The neuroscience of placebo effects: connecting context, learning and health.
Ontology highlight
ABSTRACT:
SUBMITTER: Wager TD
PROVIDER: S-EPMC6013051 | biostudies-literature | 2015 Jul
REPOSITORIES: biostudies-literature
Publications
The neuroscience of placebo effects: connecting context, learning and health.
Wager Tor D TD Atlas Lauren Y LY
Nature reviews. Neuroscience 20150701 7
Placebo effects are beneficial effects that are attributable to the brain-mind responses to the context in which a treatment is delivered rather than to the specific actions of the drug. They are mediated by diverse processes--including learning, expectations and social cognition--and can influence various clinical and physiological outcomes related to health. Emerging neuroscience evidence implicates multiple brain systems and neurochemical mediators, including opioids and dopamine. We present ...[more]
Similar Datasets
Project description:Mobile applications (apps) for learning technical scientific content are becoming increasingly popular in educational settings. Neuroscience is often considered complex and challenging for most students to understand conceptually. iNeuron is a recently developed iOS app that teaches basic neuroscience in the context of a series of scaffolded challenges to create neural circuits and increase understanding of nervous system structure and function. In this study, four different ways to implement the app within a classroom setting were explored. The goal of the study was to determine the app's effectiveness under conditions closely approximating real-world use, and to evaluate whether collaborative play and student-driven navigational features contributed to its effectiveness. Students used the app either individually or in small groups, and used a version with either a fixed or variable learning sequence. Student performance on a pre- and post- neuroscience content assessment was analyzed and compared between students who used the app and a control group receiving standard instruction, and logged app data were analyzed. Significantly greater learning gains were found for all students who used the app compared to control. All four implementation modes were effective in producing student learning gains relative to controls, but did not differ in their effectiveness to one another. In addition, students demonstrated transfer of information learned in one context to another within the app. These results suggest that teacher-led neuroscience instruction can be effectively supported by a scaffolded, technology-based curriculum which can be implemented in multiple ways to enhance student learning.
| S-EPMC6519481 | biostudies-literature
Project description:Attention is the important ability to flexibly control limited computational resources. It has been studied in conjunction with many other topics in neuroscience and psychology including awareness, vigilance, saliency, executive control, and learning. It has also recently been applied in several domains in machine learning. The relationship between the study of biological attention and its use as a tool to enhance artificial neural networks is not always clear. This review starts by providing an overview of how attention is conceptualized in the neuroscience and psychology literature. It then covers several use cases of attention in machine learning, indicating their biological counterparts where they exist. Finally, the ways in which artificial attention can be further inspired by biology for the production of complex and integrative systems is explored.
| S-EPMC7177153 | biostudies-literature
Project description:Modern cloud-based data infrastructures open new vistas for the deployment of scientometric data into the hands of practitioners. These infrastructures lower barriers to entry by making data more available and compute capacity more affordable. In addition, if data are prepared appropriately, with unique identifiers, it is possible to connect many different types of data. Bringing broader world data into the hands of practitioners (policymakers, strategists, and others) who use scientometrics as a tool can extend their capabilities. These ideas are explored through connecting Dimensions and World Bank data on Google BigQuery to study international collaboration between countries of different economic classification.
| S-EPMC9087033 | biostudies-literature
Project description:IntroductionResearch on learning in placebo and nocebo has relied predominantly on Pavlovian conditioning procedures. Operant learning procedures may more accurately model learning in real-life situations in which placebo and nocebo effects occur.ObjectivesTo investigate the development and persistence of placebo and nocebo effects using an operant avoidance learning task.MethodsPain-free participants (n = 58) could learn to avoid pain by performing movements that differed in difficulty and intensity of painful stimulation. Participants performed movements in 2 contexts. In the high cost of avoidance context, pain stimulus intensity reduced with increasing movement difficulty. In the low cost of avoidance context, contingencies were reversed. Participants rated pain expectations and pain intensity. During test, movement difficulties were unchanged, but participants always received a medium-intensity pain stimulus. Placebo and nocebo effects were defined as lower/higher pain intensity ratings for trajectories that previously resulted in low/high-intensity compared with medium-intensity stimulation.ResultsAs expected, participants acquired differential movement-pain expectations and differential movement choices. Testing with a medium-intensity pain stimulus quickly erased differences in movement choice across contexts, but differences in pain expectations were maintained. Pain modulation across context was in line with movement-pain expectations. However, we only observed placebo effects within the low cost of avoidance context and found no evidence of nocebo effects.ConclusionOperant learning can change pain expectations, pain modulation, and pain-related avoidance behavior. Persisting pain expectations suggest that acquired pain beliefs may be resistant to disconfirmation, despite self-initiated experience with novel pain-movement contingencies.
