1
Rare Rewards Amplify Dopamine Learning Responses
Kathryn M. Rothenhoefer, Tao Hong, Aydin Alikaya, William R. Stauffer*.
Affiliations:
Center for Neuroscience, Center for the Neural Basis of Cognition, Systems Neuroscience
Institute, The Brain Institute, University of Pittsburgh, Pittsburgh, PA, USA. 5
*Correspondence to: [email protected]
Abstract:
Dopamine neurons drive learning by coding reward prediction errors (RPEs), which are formalized
as subtractions of predicted values from reward values. Subtractions accommodate point estimate 10
predictions of value, such as the average value. However, point estimate predictions fail to capture
many features of choice and learning behaviors. For instance, reaction times and learning rates
consistently reflect higher moments of probability distributions. Here, we demonstrate that
dopamine RPE responses code probability distributions. We presented monkeys with rewards that
were drawn from the tails of normal and uniform reward size distributions to generate rare and 15
common RPEs, respectively. Behavioral choices and pupil diameter measurements indicated that
monkeys learned faster and registered greater arousal from rare RPEs, compared to common RPEs
of identical magnitudes. Dopamine neuron recordings indicated that rare rewards amplified RPE
responses. These results demonstrate that dopamine responses reflect probability distributions and
suggest a neural mechanism for the amplified learning and enhanced arousal associated with rare 20
events.
not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which wasthis version posted November 22, 2019. ; https://doi.org/10.1101/851709doi: bioRxiv preprint
https://doi.org/10.1101/851709
2
Main Text:
Making accurate predictions is evolutionarily adaptive. Accurate predictions enable
individuals to be in the right place at the right time, choose the best options, and efficiently scale
the vigor of responses. Dopamine neurons are crucial for building accurate reward predictions.
Phasic dopamine responses code for reward prediction errors: the differences between the values 5
of received and predicted rewards (1-8). These signals cause predictions to be modified through
associative and extinction learning (9, 10). Likewise, phasic dopamine neuron stimulation during
reward delivery increase both the dopamine responses to reward predicting cues and the choices
for those same cues (11). Although it is well understood how predicted reward values affect
dopamine responses, these predictions are simply point estimates – normally the average value – 10
of probability distributions. It is unknown how the form of probability distributions affects
dopamine learning signals.
Probability distributions affect behavioral measures of learning and decision making.
Learning the expected value takes l ...
Topic Learning TeamNumber of Pages 2 (Double Spaced)Num.docxAASTHA76
Topic: Learning Team
Number of Pages: 2 (Double Spaced)
Number of sources: 1
Writing Style: APA
Type of document: Essay
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Category: Psychology
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Language Style: English (U.S.)
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Correlation
PSYCH/610 Version 2
1
University of Phoenix Material
Correlation
A researcher is interested in investigating the relationship between viewing time (in seconds) and ratings of aesthetic appreciation. Participants are asked to view a painting for as long as they like. Time (in seconds) is measured. After the viewing time, the researcher asks the participants to provide a ‘preference rating’ for the painting on a scale ranging from 1-10. Create a scatter plot depicting the following data:
Viewing Time in Seconds
Preference Rating
10
3
12
4
24
7
5
3
16
6
3
4
11
4
5
2
21
8
23
9
9
5
3
3
17
5
14
6
What does the scatter plot suggest about the relationship between viewing time and aesthetic preference? Is it accurate to state that longer viewing times are the result of greater preference for paintings? Explain. Submit your scatter plot and your answers to the questions to your instructor.
LEARNING OBJECTIVES
· Explain how researchers use inferential statistics to evaluate sample data.
· Distinguish between the null hypothesis and the research hypothesis.
· Discuss probability in statistical inference, including the meaning of statistical significance.
· Describe the t test and explain the difference between one-tailed and two-tailed tests.
· Describe the F test, including systematic variance and error variance.
· Describe what a confidence interval tells you about your data.
· Distinguish between Type I and Type II errors.
· Discuss the factors that influence the probability of a Type II error.
· Discuss the reasons a researcher may obtain nonsignificant results.
· Define power of a statistical test.
· Describe the criteria for selecting an appropriate statistical test.
Page 267IN THE PREVIOUS CHAPTER, WE EXAMINED WAYS OF DESCRIBING THE RESULTS OF A STUDY USING DESCRIPTIVE STATISTICS AND A VARIETY OF GRAPHING TECHNIQUES.In addition to descriptive statistics, researchers use inferential statistics to draw more general conclusions about their data. In short, inferential statistics allow researchers to (a) assess just how confident they are that their results reflect what is true in the larger population and (b) assess the likelihood that their findings would still occur if their study was repeated over and over. In this chapter, we examine methods for doing so.
SAMPLES AND POPULATIONS
Inferential statistics are necessary because the results of a given study are based only on data obtained from a single sample of research participants. Researchers rarely, if ever, study entire populations; their findings are based on sample data. In addition to describing the sample data, we want to make statements ab.
Page 266LEARNING OBJECTIVES· Explain how researchers use inf.docxkarlhennesey
Page 266
LEARNING OBJECTIVES
· Explain how researchers use inferential statistics to evaluate sample data.
· Distinguish between the null hypothesis and the research hypothesis.
· Discuss probability in statistical inference, including the meaning of statistical significance.
· Describe the t test and explain the difference between one-tailed and two-tailed tests.
· Describe the F test, including systematic variance and error variance.
· Describe what a confidence interval tells you about your data.
· Distinguish between Type I and Type II errors.
· Discuss the factors that influence the probability of a Type II error.
· Discuss the reasons a researcher may obtain nonsignificant results.
· Define power of a statistical test.
· Describe the criteria for selecting an appropriate statistical test.
Page 267IN THE PREVIOUS CHAPTER, WE EXAMINED WAYS OF DESCRIBING THE RESULTS OF A STUDY USING DESCRIPTIVE STATISTICS AND A VARIETY OF GRAPHING TECHNIQUES. In addition to descriptive statistics, researchers use inferential statistics to draw more general conclusions about their data. In short, inferential statistics allow researchers to (a) assess just how confident they are that their results reflect what is true in the larger population and (b) assess the likelihood that their findings would still occur if their study was repeated over and over. In this chapter, we examine methods for doing so.
SAMPLES AND POPULATIONS
Inferential statistics are necessary because the results of a given study are based only on data obtained from a single sample of research participants. Researchers rarely, if ever, study entire populations; their findings are based on sample data. In addition to describing the sample data, we want to make statements about populations. Would the results hold up if the experiment were conducted repeatedly, each time with a new sample?
In the hypothetical experiment described in Chapter 12 (see Table 12.1), mean aggression scores were obtained in model and no-model conditions. These means are different: Children who observe an aggressive model subsequently behave more aggressively than children who do not see the model. Inferential statistics are used to determine whether the results match what would happen if we were to conduct the experiment again and again with multiple samples. In essence, we are asking whether we can infer that the difference in the sample means shown in Table 12.1 reflects a true difference in the population means.
Recall our discussion of this issue in Chapter 7 on the topic of survey data. A sample of people in your state might tell you that 57% prefer the Democratic candidate for an office and that 43% favor the Republican candidate. The report then says that these results are accurate to within 3 percentage points, with a 95% confidence level. This means that the researchers are very (95%) confident that, if they were able to study the entire population rather than a sample, the actual percentage who preferred th ...
Researchers use hypothesis testing to evaluate claims about populations by taking samples and comparing sample statistics to hypothesized population parameters. The four steps of hypothesis testing are:
1) State the null and alternative hypotheses, with the null hypothesis presuming the claim is true.
2) Set criteria for deciding whether to reject the null hypothesis.
3) Calculate a test statistic from the sample.
4) Compare the test statistic to the criteria to determine whether to reject the null hypothesis.
Researchers use hypothesis testing to evaluate claims about populations by taking samples and analyzing the results. The four steps of hypothesis testing are: 1) stating the null and alternative hypotheses, 2) setting the significance level typically at 5%, 3) computing a test statistic to quantify how unlikely the sample results would be if the null was true, and 4) making a decision to either reject or fail to reject the null hypothesis based on comparing the test statistic to the significance level. The goal is to systematically evaluate whether a hypothesized population parameter, such as a mean, is likely to be true based on the sample results.
Chapter8 introduction to hypothesis testingBOmebratu
Researchers use hypothesis testing to evaluate claims about populations by taking samples and comparing sample statistics to hypothesized population parameters. The four steps of hypothesis testing are:
1) State the null and alternative hypotheses, with the null hypothesis presuming the claim is true.
2) Set criteria for deciding whether to reject the null hypothesis.
3) Calculate a test statistic from the sample.
4) Compare the test statistic to the criteria to determine whether to reject the null hypothesis.
Researchers use hypothesis testing to evaluate claims about populations by taking samples and comparing sample statistics to hypothesized population parameters. The four steps of hypothesis testing are:
1) State the null and alternative hypotheses, with the null hypothesis presuming the claim is true.
2) Set criteria for deciding whether to reject or fail to reject the null hypothesis.
3) Calculate a test statistic from the sample.
4) Compare the test statistic to the criteria to either reject or fail to reject the null hypothesis.
BUS 308 Week 3 Lecture 1 Examining Differences - Continued.docxcurwenmichaela
BUS 308 Week 3 Lecture 1
Examining Differences - Continued
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. Issues around multiple testing
2. The basics of the Analysis of Variance test
3. Determining significant differences between group means
4. The basics of the Chi Square Distribution.
Overview
Last week, we found out ways to examine differences between a measure taken on two
groups (two-sample test situation) as well as comparing that measure to a standard (a one-sample
test situation). We looked at the F test which let us test for variance equality. We also looked at
the t-test which focused on testing for mean equality. We noted that the t-test had three distinct
versions, one for groups that had equal variances, one for groups that had unequal variances, and
one for data that was paired (two measures on the same subject, such as salary and midpoint for
each employee). We also looked at how the 2-sample unequal t-test could be used to use Excel
to perform a one-sample mean test against a standard or constant value. This week we expand
our tool kit to let us compare multiple groups for similar mean values.
A second tool will let us look at how data values are distributed – if graphed, would they
look the same? Different shapes or patterns often means the data sets differ in significant ways
that can help explain results.
Multiple Groups
As interesting as comparing two groups is, often it is a bit limiting as to what it tells us.
One obvious issue that we are missing in the comparisons made last week was equal work. This
idea is still somewhat hard to get a clear handle on. Typically, as we look at this issue, questions
arise about things such as performance appraisal ratings, education distribution, seniority impact,
etc.
Some of these can be tested with the tools introduced last week. We can see, for
example, if the performance rating average is the same for each gender. What we couldn’t do, at
this point however, is see if performance ratings differ by grade, do the more senior workers
perform relatively better? Is there a difference between ratings for each gender by grade level?
The same questions can be asked about seniority impact. This week will give us tools to expand
how we look at the clues hidden within the data set about equal pay for equal work.
ANOVA
So, let’s start taking a look at these questions. The first tool for this week is the Analysis
of Variance – ANOVA for short. ANOVA is often confusing for students; it says it analyzes
variance (which it does) but the purpose of an ANOVA test is to determine if the means of
different groups are the same! Now, so far, we have considered means and variance to be two
distinct characteristics of data sets; characteristics that are not related, yet here we are saying that
looking at one will give us insight into the other.
The reason is due to the way the variance is an.
Topic Learning TeamNumber of Pages 2 (Double Spaced)Num.docxAASTHA76
Topic: Learning Team
Number of Pages: 2 (Double Spaced)
Number of sources: 1
Writing Style: APA
Type of document: Essay
Academic Level:Master
Category: Psychology
VIP Support: N/A
Language Style: English (U.S.)
