Cornell Psych 1101: Introduction to Psychology PDF

Cornell University Psych 1101: Introduction to Psychology David Pizarro NOBA Copyright Copyright © 2023 by Diener Education Fund. This material is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en_US. The Internet addresses listed in the text were accurate at the time of publication. The inclusion of a Website does not indicate an endorsement by the authors or the Diener Education Fund, and the Diener Education Fund does not guarantee the accuracy of the information presented at these sites. Contact Information: Noba Project www.nobaproject.com [email protected] Contents About Noba & Acknowledgements 5 1. Psychology as a Science 6 1 Why Science? 7 Edward Diener 2 Thinking like a Psychological Scientist 18 Erin I. Smith 2. The Brain 40 3 The Nervous System 41 Aneeq Ahmad 4 The Brain 61 Diane Beck & Evelina Tapia 3. Sensation and Perception, and Categorization 78 5 Sensation and Perception 79 Adam John Privitera 4. Sleep and Other States of Consciousness 104 6 States of Consciousness 105 Robert Biswas-Diener & Jake Teeny 5. Attention and Memory 126 7 Attention 127 Frances Friedrich 8 Failures of Awareness: The Case of Inattentional Blindness 142 Daniel Simons 9 Memory (Encoding, Storage, Retrieval) 154 Kathleen B. McDermott & Henry L. Roediger 10 Eyewitness Testimony and Memory Biases 178 Cara Laney & Elizabeth F. Loftus 6. Learning 192 11 Conditioning and Learning 193 Mark E. Bouton 7. Developmental Psychology 219 12 Cognitive Development in Childhood 220 Robert Siegler 8. Emotions 235 13 Functions of Emotions 236 Hyisung Hwang & David Matsumoto 14 Culture and Emotion 251 Jeanne Tsai 15 Mood Disorders 274 Anda Gershon & Renee Thompson 9. Morality 297 16 Cooperation 298 Jake P. Moskowitz & Paul K. Piff 17 Helping and Prosocial Behavior 319 Dennis L. Poepsel & David A. Schroeder 18 Psychopathy 336 Chris Patrick 10. Human Judgment (and its Pitfalls) 359 19 Judgment and Decision Making 360 Max H. Bazerman 20 Persuasion: So Easily Fooled 375 Robert V. Levine 11. Language and Intelligence 396 21 Language and Language Use 397 Yoshihisa Kashima 22 Intelligence 412 Robert Biswas-Diener 23 The Nature-Nurture Question 427 Eric Turkheimer 12. Personality 440 24 Personality Traits 441 Edward Diener & Richard E. Lucas 25 Personality Assessment 459 David Watson 26 Personality Stability and Change 479 M. Brent Donnellan 13. Social Psychology 498 27 Social Cognition and Attitudes 499 Yanine D. Hess & Cynthia L. Pickett 28 Conformity and Obedience 520 Jerry M. Burger 29 Prejudice, Discrimination, and Stereotyping 534 Susan T. Fiske 30 Theory of Mind 550 Bertram Malle 14. Happiness 569 31 Happiness: The Science of Subjective Well-Being 570 Edward Diener 32 Optimal Levels of Happiness 587 Shigehiro Oishi Index 598 About Noba The Diener Education Fund (DEF) is a non-profit organization founded with the mission of re- inventing higher education to serve the changing needs of students and professors. The initial focus of the DEF is on making information, especially of the type found in textbooks, widely available to people of all backgrounds. This mission is embodied in the Noba project. Noba is an open and free online platform that provides high-quality, flexibly structured textbooks and educational materials. The goals of Noba are three-fold: To reduce financial burden on students by providing access to free educational content To provide instructors with a platform to customize educational content to better suit their curriculum To present material written by a collection of experts and authorities in the field The Diener Education Fund was co-founded by Drs. Ed and Carol Diener. Ed was a professor emeritus at the University of Illinois, Urbana Champaign, and a professor at University of Virginia and the University of Utah, and a senior scientist at the Gallup Organization but passed away in April 2021. For more information, please see http://noba.to/78vdj2x5. Carol Diener is the former director of the Mental Health Worker and the Juvenile Justice Programs at the University of Illinois. Both Ed and Carol are award- winning university teachers. Acknowledgements The Diener Education Fund would like to acknowledge the following individuals and companies for their contribution to the Noba Project: Robert Biswas-Diener as Managing Editor, Peter Lindberg as the former Operations Manager, and Nadezhda Lyubchik as the current Operations Manager; The Other Firm for user experience design and web development; Sockeye Creative for their work on brand and identity development; Arthur Mount for illustrations; Chad Hurst for photography; EEI Communications for manuscript proofreading; Marissa Diener, Shigehiro Oishi, Daniel Simons, Robert Levine, Lorin Lachs and Thomas Sander for their feedback and suggestions in the early stages of the project. 1. Psychology as a Science 1 Why Science? Edward Diener Scientific research has been one of the great drivers of progress in human history, and the dramatic changes we have seen during the past century are due primarily to scientific findings —modern medicine, electronics, automobiles and jets, birth control, and a host of other helpful inventions. Psychologists believe that scientific methods can be used in the behavioral domain to understand and improve the world. Although psychology trails the biological and physical sciences in terms of progress, we are optimistic based on discoveries to date that scientific psychology will make many important discoveries that can benefit humanity. This module outlines the characteristics of the science, and the promises it holds for understanding behavior. The ethics that guide psychological research are briefly described. It concludes with the reasons you should learn about scientific psychology Learning Objectives Describe how scientific research has changed the world. Describe the key characteristics of the scientific approach. Discuss a few of the benefits, as well as problems that have been created by science. Describe several ways that psychological science has improved the world. Describe a number of the ethical guidelines that psychologists follow. Scientific Advances and World Progress There are many people who have made positive contributions to humanity in modern times. Why Science? 7 Take a careful look at the names on the following list. Which of these individuals do you think has helped humanity the most? 1. Mother Teresa 2. Albert Schweitzer 3. Edward Jenner 4. Norman Borlaug 5. Fritz Haber The usual response to this question is “Who on earth are Jenner, Borlaug, and Haber?” Many people know that Mother Teresa helped thousands of people living in the slums of Kolkata (Calcutta). Others recall that Albert Schweitzer opened his famous hospital in Africa and went on to earn the Nobel Peace Prize. The other three historical figures, on the other hand, are far less well known. Jenner, Borlaug, and Haber were scientists whose research discoveries saved millions, and even billions, of lives. Dr. Edward Jenner is often considered the “father of immunology” because he was among the first to conceive of and test vaccinations. His pioneering work led directly to the eradication of smallpox. Many other diseases have been greatly reduced because of vaccines discovered using science—measles, pertussis, diphtheria, tetanus, typhoid, cholera, polio, hepatitis—and all are the legacy of Jenner. Fritz Haber and Norman Borlaug saved more than a billion human lives. They created the “Green Revolution” by producing hybrid agricultural crops and synthetic fertilizer. Humanity can now produce food for the seven billion people on the planet, and the starvation that does occur is related to political and economic factors rather than our collective ability to produce food. If you examine major social and Due to the breakthrough work of Dr. Edward Jenner, millions of technological changes over the past vaccinations are now administered around the world every year preventing the spread of many treatable diseases while saving the century most of them can be directly lives of people of all ages. [Image: CDC Global Health, https://goo. attributed to science. The world in 1914 gl/hokiWz, CC BY 2.0, https://goo.gl/9uSnqN] was very different than the one we see Why Science? 8 today (Easterbrook, 2003). There were few cars and most people traveled by foot, horseback, or carriage. There were no radios, televisions, birth control pills, artificial hearts or antibiotics. Only a small portion of the world had telephones, refrigeration or electricity. These days we find that 80% of all households have television and 84% have electricity. It is estimated that three quarters of the world’s population has access to a mobile phone! Life expectancy was 47 years in 1900 and 79 years in 2010. The percentage of hungry and malnourished people in the world has dropped substantially across the globe. Even average levels of I.Q. have risen dramatically over the past century due to better nutrition and schooling. All of these medical advances and technological innovations are the direct result of scientific research and understanding. In the modern age it is easy to grow complacent about the advances of science but make no mistake about it—science has made fantastic discoveries, and continues to do so. These discoveries have completely changed our world. What Is Science? What is this process we call “science,” which has so dramatically changed the world? Ancient people were more likely to believe in magical and supernatural explanations for natural phenomena such as solar eclipses or thunderstorms. By contrast, scientifically minded people try to figure out the natural world through testing and observation. Specifically, science is the use of systematic observation in order to acquire knowledge. For example, children in a science class might combine vinegar and baking soda to observe the bubbly chemical reaction. These empirical methods are wonderful ways to learn about the physical and biological world. Science is not magic—it will not solve all human problems, and might not answer all our questions about behavior. Nevertheless, it appears to be the most powerful method we have for acquiring knowledge about the observable world. The essential elements of science are as follows: 1. Systematic observation is the core of science. Scientists observe the world, in a very organized way. We often measure the phenomenon we are observing. We record our observations so that memory biases are less likely to enter in to our conclusions. We are systematic in that we try to observe under controlled conditions, and also systematically vary the conditions of our observations so that we can see variations in the phenomena and understand when they occur and do not occur. 