| S-EPMC6749895 | biostudies-literature
Project description:BackgroundMindfulness and meditation have a rich historical tradition, and a growing scientific base of evidence supports their use in creating positive psychological and neuroplastic changes for practitioners. Although meditation can be taught in various ways, the scientific community has yet to systematically study the impact of different types of meditation on neuropsychological outcomes, especially as it pertains to digital implementation. Therefore, it is critical that the instruction of mindfulness be evidence based because meditation is being used in both scientific and clinical settings.ObjectiveThis study investigated the use of teacher cueing and the integration of neuroscience education into a meditation program. Compassion cueing was chosen as the element of experimental manipulation because traditional lineages of Buddhist meditation teach compassion for self and others as one of the primary outcomes of meditation. We hypothesized that participants receiving compassion cueing would have enhanced neuropsychological outcomes compared with those receiving functional cueing and that gains in neuroscience knowledge would relate to positive neuropsychological outcomes.MethodsParticipants (n=89) were randomized to receive either functional cueing (control group) or compassion cueing (experimental group) and engaged with five 10-minute meditation sessions a week for 4 weeks. All intervention sessions were administered through digital presentation. All participants completed ecological momentary assessments before and after the daily intervention, as well as pre- and postintervention questionnaires.ResultsParticipants demonstrated significant benefits over time, including increased mindfulness and self-compassion, decreased depression, and gains in neuroscience content (all P<.001); however, no significant between-group differences were found. Daily scores from each day of the intervention showed a statistically significant shift from active toward settled. Importantly, long-term increases in mindfulness were positively correlated to changes in compassion (r=0.326; P=.009) and self-compassion (r=0.424; P<.001) and negatively correlated to changes in anxiety (r=-0.266; P=.03) and depression (r=-0.271; P=.03). Finally, the acute effects of meditation were significantly correlated to the longitudinal outcomes (with a small-to-medium effect size), especially those relevant to mindfulness.ConclusionsWe developed a novel neuroscience-based education-meditation program that enhanced self-regulation as evidenced by improved mindfulness, self-compassion, and mood state. Our findings demonstrate the behavioral importance of engaging with mindfulness meditation and reinforce the idea that the benefits of meditation are independent of teacher cueing behavior. Future studies will need to investigate the brain-based changes underlying these meditation-induced outcomes.
| S-EPMC9896358 | biostudies-literature
Project description:Translational research in neuroscience is increasingly focusing on the analysis of multi-modal data, in order to account for the biological complexity of suspected disease mechanisms. Recent advances in machine learning have the potential to substantially advance such translational research through the simultaneous analysis of different data modalities. This review focuses on one of such approaches, the so-called "multi-task learning" (MTL), and describes its potential utility for multi-modal data analyses in neuroscience. We summarize the methodological development of MTL starting from conventional machine learning, and present several scenarios that appear particularly suitable for its application. For these scenarios, we highlight different types of MTL algorithms, discuss emerging technological adaptations, and provide a step-by-step guide for readers to apply the MTL approach in their own studies. With its ability to simultaneously analyze multiple data modalities, MTL may become an important element of the analytics repertoire used in future neuroscience research and beyond.
| S-EPMC9714033 | biostudies-literature
Project description:Although a number of studies have explored deep learning in neuroscience, the application of these algorithms to neural systems on a microscopic scale, i.e. parameters relevant to lower scales of organization, remains relatively novel. Motivated by advances in whole-brain imaging, we examined the performance of deep learning models on microscopic neural dynamics and resulting emergent behaviors using calcium imaging data from the nematode C. elegans. As one of the only species for which neuron-level dynamics can be recorded, C. elegans serves as the ideal organism for designing and testing models bridging recent advances in deep learning and established concepts in neuroscience. We show that neural networks perform remarkably well on both neuron-level dynamics prediction and behavioral state classification. In addition, we compared the performance of structure agnostic neural networks and graph neural networks to investigate if graph structure can be exploited as a favourable inductive bias. To perform this experiment, we designed a graph neural network which explicitly infers relations between neurons from neural activity and leverages the inferred graph structure during computations. In our experiments, we found that graph neural networks generally outperformed structure agnostic models and excel in generalization on unseen organisms, implying a potential path to generalizable machine learning in neuroscience.