Order Instructions:
I will attach the instruction. On this paper please follow the instructions carefully. Thank you
Correlation
PSYCH/610 Version 2
1
University of Phoenix Material
Correlation
A researcher is interested in investigating the relationship between viewing time (in seconds) and ratings of aesthetic appreciation. Participants are asked to view a painting for as long as they like. Time (in seconds) is measured. After the viewing time, the researcher asks the participants to provide a ‘preference rating’ for the painting on a scale ranging from 1-10. Create a scatter plot depicting the following data:
Viewing Time in Seconds
Preference Rating
10
3
12
4
24
7
5
3
16
6
3
4
11
4
5
2
21
8
23
9
9
5
3
3
17
5
14
6
What does the scatter plot suggest about the relationship between viewing time and aesthetic preference? Is it accurate to state that longer viewing times are the result of greater preference for paintings? Explain. Submit your scatter plot and your answers to the questions to your instructor.
LEARNING OBJECTIVES
· Explain how researchers use inferential statistics to evaluate sample data.
· Distinguish between the null hypothesis and the research hypothesis.
· Discuss probability in statistical inference, including the meaning of statistical significance.
· Describe the t test and explain the difference between one-tailed and two-tailed tests.
· Describe the F test, including systematic variance and error variance.
· Describe what a confidence interval tells you about your data.
· Distinguish between Type I and Type II errors.
· Discuss the factors that influence the probability of a Type II error.
· Discuss the reasons a researcher may obtain nonsignificant results.
· Define power of a statistical test.
· Describe the criteria for selecting an appropriate statistical test.
Page 267IN THE PREVIOUS CHAPTER, WE EXAMINED WAYS OF DESCRIBING THE RESULTS OF A STUDY USING DESCRIPTIVE STATISTICS AND A VARIETY OF GRAPHING TECHNIQUES.In addition to descriptive statistics, researchers use inferential statistics to draw more general conclusions about their data. In short, inferential statistics allow researchers to (a) assess just how confident they are that their results reflect what is true in the larger population and (b) assess the likelihood that their findings would still occur if their study was repeated over and over. In this chapter, we examine methods for doing so.
SAMPLES AND POPULATIONS
Inferential statistics are necessary because the results of a given study are based only on data obtained from a single sample of research participants. Researchers rarely, if ever, study entire populations; their findings are based on sample data. In addition to describing the sample data, we want to make statements ab.
Page 266LEARNING OBJECTIVES· Explain how researchers use inf.docxkarlhennesey
Page 266
LEARNING OBJECTIVES
· Explain how researchers use inferential statistics to evaluate sample data.
· Distinguish between the null hypothesis and the research hypothesis.
· Discuss probability in statistical inference, including the meaning of statistical significance.
· Describe the t test and explain the difference between one-tailed and two-tailed tests.
· Describe the F test, including systematic variance and error variance.
· Describe what a confidence interval tells you about your data.
· Distinguish between Type I and Type II errors.
· Discuss the factors that influence the probability of a Type II error.
· Discuss the reasons a researcher may obtain nonsignificant results.
· Define power of a statistical test.
· Describe the criteria for selecting an appropriate statistical test.
Page 267IN THE PREVIOUS CHAPTER, WE EXAMINED WAYS OF DESCRIBING THE RESULTS OF A STUDY USING DESCRIPTIVE STATISTICS AND A VARIETY OF GRAPHING TECHNIQUES. In addition to descriptive statistics, researchers use inferential statistics to draw more general conclusions about their data. In short, inferential statistics allow researchers to (a) assess just how confident they are that their results reflect what is true in the larger population and (b) assess the likelihood that their findings would still occur if their study was repeated over and over. In this chapter, we examine methods for doing so.
SAMPLES AND POPULATIONS
Inferential statistics are necessary because the results of a given study are based only on data obtained from a single sample of research participants. Researchers rarely, if ever, study entire populations; their findings are based on sample data. In addition to describing the sample data, we want to make statements about populations. Would the results hold up if the experiment were conducted repeatedly, each time with a new sample?
In the hypothetical experiment described in Chapter 12 (see Table 12.1), mean aggression scores were obtained in model and no-model conditions. These means are different: Children who observe an aggressive model subsequently behave more aggressively than children who do not see the model. Inferential statistics are used to determine whether the results match what would happen if we were to conduct the experiment again and again with multiple samples. In essence, we are asking whether we can infer that the difference in the sample means shown in Table 12.1 reflects a true difference in the population means.
Recall our discussion of this issue in Chapter 7 on the topic of survey data. A sample of people in your state might tell you that 57% prefer the Democratic candidate for an office and that 43% favor the Republican candidate. The report then says that these results are accurate to within 3 percentage points, with a 95% confidence level. This means that the researchers are very (95%) confident that, if they were able to study the entire population rather than a sample, the actual percentage who preferred th ...
Researchers use hypothesis testing to evaluate claims about populations by taking samples and comparing sample statistics to hypothesized population parameters. The four steps of hypothesis testing are:
1) State the null and alternative hypotheses, with the null hypothesis presuming the claim is true.
2) Set criteria for deciding whether to reject the null hypothesis.
3) Calculate a test statistic from the sample.
4) Compare the test statistic to the criteria to determine whether to reject the null hypothesis.
Researchers use hypothesis testing to evaluate claims about populations by taking samples and analyzing the results. The four steps of hypothesis testing are: 1) stating the null and alternative hypotheses, 2) setting the significance level typically at 5%, 3) computing a test statistic to quantify how unlikely the sample results would be if the null was true, and 4) making a decision to either reject or fail to reject the null hypothesis based on comparing the test statistic to the significance level. The goal is to systematically evaluate whether a hypothesized population parameter, such as a mean, is likely to be true based on the sample results.
Chapter8 introduction to hypothesis testingBOmebratu
Researchers use hypothesis testing to evaluate claims about populations by taking samples and comparing sample statistics to hypothesized population parameters. The four steps of hypothesis testing are:
1) State the null and alternative hypotheses, with the null hypothesis presuming the claim is true.
2) Set criteria for deciding whether to reject the null hypothesis.
3) Calculate a test statistic from the sample.
4) Compare the test statistic to the criteria to determine whether to reject the null hypothesis.
Researchers use hypothesis testing to evaluate claims about populations by taking samples and comparing sample statistics to hypothesized population parameters. The four steps of hypothesis testing are:
1) State the null and alternative hypotheses, with the null hypothesis presuming the claim is true.
2) Set criteria for deciding whether to reject or fail to reject the null hypothesis.
3) Calculate a test statistic from the sample.
4) Compare the test statistic to the criteria to either reject or fail to reject the null hypothesis.
BUS 308 Week 3 Lecture 1 Examining Differences - Continued.docxcurwenmichaela
BUS 308 Week 3 Lecture 1
Examining Differences - Continued
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. Issues around multiple testing
2. The basics of the Analysis of Variance test
3. Determining significant differences between group means
4. The basics of the Chi Square Distribution.
Overview
Last week, we found out ways to examine differences between a measure taken on two
groups (two-sample test situation) as well as comparing that measure to a standard (a one-sample
test situation). We looked at the F test which let us test for variance equality. We also looked at
the t-test which focused on testing for mean equality. We noted that the t-test had three distinct
versions, one for groups that had equal variances, one for groups that had unequal variances, and
one for data that was paired (two measures on the same subject, such as salary and midpoint for
each employee). We also looked at how the 2-sample unequal t-test could be used to use Excel
to perform a one-sample mean test against a standard or constant value. This week we expand
our tool kit to let us compare multiple groups for similar mean values.
A second tool will let us look at how data values are distributed – if graphed, would they
look the same? Different shapes or patterns often means the data sets differ in significant ways
that can help explain results.
Multiple Groups
As interesting as comparing two groups is, often it is a bit limiting as to what it tells us.
One obvious issue that we are missing in the comparisons made last week was equal work. This
idea is still somewhat hard to get a clear handle on. Typically, as we look at this issue, questions
arise about things such as performance appraisal ratings, education distribution, seniority impact,
etc.
Some of these can be tested with the tools introduced last week. We can see, for
example, if the performance rating average is the same for each gender. What we couldn’t do, at
this point however, is see if performance ratings differ by grade, do the more senior workers
perform relatively better? Is there a difference between ratings for each gender by grade level?
The same questions can be asked about seniority impact. This week will give us tools to expand
how we look at the clues hidden within the data set about equal pay for equal work.
ANOVA
So, let’s start taking a look at these questions. The first tool for this week is the Analysis
of Variance – ANOVA for short. ANOVA is often confusing for students; it says it analyzes
variance (which it does) but the purpose of an ANOVA test is to determine if the means of
different groups are the same! Now, so far, we have considered means and variance to be two
distinct characteristics of data sets; characteristics that are not related, yet here we are saying that
looking at one will give us insight into the other.
The reason is due to the way the variance is an.
1. Descriptive statistics provide a simple summary of data through measures of central tendency, frequency, and variability.
2. Common measures include the mean, median, mode, standard deviation, and outliers.
3. Inferential statistics allow researchers to make generalizations about populations based on analyses of samples. They include t-tests, ANOVA, correlation, and regression.
ANOVA is a statistical technique used to analyze differences between group means and their associated procedures. It was developed by Ronald Fisher and partitions variance into components that can be attributed to different sources. In its simplest form, ANOVA provides a statistical test of whether population means of several groups are equal, generalizing the t-test to more than two groups. It is useful for comparing three or more group means for statistical significance.
This document discusses inferential statistics and hypothesis testing. It provides examples of researchers formulating hypotheses and collecting data to test them. Researchers take random samples from populations to test if there are meaningful differences between groups. Hypothesis testing involves comparing experimental and control groups after exposing them to different levels of an independent variable. The goal is to determine if the independent variable caused a detectable change in the dependent variable. Inferential statistics are used to test if sample means differ significantly, which would suggest the hypothesis is supported or not supported. Proper sampling and estimating sampling distributions, standard errors, and variability are important concepts for accurately testing hypotheses about populations based on sample data.
The document discusses reward processing and decision making in the brain. It describes how dopamine neurons encode reward prediction errors and transmit information about rewards. It also discusses three types of values - Pavlovian values encoded in the amygdala and orbitofrontal cortex, goal values encoded in the dorsomedial striatum that guide goal-directed actions, and habit values encoded in the dorsolateral striatum that drive habitual behaviors independent of goals or values. The actor-critic model proposes separate neural systems for learning state values (critic) and selecting actions (actor).
Based on data from 11 responses from Unanimous AI asking about a fair movie ticket price, this document compares the performance of Unanimous to simulated questionnaires. With the full 11 responses, 54.5% of Unanimous answers were within $0.25 of the average price, compared to 44% of simulated 36-person questionnaires, a non-significant difference. However, with samples of 15 or less, Unanimous responses were more accurate, with 80% within $0.25 of the average for samples of 5, significantly more accurate than the 23% of simulated 9-person questionnaires. More data is needed to generalize these results.
Multiple sample test - Anova, Chi-square, Test of association, Goodness of Fit Rupak Roy
Detailed demonstration of Multiple Sample Test like Analysis of Variance (ANOVA), kinds of ANOVA One Way, Two Way, Chi-square with their assumptions and applications using excel, and much more.
Let me know if anything is needed. Happy to help. ping @ #bobrupakroy
The document provides examples of hypothesis testing using z-tests, t-tests, F-tests (ANOVA), and describes how to conduct each test. It includes examples testing hypotheses about means of different groups for variables like exam scores, car crash tests, and sales data. The final example tests whether the monthly sales means are equal to determine which salesman is most likely to be promoted.
Statistical tests help justify if sample results can be applied to a population. ANOVA compares group means and is preferred over t-tests for 3+ groups. It calculates variation between and within groups to obtain an F-ratio. If the F-ratio exceeds its critical value, the null hypothesis that group means are equal is rejected, showing group means differ significantly. Two-way ANOVA extends this to consider two factors' influence, computing interaction effects between factors.
Chapter 5 part2- Sampling Distributions for Counts and Proportions (Binomial ...nszakir
Mathematics, Statistics, Sampling Distributions for Counts and Proportions, Binomial Distributions for Sample Counts,
Binomial Distributions in Statistical Sampling, Binomial Mean and Standard Deviation, Sample Proportions, Normal Approximation for Counts and Proportions, Binomial Formula
The document discusses normal and standard normal distributions. It provides examples of using a normal distribution to calculate probabilities related to bone mineral density test results. It shows how to find the probability of a z-score falling below or above certain values. It also explains how to determine the sample size needed to estimate an unknown population proportion within a given level of confidence.