2. Observation leads to hypotheses we can test. When we develop hypothesesand theories, we state them in a way that can be tested. For example, you might make the claim that candles made of paraffin wax burn more slowly than do candles of the exact same size and shape made from bee’s wax. This claim can be readily tested by timing the burning speed of Why Science? 9 candles made from these materials. 3. Science is democratic. People in ancient times may have been willing to accept the views of their kings or pharaohs as absolute truth. These days, however, people are more likely to want to be able to form their own opinions and debate conclusions. Scientists are skeptical and have open discussions about their observations and theories. These debates often occur as scientists publish competing findings with the idea that the best data will win the argument. 4. Science is cumulative. We can learn the Systematic observation is the core of science. [Image: Cvl important truths discovered by earlier Neuro, https://goo.gl/Avbju7, CC BY-SA 3.0, https://goo.gl/ scientists and build on them. Any physics uhHola] student today knows more about physics than Sir Isaac Newton did even though Newton was possibly the most brilliant physicist of all time. A crucial aspect of scientific progress is that after we learn of earlier advances, we can build upon them and move farther along the path of knowledge. Psychology as a Science Even in modern times many people are skeptical that psychology is really a science. To some degree this doubt stems from the fact that many psychological phenomena such as depression, intelligence, and prejudice do not seem to be directly observable in the same way that we can observe the changes in ocean tides or the speed of light. Because thoughts and feelings are invisible many early psychological researchers chose to focus on behavior. You might have noticed that some people act in a friendly and outgoing way while others appear to be shy and withdrawn. If you have made these types of observations then you are acting just like early psychologists who used behavior to draw inferences about various types of personality. By using behavioral measures and rating scales it is possible to measure thoughts and feelings. This is similar to how other researchers explore “invisible” phenomena such as the way that educators measure academic performance or economists measure quality of life. One important pioneering researcher was Francis Galton, a cousin of Charles Darwin who lived in England during the late 1800s. Galton used patches of color to test people’s ability to distinguish between them. He also invented the self-report questionnaire, in which people Why Science? 10 offered their own expressed judgments or opinions on various matters. Galton was able to use self-reports to examine—among other things—people’s differing ability to accurately judge distances. Although he lacked a modern understanding of genetics Galton also had the idea that scientists could look at the behaviors of identical and fraternal twins to estimate the degree to which genetic and social factors contribute to personality; a puzzling issue we currently refer to as the “nature-nurture question.” In modern times psychology has become more sophisticated. Researchers now use better measures, more sophisticated study designs and better statistical analyses to explore human nature. Simply take the example of studying the emotion of happiness. How would you go In 1875 Francis Galton did pioneering studies of twins to determine about studying happiness? One straight how much the similarities and differences in twins were affected forward method is to simply ask people by their life experiences. In the course of this work he coined the phrase "Nature versus Nurture". [Image: XT Inc., https://goo.gl/ about their happiness and to have them F1Wvu7, CC BY-NC-SA 2.0, https://goo.gl/Toc0ZF] use a numbered scale to indicate their feelings. There are, of course, several problems with this. People might lie about their happiness, might not be able to accurately report on their own happiness, or might not use the numerical scale in the same way. With these limitations in mind modern psychologists employ a wide range of methods to assess happiness. They use, for instance, “peer report measures” in which they ask close friends and family members about the happiness of a target individual. Researchers can then compare these ratings to the self-report ratings and check for discrepancies. Researchers also use memory measures, with the idea that dispositionally positive people have an easier time recalling pleasant events and negative people have an easier time recalling unpleasant events. Modern psychologists even use biological measures such as saliva cortisol samples (cortisol is a stress related hormone) or fMRI images of brain activation (the left pre-frontal cortex is one area of brain activity associated with good moods). Despite our various methodological advances it is true that psychology is still a very young science. While physics and chemistry are hundreds of years old psychology is barely a hundred Why Science? 11 and fifty years old and most of our major findings have occurred only in the last 60 years. There are legitimate limits to psychological science but it is a science nonetheless. Psychological Science is Useful Psychological science is useful for creating interventions that help people live better lives. A growing body of research is concerned with determining which therapies are the most and least effective for the treatment of psychological disorders. For example, many studies have shown that cognitive behavioral therapy can help many people suffering from depression and anxiety disorders (Butler, Chapman, Forman, & Beck, 2006; Hoffman & Smits, 2008). In contrast, research reveals that some types of therapies actually might be harmful on average (Lilienfeld, 2007). In organizational psychology, a number of psychological interventions have been found by researchers to produce greater productivity and satisfaction in the workplace (e.g., Guzzo, Jette, & Katzell, 1985). Human factor engineers have greatly increased the Cognitive Behavioral Therapy has shown to be effective in safety and utility of the products we use. For treating a variety of conditions, including depression. [Image: example, the human factors psychologist SalFalco, https://goo.gl/3knLoJ, CC BY-NC 2.0, https://goo.gl/ Alphonse Chapanis and other researchers HEXbAA] redesigned the cockpit controls of aircraft to make them less confusing and easier to respond to, and this led to a decrease in pilot errors and crashes. Forensic sciences have made courtroom decisions more valid. We all know of the famous cases of imprisoned persons who have been exonerated because of DNA evidence. Equally dramatic cases hinge on psychological findings. For instance, psychologist Elizabeth Loftus has conducted research demonstrating the limits and unreliability of eyewitness testimony and memory. Thus, psychological findings are having practical importance in the world outside the laboratory. Psychological science has experienced enough success to demonstrate that it works, but there remains a huge amount yet to be learned. Why Science? 12 Ethics of Scientific Psychology Psychology differs somewhat from the natural sciences such as chemistry in that researchers conduct studies with human research participants. Because of this there is a natural tendency to want to guard research participants against potential psychological harm. For example, it might be interesting to see how people handle ridicule but it might not be advisable to ridicule research participants. Scientific psychologists follow a specific set of guidelines for research known as a code of ethics. There are extensive ethical guidelines for how human participants should be treated in psychological research (Diener & Crandall, 1978; Sales & Folkman, 2000). Following are a few Diagram of the Milgram Experiment in which the highlights: "teacher" (T) was asked to deliver a (supposedly) painful electric shock to the "learner"(L). Would this 1. Informed consent. In general, people should know experiment be approved by a review board today? [Image: Fred the Oyster, https://goo.gl/ZIbQz1, CC when they are involved in research, and BY-SA 4.0, https://goo.gl/X3i0tq] understand what will happen to them during the study. They should then be given a free choice as to whether to participate. 2. Confidentiality. Information that researchers learn about individual participants should not be made public without the consent of the individual. 3. Privacy. Researchers should not make observations of people in private places such as their bedrooms without their knowledge and consent. Researchers should not seek confidential information from others, such as school authorities, without consent of the participant or his or her guardian. 4. Benefits. Researchers should consider the benefits of their proposed research and weigh these against potential risks to the participants. People who participate in psychological studies should be exposed to risk only if they fully understand these risks and only if the likely benefits clearly outweigh the risks. 5. Deception. Some researchers need to deceive participants in order to hide the true nature of the study. This is typically done to prevent participants from modifying their behavior Why Science? 13 in unnatural ways. Researchers are required to “debrief” their participants after they have completed the study. Debriefing is an opportunity to educate participants about the true nature of the study. Why Learn About Scientific Psychology? I once had a psychology professor who asked my class why we were taking a psychology course. Our responses give the range of reasons that people want to learn about psychology: 1. To understand ourselves 2. To understand other people and groups 3. To be better able to influence others, for example, in socializing children or motivating employees 4. To learn how to better help others and improve the world, for example, by doing effective psychotherapy 5. To learn a skill that will lead to a profession such as being a social worker or a professor 6. To learn how to evaluate the research claims you hear or read about 7. Because it is interesting, challenging, and fun! People want to learn about psychology because this is exciting in itself, regardless of other positive outcomes it might have. Why do we see movies? Because they are fun and exciting, and we need no other reason. Thus, one good reason to study psychology is that it can be rewarding in itself. Conclusions The science of psychology is an exciting adventure. Whether you will become a scientific psychologist, an applied psychologist, or an educated person who knows about psychological research, this field can influence your life and provide fun, rewards, and understanding. My hope is that you learn a lot from the modules in this e-text, and also that you enjoy the experience! I love learning about psychology and neuroscience, and hope you will too! Why Science? 