| S-EPMC7971515 | biostudies-literature
Project description:Service learning is becoming a keystone of the undergraduate learning experience. At Stonehill College, we implemented a service learning course, called a Learning Community, in Neuroscience. This course was created to complement the basic research available to Stonehill Neuroscience majors with experience in a more applied and "clinical" setting. The Neuroscience Learning Community is designed to promote a deep understanding of Neuroscience by combining traditional classroom instruction with clinical perspectives and real-life experiences. This Neuroscience Learning Community helps students translate abstract concepts within the context of neurodevelopment by providing students with contextual experience in a real-life, unscripted setting. The experiential learning outside of the classroom enabled students to participate in informed discussions in the classroom, especially with regard to neurodevelopmental disorders. We believe that all students taking this course gain an understanding of the importance of basic and applied Neuroscience as it relates to the individual and the community. Students also have used this concrete, learning-by-doing experience to make informed decisions about career paths and choice of major.
| S-EPMC3852872 | biostudies-literature
Project description:BackgroundComputer-assisted learning has been suggested to improve enjoyment and learning efficacy in medical education and more specifically, in neuroscience. These range from text-based websites to interactive electronic modules (eModules). It remains uncertain how these can best be implemented. To assess the effects of interactivity on learning perceptions and efficacy, we compared the utility of an eModule using virtual clinical cases and graphics against a Wikipedia-like page of matching content to teach clinical neuroscience: fundamentals of stroke and cerebrovascular anatomy.MethodsA randomized control trial of using an interactive eModule versus a Wikipedia-like page without interactivity was performed. Participants remotely accessed their allocated learning activity once, for approximately 30 min. The primary outcome was the difference in perceptions on enjoyability, engagement and usefulness. The secondary outcome was the difference in learning efficacy between the two learning activities. These were assessed using a Likert-scale survey and two knowledge quizzes: one immediately after the learning activity and one repeated eight weeks later. Assessments were analysed using Mann-Whitney U and T-tests respectively.ResultsThirty-two medical students participated: allocated evenly between the two groups through randomisation. The eModule was perceived as significantly more engaging (p = 0.0005), useful (p = 0.01) and enjoyable (p = 0.001) by students, with the main contributing factors being interactivity and clinical cases. After both learning activities, there was a significant decrease between the first and second quiz scores for both the eModule group (-16%, p = 0.001) and Wikipedia group (-17%, p = 0.003). There was no significant difference in quiz scores between the eModule and Wikipedia groups immediately afterwards (86% vs 85%, p = 0.8) or after eight weeks (71% vs 68%, p = 0.7).ConclusionOur study shows that increased student satisfaction associated with interactive computer-assisted learning in the form of an eModule does not translate into increased learning efficacy as compared to using a Wikipedia-like webpage. This suggests the matched content of the passive webpage provides a similar learning efficacy. Still, eModules can help motivate self-directed learners and overcome the perceived difficulty associated with neuroscience. As computer assisted learning continues to rapidly expand among medical schools, we suggest educators critically evaluate the usage and cost-benefit of eModules.
| S-EPMC9250740 | biostudies-literature
Project description:In the Stroop task, participants identify the print color of color words. The congruency effect is the observation that response times and errors are increased when the word and color are incongruent (e.g., the word "red" in green ink) relative to when they are congruent (e.g., "red" in red). The proportion congruent (PC) effect is the finding that congruency effects are reduced when trials are mostly incongruent rather than mostly congruent. This PC effect can be context-specific. For instance, if trials are mostly incongruent when presented in one location and mostly congruent when presented in another location, the congruency effect is smaller for the former location. Typically, PC effects are interpreted in terms of strategic control of attention in response to conflict, termed conflict adaptation or conflict monitoring. In the present manuscript, however, an episodic learning account is presented for context-specific proportion congruent (CSPC) effects. In particular, it is argued that context-specific contingency learning can explain part of the effect, and context-specific rhythmic responding can explain the rest. Both contingency-based and temporal-based learning can parsimoniously be conceptualized within an episodic learning framework. An adaptation of the Parallel Episodic Processing model is presented. This model successfully simulates CSPC effects, both for contingency-biased and contingency-unbiased (transfer) items. The same fixed-parameter model can explain a range of other findings from the learning, timing, binding, practice, and attentional control domains.
| S-EPMC5110540 | biostudies-literature