This document discusses non-parametric tests and how to use them to compare groups when assumptions of parametric tests are violated. It explains that non-parametric tests like the Wilcoxon and Kruskal-Wallis tests can be used when samples are small or data is not normally distributed. The Kruskal-Wallis test allows comparison of more than two groups by ranking all data and comparing mean ranks between groups. An example compares student grades under different teaching methods using both Kruskal-Wallis and ANOVA tests.
The four steps of hypothesis testing are:
1. State the null and alternative hypotheses. The null hypothesis assumes the claim is true while the alternative contradicts it.
2. Set the significance level, typically 5%, which is the probability of a Type I error of rejecting a true null hypothesis.
3. Compute the test statistic to determine how far the sample mean is from the population mean stated in the null hypothesis.
4. Make a decision by comparing the p-value to the significance level. If p < 0.05, reject the null hypothesis.
Hypothesis Testing. Inferential Statistics pt. 2John Labrador
A hypothesis test is a statistical test that is used to determine whether there is enough evidence in a sample of data to infer that a certain condition is true for the entire population. A hypothesis test examines two opposing hypotheses about a population: the null hypothesis and the alternative hypothesis.
The document discusses parametric hypothesis testing concepts like directional vs non-directional hypotheses, p-values, critical values, and types of parametric tests including t-tests, ANOVA, and when each should be used. It provides examples of one-way and two-way ANOVA, describing how one-way ANOVA is used when groups differ on one factor and two-way is used when groups differ on two or more factors. Key assumptions for parametric tests like normality and sample size are also outlined.
This document discusses concepts related to sampling and sampling distributions. It begins with definitions of key terms like population, sample, parameter, and statistic. It then explains different sampling methods, focusing on simple random sampling. Different measures of central tendency and variability are outlined like mean, median, mode, range, variance, and standard deviation. The central limit theorem is introduced, which states that the sampling distribution of the mean will approximate a normal distribution for large sample sizes regardless of the population distribution. Examples are provided to illustrate these concepts.
This document discusses inferential statistics and epidemiological research. It introduces concepts like the central limit theorem, standard error, confidence intervals, hypothesis testing, and different statistical tests. Specifically, it covers:
- The central limit theorem states that sample means will follow a normal distribution, even if the population is not normally distributed.
- Standard error is used to measure sampling variation and determine confidence intervals around sample statistics to estimate population parameters.
- Hypothesis testing involves a null hypothesis of no difference and an alternative hypothesis of a significant difference.
- Common tests discussed include chi-square tests to compare proportions between groups and determine if differences are significant.
1. The document discusses hypothesis testing using the z-test. It outlines the steps of hypothesis testing including stating hypotheses, setting the criterion, computing test statistics, comparing to the criterion, and making a decision.
2. Examples are provided to demonstrate a non-directional and directional z-test, including stating hypotheses, computing test statistics, comparing to criteria, and interpreting results.
3. Key concepts reviewed are the central limit theorem, type I and II errors, significance levels, rejection regions, p-values, and confidence intervals in hypothesis testing.
Statistics is used to organize and understand data through research design, quantification, description, and analysis. It involves measures of central tendency like mean, median, and mode to provide an overview of a population or sample, as well as measures of variability. Inferential statistics uses probability to compare sample means and make conclusions about populations. The goal is to determine the probability that differences observed in a sample reflect real differences in the overall population from which the sample was drawn.
A short introduction to sample size estimation for Research methodology workshop at Dr. BVP RMC, Pravara Institute of Medical Sciences(DU), Loni by Dr. Mandar Baviskar
1. IntroversionScore 11 pts.4 - 22 pts.Feedback Some peopMartineMccracken314
1. Introversion
Score : 11 pts.
4 - 22 pts.
Feedback: Some people thrive in teleworking arrangements, whereas others discover that it is neither a satisfying nor productive work environment for them. This scale assesses three personal dispositions that are identified in the literature as characteristics of effective teleworkers: (a) high company alignment, (b) low social needs at work and (c) independent initiative.
Company alignment
Company alignment estimates the extent to which you follow company procedures and have values congruent with company values. The greater the alignment, the more likely that you can abide by company practices while working alone and with direct supervision. While some deviation from company practices may be appropriate, teleworkers need to agree with company values and provide work that is consistent with company expectations most of the time. Scores on this scale range from 4 to 20.
Extroversion
Score: 17 pts.
4 - 22 pts.
Feedback: Low individualism
Individualism refers to the extent that you value independence and personal uniqueness. Highly individualist people value personal freedom, self-sufficiency, control over their own lives, and appreciation of their unique qualities that distinguish them from others.
However, keep in mind that the average level of individualism is higher in some cultures (such as Australia) than in others.
2. Total score: 8 pts.
RANGE BASED FEEDBACK:
6-12 pts.
Feedback: Low work centrality
People with high work centrality define themselves mainly by their work roles and view non-work roles as much less significant. Consequently, people with a high work centrality score likely have lower complexity in their self-concept. This can be a concern because if something goes wrong with their work role, their non-work roles are not of sufficient value to maintain a positive self-evaluation. At the same time, work dominates our work lives, so those with very low scores would be more of the exception than the rule in most societies. Scores range from 6 to 36 with higher scores indicating higher work centrality. The norms in the following table are based on a large sample of Canadian employees (average score was 20.7). However, work centrality norms vary from one group to the next. For example, the average score in a sample of Canadian nurses was around 17 (translated to the scale range used here).
3. Total score: 32 pts.
RANGE BASED FEEDBACK:
28-32 pts.
Feedback: High need for social approval
The need for social approval scale estimates the extent to which you are motivated to seek favourable evaluation from others. Founded on the drive to bond, the need for social approval is a secondary need, because people vary in this need based on their self-concept, values, personality and possibly social norms. This scale ranges from 0 to 32. How high or low is your need for social approval? The ideal would be to compare your score with the collective results of other students in your class. Otherwi ...
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1. Descriptive statistics provide a simple summary of data through measures of central tendency, frequency, and variability.
2. Common measures include the mean, median, mode, standard deviation, and outliers.
3. Inferential statistics allow researchers to make generalizations about populations based on analyses of samples. They include t-tests, ANOVA, correlation, and regression.
ANOVA is a statistical technique used to analyze differences between group means and their associated procedures. It was developed by Ronald Fisher and partitions variance into components that can be attributed to different sources. In its simplest form, ANOVA provides a statistical test of whether population means of several groups are equal, generalizing the t-test to more than two groups. It is useful for comparing three or more group means for statistical significance.
This document discusses inferential statistics and hypothesis testing. It provides examples of researchers formulating hypotheses and collecting data to test them. Researchers take random samples from populations to test if there are meaningful differences between groups. Hypothesis testing involves comparing experimental and control groups after exposing them to different levels of an independent variable. The goal is to determine if the independent variable caused a detectable change in the dependent variable. Inferential statistics are used to test if sample means differ significantly, which would suggest the hypothesis is supported or not supported. Proper sampling and estimating sampling distributions, standard errors, and variability are important concepts for accurately testing hypotheses about populations based on sample data.
The document discusses reward processing and decision making in the brain. It describes how dopamine neurons encode reward prediction errors and transmit information about rewards. It also discusses three types of values - Pavlovian values encoded in the amygdala and orbitofrontal cortex, goal values encoded in the dorsomedial striatum that guide goal-directed actions, and habit values encoded in the dorsolateral striatum that drive habitual behaviors independent of goals or values. The actor-critic model proposes separate neural systems for learning state values (critic) and selecting actions (actor).
Based on data from 11 responses from Unanimous AI asking about a fair movie ticket price, this document compares the performance of Unanimous to simulated questionnaires. With the full 11 responses, 54.5% of Unanimous answers were within $0.25 of the average price, compared to 44% of simulated 36-person questionnaires, a non-significant difference. However, with samples of 15 or less, Unanimous responses were more accurate, with 80% within $0.25 of the average for samples of 5, significantly more accurate than the 23% of simulated 9-person questionnaires. More data is needed to generalize these results.
Multiple sample test - Anova, Chi-square, Test of association, Goodness of Fit Rupak Roy
Detailed demonstration of Multiple Sample Test like Analysis of Variance (ANOVA), kinds of ANOVA One Way, Two Way, Chi-square with their assumptions and applications using excel, and much more.
Let me know if anything is needed. Happy to help. ping @ #bobrupakroy
The document provides examples of hypothesis testing using z-tests, t-tests, F-tests (ANOVA), and describes how to conduct each test. It includes examples testing hypotheses about means of different groups for variables like exam scores, car crash tests, and sales data. The final example tests whether the monthly sales means are equal to determine which salesman is most likely to be promoted.
Statistical tests help justify if sample results can be applied to a population. ANOVA compares group means and is preferred over t-tests for 3+ groups. It calculates variation between and within groups to obtain an F-ratio. If the F-ratio exceeds its critical value, the null hypothesis that group means are equal is rejected, showing group means differ significantly. Two-way ANOVA extends this to consider two factors' influence, computing interaction effects between factors.
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This document discusses concepts related to sampling and sampling distributions. It begins with definitions of key terms like population, sample, parameter, and statistic. It then explains different sampling methods, focusing on simple random sampling. Different measures of central tendency and variability are outlined like mean, median, mode, range, variance, and standard deviation. The central limit theorem is introduced, which states that the sampling distribution of the mean will approximate a normal distribution for large sample sizes regardless of the population distribution. Examples are provided to illustrate these concepts.
This document discusses inferential statistics and epidemiological research. It introduces concepts like the central limit theorem, standard error, confidence intervals, hypothesis testing, and different statistical tests. Specifically, it covers:
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1. The document discusses hypothesis testing using the z-test. It outlines the steps of hypothesis testing including stating hypotheses, setting the criterion, computing test statistics, comparing to the criterion, and making a decision.
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Similar to 1 Rare Rewards Amplify Dopamine Learning Responses (20)
1. IntroversionScore 11 pts.4 - 22 pts.Feedback Some peopMartineMccracken314
1. Introversion
Score : 11 pts.
4 - 22 pts.
Feedback: Some people thrive in teleworking arrangements, whereas others discover that it is neither a satisfying nor productive work environment for them. This scale assesses three personal dispositions that are identified in the literature as characteristics of effective teleworkers: (a) high company alignment, (b) low social needs at work and (c) independent initiative.
Company alignment
Company alignment estimates the extent to which you follow company procedures and have values congruent with company values. The greater the alignment, the more likely that you can abide by company practices while working alone and with direct supervision. While some deviation from company practices may be appropriate, teleworkers need to agree with company values and provide work that is consistent with company expectations most of the time. Scores on this scale range from 4 to 20.
Extroversion
Score: 17 pts.
4 - 22 pts.
Feedback: Low individualism
Individualism refers to the extent that you value independence and personal uniqueness. Highly individualist people value personal freedom, self-sufficiency, control over their own lives, and appreciation of their unique qualities that distinguish them from others.
However, keep in mind that the average level of individualism is higher in some cultures (such as Australia) than in others.
2. Total score: 8 pts.
RANGE BASED FEEDBACK:
6-12 pts.
Feedback: Low work centrality
People with high work centrality define themselves mainly by their work roles and view non-work roles as much less significant. Consequently, people with a high work centrality score likely have lower complexity in their self-concept. This can be a concern because if something goes wrong with their work role, their non-work roles are not of sufficient value to maintain a positive self-evaluation. At the same time, work dominates our work lives, so those with very low scores would be more of the exception than the rule in most societies. Scores range from 6 to 36 with higher scores indicating higher work centrality. The norms in the following table are based on a large sample of Canadian employees (average score was 20.7). However, work centrality norms vary from one group to the next. For example, the average score in a sample of Canadian nurses was around 17 (translated to the scale range used here).
3. Total score: 32 pts.
RANGE BASED FEEDBACK:
28-32 pts.
