers with feedback (positive, neutral or negative) to the participant's personal profile. Next, participants responded to the peer feedback by pressing a button, thereby producing a loud noise toward the peer, as an index of aggression. Behavioral analyses showed that negative feedback led to more aggression (longer noise blasts). Conjunction neuroimaging analyses revealed that both positive and negative feedback were associated with increased activity in the medial prefrontal cortex (PFC) and bilateral insula. In addition, more activation in the right dorsal lateral PFC (dlPFC) during negative feedback vs neutral feedback was associated with shorter noise blasts in response to negative social feedback, suggesting a potential role of dlPFC in aggression regulation, or top-down control over affective impulsive actions. This study demonstrates a role of the dlPFC in the regulation of aggressive social behavior.