14 Outside Resources Web: Science Heroes- A celebration of people who have made lifesaving discoveries. http://www.scienceheroes.com/index.php?option=com_content&view=article&id=258&Itemid=27 Discussion Questions 1. Some claim that science has done more harm than good. What do you think? 2. Humanity is faced with many challenges and problems. Which of these are due to human behavior, and which are external to human actions? 3. If you were a research psychologist, what phenomena or behaviors would most interest you? 4. Will psychological scientists be able to help with the current challenges humanity faces, such as global warming, war, inequality, and mental illness? 5. What can science study and what is outside the realm of science? What questions are impossible for scientists to study? 6. Some claim that science will replace religion by providing sound knowledge instead of myths to explain the world. They claim that science is a much more reliable source of solutions to problems such as disease than is religion. What do you think? Will science replace religion, and should it? 7. Are there human behaviors that should not be studied? Are some things so sacred or dangerous that we should not study them? Why Science? 15 Vocabulary Empirical methods Approaches to inquiry that are tied to actual measurement and observation. Ethics Professional guidelines that offer researchers a template for making decisions that protect research participants from potential harm and that help steer scientists away from conflicts of interest or other situations that might compromise the integrity of their research. Hypotheses A logical idea that can be tested. Systematic observation The careful observation of the natural world with the aim of better understanding it. Observations provide the basic data that allow scientists to track, tally, or otherwise organize information about the natural world. Theories Groups of closely related phenomena or observations. Why Science? 16 References Butler, A. C., Chapman, J. E., Forman, E. M., & Beck, A. T. (2006). The empirical status of cognitive- behavioral therapy: A review of meta-analyses. Clinical Psychology Review, 26, 17–31. Diener, E., & Crandall, R. (1978). Ethics in social and behavioral research. Chicago, IL: University of Chicago Press. Easterbrook, G. (2003). The progress paradox. New York, NY: Random House. Guzzo, R. A., Jette, R. D., & Katzell, R. A. (1985). The effects of psychologically based intervention programs on worker productivity: A meta-analysis. Personnel Psychology, 38, 275.291. Hoffman, S. G., & Smits, J. A. J. (2008). Cognitive-behavioral therapy for adult anxiety disorders. Journal of Clinical Psychiatry, 69, 621–32. Lilienfeld, S. O. (2007). Psychological treatments that cause harm. Perspectives on Psychological Science, 2, 53–70. Moore, D. (2003). Public lukewarm on animal rights. Gallup News Service, May 21. http://www.gallup.com/poll/8461/public-lukewarm-animal-rights.aspx Sales, B. D., & Folkman, S. (Eds.). (2000). Ethics in research with human participants. Washington, DC: American Psychological Association. 2 Thinking like a Psychological Scientist Erin I. Smith We are bombarded every day with claims about how the world works, claims that have a direct impact on how we think about and solve problems in society and our personal lives. This module explores important considerations for evaluating the trustworthiness of such claims by contrasting between scientific thinking and everyday observations (also known as “anecdotal evidence”). Learning Objectives Compare and contrast conclusions based on scientific and everyday inductive reasoning. Understand why scientific conclusions and theories are trustworthy, even if they are not able to be proven. Articulate what it means to think like a psychological scientist, considering qualities of good scientific explanations and theories. Discuss science as a social activity, comparing and contrasting facts and values. Introduction Why are some people so much happier than others? Is it harmful for children to have imaginary companions? How might students study more effectively? Even if you’ve never considered these questions before, you probably have some guesses about their answers. Maybe you think getting rich or falling in love leads to happiness. Perhaps Thinking like a Psychological Scientist 18 you view imaginary friends as expressions of a dangerous lack of realism. What’s more, if you were to ask your friends, they would probably also have opinions about these questions—opinions that may even differ from your own. A quick internet search would yield even more answers. We live in the “Information Age,” with people having access to more explanations and answers than at any other time in history. But, although the quantity of information is continually increasing, it’s always good practice to Today, people are overwhelmed with information although it consider the quality of what you read or varies in quality. [Image: Mark Smiciklas, https://goo.gl/TnZCoH, watch: Not all information is equally CC BY-NC 2.0, https://goo.gl/AGYuo9] trustworthy. The trustworthiness of information is especially important in an era when “fake news,” urban myths, misleading “click-bait,” and conspiracy theories compete for our attention alongside well-informed conclusions grounded in evidence. Determining what information is well-informed is a crucial concern and a central task of science. Science is a way of using observable data to help explain and understand the world around us in a trustworthy way. In this module, you will learn about scientific thinking. You will come to understand how scientific research informs our knowledge and helps us create theories. You will also come to appreciate how scientific reasoning is different from the types of reasoning people often use to form personal opinions. Scientific Versus Everyday Reasoning Each day, people offer statements as if they are facts, such as, “It looks like rain today,” or, “Dogs are very loyal.” These conclusions represent hypotheses about the world: best guesses as to how the world works. Scientists also draw conclusions, claiming things like, “There is an 80% chance of rain today,” or, “Dogs tend to protect their human companions.” You’ll notice that the two examples of scientific claims use less certain language and are more likely to be associated with probabilities. Understanding the similarities and differences between scientific and everyday (non-scientific) statements is essential to our ability to accurately Thinking like a Psychological Scientist 19 evaluate the trustworthiness of various claims. Scientific and everyday reasoning both employ induction: drawing general conclusions from specific observations. For example, a person’s opinion that cramming for a test increases performance may be based on her memory of passing an exam after pulling an all-night study session. Similarly, a researcher’s conclusion against cramming might be based on studies comparing the test performances of people who studied the material in different ways (e.g., cramming versus study sessions spaced out over time). In these scenarios, both scientific and everyday conclusions are drawn from a limited sample of potential observations. The process of induction, alone, does not seem suitable enough to provide trustworthy information—given the contradictory results. What should a student who wants to perform well on exams do? One source of information encourages her to cram, while another suggests that spacing out her studying time is the best strategy. To make the best decision with the information at hand, we need to appreciate the differences between personal opinions and scientific statements, which requires an understanding of science and the nature of scientific reasoning. Table 1. Features of good scientific theories (Kuhn, 2011) Thinking like a Psychological Scientist 20 There are generally agreed-upon features that distinguish scientific thinking—and the theories and data generated by it—from everyday thinking. A short list of some of the commonly cited features of scientific theories and data is shown in Table 1. One additional feature of modern science not included in this list but prevalent in scientists’ thinking and theorizing is falsifiability, a feature that has so permeated scientific practice that it warrants additional clarification. In the early 20th century, Karl Popper (1902-1994) suggested that science can be distinguished from pseudoscience (or just everyday reasoning) because scientific claims are capable of being falsified. That is, a claim can be conceivably demonstrated to be untrue. For example, a person might claim that “all people are right handed.” This claim can be tested and—ultimately—thrown out because it can be shown to be false: There are people who are left-handed. An easy rule of thumb is to not get confused by the term “falsifiable” but to understand that—more or less—it means testable. On the other hand, some claims cannot be tested and falsified. Imagine, for instance, that a magician claims that he can teach people to move objects with their minds. The trick, he explains, is to truly believe in one’s ability for it to work. When his students fail to budge chairs with their minds, the magician scolds, “Obviously, you don’t truly believe.” The magician’s claim does not qualify as falsifiable because there is no way to disprove it. It is unscientific. Popper was particularly irritated about nonscientific claims because he believed they were a threat to the science of psychology. Specifically, he was dissatisfied with Freud’s explanations for mental illness. Freud believed that when a person suffers a mental illness it is often due to problems stemming from childhood. For instance, imagine a person who grows up to be an obsessive perfectionist. If she were raised by messy, relaxed parents, Freud might argue that her adult perfectionism is a reaction to her early family experiences—an effort to maintain order and routine instead of chaos. Alternatively, imagine the same person being raised by harsh, orderly parents. In this case, Freud might argue that her adult tidiness is simply her internalizing