Feedback: High need for social approval
The need for social approval scale estimates the extent to which you are motivated to seek favourable evaluation from others. Founded on the drive to bond, the need for social approval is a secondary need, because people vary in this need based on their self-concept, values, personality and possibly social norms. This scale ranges from 0 to 32. How high or low is your need for social approval? The ideal would be to compare your score with the collective results of other students in your class. Otherwi ...
1. International financial investors are moving funds from Talona MartineMccracken314
1. International financial investors are moving funds from Talona to other countries. This depreciation is causing even more disenchantment with this Talona's currency. Describe the affects will this have on the supply and demand curves for this currency on the foreign exchange markets?
2. Using a supply and demand diagram, demonstrate how a negative externality leads to market inefficiency. How might the government help to eliminate this inefficiency?
3. Briefly discuss the shortcomings of environmental command-and-control regulations.
4. Some data that at first might seem puzzling: The share of GDP devoted to investment was similar for the United States and South Korea from 1960-1991. However, during these same years South Korea had a 6 percent growth rate of average annual income per person, while the United States had only a 2 percent growth rate. If the saving rates were the same, why were the growth rates so different?
5. “Block Imports—Save Jobs for Some Americans, Lose a Roughly Equal Number of Jobs for Other Americans, and Also Pay High Prices.” Discuss this statement within the context of protectionism.
6. Steve and Craig have been shipwrecked on a deserted island in the South Pacific. Their economic activity consists of either gathering pineapples or fishing. We know Steve can catch four fish in one hour or harvest two baskets of pineapples. In the same time Craig can reel in two fish or harvest two baskets of pineapples.
Assume Craig and Steve both operate on straight-line production possibilities curves. What is Steve's opportunity cost of producing a basket of pineapples? Of a producing a fish? What is Craig's opportunity cost of producing a basket of pineapples? Of a producing a fish?
7. Provide examples of market-oriented environmental policies.
Running head: SC PLAN 1
SC PLAN 4
SC PLAN
Student’s Name
Institution Affiliation
SC PLAN
1. Describe the actions you will take to increase your net cash flows in the near future.
The first step is to reduce living expenditures. It is critical to lessen the amount spent on living expenses and other variables and save for future use. I will have to prevent luxuries such as vacation costs or keep them in check to avoid spending a hefty amount on them. I should check the option to cook for myself and avoid buying food. Also, I will choose a destination I can drive myself to save on rental car expenditures and airfare. I will have a detailed budget indicating the amount required for savings, debt repayment, and investment that will assist only to spend the money on essential expenditures. Further, the savings can help to start a business and become self-employed in the distant future.
I would have to look for a job that pays well or engage in a robust salary negotiation. The right time to negotiate for salary is during a performance review, compensation meeting, or job promotion (Bellon, Cookson, Gilje, & Heimer, 2020). I will ensure that I expand my education and technic ...
1. Interventionstreatment· The viral pinkeye does not need any MartineMccracken314
1. Interventions/treatment
· The viral pinkeye does not need any medication
· The bacterial pinkeye is treated with ointment or eye droplets
2. Possible nursing diagnosis
· Checking the specific infection affecting the eye
· Identifying burning eyes
· Increased anxiety with red eyes
3. Sign and symptoms
· Eye irritation
· Eye tearing
· Eye redness
· Eye discomfort
4. Nursing Interventions
· Putting some droplets in the kid’s eye
· Using a antibiotic ointment
· Administering ibuprofen to the kid
5. Risk factors
· Allergies
· A women having an STD during pregnancy
· Exposing the child to areas with lots of bacteria
6. Pathophysiology
The infected eye shows through an inflammation that is swollen and red. The conjunctiva shows and this is the clear membrane seen in the part where the eye is white. It remains this way if not treated for a while before it ends with medication administered or just ends naturally.
7. Complications
· A scaring in the child’s eye if the conjunctivitis is caused by allergic reactions
· It can aggravate to cause different conditions such as meningitis
8. Diagnostic Procedure
· Administering the medicine using eye droplets
· Rubbing the eye area with the ointment
...
1. Introduction and background information about solvatochromism uMartineMccracken314
1. Introduction and background information about solvatochromism using Reichardt’s dye? (400-500 words)
2. Discuss the properties of Reichardt’s dye that cause it to change its wavelength of maximum absorbance in the presence of solvents of differing polarities.
3. Discuss solvatochromism. Are there other dyes which exhibit this effect?
4. Would it be possible to use the wavelength of maximum absorbance in the presence of Reichardt’s dye to determine the water content of acetone solutions?
...
1. Integrity, the basic principle of healthcare leadership.ContaMartineMccracken314
1. Integrity, the basic principle of healthcare leadership.
Contains unread posts
Mateo Alba posted May 12, 2021 10:04 PM
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Integrity of any organization regardless whether it is in healthcare or business or government is paramount. Because of integrity comes trust. Having trust in a healthcare organization is nonnegotiable. It is the foundation of a world-class organization. Executives who ignore ethics run the risk of personal and corporate liability in today’s increasingly tough legal environment (Lynn S. Paine, 1994, Managing for Organizational Integrity, pp. 2-21)
First, the healthcare organization. The healthcare organization is the head or the governing body. It is charged of day-to-day functions, establish policies, guidance, business process, safety, security and all the administrative duties. Integrity is and must be the cornerstone of any healthcare organization. Without it, no clinicians or workers that would knowingly work for an organization that they cannot trust or feel safe. And most importantly, if the patients do not have trust in the organization, they will avoid that facility at all cost.
Second, the clinicians. The clinicians are what makes the organization or facility function. Whether they are the providers, nurses or staff it is important that they have the integrity to always do what is right not only for the healthcare team or the organization, but most specially for the patient. It starts with the clinical leaders building trust to their subordinate staff by having the integrity and values of what a leader should be. Once that is established, then it permeates throughout the entire team. Thereby improving the healthcare delivery.
Lastly, and the most important is the patient. At the center of the entire system needs to be the patient. Once the patient recognizes the integrity or values of the healthcare organization and the clinicians delivering healthcare, patient trust is established. The patient satisfaction also increases. According to Cowing, Davino-Ramaya, Ramaya, Szmerekovsky, 2009, pp.72, “if patients are satisfied with clinician-patient interactions, they are likely to be more compliant with their treatment plan, to understand their role in the recovery process, and to follow through with the recommended treatment”. Having integrity or values in the healthcare delivery is the basic principle of healthcare leadership.
Cowing, M., Davino-Ramaya, C. M., Ramaya, K., & Szmerekovsky, J. (2009). Health care delivery performance: service, outcomes, and resource stewardship. The Permanente Journal, 13(4), 72–78. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2911834/
Lynn S. Paine, 1994, Managing for Organizational Integrity. Harvard business review, 2-21. Retrieved from Managing for Organizational Integrity (hbr.org)
2. Medical Delivery Influences
Contains unread posts
Robert Breeden posted May 12, 2021 9:44 AM
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Hello,
The influence within the medical community is so important and ...
1. Information organized and placed in a logical sequence (10 poMartineMccracken314
1.
Information organized and placed in a logical sequence (10 points)
Points Awarded
2.
Demonstrated knowledge of ethical dilemma presented by:
2a. Summarized the situation (10)
2b. Explained the ethical dilemma (5)
2c. Solved the problem as a professional RN (15)
3.
Responses supported with specific ANA Codes
(20)
4.
Visual aids professional, visually interesting
& aided in understanding material; proper grammar/spelling/punctuation-no more than 2 errors in presentation(10)
5.
Maintained eye contact of audience (10)
6.
Voice clear & audible (10)
7.
Encouraged class participation (5)
8.
Reference slide that includes references in APA
format (5)
Total points possible = 100
NSG 100
Case Study in-class Presentations Assignment
1): Moral Courage with a Dying Patient
Mr. T. is an 82-year-old widower who has been a patient on your unit several times over the past 5 years. His CHF, COPD, and diabetes have taken a toll on his body. He now needs oxygen 24 hours a day and still has dyspnea and tachycardia at rest. On admission, his ejection fraction is less than 20%, EKG shows a QRS interval of greater than 0.13 seconds, and his functional class is IV on NYHA assessment.
He has remained symptomatic despite maximum medical management with a vasodilator and diuretics. He tells you, "This is my last trip; I am glad I have made peace with my family and God. Nurse, I am ready to die." You ask about an advance directive and he tells you his son knows that he wants no heroics, but they just have never gotten around to filling out the form. When the son arrives, you suggest that he speak with the social worker to complete the advance directive and he agrees reluctantly. You page the physician to discuss DNR status with the son. Unfortunately, Mr. T. experiences cardiac arrest before the discussion occurs and you watch helplessly as members of the Code Blue Team perform resuscitation. Mr. T. is now on a ventilator and the son has dissolved into tears with cries of, "Do not let him die!"
2): Moral Courage to Confront Bullying
Melissa started on the unit as a new graduate 5 weeks ago. She is still in orientation and has a good relationship with her preceptor. The preceptor has been assigned consistently to Melissa for most of the last 4 weeks, but due to family emergency has not been available in the last week. Melissa has been told that she will be precepted by a different nurse for the remainder of her orientation. The new preceptor has not been welcoming, supportive, or focused on the educational goals of the orientation. In fact, this new preceptor has voiced to all who will listen her feelings about the incompetence of new BSN graduates. The crisis occurs when Melissa fails to recognize a patient's confusion as a result of an adverse medication effect. The preceptor berates Melissa in the nurses' station, makes sarcastic comments in shift report abou ...
1. In our grant application, we included the following interventioMartineMccracken314
1. In our grant application, we included the following interventions as our evidence-based programs: Family Therapy (to promote family acceptance and support, a key factor for overall health outcomes for this population), Motivational Interviewing (to address higher co-occurrence of substance use concerns), Trauma-Focused Treatment (including EMDR Therapy and TF-CBT, to address higher rates of complex trauma including from systemic oppression), and CBT (a gold standard treatment modality, but adapted to meet the needs of our client population by incorporating elements of
Solution
s-Focused or Narrative approaches to make it more strengths-based).
For questions 2-4, you would need to do some of your own research in the literature on these treatment modalities and determine for yourself if there were best practices that should be incorporated into the plan used at the agency.
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Cultural Competency: A Key to Effective Future Social Work With Racially and Ethnically Diverse E...
Min, Jong Won
Families in Society; Jul-Sep 2005; 86, 3; ProQuest One Academic
pg. 347
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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
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...
1. I believe that the protagonist is Nel because she is the one thMartineMccracken314
1. I believe that the protagonist is Nel because she is the one that goes through different changes throughout the book. I also think she is the protagonist because most people can relate to her more. Nel was done wrong by Sula and her husband Jude Green. Sula did the one thing that a best friend should never do and, that is sleep with your best friend's husband. Even though Sula did a terrible thing Nel still cares about her best friend because she goes and visits her when she is sick even after all the pain she caused her. Nel is also deeply saddened when she visits Sulas grave. That is not the only thing that happened to Nel. Nel not only had to deal with the affair but also accepted her guilt in Chicken Little's drowning. But in the end, Nel realized she enjoyed watching him drown.
Everything changed when Sula came back to Nels life. Nel was happy before. She was happy with her family and her husband, but when Sula came back that all changed. After the affair and Sulas death, Nel was alone. Nel became a single mother and, she no longer has a good relationship with another man.
2. I believe that although the title of the story is Sula, the main protaginist of the story is Nel. Nel is kept until the end of the story and Sulay passes away and exit's the story. I think in this pivitol moment is when the author wanted to make Nel the main character. Nel contained her emotion until towards the end of the story when she has a conversation with Eva, Nel nervously comments "Who told you all these lies? Miss Peace? Who told you? Why are you telling lies on me?" I believe the author wanted us to feel the anxiousness and wonder that Nel found out that somebody finally knew about the little boy being thrown. I believe this admission of guilt to Eva brings closure to Nel. Nel was trying to hide her emotions the entire time and it wasn't after being confronted that she broke down about it and visited Sulay's grave. Nel even stated "I don't know. No." when asked whether somebody saw the boy being thrown into the river. This shows that Nel was not sure at all in the moment it happened whether somebody knew. Nel wanted to not think about what happen forever and try to mute the situation but Eva bringing it up, made Nel feel terrible about what happened which is why she ended up visting Sulay's grave. I think muting herself from knowing the little boy was thrown was still not a 'good' way to look at it, from her end. She wanted to believe a lie by just pretending it never happened. It wasn't after someone brought up the situation to her that her feelings change.
3. Although the novel is titled Sula, the real protagonist is Nel because she is the one who is transformed by the end. Sula and Nel were very great friends and were very dedicated to each other. But they were also very different. Nel was known as the more mature and "good person" while Sula is more impulsive. "Nel is the product of a family that believes deeply in social conventions, hers is a st ...
1. If the profit from the sale of x units of a product is P = MartineMccracken314
The document provides 11 math word problems related to profit, costs, revenue, supply and demand functions, and other economics topics. Students are asked to solve the problems by finding break-even points, maximum or minimum values, equilibrium quantities and prices, and other values. The problems cover concepts like profit maximization, optimal production levels, and using equations to model economic relationships.
1. How does CO2 and other greenhouse gases promote global warminMartineMccracken314
1. How does CO2 and other greenhouse gases promote global warming? Discuss your opinion on the use of geoengineering measures to mitigate the effects of global warming.
Your response should be at least 250 words in length.
2. How does CO2 and other greenhouse gases promote global warming? Discuss your opinion on the use of geoengineering measures to mitigate the effects of global warming.
Your response should be at least 250 words in length.
Raw DataNamePayResponsibilitiesSupervisionGenderDepartmentRudolph211MaleAccountingOlga211FemaleAccountingInstructionsErnest211MaleAccountingEmily211FemaleAccountingThe sheet labeled "Raw Data" lists 366 employees and their rating (1-5) of their satisfaction with their Pay, Responsibilities, and Supervision. A rating of 5 is the highest satisfaction.Bobby211MaleAccountingRaw Data also includes the Gender and Department for each employee.Benjamin211MaleAccountingBeatrice211FemaleAccountingInsert a new column in EKeith211MaleAccountingLabel this new column "Overall Satisfaction Rating"Hilda211FemaleAccountingFor each employee, compute the Overall Satisfaction Rating as the Average of Pay, Responsibilities, and Supervision.Leslie311MaleAccountingFormat Overall Satisfaction Rating to one decimal place.Curtis311MaleAccountingAlice311FemaleAccountingOn a New sheet titled Results, create a Pivot Chart & Pivot TableSophie311FemaleAccountingAssign Gender to Columns, Department to rows, and Pay to Values. Change the value field setting from Sum to Average if necessary.Sally311FemaleAccountingSort the departments in descending order of satisfaction.Melvin311MaleAccountingCreate a title for the chart, which includes your last namePearl411FemaleAccountingBe sure your chart includes a legend for male & female employees, change male color to blue and female to orangeJohnny411MaleAccountingBe sure to include axis titlesEunice411FemaleAccountingFormat the vertical axis for a max of 5 and major tick marks at 1 and one decimal place.Opal212FemaleAccountingJulia212FemaleAccountingCreate a new sheet titled "Graphs".Jimmie212MaleAccountingCopy & Paste as Picture your graph of Pay SatisfactionEsther212FemaleAccountingAlbert212MaleAccountingAlter your Pivot chart/table to display Responsibilities Satisfaction. Change titles as needed.Mike212MaleAccountingPaste this chart on the Graphs sheetMarion212MaleAccountingJosephine212FemaleAccountingAlter your Pivot chart/table to display Supervision Satisfaction. Change titles as needed.Ida212FemaleAccountingPaste this chart on the Graphs sheetGerald212MaleAccountingCaroline212FemaleAccountingAlter your Pivot chart/table to display Overall Satisfaction. Change titles as needed.Alberta212FemaleAccountingPaste this chart on the Graphs sheetLeroy312MaleAccountingLeave Results sheet with the Pivot Table & Chart displaying the Overall Satisfaction.Anita312FemaleAccountingMildred412FemaleAccountingBeulah412FemaleAccountingAda412FemaleAccountingClayton212MaleAccountingWayne312MaleA ...
1. How do you think communication and the role of training addressMartineMccracken314
1. How do you think communication and the role of training address performance gaps or training needs as it relates to how Adults learn?
2. There are many ways – or methods – available to gather data during a need’s assessment. Each one has advantages and disadvantages. What is important is to select the appropriate method based on your business problem. The most common methods for data gathering are:
· Document reviews or Extant Data Analysis – reviewing existing material like process maps, procedure guides, previous training material, etc.,
· Needs Assessment
· Interviews
· Focus groups
· Surveys
· Questionnaires
· Direct Observations
· Testing
· Subject Matter Expert Analysis
Select one of these data gathering methods to discuss and share what you see as the advantages and disadvantages associated with using the selected method.
1. Team teaching
In team teaching, both teachers are in the room at the same time but take turns teaching the whole class. Team teaching is sometimes called “tag team teaching.” You and your co-teacher teacher are a bit like co-presenters at a conference or the Oscars. You don’t necessarily plan who takes which part of the lesson, and when one of you makes a point, the other can jump in and elaborate if needed.
Team teaching can make you feel vulnerable. It asks you to step outside of your comfort zone and allow another teacher to see how you approach a classroom full of students. However, it also gives you the opportunity to learn about and improve your teaching skills by having a partner who can provide feedback and — in some cases — mentorship.
In team teaching, as well as the five other co-teaching models below, a teacher team may be made up of two general education teachers, two special education teachers, or one of each. Or, in some cases, it may be a teacher and a paraprofessional working together. Some IEPs specify that a student’s teaching team needs to include a general education teacher and a special education teacher.
Here’s what you need to know about the team teaching method:
What it looks like in the classroom
Both teachers teach at the front of the room and move about to check in with students (as needed).
Benefits
· Provides both teachers with an active instructional role
· Introduces students to complementary teaching styles and personalities
· Allows for lessons to be presented by two different people with different teaching styles
· Models multiple ways of presenting and engaging with information
· Models for students what a successful collaborative working relationship can look like
· Provides more opportunities to pursue teachable moments that may arise
Challenges
· Takes time and trust for teachers to build a working relationship that values each teacher equally in the classroom
· Necessitates a lot of planning time and coordination of schedules
· Requires teachers to have equal involvement not just in planning, but also in grading, which means assignments need to be evaluated ...
1. How brain meets its requirement for its energy in terms of wellMartineMccracken314
1. How brain meets its requirement for its energy in terms of well-fed and during starvation or fasting?
2. Explain the utilization of different sources of energy in muscle during anaerobic and aerobic conditions of high physical activity and resting?
3. Why and how adipose tissue and kidney are significant for fuel metabolism?
4. Explain in detail why liver is significant for metabolism of mammals and how does it coordinate the different metabolic pathways essential for organism?
5. Explain the Cori cycle and glucose-alanine cycle for interorgan fuel metabolism?
...
1. Give an introduction to contemporary Chinese art (Talk a littleMartineMccracken314
1. Give an introduction to contemporary Chinese art (Talk a little bit about some of the major changes in Chinese art)
2. Read the article that is provided. Do some research on the artist, Xu Bing. According to the article, give some background information about Xu Bing, and investigate the body of work.
3. Select one piece of his artwork to write about. It could be a traditional work of art, such as drawing, painting, or sculpture, or something more experimental like performance art, body art, or installation art.
4. Write a 3-page analysis of the artwork you select. The paper should have a short introduction and conclusion, but the body should focus on your analysis of the artwork. Some of the questions that you might want to work through in the paper include: Why is the work important? In what ways does it challenge the viewer? Is there an allegorical meaning to the work? How is it in dialogue with Western art traditions or earlier Chinese art traditions? Does it engage with Chinese history? Etc.
5. Be sure to include an image of the work you select into the paper, and the paper must be grammatically correct.
...
1. For this reaction essay is a brief written reaction to the readMartineMccracken314
1. For this reaction essay is a brief written reaction to the readings. It may be somewhat informal (and I would encourage you to be personal), but it must be well-written and well-organized. It must not be more than 2 pages, use 12-point font, single-spaced, at least 1" margins. You will react to the results of this systematic review article on Telemedicine " Effectiveness of Telemedicine A Systematic Review of Reviews.pdf
Focus on the results of the synthesis only, react to the authors' conclusions- do you agree or disagree with their synthesis? Discuss your opinion, are there faults in their conclusions?
Telemedicine is increasingly being suggested as an alternative for an in-person visit, especially with emergent diseases that call for person-to-person distancing. What are the potential concerns with this suggestion? What are in the authors' synthesis and conclusions underscore the limitations of this suggestion?
2. The next day a representative from Bristol Myers Squibb visits your office and tells you that Plavix® (clopidogrel) decreases cardiovascular events by 8.7% compared to aspirin. That sure sounds good to you, as you have many elderly patients at risk of heart attacks and strokes and many are already on aspirin. The brochure quotes the CAPRIE study, and you decide to investigate this further. A review of the 1996 article reveals that study patients on Plavix® experienced cardiovascular events 9.78% of the time compared to 10.64% of the time with aspirin. Plavix® was approved by the FDA based on this one study. Cost of Plavix/day=$6.50. Cost of aspirin/day = $1.33
• What was the NNT?
• How much does Plavix® cost monthly?
• What meaning do these values have for this problem?
• Be sure to include your actual calculations/math
i n t e r n a t i o n a l j o u r n a l o f m e d i c a l i n f o r m a t i c s 7 9 ( 2 0 1 0 ) 736–771
j o u r n a l h o m e p a g e : w w w . i n t l . e l s e v i e r h e a l t h . c o m / j o u r n a l s / i j m i
Effectiveness of telemedicine: A systematic review of
reviews
Anne G. Ekeland a,∗, Alison Bowes b, Signe Flottorp c,d
a Norwegian Centre for Integrated Care and Telemedicine, University Hospital of North Norway, P.O. Box 6060, N-9038 Tromsø, Norway
b Department of Applied Social Science, University of Stirling, Scotland, UK
c Norwegian Knowledge Centre for the Health Services, Oslo, Norway
d Department of Public Health and Primary Health Care, University of Bergen, Norway
a r t i c l e i n f o
Article history:
Received 23 April 2010
Received in revised form
11 July 2010
Accepted 29 August 2010
Keywords:
Telemedicine
Telecare
Systematic review
Effectiveness
Outcome
a b s t r a c t
Objectives: To conduct a review of reviews on the impacts and costs of telemedicine services.
Methods: A review of systematic reviews of telemedicine interventions was conducted. Inter-
ventions included all e-health interventions, information and communication technologies
for communication ...
1. Find something to negotiate in your personal or professional liMartineMccracken314
1. Find something to negotiate in your personal or professional life. Examples include: redistribution of household chores, a personal or professional purchase, a contract at work, asking for a raise, booking a vacation, hiring a contractor, etc. The deal does not have to be implemented for the purposes of this class (e.g. you can finalize the price for something you’re thinking of buying without following through on the purchase right now). The scenario you choose should be significant enough to allow you to do substantial research and detail for your paper. Submit a five page paper (minimum), double spaces, utilizing proper grammar and spelling, which summarizes the following:
1. Your Preparation – Describe the process you used and results of your preparation. You should also discuss your strategies, targets, and negotiating plan. Make sure you do your research, working on both your BATNA and the other party’s. (Consider newspapers, bookstores, libraries, the internet, and personal calls and visits as possible sources of information). This is the most important step, so being thorough is critical.
1. The Negotiating Process – Describe what happened in the negotiation itself. List he sequence of events and how you reacted/adjusted to the other party’s position. What was the negotiation style of the other party? What “tricks” did they try? How did you react? Were there any other influencing factors (e.g. cultural differences, misperceptions, emotion, etc.)?
1. The Outcome – What was the outcome and how did you feel about it? What worked well? What would you have done differently? Do you feel the result you arrived at was better than it would have been if you hadn’t taken the class? Why/Why not?