her parents’ way of being. As you can see, according to Freud’s rationale, both opposing scenarios are possible; no matter what the disorder, Freud’s theory could explain its childhood origin—thus failing to meet the principle of falsifiability. Popper argued against statements that could not be falsified. He claimed that they blocked scientific progress: There was no way to advance, refine, or refute knowledge based on such claims. Popper’s solution was a powerful one: If science showed all the possibilities that were not true, we would be left only with what is true. That is, we need to be able to articulate— beforehand—the kinds of evidence that will disprove our hypothesis and cause us to abandon it. Thinking like a Psychological Scientist 21 This may seem counterintuitive. For example, if a scientist wanted to establish a comprehensive understanding of why car accidents happen, she would systematically test all potential causes: alcohol consumption, speeding, using a cell phone, fiddling with the radio, wearing sandals, eating, chatting with a passenger, etc. A complete understanding could only be achieved once all possible explanations were explored and either falsified or not. After all the testing was concluded, the evidence would be evaluated against the criteria for falsification, and only the real Karl Popper was an influential thinker regarding scientific theory causes of accidents would remain. The and reasoning. [Image: Lucinda Douglas-Menzies, https://goo.gl/ scientist could dismiss certain claims (e.g., uuqxCe] sandals lead to car accidents) and keep only those supported by research (e.g., using a mobile phone while driving increases risk). It might seem absurd that a scientist would need to investigate so many alternative explanations, but it is exactly how we rule out bad claims. Of course, many explanations are complicated and involve multiple causes—as with car accidents, as well as psychological phenomena. ------------------------------------------------------------------------------------------------------------------------------------------------------ Test Yourself 1: Can It Be Falsified? Which of the following hypotheses can be falsified? For each, be sure to consider what kind of data could be collected to demonstrate that a statement is not true. A. Chocolate tastes better than pasta. B. We live in the most violent time in history. C. Time can run backward as well as forward. D. There are no planets other than Earth that have water on them. Thinking like a Psychological Scientist 22 [See answer at end of this module] ------------------------------------------------------------------------------------------------------------------------------------------------------ Although the idea of falsification remains central to scientific data and theory development, these days it’s not used strictly the way Popper originally envisioned it. To begin with, scientists aren’t solely interested in demonstrating what isn’t. Scientists are also interested in providing descriptions and explanations for the way things are. We want to describe different causes and the various conditions under which they occur. We want to discover when young children start speaking in complete sentences, for example, or whether people are happier on the weekend, or how exercise impacts depression. These explorations require us to draw conclusions from limited samples of data. In some cases, these data seem to fit with our hypotheses and in others they do not. This is where interpretation and probability come in. The Interpretation of Research Results Imagine a researcher wanting to examine the hypothesis—a specific prediction based on previous research or scientific theory—that caffeine enhances memory. She knows there are several published studies that suggest this might be the case, and she wants to further explore the possibility. She designs an experiment to test this hypothesis. She randomly assigns some participants a cup of fully caffeinated tea and some a cup of herbal tea. All the participants are instructed to drink up, study a list of words, then complete a memory test. There are three possible outcomes of this proposed study: 1. The caffeine group performs better (support for the hypothesis). 2. The no-caffeine group performs better (evidence against the hypothesis). 3. There is no difference in the performance between the two groups (also evidence against Thinking like a Psychological Scientist 23 the hypothesis). Let’s look, from a scientific point of view, at how the researcher should interpret each of these three possibilities. First, if the results of the memory test reveal that the caffeine group performs better, this is a piece of evidence in favor of the hypothesis: It appears, at least in this case, that caffeine is associated with better memory. It does not, however, prove that caffeine is associated with better memory. There are still many questions left unanswered. How long does the memory boost last? Does caffeine work the same way with people of all ages? Is there a difference in memory performance between people who drink caffeine regularly and those who never drink it? Could the results be a freak occurrence? Because of these uncertainties, we do not say that a study—especially a single study—proves a hypothesis. Instead, we say the results of the study offer evidence in support of the hypothesis. Even if we tested this across 10 thousand or 100 thousand people we still could not use the word “proven” to describe this phenomenon. This is because inductive reasoning is based on probabilities. Probabilities are always a matter of degree; they may be extremely likely or unlikely. Science is better at shedding light on the likelihood—or probability—of something than at proving it. In this way, data is still highly useful even if it doesn’t fit Popper’s absolute standards. The science of meteorology helps illustrate this point. You might look at your local weather forecast and see a high likelihood of rain. This is because the meteorologist has used inductive reasoning to create her forecast. She has taken current observations—lots of dense clouds coming toward your city—and compared them to historical weather patterns associated with rain, making a reasonable prediction of a high probability of rain. The meteorologist has not proven it will rain, however, by pointing out the oncoming clouds. Proof is more associated with deductive reasoning. Deductive reasoning starts with general principles that are applied to specific instances (the reverse of inductive reasoning). When the general principles, or premises, are true, and the structure of the argument is valid, the conclusion is, by definition, proven; it must be so. A deductive truth must apply in all relevant circumstances. For example, all living cells contain DNA. From this, you can reason— deductively—that any specific living cell (of an elephant, or a person, or a snake) will therefore contain DNA. Given the complexity of psychological phenomena, which involve many contributing factors, it is nearly impossible to make these types of broad statements with certainty. ------------------------------------------------------------------------------------------------------------------------------------------------------ Thinking like a Psychological Scientist 24 Test Yourself 2: Inductive or Deductive? A. The stove was on and the water in the pot was boiling over. The front door was standing open. These clues suggest the homeowner left unexpectedly and in a hurry. B. Gravity is associated with mass. Because the moon has a smaller mass than the Earth, it should have weaker gravity. C. Students don’t like to pay for high priced textbooks. It is likely that many students in the class will opt not to purchase a book. D. To earn a college degree, students need 100 credits. Janine has 85 credits, so she cannot graduate. [See answer at end of this module] ------------------------------------------------------------------------------------------------------------------------------------------------------ The second possible result from the caffeine-memory study is that the group who had no caffeine demonstrates better memory. This result is the opposite of what the researcher expects to find (her hypothesis). Here, the researcher must admit the evidence does not support her hypothesis. She must be careful, however, not to extend that interpretation to other claims. For example, finding increased memory in the no-caffeine group would not be evidence that caffeine harms memory. Again, there are too many unknowns. Is this finding a freak occurrence, perhaps based on an unusual sample? Is there a problem with the design of the study? The researcher doesn’t know. She simply knows that she was not able to observe support for her hypothesis. There is at least one additional consideration: The researcher originally developed her caffeine- benefits-memory hypothesis based on conclusions drawn from previous research. That is, previous studies found results that suggested caffeine boosts memory. The researcher’s single study should not outweigh the conclusions of many studies. Perhaps the earlier research employed participants of different ages or who had different baseline levels of caffeine intake. This new study simply becomes a piece of fabric in the overall quilt of studies of the caffeine- memory relationship. It does not, on its own, definitively falsify the hypothesis. Finally, it’s possible that the results show no difference in memory between the two groups. How should the researcher interpret this? How would you? In this case, the researcher once Thinking like a Psychological Scientist 25 again has to admit that she has not found support for her hypothesis. Interpreting the results of a study—regardless of outcome—rests on the quality of the observations from which those results are drawn. If you learn, say, that each group in a study included only four participants, or that they were all over 90 years old, you might have concerns. Specifically, you should be concerned that the observations, even if accurate, aren’t representative of the general population. This is one of the defining differences between conclusions drawn from personal anecdotes and those drawn from scientific observations. Anecdotal evidence—derived from personal experience and unsystematic observations (e. g., “common sense,”)—is limited by the quality and representativeness of observations, and by memory shortcomings. Well-designed research, on the other hand, relies on observations that are systematically recorded, of high quality, and representative of the population it claims to describe. Why Should I Trust Science If It Can’t Prove Anything? It’s worth delving a bit deeper into why we ought to trust the scientific inductive process, even when it relies on limited samples that don’t offer absolute “proof.” To do this, let’s examine a widespread practice in psychological science: null-hypothesis significance testing. To understand this concept, let’s begin with another research example. Imagine, for instance, a researcher is curious about the ways maturity affects academic performance. She might have a hypothesis that mature students are more likely to be responsible about studying and completing homework and, therefore, will do better in their courses. To test this hypothesis, the researcher needs a measure of maturity and a measure of course performance. She might calculate the correlation—or relationship—between student age (her measure of maturity) and points earned in a course (her measure of academic Is there a relationship between student age and academic performance). Ultimately, the researcher is performance? How could we research this question? How interested in the likelihood—or probability— confident can we be that our observations reflect that these two variables closely relate to one reality? [Image: Jeremy Wilburn, https://goo.gl/i9MoJb, CC BY- another. Null-hypothesis significance testing NC-ND 2.0, https://goo.gl/SjTsDg] Thinking like a Psychological Scientist 26 (NHST) assesses the probability that the collected data (the observations) would be the same if there were no relationship between the variables in the study. Using our example, the NHST would test the probability that the researcher would find a link between age and class performance if there were, in reality, no such link. Now, here’s where it gets a little complicated. NHST involves a null hypothesis, a statement that two variables are not related (in this case, that student maturity and academic performance are not related in any meaningful way). NHST also involves an alternative hypothesis, a statement that two variables are related (in this case, that student maturity and academic performance go together). To evaluate these two hypotheses, the researcher collects data. The researcher then compares what she expects to find (probability) with what she actually finds (the collected data) to determine whether she can falsify, or reject, the null hypothesis in favor of the alternative hypothesis. How does she do this? By looking at the distribution of the data. The distribution is the spread of values—in our example, the numeric values of students’ scores in the course. The researcher will test her hypothesis by comparing the observed distribution of grades earned by older students to those earned by younger students, recognizing that some distributions are more or less likely. Your intuition tells you, for example, that the chances of every single person in the course getting a perfect score are lower than their scores being distributed across all levels of performance. The researcher can use a probability table to assess the likelihood of any distribution she finds in her class. These tables reflect the work, over the past 200 years, of mathematicians and scientists from a variety of fields. You can see, in Table 2a, an example of an expected distribution if the grades were normally distributed (most are average, and relatively few are amazing or terrible). In Table 2b, you can see possible results of this imaginary study, and can clearly see how they differ from the expected distribution. In the process of testing these hypotheses, there are four possible outcomes. These are determined by two factors: 1) reality, and 2) what the researcher finds (see Table 3). The best possible outcome is accurate detection. This means that the researcher’s conclusion mirrors reality. In our example, let’s pretend the more mature students do perform slightly better. If this is what the researcher finds in her data, her analysis qualifies as an accurate detection of reality. Another form of accurate detection is when a researcher finds no evidence for a phenomenon, but that phenomenon doesn’t actually exist anyway! Using this same example, let’s now pretend that maturity has nothing to do with academic performance. Perhaps academic performance is instead related to intelligence or study habits. If the researcher finds no evidence for a link between maturity and grades and none actually exists, she will have Thinking like a Psychological Scientist 27 also achieved accurate detection. Table 2a (Above): Expected grades if there were no difference between the two groups. Table 2b (Below): Course grades by age There are a couple of ways that research conclusions might be wrong. One is referred to as a type I error—when the researcher concludes there is a relationship between two variables but, in reality, there is not. Back to our example: Let’s now pretend there’s no relationship between maturity and grades, but the researcher still finds one. Why does this happen? It may be that her sample, by chance, includes older students who also have better study habits and perform better: The researcher has “found” a relationship (the data appearing to show Thinking like a Psychological Scientist 28 age as significantly correlated with academic performance), but the truth is that the apparent relationship is purely coincidental—the result of these specific older students in this particular sample having better-than-average study habits (the real cause of the relationship). They may have always had superior study habits, even when they were young. Another possible outcome of NHST is a type II error, when the data fail to show a relationship between variables that actually exists. In our example, this time pretend that maturity is —in reality—associated with academic performance, but the researcher doesn’t find it in her sample. Perhaps it was just her bad luck that her older students are just having an off day, suffering from test anxiety, or were uncharacteristically careless with their homework: The peculiarities of her particular sample, by chance, prevent the researcher from identifying the real relationship between maturity and academic performance. These types of errors might worry you, that there is just no way to tell if data are any good or not. Researchers share your concerns, and address them by using probability values (p- values) to set a threshold for type I or type II errors. When researchers write that a particular finding is “significant at a p <.05 level,” they’re saying that if the same study were repeated 100 times, we should expect this result to occur—by chance—fewer than five times. That is, in this case, a Type I error is unlikely. Scholars sometimes argue over the exact threshold that should be used for probability. The most common in psychological science are.05 (5% chance),.01 (1% chance), and.001 (1/10th of 1% chance). Remember, psychological science doesn’t rely on definitive proof; it’s about the probability of seeing a specific result. This is also why it’s so important that scientific findings be replicated in additional studies. Table 3: Accurate detection and errors in research It’s because of such methodologies that science is generally trustworthy. Not all claims and explanations are equal; some conclusions are better bets, so to speak. Scientific claims are Thinking like a Psychological Scientist 29 more likely to be correct and predict real outcomes than “common sense” opinions and personal anecdotes. This is because researchers consider how to best prepare and measure their subjects, systematically collect data from large and—ideally—representative samples, and test their findings against probability. Scientific Theories The knowledge generated from research is organized according to scientific theories. A scientific theory is a comprehensive framework for making sense of evidence regarding a particular phenomenon. When scientists talk about a theory, they mean something different from how the term is used in everyday conversation. In common usage, a theory is an educated guess—as in, “I have a theory about which team will make the playoffs,” or, “I have a theory about why my sister is always running late for appointments.” Both of these beliefs are liable to be heavily influenced by many untrustworthy factors, such as personal opinions and memory biases. A scientific theory, however, enjoys support from many research studies, collectively providing evidence, including, but not limited to, that which has falsified competing explanations. A key component of good theories is that they describe, explain, and predict in a way that can be empirically tested and potentially falsified. Theories are open to revision if new evidence comes to light that compels reexamination of the accumulated, relevant data. In ancient times, for instance, people thought the Sun traveled around the Earth. This seemed to make sense and fit with many observations. In the 16th century, however, astronomers began systematically charting visible objects in the sky, and, over a 50-year period, with repeated testing, critique, and refinement, they provided evidence for a revised theory: The Earth and other cosmic objects revolve around the Sun. In science, we believe what the best and Early theories placed the Earth at the center of the solar system. most data tell us. If better data come We now know that the Earth revolves around the sun. [Image: along, we must be willing to change our Pearson Scott Foresman, https://goo.gl/W3izMR, Public Domain] views in accordance with the new Thinking like a Psychological Scientist 30 evidence. Is Science Objective? Thomas Kuhn (2012), a historian of science, argued that science, as an activity conducted by humans, is a social activity. As such, it is—according to Kuhn—subject to the same psychological influences of all human activities. Specifically, Kuhn suggested that there is no such thing as objective theory or data; all of science is informed by values. Scientists cannot help but let personal/cultural values, experiences, and opinions influence the types of questions they ask and how they make sense of what they find in their research. Kuhn’s argument highlights a distinction between facts (information about the world), and values (beliefs about the way the world is or ought to be). This distinction is an important one, even if it is not always clear. To illustrate the relationship between facts and values, consider the problem of global warming. A vast accumulation of evidence (facts) substantiates the adverse impact that human activity has on the levels of greenhouse gases in Earth’s atmosphere leading to changing weather patterns. There is also a set of beliefs (values), shared by many people, that influences their choices and behaviors in an attempt to address that impact (e.g., purchasing electric vehicles, recycling, bicycle commuting). Our values—in this case, that Earth as we know it is in danger and should be protected—influence how we engage with facts. People (including scientists) who strongly endorse this value, for example, might be more attentive to research on renewable energy. The primary point of this illustration is that (contrary to the image of scientists as outside observers to the facts, gathering them neutrally and without bias from the natural world) all science—especially social sciences like psychology—involves values and interpretation. As a result, science functions best when people with diverse values and backgrounds work collectively to understand complex natural phenomena. Indeed, science can benefit from multiple perspectives. One approach to achieving this is through levels of analysis. Levels of Thinking like a Psychological Scientist 31 analysis is the idea that a single phenomenon may be explained at different levels simultaneously. Remember the question concerning cramming for a test versus studying over time? It can be answered at a number of different levels of analysis. At a low level, we might use brain scanning technologies to investigate whether biochemical processes differ between the two study strategies. At a higher level—the level of thinking—we might investigate processes of decision making (what to study) and ability to focus, as they relate to cramming versus spaced practice. At even higher levels, we might be interested in real world behaviors, such as how long people study using each of the strategies. Similarly, we might be interested in how the presence of others influences learning across these two strategies. Levels of analysis suggests that one level is not more correct—or truer—than another; their appropriateness depends on the specifics of the question asked. Ultimately, levels of analysis would suggest that we cannot understand the world around us, including human psychology, by reducing the phenomenon to only the biochemistry of genes and dynamics of neural networks. But, neither can we understand humanity without considering the functions of the human nervous system. Science in Context There are many ways to interpret the world around us. People rely on common sense, personal experience, and faith, in combination and to varying degrees. All of these offer legitimate benefits to navigating one’s culture, and each offers a unique perspective, with specific uses and limitations. Science provides another important way of understanding the world and, while it has many crucial advantages, as with all methods of interpretation, it also has limitations. Understanding the limits of science—including its subjectivity and uncertainty— does not render it useless. Because it is systematic, using testable, reliable data, it can allow us to determine causality and can help us generalize our conclusions. By understanding how scientific conclusions are reached, we are better equipped to use science as a tool of knowledge. ------------------------------------------------------------------------------------------------------------------------------------------------------ Answer - Test Yourself 1: Can It Be Falsified? Answer explained: There are 4 hypotheses presented. Basically, the question asks “which of these could be tested and demonstrated to be false?". We can eliminate answers A, B and C. A is a matter of personal opinion. C is a concept for which there are currently no existing measures. B is a little trickier. A person could look at data on wars, assaults, and other forms of violence to draw a conclusion about which period is the most violent. The problem here is Thinking like a Psychological Scientist 32 that we do not have data for all time periods, and there is no clear guide to which data should be used to address this hypothesis. The best answer is D, because we have the means to view other planets and to determine whether there is water on them (for example, Mars has ice). ------------------------------------------------------------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------------------------------------------------------------ Answer - Test Yourself 2: Inductive or Deductive Answer explained: This question asks you to consider whether each of 4 examples represents inductive or deductive reasoning. 1) Inductive—it is possible to draw the conclusion—the homeowner left in a hurry—from specific observations such as the stove being on and the door being open. 2) Deductive—starting with a general principle (gravity is associated with mass), we draw a conclusion about the moon having weaker gravity than does the Earth because it has smaller mass. 3) Deductive—starting with a general principle (students do not like to pay for textbooks) it is possible to make a prediction about likely student behavior (they will not purchase textbooks). Note that this is a case of prediction rather than using observations. 4) Deductive—starting with a general principle (students need 100 credits to graduate) it is possible to draw a conclusion about Janine (she cannot graduate because she has fewer than the 100 credits required). ------------------------------------------------------------------------------------------------------------------------------------------------------ Thinking like a Psychological Scientist 33 Outside Resources Article: A meta-analysis of research on combating mis-information http://journals.sagepub.com/doi/full/10.1177/0956797617714579 Article: Fixing the Problem of Liberal Bias in Social Psychology https://www.scientificamerican.com/article/fixing-the-problem-of-liberal-bias-in-social-psychology/ Article: Flat out science rejection is rare, but motivated rejection of key scientific claims is relatively common. https://blogs.scientificamerican.com/guest-blog/who-are-you-calling-anti-science/ Article: How Anecdotal Evidence Can Undermine Scientific Results https://www.scientificamerican.com/article/how-anecdotal-evidence-can-undermine-scientific-results/ Article: How fake news is affecting your memory http://www.nature.com/news/how-facebook-fake-news-and-friends-are-warping-your-memory-1.21596 Article: New Study Indicates Existence of Eight Conservative Social Psychologists https://heterodoxacademy.org/2016/01/07/new-study-finds-conservative-social-psychologists/ Article: The Objectivity Thing (or, Why Science Is a Team Sport). https://blogs.scientificamerican.com/doing-good-science/httpblogsscientificamericancomdo ing-good-science20110720the-objectivity-thing-or-why-science-is-a-team-sport/ Article: Thomas Kuhn: the man who changed the way the world looked at science https://www.theguardian.com/science/2012/aug/19/thomas-kuhn-structure-scientific-revolutions Video: Karl Popper's Falsification - Karl Popper believed that human knowledge progresses through 'falsification'. A theory or idea shouldn't be described as scientific unless it could, in principle, be proven false. https://www.youtube.com/watch?v=wf-sGqBsWv4 Video: Karl Popper, Science, and Pseudoscience: Crash Course Philosophy #8 https://www.youtube.com/watch?v=-X8Xfl0JdTQ Video: Simple visualization of Type I and Type II errors https://www.youtube.com/watch?v=Dsa9ly4OSBk Thinking like a Psychological Scientist 34 Web: An overview and history of the concept of fake news. https://en.wikipedia.org/wiki/Fake_news Web: Heterodox Academy - an organization focused on improving "the quality of research and education in universities by increasing viewpoint diversity, mutual understanding, and constructive disagreement". https://heterodoxacademy.org/ Web: The People's Science - An orgnization dedicated to removing barriers between scientists and society. See examples of how researchers, including psychologists, are sharing their research with students, colleagues and the general public. http://thepeoplesscience.org/science-topic/human-sciences/ Discussion Questions 1. When you think of a “scientist,” what image comes to mind? How is this similar to or different from the image of a scientist described in this module? 2. What makes the inductive reasoning used in the scientific process different than the inductive reasoning we employ in our daily lives? How do these differences influence our trust in the conclusions? 3. Why aren’t horoscopes considered scientific? 4. If science cannot “prove” something, why do you think so many media reports of scientific research use this word? As an educated consumer of research, what kinds of questions should you ask when reading these secondary reports? 5. In thinking about the application of research in our lives, which is more meaningful: individual research studies and their conclusions or scientific theories? Why? 6. Although many people believe the conclusions offered by science generally, there is often a resistance to specific scientific conclusions or findings. Why might this be? Thinking like a Psychological Scientist 35 Vocabulary Anecdotal evidence A piece of biased evidence, usually drawn from personal experience, used to support a conclusion that may or may not be correct. Causality In research, the determination that one variable causes—is responsible for—an effect. Correlation In statistics, the measure of relatedness of two or more variables. Data (also called observations) In research, information systematically collected for analysis and interpretation. Deductive reasoning A form of reasoning in which a given premise determines the interpretation of specific observations (e.g., All birds have feathers; since a duck is a bird, it has feathers). Distribution In statistics, the relative frequency that a particular value occurs for each possible value of a given variable. Empirical Concerned with observation and/or the ability to verify a claim. Fact Objective information about the world. Falsify In science, the ability of a claim to be tested and—possibly—refuted; a defining feature of science. Generalize In research, the degree to which one can extend conclusions drawn from the findings of a study to other groups or situations not included in the study. Hypothesis Thinking like a Psychological Scientist 36 A tentative explanation that is subject to testing. Induction To draw general conclusions from specific observations. Inductive reasoning A form of reasoning in which a general conclusion is inferred from a set of observations (e. g., noting that “the driver in that car was texting; he just cut me off then ran a red light!” (a specific observation), which leads to the general conclusion that texting while driving is dangerous). Levels of analysis In science, there are complementary understandings and explanations of phenomena. Null-hypothesis significance testing (NHST) In statistics, a test created to determine the chances that an alternative hypothesis would produce a result as extreme as the one observed if the null hypothesis were actually true. Objective Being free of personal bias. Population In research, all the people belonging to a particular group (e.g., the population of left handed people). Probability A measure of the degree of certainty of the occurrence of an event. Probability values In statistics, the established threshold for determining whether a given value occurs by chance. Pseudoscience Beliefs or practices that are presented as being scientific, or which are mistaken for being scientific, but which are not scientific (e.g., astrology, the use of celestial bodies to make predictions about human behaviors, and which presents itself as founded in astronomy, the actual scientific study of celestial objects. Astrology is a pseudoscience unable to be falsified, whereas astronomy is a legitimate scientific discipline). Representative Thinking like a Psychological Scientist 37 In research, the degree to which a sample is a typical example of the population from which it is drawn. Sample In research, a number of people selected from a population to serve as an example of that population. Scientific theory An explanation for observed phenomena that is empirically well-supported, consistent, and fruitful (predictive). Type I error In statistics, the error of rejecting the null hypothesis when it is true. Type II error In statistics, the error of failing to reject the null hypothesis when it is false. Value Belief about the way things should be. Thinking like a Psychological Scientist 38 References Kuhn, T. S. (2012). The structure of scientific revolutions: 50th anniversary edition. Chicago, USA: University of Chicago Press. Kuhn, T. S. (2011). Objectivity, value judgment, and theory choice, in T. S. Kuhn (Ed.), The essential tension: Selected studies in scientific tradition and change (pp. 320-339). Chicago: University of Chicago Press. Retrieved from http://ebookcentral.proquest.com 2. The Brain 3 The Nervous System Aneeq Ahmad The mammalian nervous system is a complex biological organ, which enables many animals including humans to function in a coordinated fashion. The original design of this system is preserved across many animals through evolution; thus, adaptive physiological and behavioral functions are similar across many animal species. Comparative study of physiological functioning in the nervous systems of different animals lend insights to their behavior and their mental processing and make it easier for us to understand the human brain and behavior. In addition, studying the development of the nervous system in a growing human provides a wealth of information about the change in its form and behaviors that result from this change. The nervous system is divided into central and peripheral nervous systems, and the two heavily interact with one another. The peripheral nervous system controls volitional (somatic nervous system) and nonvolitional (autonomic nervous system) behaviors using cranial and spinal nerves. The central nervous system is divided into forebrain, midbrain, and hindbrain, and each division performs a variety of tasks; for example, the cerebral cortex in the forebrain houses sensory, motor, and associative areas that gather sensory information, process information for perception and memory, and produce responses based on incoming and inherent information. To study the nervous system, a number of methods have evolved over time; these methods include examining brain lesions, microscopy, electrophysiology, electroencephalography, and many scanning technologies. Learning Objectives Describe and understand the development of the nervous system. Learn and understand the two important parts of the nervous system. Explain the two systems in the peripheral nervous system and what you know about the different regions and areas of the central nervous system. The Nervous System 41 Learn and describe different techniques of studying the nervous system. Understand which of these techniques are important for cognitive neuroscientists. Describe the reasons for studying different nervous systems in animals other than human beings. Explain what lessons we learn from the evolutionary history of this organ. Evolution of the Nervous System Many scientists and thinkers (Cajal, 1937; Crick & Koch, 1990; Edelman, 2004) believe that the human nervous system is the most complex machine known to man. Its complexity points to one undeniable fact—that it has evolved slowly over time from simpler forms. Evolution of the nervous system is intriguing not because we can marvel at this complicated biological structure, but it is fascinating because it inherits a lineage of a long history of many less complex nervous systems (Figure 1), and it documents a record of adaptive behaviors observed in life forms other than humans. Thus, evolutionary study of the nervous system is important, and it is the first step in understanding its design, its workings, and its functional interface with the environment. Figure 1 The brains of various animals The brains of some animals, like apes, monkeys, and rodents, are structurally similar to humans (Figure 1), while others are not (e.g., invertebrates, single-celled organisms). Does anatomical similarity of these brains suggest that behaviors that emerge in these species are also similar? Indeed, many animals display behaviors that are similar to humans, e.g., apes use nonverbal communication signals with their hands and arms that resemble nonverbal forms of communication in humans (Gardner & Gardner, 1969; Goodall, 1986; Knapp & Hall, 2009). If we study very simple behaviors, like physiological responses made by individual neurons, then brain-based behaviors of invertebrates (Kandel & Schwartz, 1982) look very similar to humans, suggesting that from time immemorial such basic behaviors have been conserved in the brains of many simple animal forms and in fact are the foundation of more complex behaviors in animals that evolved later (Bullock, 1984). The Nervous System 42 Even at the micro-anatomical level, we note that individual neurons differ in complexity across animal species. Human neurons exhibit more intricate complexity than other animals; for example, neuronal processes (dendrites) in humans have many more branch points, branches, and spines. Complexity in the structure of the nervous system, both at the macro- and micro-levels, give rise to complex behaviors. We can observe similar movements of the limbs, as in nonverbal communication, in apes and humans, but the variety and intricacy of nonverbal behaviors using hands in humans surpasses apes. Deaf individuals who use American Sign Language (ASL) express themselves in English nonverbally; they use this language with such fine gradation that many accents of ASL exist (Walker, 1987). Complexity of behavior with increasing complexity of the nervous system, especially the cerebral cortex, can be observed in the genus Homo (Figure 2). If we compare sophistication of material culture in Homo habilis (2 million years ago; brain volume ~650 cm3) and Homo sapiens (300,000 years to now; brain volume ~1400 cm3), the evidence shows that Homo habilis used crude stone tools compared with modern tools used by Homo sapiens to erect cities, develop written languages, embark on space travel, and study her own self. All of this is due to increasing complexity of the nervous system. Figure 2 Changes in cerebral volume across evolution What has led to the complexity of the brain and nervous system through evolution, to its behavioral and cognitive refinement? Darwin (1859, 1871) proposed two forces of natural and sexual selection as work engines behind this change. He prophesied, “psychology will be based The Nervous System 43 on a new foundation, that of the necessary acquirement of each mental power and capacity by gradation” that is, psychology will be based on evolution (Rosenzweig, Breedlove, & Leiman, 2002). Development of the Nervous System Where the study of change in the nervous system over eons is immensely captivating, studying the change in a single brain during individual development is no less engaging. In many ways the ontogeny (development) of the nervous system in an individual mimics the evolutionary advancement of this structure observed across many animal species. During development, the nervous tissue emerges from the ectoderm (one of the three layers of the mammalian embryo) through the process of neural induction. This process causes the formation of the neural tube, which extends in a rostrocaudal (head-to-tail) plane. The tube, which is hollow, seams itself in the rostrocaudal direction. In some disease conditions, the neural tube does not close caudally and results in an abnormality called spina bifida. In this pathological condition, the lumbar and sacral segments of the spinal cord are disrupted. As gestation progresses, the neural tube balloons up (cephalization) at the rostral end, and forebrain, midbrain, hindbrain, and the spinal cord can be visually delineated (day 40). About 50 days into gestation, six cephalic areas can be anatomically discerned (also see below for a more detailed description of these areas). The progenitor cells (neuroblasts) that form the lining (neuroepithelium) of the neural tube generate all the neurons and glial cells of the central nervous system. During early stages of this development, neuroblasts rapidly divide and specialize into many varieties of neurons and glial cells, but this proliferation of cells is not uniform along the neural tube—that is why we see the forebrain and hindbrain expand into larger cephalic tissues than the midbrain. The neuroepithelium also generates a group of specialized cells that migrate outside the neural tube to form the neural crest. This structure gives rise to sensory and autonomic neurons in the peripheral nervous system. The Structure of the Nervous System The mammalian nervous system is divided into central and peripheral nervous systems. The Peripheral Nervous System The Nervous System 44 Figure 3 The various components of the peripheral nervous system The peripheral nervous system is divided into somatic and autonomic nervous systems (Figure 3). Where the somatic nervous system consists of cranial nerves (12 pairs) and spinal nerves (31 pairs) and is under the volitional control of the individual in maneuvering bodily muscles, the autonomic nervous system also running through these nerves lets the individual have little control over muscles and glands. Main divisions of the autonomic nervous system that control visceral structures are the sympathetic and parasympathetic nervous systems. At an appropriate cue (say a fear-inducing object like a snake), the sympathetic division generally energizes many muscles (e.g., heart) and glands (e.g., adrenals), causing activity and release of hormones that lead the individual to negotiate the fear-causing snake with fight- or-flight responses. Whether the individual decides to fight the snake or run away from it, either action requires energy; in short, the sympathetic nervous system says “go, go, go.” The parasympathetic nervous system, on the other hand, curtails undue energy mobilization into muscles and glands and modulates the response by saying “stop, stop, stop.” This push– Figure 4 the central nervous system and its components The Nervous System 45 pull tandem system regulates fight-or-flight responses in all of us. The Central Nervous System The central nervous system is divided into a number of important parts (see Figure 4), including the spinal cord, each specialized to perform a set of specific functions. Telencephalon or cerebrum is a newer development in the evolution of the mammalian nervous system. In humans, it is about the size of a large napkin and when crumpled into the skull, it forms furrows called sulci (singular form, sulcus). The bulges between sulci are called gyri (singular form, gyrus). The cortex is divided into two hemispheres, and each hemisphere is further divided into four lobes (Figure 5a), which have specific functions. The division of these lobes is based on two delineating sulci: the central sulcus divides the hemisphere into frontal and parietal-occipital lobes and the lateral sulcus marks the temporal lobe, which lies below. Just in front of the central sulcus lies an area called the primary motor cortex (precentral gyrus), which connects to the muscles of the body, and on volitional command moves them. From mastication to movements in the genitalia, the body map is represented on this strip (Figure 5b). Some body parts, like fingers, thumbs, and lips, occupy a greater representation on the strip than, say, the trunk. This disproportionate representation of the body on the primary motor cortex is called the magnification factor (Rolls & Cowey, 1970) and is seen in other motor and Figure 5a The lobes of the brain sensory areas. At the lower end of the central sulcus, close to the lateral sulcus, lies the Broca’s area (Figure 6b) in the left frontal lobe, which is involved with language production. Damage to this part of the brain led Pierre Paul Broca, a French neuroscientist in 1861, to document many different forms of aphasias, in which his patients would lose the ability to speak or would retain partial speech impoverished in syntax and grammar (AAAS, 1880). It is no wonder that others have found subvocal rehearsal and central executive processes of working memory in this frontal lobe (Smith & Jonides, 1997, 1999). The Nervous System 46 Figure 5b. Specific body parts like the tongue or fingers are mapped onto certain areas of the brain including the primary motor cortex. Just behind the central gyrus, in the parietal lobe, lies the primary somatosensory cortex (Figure 6a) on the postcentral gyrus, which represents the whole body receiving inputs from the skin and muscles. The primary somat osensory cortex parallels, abuts, and connects heavily to the primary motor cortex and resembles it in terms of areas devoted to bodily representation. All spinal and some cranial nerves (e.g., the facial nerve) send sensory signals from skin (e.g., touch) and muscles to the primary somatosensory cortex. Close to the lower (ventral) end of this strip, curved inside the parietal lobe, is the taste area (secondary somatosensory cortex), which is involved with taste experiences that originate from the tongue, pharynx, epiglottis, and so forth. Just below the parietal lobe, and under the Figure 6a The Primary Somatosensory Cortex The Nervous System 47 caudal end of the lateral fissure, in the temporal lobe, lies the Wernicke’s area (Demonet et al., 1992). This area is involved with language comprehension and is connected to the Broca’s area through the arcuate fasciculus, nerve fibers that connect these two regions. Damage to the Wernicke’s area (Figure 6b) results in many kinds of agnosias; agnosia is defined as an inability to know or understand language and speech-related behaviors. So an individual may show word deafness, which is an inability to recognize spoken language, or word blindness, which is an inability to recognize written or printed language. Close in proximity to the Wernicke’s area is the primary auditory cortex, which is involved with audition, and finally the brain region devoted to smell (olfaction) is tucked away inside the primary olfactory cortex (prepyriform cortex). At the very back of the cerebral cortex lies the occipital lobe housing the primary visual cortex. Optic nerves travel all the way to the thalamus (lateral geniculate nucleus, LGN) and then to visual cortex, where images that are received on the retina are projected (Hubel, 1995). In the past 50 to 60 years, visual sense and visual pathways have been studied extensively, and our understanding about them has increased manifold. We now understand that all objects that form Figure 6b Wernicke's area images on the retina are transformed (transduction) in neural language handed down to the visual cortex for further processing. In the visual cortex, all attributes (features) of the image, such as the color, texture, and orientation, are decomposed and processed by different visual cortical modules (Van Essen, Anderson & Felleman, 1992) and then recombined to give rise to singular perception of the image in question. If we cut the cerebral hemispheres in the middle, a new set of structures come into view. Many of these perform different functions vital to our being. For example, the limbic system contains a number of nuclei that process memory (hippocampus and fornix) and attention and emotions (cingulate gyrus); the globus pallidus is involved with motor movements and their coordination; the hypothalamus and thalamus are involved with drives, motivations, and trafficking of sensory and motor throughputs. The hypothalamus plays a key role in regulating endocrine hormones in conjunction with the pituitary gland that extends from the hypothalamus through a stalk (infundibulum). The Nervous System 48 As we descend down the thalamus, the midbrain comes into view with superior and inferior colliculi, which process visual and auditory information, as does the substantia nigra, which is involved with notorious Parkinson’s disease, and the reticular formation regulating arousal, sleep, and temperature. A little lower, the hindbrain with the pons processes sensory and motor information employing the cranial nerves, works as a bridge that Figure 7 The interior of the brain connects the cerebral cortex with the medulla, and reciprocally transfers information back and forth between the brain and the spinal cord. The medulla oblongata processes breathing, digestion, heart and blood vessel function, swallowing, and sneezing. The cerebellum controls motor movement coordination, balance, equilibrium, and muscle tone. The midbrain and the hindbrain, which make up the brain stem, culminate in the spinal cord. Whereas inside the cerebral cortex, the gray matter (neuronal cell bodies) lies outside and white matter (myelinated axons) inside; in the spinal cord this arrangement reverses, as the gray matter resides inside and the white matter outside. Paired nerves (ganglia) exit the spinal cord, some closer in direction towards the back (dorsal) and others towards the front (ventral). The dorsal nerves (afferent) receive sensory information from skin and muscles, and ventral nerves (efferent) send signals to muscles and organs to respond. Studying the Nervous System The study of the nervous system involves anatomical and physiological techniques that have improved over the years in efficiency and caliber. Clearly, gross morphology of the nervous system requires an eye-level view of the brain and the spinal cord. However, to resolve minute components, optical and electron microscopic techniques are needed. Light microscopes and, later, electron microscopes have changed our understanding of the intricate connections that exist among nerve cells. For example, modern staining procedures (immunocytochemistry) make it possible to see selected neurons that are of one type or another or are affected by growth. With better resolution of the electron microscopes, fine structures like the synaptic cleft between the pre- and post-synaptic neurons can be studied in detail. The Nervous System 49 Along with the neuroanatomical techniques, a number of other methodologies aid neuroscientists in studying the function and physiology of the nervous system. Early on, lesion studies in animals (and study of neurological damage in humans) provided information about the function of the nervous system, by ablating (removing) parts of the nervous system or using neurotoxins to destroy them and documenting the effects on behavior or mental processes. Later, more sophisticated microelectrode techniques were introduced, which led to recording from single neurons in the animal brains and investigating their physiological functions. Such studies led to formulating theories about how sensory and motor information are processed in the brain. To study many neurons (millions of them at a time) electroencephalographic (EEG) techniques were introduced. These methods are used to study how large ensembles of neurons, representing different parts of the nervous system, with (event-related potentials) or without stimulation function together. In addition, many scanning techniques that visualize the brain in conjunction with methods mentioned above are used to understand the details of the structure and function of the brain. These include computerized axial tomography (CAT), which uses X-rays to capture many pictures of the brain and sandwiches them into 3-D models to study it. The resolution of this method is inferior to magnetic resonance imaging (MRI), which is yet another way to capture brain images using large magnets that bobble (precession) hydrogen nuclei in the brain. Although the resolution of MRI scans is much better than CAT scans, they do not provide any functional information about the brain. Positron Emission Tomography (PET) involves the acquisiti

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