Your understanding of the appropriate preparation and process steps to take in negotiating this deal is more important than the final outcome.
Be sure to cite your sources, and include copies of necessary quotes/documentation.
1.
Find something to negotiate in your personal or professional life. Examples include:
redistributi
on of household chores, a personal or professional purchase, a contract at work,
asking for a raise, booking a vacation, hiring a contractor, etc. The deal does not have to be
implemented for the purposes of this class (e.g. you can finalize the price for
something you’re
thinking of buying without following through on the purchase right now). The scenario you
choose should be significant enough to allow you to do substantial research and detail for your
paper. Submit a five page paper (minimum), double
spaces, utilizing proper grammar and
spelling, which summarizes the following:
2.
Your Preparation
–
Describe the process you us
ed and results of your preparation. You should
also discuss your strategies, targets, and negotiating plan. Make sure you do your research,
working on both your BATNA and the other party’s. (Consider newspapers, bookstores, libraries,
the internet, and p
ers ...
1. FAMILYMy 57 year old mother died after a short illness MartineMccracken314
1. FAMILY
My 57 year old mother died after a short illness last June. She was a wonderful mother and my 66 year old father
adored her. They had been married for 38 years. He is finding it extremely difficult to cope without her. To make
matters worse, he retired just two months before she died and is at a loss to fill his days.
He is disorganized and has not established any pattern in his life. I invite him for meals and outings, but he is
detached and depressed. He doesn’t seem to be part of the world any more. I am terribly worried about him. How
long will he be like this? I am 34 and have small children. I thought being with the children would help him, but it’s
as though he doesn’t see or know them. He just sits and stares into space for much of the day. He seems locked
into his grief.
2. FAMILY
One of our 17 year old son’s best friends took his life several months ago. Our son didn’t say much at the time, but
he was very shaken. Since then he has gradually “retired” into himself. He stays in his room most of the time
listening to rock music.
He is unemployed and no longer sees his former schoolmates. We are very worried about him. How do we get him
out of himself? He has always been a quiet guy but his present behavior is beyond “quiet.” We have two other
children, girls aged 13 and 10, but our son now just ignores them.
3. FAMILY - rural
Ken is a 67 year old farmer who lives with his wife Margaret. Ken and Margaret had hoped to retire late in their 60s
and move to the west coast to be closer to their children, reluctantly selling the family property that has been
struggling financially. They have limited investment funds set aside to support their retirement and have been told
it is unlikely that they would be successful in selling their farm. Ken also suffers chronic back pain from a previous
farm injury. A neighbor has become concerned about Ken’s ability to cope with his property, and has visited Ken
and Margaret a number of times due to problems with his stock and pasture management. Margaret believes the
farm is “too much for them now,” but feels she can’t talk to Ken about this. Ken has become withdrawn and
refuses to discuss the issue. He talks about there being “no way out of this,” and that it “might as well be over.” He
sees his physician infrequently, having difficulty traveling the 60 miles to the nearby town.
4. FAMILY - rural
Jason is 34 years old and lives with his wife Jenny and their two children (8 and 3 years old). After completing a
mechanical trade apprenticeship in Boston, he has returned home with plans to build his future as a farmer. He has
become increasingly irritable and frustrated with what he believes is his failure to “get on top of things” on the
farm, and they are struggling to manage financially.
Jason is drinking heavily, mostly at home, but still drives his car into town. Jenny is angry and worried about this.
She is feeling isolated, having few friends in the area, and relying on Jas ...
1. Explain the four characteristics of B-DNA structure DifferentiMartineMccracken314
1. Explain the four characteristics of B-DNA structure? Differentiate between the A-DNA and Z-DNA structural features?
2. Describe the supercoiled DNA with its properties and how naturally occurring DNA under wound?
3. What are topoisomerases? Explain the two types of topoisomerases with their mechanism of action?
4. Explain the three interactions that are required to stabilize nucleic acids? How DNA denatures and renatures?
5. What are ribozymes and explain their properties?
Case 20 Restructuring
General Electric
The appointment of Larry Culp as the chairman and CEO of the General Electric
Company (GE) on October 1st, 2018 was a clear indication of the seriousness of the
problems that had engulfed the company. Culp, the former CEO of the highly-successful
conglomerate, Danaher Corporation, had been appointed a GE director only six months
previously and was the first outsider to lead GE—every one of GE’s previous CEOs had
been a career manager at the company. On the same day as Culp’s appointment, GE
abandoned its earning guidance for the year and announced a $23 billion accounting
charge arising from a write-down of goodwill at its troubled electrical power division.1
Culp’s predecessor, John Flannery had been CEO for a mere 14 months—a sharp
contrast to GE’s two previous CEOs: Jeff Immelt (16 years) and Jack Welch (20 years).
Flannery’s tenure at GE has coincided with of the company’s most difficult periods in its
entire 126-year history. In November 2017, amidst deteriorating financial performance,
Flannery announced a halving of GE’s quarterly dividend, the proposed sale of its
lighting and locomotive units—two of GE’s oldest businesses—and the elimination of
12,000 jobs in the power division.
In 2018, the situation worsened. In January, GE announced that it would be paying
$15 bn. to cover liabilities at insurance companies it had sold 12 years previously. In
February, GE confirmed suspicions over its dubious accounting practices by restating its
revenues and earnings for the previous two years, while also announcing the likelihood
of legal claims arising from its its subprime mortgage lending over a decade earlier.
The outcome was a precipitous fall in GE’s share price (see Figure 1) that culminated
in GE’s dismissal from the Dow Jones Industrial Average (DJIA). Until June 2018, GE
was the sole surviving member of the DJIA when it was created in 1896.
The crisis at GE presented the board with two central questions. First, should GE
be broken up? Second, if GE was to continue as a widely-diversified company, how
should it be managed?
As a diversified corporation that extended from jet engines, to oil and gas equipment,
to healthcare products, to financial services, GE was an anomaly. For three decades, con-
glomerates—diversified companies comprising unrelated or loosely related businesses—
had been deeply unfashionable. CEOs, Jack Welch and Jeff Immelt, had claimed that,
by virtue of its integrated m ...
1. examine three of the upstream impacts of mining. Which of theseMartineMccracken314
1. examine three of the upstream impacts of mining. Which of these do you think would be most difficult to estimate in a life cycle assessment?
Your response should be at least 250 words in length.
2. Discuss the pollutants that are emitted during the operation stage of a life cycle assessment for a fossil fuel source.
Your response should be at least 250 words in length
Body Ritual among the Nacirema
H O R A C E M I N E R
University of Michigan
HE anthropologist has become so familiar with the diversity of ways iq T which different peoples behave in similar situations that he is not a p t to.
be surprised by even the most exotic customs. I n fact, if all of thelogically
possible combinations of behavior have not been found somewhere in the
world, he is a p t to suspect that they must be present in some yet undescribed
tribe. This point has, in fact, been expressed with respect to clan organization
by Murdock (1949: 7 1 ) . I n this light, the magical beliefs and practices of the
Nacirema present such unusual aspects that i t seems desirable t o describe
them a s an example of the extremes to which human behavior can go.
Professor Linton first brought the ritual of the Nacirema to the attention
of anthropologists twenty years ago (1936:326), but the culture of this people
is still very poorly understood. They are a North American group living in the
territory between the Canadian Cree, the Yaqui and Tarahumare of Mexico,
and the Carib and Arawak of the Antilles. Little is known of their origin, al-
though tradition states that they came from the east. According to Nacirema
mythology, their nation was originated by a culture hero, Notgnihsaw, who is
otherwise known for two great feats of strength-the throwing of a piece of
wampum across the river Pa-To-Mac and the chopping down of a cherry tree
in which the Spirit of Truth resided.
Nacirema culture is characterized by a highly developed market economy
which has evolved in a rich natural habitat. While much of the people’s time
is devoted to economic pursuits, a large part of the fruits of these labors and a
considerable portion of the day are spent in ritual activity. The focus of this
activity is the human body, the appearance and health of which loom a s a
dominant concern in the ethos of the people. While such a concern is certainly
not unusual, its ceremonial aspects and associated philosophy are unique.
The fundamental belief underlying the whole system appears to be that the
human body is ugly and that its natural tendency is t o debility and disease.
Incarcerated in such a body, man’s only hope is to avert these characteristics
through the use of the powerful influences of ritual and ceremony. Every house-
hold has one or more shrines devoted to this purpose. The more powerful in-
dividuals in the society have several shrines in their houses and, in fact, the
opulence of a house is often referred to in terms of the num ...
1. Examine Hofstedes model of national culture. Are all four dimeMartineMccracken314
1. Examine Hofstede's model of national culture. Are all four dimensions still important in today's society as it relates to the success of the multinational manager? Why, or why not? Which do you think is the least important as it relates to multinational management? Why?
2. More companies are seeking to fill multinational management positions due to the influx of business growth abroad. If you were offered and accepted a position as a multinational manager, what would you do to personally prepare for the culture of a different country? Where would you seek information? What overall responsibilities would you expect of the job? How do you think the managerial responsibilities would be different from those you would face in the United States?
3. Multinational managers encounter many levels of culture. Which of the culture levels do you think might be the most difficult to manage? Why? Share an example. Which culture level do you think might be the easiest to understand? Why? Give an example of this.
4. In your own words, what is your perception of free trade? Think about the advantages of free trade; what are two benefits that result from free trade? There is also a downside to free trade; what are two disadvantages resulting from free trade? Provide reasoning for your choices.
5. What are the three major economic systems that nations utilize, and what is the role of each? How does each affect and influence individuals, multinational managers, and corporations?
6. How would you define ethical convergence? What are the four basic reasons for ethical convergence? Which might be the most difficult for multinational companies to follow, and why?
7. Describe the four major world religions. What are the impacts of each religion type on an economic environment? What do you think makes religion a concern in societies?
8. If you were a multinational manager, and you encountered an ethical dilemma within the multinational company, what heuristic questions would you use to decide between ethical relativism and ethical universalism? Of the different heuristic questions, which one do you think is most important? Explain your reasoning.
1
Week Two Instructor’s Notes
PHIL 1103 Summer
This week you will be learning in detail about the four different moral perspectives that
we will use to analyze moral questions.
Notice two things right at the start. First, because normative ethics is our main focus this
term, we are not going to attempt to settle the question of whether any moral perspective at all
could be correct or known to be correct—that is a task for metaethics. Our task in this second
week is to learn in some detail about four different kinds of consideration or value that often
seem relevant when we try to decide what is morally right or wrong in particular cases, namely:
(1) Respect for the rights and autonomy of the persons involved
(2) Increasing the overall well-being of the most individuals possible
(3) Asking wha ...
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
1. 1
Rare Rewards Amplify Dopamine Learning Responses
Kathryn M. Rothenhoefer, Tao Hong, Aydin Alikaya, William
R. Stauffer*.
Affiliations:
Center for Neuroscience, Center for the Neural Basis of
Cognition, Systems Neuroscience
Institute, The Brain Institute, University of Pittsburgh,
Pittsburgh, PA, USA. 5
*Correspondence to: [email protected]
Abstract:
Dopamine neurons drive learning by coding reward prediction
errors (RPEs), which are formalized
as subtractions of predicted values from reward values.
Subtractions accommodate point estimate 10
predictions of value, such as the average value. However, point
estimate predictions fail to capture
many features of choice and learning behaviors. For instance,
reaction times and learning rates
2. consistently reflect higher moments of probability distributions.
Here, we demonstrate that
dopamine RPE responses code probability distributions. We
presented monkeys with rewards that
were drawn from the tails of normal and uniform reward size
distributions to generate rare and 15
common RPEs, respectively. Behavioral choices and pupil
diameter measurements indicated that
monkeys learned faster and registered greater arousal from rare
RPEs, compared to common RPEs
of identical magnitudes. Dopamine neuron recordings indicated
that rare rewards amplified RPE
responses. These results demonstrate that dopamine responses
reflect probability distributions and
suggest a neural mechanism for the amplified learning and
enhanced arousal associated with rare 20
events.
not certified by peer review) is the author/funder. All rights
reserved. No reuse allowed without permission.
The copyright holder for this preprint (which wasthis version
posted November 22, 2019. ; https://doi.org/10.1101/851709doi:
bioRxiv preprint
https://doi.org/10.1101/851709
3. 2
Main Text:
Making accurate predictions is evolutionarily adaptive.
Accurate predictions enable
individuals to be in the right place at the right time, choose the
best options, and efficiently scale
the vigor of responses. Dopamine neurons are crucial for
building accurate reward predictions.
Phasic dopamine responses code for reward prediction errors:
the differences between the values 5
of received and predicted rewards (1-8). These signals cause
predictions to be modified through
associative and extinction learning (9, 10). Likewise, phasic
dopamine neuron stimulation during
reward delivery increase both the dopamine responses to reward
predicting cues and the choices
for those same cues (11). Although it is well understood how
predicted reward values affect
dopamine responses, these predictions are simply point
estimates – normally the average value – 10
of probability distributions. It is unknown how the form of
4. probability distributions affects
dopamine learning signals.
Probability distributions affect behavioral measures of learning
and decision making.
Learning the expected value takes longer when rewards are
sampled from broader distributions,
compared to when they are drawn from narrower distributions
(12). Likewise, even with well 15
learned values, decision makers take a longer time to choose
between options when the value
difference between rewards is smaller, compared to when the
difference is larger (13, 14). These
behavioral measures of learning and decision making
demonstrate that the probability distributions
over reward values, and not simply the point estimates of
values, influence learning and decision
making. Prior electrophysiological recordings have
demonstrated that dopamine signals adapt to 20
the range between two outcomes (15), but it is not known
whether these same dopamine signals
reflect probability distributions over reward values.
not certified by peer review) is the author/funder. All rights
reserved. No reuse allowed without permission.
The copyright holder for this preprint (which wasthis version
5. posted November 22, 2019. ; https://doi.org/10.1101/851709doi:
bioRxiv preprint
https://doi.org/10.1101/851709
3
Here, we asked whether the shapes of probability distributions
were reflected in dopamine
reward prediction error responses. We created two discrete
reward size distributions that reflected,
roughly, the shapes of normal and uniform distributions. We
trained monkeys to predict rewards
drawn from these distributions. Crucially, the normal
distribution resulted in rare prediction errors
following rewards drawn from the tails of that distribution,
whereas the same rewards, with 5
identical prediction errors, were drawn with greater frequency
from the uniform distribution. We
found that monkeys learned to choose the better option within
fewer trials when rewards were
drawn from normal distributions, compared to when rewards
were drawn from uniform
distributions. Moreover, we found that pupil diameter was
correlated with rare prediction errors,
6. but uncorrelated with common prediction errors of the same
magnitude, suggesting greater 10
vigilance to rare outcomes. Using single neuron recording, we
show that dopamine responses
reflect the shape of predicted reward distribution. Specifically,
rare prediction errors evoked
significantly larger responses than common prediction errors
with identical magnitudes. Together,
these results demonstrate a complementary but distinct
mechanism from TD-like reward prediction
error responses for learning based on probability distributions.
15
Results
Reward size distributions
We used non-informative images (fractal pictures) to predict
rewards drawn from
differently shaped distributions. Distribution shapes were
defined according to relative reward 20
frequency. One fractal image predicted an equal probability of
receiving a small, medium, or large
volume of juice reward. We define this as the uniform reward
size distribution (Fig. 1A left). A
7. second fractal image predicted that the small and the large
reward would be given 2 out of 15
not certified by peer review) is the author/funder. All rights
reserved. No reuse allowed without permission.
The copyright holder for this preprint (which wasthis version
posted November 22, 2019. ; https://doi.org/10.1101/851709doi:
bioRxiv preprint
https://doi.org/10.1101/851709
4
times, and the medium sized reward would be given the
remaining 11 out of 15 times (normal
reward size distribution, Fig. 1A right). Importantly, both
reward size distributions were
symmetrical and were comprised of the same three reward
magnitudes. Therefore, the uniform and
normal distributions had identical Pascalian expected values.
However, rewards drawn from the
tails of the normal distribution were rare, compared to the
frequency of identical rewards drawn 5
from the tails of the uniform distribution. Anticipatory licking
reflected the expected value of both
the distributions, as well as the expected value of safe cues
(Figure 1B, p = 0.019, Linear
8. Regression). Thus, the animals learned that the cues predicted
rewards.
Dopamine neurons code prediction errors in the subjective
values of rewards (16),
according to the following equation: 10
���������� ����� = ������ ����������
����� − ��������� ���������� �����.
Eq.1
To investigate whether dopamine responses were sensitive to
probability distributions, we sought
to compare dopamine responses when the conventional
prediction errors, defined according to Eq. 15
1, were identical. Therefore, we created reward distributions
with the same expected values and
then measured the distribution of the subjective values. We
measured the subjective values of six
reward distributions – three normal reward distributions with
center locations of 0.3, 0.4, and 0.5
ml and three uniform distributions with center locations of 0.3,
0.4, and 0.5 ml. Monkeys made
choices between cues that predicted a distribution and safe
values (Fig. 1C, Methods). We plotted 20
9. the probability of choosing the safe option as a function of the
safe option volume and generated
psychometric functions (Figure 1D). We used the certainty
equivalents (CEs) – defined as the safe
volume at the point of subjective equivalence between the two
options (Fig. 1D, vertical dashed.
not certified by peer review) is the author/funder. All rights
reserved. No reuse allowed without permission.
The copyright holder for this preprint (which wasthis version
posted November 22, 2019. ; https://doi.org/10.1101/851709doi:
bioRxiv preprint
https://doi.org/10.1101/851709
5
Line) – as a measure of the distribution subjective value.
Analysis of variance (ANOVA) on the
CEs for the normal and uniform distribution cues indicated a
significant effect of EV on CE (F =
71.81, p < 0.001, ANOVA). Crucially, we found no significant
effect of the distribution type on
the CEs (F = 3.57, p = 0.07, ANOVA). To increase our power to
detect small differences in
subjective value, we combined the CE data from all three EVs,
yet still found no significant 5
10. difference between the subjective values of the normal and
uniform distributions (p = 0.20, two-
sample t-test). Therefore, within the limited range of reward
sizes we used, the data indicate that
the normal and uniform reward size distributions had similar
subjective values. These results
indicated that the prediction errors generated from the
distributions could be readily compared and
ensured that disparities between prediction error responses were
not driven by differences in the 10
predicted subjective values.
15
not certified by peer review) is the author/funder. All rights
reserved. No reuse allowed without permission.
The copyright holder for this preprint (which wasthis version
posted November 22, 2019. ; https://doi.org/10.1101/851709doi:
bioRxiv preprint
https://doi.org/10.1101/851709
6
11. Figure 1. Reward size distributions. A. Fractal images
predicted that one of three juice reward sizes would be drawn
from a uniform (left) or normal (right) reward size distribution.
The uniform distribution predicted that each size would be
drawn with equal relative frequency (R.F.) (1/3 for small, 5
medium, and large sized rewards). The normal distribution
predicted that the small and large reward volumes would be
drawn 2/15 times, and the medium reward volume would be
drawn the remaining 11/15 times. B. Anticipatory licking
indicated that the monkeys learned the predicted reward 10
values. Black dots indicate the normalized licking duration
data for predicted reward volume, and the grey dotted line
indicates the linear fit to the data. Error bars indicate SEM
across session. C. The choice task used to measure subjective
value. Animals made saccade-
directed choices between a distribution predicting cue and a
safe alternative option. The safe 15
alternative option was a value bar with a minimum and
maximum of 0 and 0.8 ml at the bottom
and top, respectively. The intersection between the horizontal
12. bar and the scale indicated the
volume of juice that would be received if monkeys selected the
safe cue. D. Probability of choosing
the safe cue as a function of the value of the safe option, when
the distribution predicting cue had
an expected value (EV) of 0.4ml. Dots show average choice
probability for 9 safe value options 20
for monkey B (left), and Monkey S (right). Solid lines are a
logistic fit to the data. Red indicates
data from normal distribution blocks, grey indicates data from
uniform distribution blocks. The
dashed horizontal lines indicate subjective equivalence, and the
CE for each distribution type is
indicated with the dashed vertical lines.
25
Distribution shape affects learning
We next investigated how probability distributions affected
learning (Fig. 2A). Each
learning block consisted of 15-25 trials and used two, never-
before-seen cues (Supplementary
Methods). Both cues predicted either normal or uniform
distributions and had two different
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expected values (EVs). The animals had to learn the relative
EVs to maximize reward. We
analyzed behavioral choice data from two animals across 16
sessions, each of which included a
mixture of normal and uniform blocks (Supplementary
Methods). As expected, the probability of
choosing the higher value option on the first trial of each block
was not significantly different from
chance (Fig. 2B, left). Comparison between the first and
fifteenth trial of each block revealed that 5
the animals learned to choose the higher value option on 87%
and 85% of trials, in the normal and
uniform blocks, respectively (Fig. 2B, p < 0.001, paired t-test
for both distributions). There was
no significant difference in the average performance between
the fifteenth trials of normal and
14. uniform blocks (Fig. 2B, right, p = 0.7, t-test). Together, these
results demonstrate that the animals
learned which option had a higher EV. 10
We hypothesized that the animals would learn faster from
reward sizes sampled from the
normal distribution compared to the uniform distribution. We
used a reinforcement learning (RL)
model to quantify the learned values (Supplementary Methods).
Our model, fit to the behavioral
choices, performed well at predicting the true values of the two
choice options (Fig 2C). To
differentiate active learning from stable, asymptotic-like
behavior, we fit a logarithmic function to 15
the estimated prediction errors (Fig. 2D). When the change in
the log-fitted values went below a
robust threshold, we considered the values to be learned
(Supplementary Methods). Using this
approach, we determined the block-wise number of trials needed
to learn. We found that, on
average, animals needed 4 additional trials to learn in the
uniform blocks, compared to the normal
blocks (Fig. 2E, p < 0.001, Mann-Whitney U test). These data
demonstrate that the monkeys 20
learned faster from normal reward distributions, compared to
15. uniform reward distribution. Thus,
rare prediction errors have greater behavioral relevance than
common prediction errors of the same
magnitude.
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Figure 2. Faster learning from normal reward
distributions. A. Animals made choices between two never-
before-seen fractal images with different expected values
(EVs), but the same distribution type (normal or uniform). B.
Box and whisker plots show the probability of choosing the 5
higher-valued option on trials 1 and 15, separated by normal
(red) and uniform (grey) blocks. Triangles represent the
averages. n.s. = not significantly different. C. Actual (blue) and
16. estimated (orange) value differences for two choice options.
The primary y-axis shows the EV differences between the two
10
choice options, and the x-axis shows trial number. The block-
wise changes in the true value difference are reflected by the
model. The black tick marks correspond to correct and
incorrect choices, defined by the relative expected values, and
indicated by the secondary y-axis.
D. Fitted prediction errors as a function of trial within normal
(red) and uniform (grey) blocks. 15
Dashed line indicated the threshold for designating that block
values have been learned. Dots
indicate the transition from the learning to the stable phase. E.
Box and whisker plot showing the
number of trials in the learning phase for normal and uniform
distributions. The plot follows the
same format as panel B.
20
To investigate autonomic responses to prediction errors, we
analyzed the pupil responses
during the choice task (Fig 3A). We used deconvolution to
separate the effects of distinct trial
17. events on pupil responses (Supplementary Methods). The
deconvolution analysis indicated that
the correlation between pupil responses and RPE was higher
during the learning phase of normal
blocks, compared to the learning phase of uniform blocks (Fig.
3B. p = 0.014, Wilcoxon rank-sum 25
test). There were no significant differences in the correlation
during the stable phases of normal
and uniform blocks (Fig. 3B, p = 0.78, Wilcoxon rank-sum test).
Thus, pupil responses indicated
greater arousal to rare prediction errors during learning.
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Figure 3. Pupil diameter was correlated with reward
prediction error during learning normal distributions. A.
Averaged normalized pupil diameter response (light grey) to
18. different epochs during behavior. B. Box and whisker plots 5
showing the correlation between pupil diameter and RPE, in
the learning and stable phases of normal (red) and uniform
(grey) blocks. The plot follows the
same format as Figure 2B.
Dopamine responses to rare rewards 10
After showing that probability distributions were reflected in
learning behavior and
autonomic responses, we investigated the neural correlates of
probability distributions. To do so,
we recorded extracellular dopamine neuron action potentials in
a passive viewing task wherein
monkeys viewed distribution-predicting cues and received
rewards (Supplementary Methods).
Dopamine neurons were similarly activated by both distribution
predicting cues, as would be 15
expected from responses to cues that had similar values (Fig.
4A). At the time of reward delivery,
dopamine neurons are activated or suppressed by rewards that
are better or worse than predicted,
respectively. Therefore, we expected dopamine neurons to be
activated by delivery of 0.6 ml and
19. suppressed by delivery of 0.2 ml. Surprisingly, dopamine
activations to delivery of 0.6 ml were
larger in normal distribution trials, compared to dopamine
activations following delivery of the 20
same volume reward in uniform distribution trials (Fig 4B, solid
lines). Similarly, dopamine
responses were more strongly suppressed by delivery of 0.2 ml
reward during normal distribution
trials, compared to response suppressions to the same reward
during uniform distribution trials
(Fig. 4B, dashed lines). As the reward predicting cues had the
same subjective values, this response
amplification occurred despite the fact that the conventional
reward prediction errors were 25
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10
identical (Eq. 1). Moreover, because the amplification was bi-
20. directional – both activations and
suppressions were amplified in single neurons – this effect
could not be attributed to differences
in predicted subjective values. Thus, the effects we observed
were robust to errors in the
measurement of subjective value.
We sought to quantify the response amplification (Fig. 4B)
across neurons. The dynamic 5
ranges of dopamine responses are larger in the positive domain,
compared to the negative domain.
Therefore, we transformed the responses across the positive and
negative domains onto a linear
scale (Supplementary Methods). We then used linear regression
to measure the response slopes of
each neuron to positive and negative RPEs generated during
normal and uniform distribution trials.
We plotted the response slopes for each neuron in both
conditions (Fig. 4C). A majority of neurons 10
had a steeper slope for rare RPEs generated during normal
distribution trials, compared to common
RPEs generated during uniform distribution trials (Fig. 4C
inset, p = 0.001, n = 60 neurons, one-
sample t-test). To ensure that the amplification effect was bi-
directional and not driven solely by
21. positive responses, we separately analyzed the positive and
negative prediction error responses for
the 39 neurons above the unity line in Figure 4C. Across this
subpopulation, both activations and 15
suppressions were significantly amplified (Fig. 4D, negative
and positive prediction error
responses p < 0.01 and 0.001, respectively, Wilcoxon rank-sum
test). There were no significant
differences in the positive or negative responses in the
subpopulation below the unity line (p >
0.05, n = 21, Wilcoxon rank-sum test). Population peri-
stimulus time histograms (PSTHs) for the
amplified subpopulation show differences in both the positive
and negative response domains (Fig. 20
5E). Thus, phasic dopamine responses are amplified by rare
RPEs. These data demonstrate that
RPE responses are sensitive to predicted probabi lity
distributions.
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22. 11
Figure 4. Amplified
dopamine reward
prediction error
responses to rare 5
rewards. A. Population
PSTHs of conditioned
stimuli (CS) responses to
the normal and uniform
distribution predicting 10
cues. There was no
significant difference
between the population
responses (p > 0.6 for
both the early and late response components, indicated on the x-
axis by the light and dark grey 15
23. bars, respectively). B. Single neuron reward responses from
both monkeys. Top: PSTHs show
impulse rate as a function of time, aligned to the time of reward
(black vertical bar). Light grey
bars along with x-axis indicates the response window used for
analysis. Bottom: Raster plots,
separated by normal and uniform distributions and by reward
sizes 0.2 and 0.6 ml. Every line
represents the time of an action potential, and every row
represents a trial. C. Reward response 20
slopes for every neuron in normal and uniform distribution
trials. Each dot represents one neuron
(n = 60 neurons), and the two circled neurons are the example
neurons in B. Inset shows the data
as a function of the difference from the unity line (diagonal). D.
Change in normalized impulse
rate from baseline in normal and uniform distribution trials, for
negative RPE responses (left), and
of the mean (SEM) across 39 25
neurons above the unity line. E. Population PSTH for neurons
that showed amplified responses to
the same rewards in the normal and uniform distribution trials.
In panels A, B, D, and E, red and
24. grey colors indicate normal and uniform distributions,
respectively. In A, B, and E, solid PSTHs
represent positive prediction error responses, and dashed PSTHs
represent negative prediction
error responses. 30
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The passive viewing task used to collect the neural data (Fig. 4)
offers the greatest level of
control, because during each trial only one reward-predictive
cue is shown to the animals and no
choices are required. To validate that the neuronal sensitivity to
probability distributions was also
present during complex behaviors, we recorded an additional 8
and 12 neurons from monkey B
and S, respectively, while the animals learned the predicted
values and made choices (Fig. 1C). 5
25. We used the trial-wise RPE’s derived from the model to
categorize each response as a positive or
negative prediction error response (Supplementary Methods).
We found that positive and negative
RPEs elicited amplified neural responses in normal distribution
trials, compared to uniform
distributions trials (Fig. 5A). The amplification effect was
significant in the population (Fig. 5B,
negative RPE: p = 0.019; positive RPE: p = 0.001, n = 20
neurons, Wilcoxon rank-sum test). These 10
findings confirm that dopamine responses are sensitive to
probability distributions during complex
behaviors and demonstrate that amplified dopamine responses
can be used to guide active learning
and decision making.
Figure 5. Amplification in dopamine neurons persistent in
learning conditions. A. Single neuron PSTHs aligned to 15
reward (black vertical line) show amplified responses to
positive and negative RPEs in normal distribution trials,
compared to uniform distribution trials. Solid PSTHs indicate
positive prediction error responses,
and dashed PSTHs are negative prediction error responses. The
26. light grey bar along the x-axis
indicates the response window used for analysis. B. Change in
normalized impulse rate from 20
baseline in normal and uniform conditions, for negative RPE
responses (left), and positive RPE
panels, red and grey colors indicate
normal and uniform distributions, respectively.
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Discussion
Here we show that dopamine-dependent learning behavior and
dopamine reward prediction
error responses reflect probability distributions. Rare
prediction errors – compared to commonly
occurring prediction errors of the same magnitude – evoke
faster learning, increased autonomic
27. arousal, and amplified neural learning signals. Together, these
data reveal a novel computational 5
paradigm for phasic dopamine responses that is distinct from,
but complementary to, conventional
reward prediction errors. Rare events are often highly
significant (17). Our data show decision
makers exhibit greater vigilance towards rare rewards and learn
more from rare reward prediction
errors. Amplified dopamine prediction error responses provide a
mechanistic account for these
behavioral effects. 10
More than 20 years ago, single unit recordings demonstrated the
importance of
unpredictability for dopamine neuron responses (2). Since then,
the successful application of TD
learning theory to dopamine signals has largely subsumed the
role of unpredictability, and recast
it within the framework of value-based prediction errors (18,
19). In most experimental paradigms,
reward unpredictability is captured by frequentist probability of
rewards and, therefore, 15
unpredictability is factored directly into expected value (5, 7,
20-22). The resulting Pascalian
28. expected values are learned by TD models (23) and reflected in
dopamine responses (5, 20, 22).
Here, we dissociated unpredictability from expected value by
pseudorandomly drawing rewards
from symmetric probability distributions with equal values.
Monkeys learned faster from the more
unpredictable rewards drawn from the tails of normal
distributions. Likewise, dopamine responses 20
were amplified by greater unpredictability, even when the
conventional prediction error described
in Eq. 1 was identical. These data reinforce the importance of
unpredictability for dopamine
responses and learning.
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The amplified dopamine responses to rare rewards suggest that
reinforcement learning
29. approaches that acquire only the average value of past outcomes
are insufficient to describe the
phasic activity of dopamine neurons. In fact, dopamine
responses distinguish between predicted
reward distributions, even when the average expected values
and average subjective values of
those distributions are identical. Consequently, monkeys and
their dopamine neurons appear to 5
learn probability distributions, and this information is used to
boost their performance and gain
more reward. Therefore, these data suggest that an updated RL
algorithm that learns higher
moments of probability distributions, such as Kalman TD (24),
will provide the proper conceptual
framework to explain information processing in dopamine
responses.
Distribution learning is critical to Bayesian inference and,
indeed, the results that we show 10
here are consistent with a signal that could guide Bayesian
inference to optimize choices and
maximize rewards. However, further experimentation is
required to understand whether dopamine
signals actually support Bayesian inference. Distribution
learning is also emerging as an important
30. approach in machine learning and artificial intelligence (AI)
(25). Biological learning signals have
inspired deep reinforcement learning algorithms with
performance that exceeds expert human 15
performance on Atari games, chess, and Go (26, 27). The
distribution-sensitive neural signals that
we observed here could offer further guidance to computational
learning; emphasizing the impact
of rare events drawn from tails of reward distribution could
restrict the necessary search space.
Dopamine reward prediction error signals participate in Hebbian
learning (28, 29), and
these signals are likely responsible for updating action values
stored in the striatum (30). The 20
amplified dopamine responses coupled with the faster learning
dynamics observed here, suggest
that the magnitude of dopamine release affected cellular
learning mechanisms in the striatum.
Moreover, amplified dopamine responses have the ability to
modulate dopamine concentrations in
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15
the prefrontal cortex (PFC). The level of PFC dopamine is
tightly linked to neuronal signaling and
working memory performance (31). Therefore, amplification of
dopamine could explain the
exaggerated salience of real – and possibly imagined – rare
events, and postulates a neural
mechanism to explain aberrant learning observed in mental
health disorders, such as
schizophrenia. 5
Conclusion:
Dopamine neurons code reward prediction errors (RPEs), which
are classically defined as
the differences between received and predicted rewards.
However, our data show that predicted
probability distributions, rather than just the predicted average
values, affects dopamine responses. 10
Specifically, rare rewards drawn from normal distributions
amplified dopamine responses,
32. compared to the same rewards drawn more frequently from
uniform distributions. Crucially, the
classically defined RPEs had identical magnitudes, following
rare and common rewards. From a
behavioral perspective, rare prediction errors often signal
underlying changes in the environment
and therefore demand greater vigilance. From a theoretical
perspective, this result demonstrates 15
that biologically inspired reinforcement learning algorithms
should account for full probability
distributions, rather than just point estimates.
20
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Acknowledgments: The authors would like to thank Andreea
Bostan for thoughtful comments 25
and discussion of the manuscript, and Jacquelyn Breter for her
work in animal care and
enrichment for this project. Funding: This research was
supported by University of Pittsburgh
Brain Institute (WRS), and NIH DP2MH113095 (WRS). Author
contributions: KMR and
WRS conceptualized and designed the experiments. KMR, AA,
and WRS collected the data.
KMR, TH, and WRS analyzed the data. All authors discussed
the results of the experiment. 30
39. KMR, TH, and WRS wrote the manuscript. All authors revised
the manuscript. Competing
interests: The authors declare no competing interests. Data and
materials availability:
Available upon request.
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Supplementary Materials:
Materials and Methods
Figures S1-S2
References
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bioRxiv preprint
https://doi.org/10.1101/851709Rare Rewards Amplify Dopamine
40. Learning ResponsesAbstract:Dopamine neurons drive learning
by coding reward prediction errors (RPEs), which are
formalized as subtractions of predicted values from reward
values. Subtractions accommodate point estimate predictions of
value, such as the average value. However, p...ResultsReward
size distributionsDopamine responses to rare
rewardsDiscussionSupplementary Materials: