Decline effect

Last updated

The decline effect may occur when scientific claims receive decreasing support over time. The term was first described by parapsychologist Joseph Banks Rhine in the 1930s to describe the disappearing of extrasensory perception (ESP) of psychic experiments conducted by Rhine over the course of study or time. In its more general term, Cronbach, in his review article of science "Beyond the two disciplines of scientific psychology" referred to the phenomenon as "generalizations decay." [1] The term was once again used in a 2010 article by Jonah Lehrer published in The New Yorker . [2]

Contents

Examples

In his article, Lehrer gives several examples where the decline effect is allegedly showing. In the first example, the development of second generation anti-psychotic drugs, reveals that the first tests had demonstrated a dramatic decrease in the subjects' psychiatric symptoms. [2] However, after repeating tests this effect declined and in the end it was not possible to document that these drugs had any better effect than the first generation anti-psychotics.

A well-known example of the decline effect can be seen in early experiments conducted by Professor Jonathan Schooler examining the effects of verbalization on non-verbal cognition. In an initial series of studies Schooler found evidence that verbal rehearsal of previously seen faces or colors markedly impaired subsequent recognition. [3] This phenomenon is referred to as verbal overshadowing . Although verbal overshadowing effects have been repeatedly observed by Schooler, as well as other researchers, they have also proven to be somewhat challenging to replicate. [2] [4] [5] Verbal overshadowing effects in a variety of domains were initially easy to find, but then became increasingly difficult to replicate indicating a decline effect in the phenomenon. Schooler has now become one of the more prominent researchers examining the decline effect. He has argued that addressing the decline effect may require a major revision to the scientific process whereby scientists log their protocols before conducting their research and then, regardless of outcome, report their findings in an open access repository (such as Brain Nosek's "Project Implicit"). [6] Schooler is currently working with the Fetzer Foundation to organize a major meeting of scientists from various disciplines to consider alternative accounts of the decline effect and approaches for rigorously addressing it. [7]

In 1991, Danish zoologist Anders Møller discovered a connection between symmetry and sexual preference of female birds in nature. This sparked a huge interest in the topic and a lot of follow-up research was published. In three years following the original discovery, 90% of studies confirmed Møller's hypothesis. However, the same outcome was published in just four out of eight research papers in 1995, and only a third in next three years. [8]

The decline effect in ocean acidification effects on fish behavior. The strength of effect declined by a order of magnitude over a decade of research on this topic. The decline effect in this field appears to be driven by publication bias, citation bas, sample sizes, and particular investigators publishing large effect sizes. The decline effect in ocean acidification effects on fish behavior.jpg
The decline effect in ocean acidification effects on fish behavior. The strength of effect declined by a order of magnitude over a decade of research on this topic. The decline effect in this field appears to be driven by publication bias, citation bas, sample sizes, and particular investigators publishing large effect sizes.

A study published in 2022 reported perhaps one of the most striking examples of the decline effect in the field of ecology, where effect sizes of published studies testing for ocean acidification effects on fish behavior have declined by an order of magnitude over a decade of research on this topic. [9]

Explanations

One of the explanations of the effect is regression toward the mean (this is a statistical phenomenon happening when a variable is extreme on the first experiments and by later experiments tend to regress towards average), although this does not explain why sequential results decline in a linear fashion, rather than fluctuating about the true mean as would be expected. [5]

Another reason may be the publication bias: scientists and scientific journals prefer to publish positive results of experiments and tests over null results, especially with new ideas. [2] As a result, the journals may refuse to publish papers that do not prove that the idea works. Later, when an idea is accepted, journals may refuse to publish papers that support it.

In the debate that followed the original article, Lehrer answered some of the questions by claiming that scientific observations might be shaped by one's expectations and desires, sometimes even unconsciously, thus creating a bias towards the desired outcome. [8]

A significant factor contributing to the decline effect can also be the sample size of the scientific research, since smaller sample size is very likely to give more extreme results, suggesting a significant breakthrough, but also a higher probability of an error. Typical examples of this effect are the opinion polls, where those including a larger number of people are closer to reality than those with a small pool of respondents. [10] This suggestion would not appear to account for the observed decrease over time regardless of sample size. Researcher John Ioannidis offers some explanation. He states that early research is usually small and more prone to highly positive results supporting the original idea, including early confirmatory studies. Later, as larger studies are being made, they often show regression to the mean and a failure to repeat the early exaggerated results. [11] [12] [13]

A 2012 report by National Public Radio's show "On The Media" [14] covered scientists who are exploring another option: that the act of observing the universe changes the universe, and that repeated measurement might actually be rendering earlier results invalid. In other words, antipsychotic drugs did work originally, but the more we measured their effectiveness, the more the laws governing those drugs changed so they ceased to be effective. Science fiction author Geoff Ryman explores this idea and its possible ramifications further in his 2012 short story What We Found, [15] which won the Nebula Award for Best Novelette in 2012. [16]

Another reason for some decline effects may be that certain researchers tend to publish larger effect sizes than others. For example, alongside publication bias and sample size effects, the decline effect in ocean acidification effects on fish behavior [9] was largely driven by outstanding effect sizes reported by two particular investigators from the same laboratory who are currently under investigation for potential scientific misconduct and data fabrication. [17]

Contesting views

Several commenters have contested Jonah Lehrer's view of the decline effect being a problematic side of the phenomenon, as presented in his New Yorker article. "The decline effect is troubling because it reminds us how difficult it is to prove anything. We like to pretend that our experiments define the truth for us. But that's often not the case. Just because an idea is true doesn't mean it can be proved. And just because an idea can be proved doesn't mean it's true. When the experiments are done, we still have to choose what to believe." [2]

Steven Novella also challenges Lehrer's view of the decline effect, arguing that Lehrer is concentrating on new discoveries on the cutting edge of scientific research and applying the conclusions to all areas of science. Novella points out that most of the examples used by Lehrer come from medicine, psychology and ecology, scientific fields most influenced by a complex human aspect and that there is not much evidence of the decline effect in other areas of science, such as physics. [18]

Another scientist, Paul Zachary Myers, is also contesting Lehrer's view on the decline effect being a surprising phenomenon in science, claiming that: "This isn't surprising at all. It's what we expect, and there are many very good reasons for the shift." [19]

Lehrer's statements about the difficulty of proving anything and publication bias find support from Jerry A. Coyne. Coyne holds that in the fields of genetics and evolutionary biology, almost no research is replicated and there is a premium motivation offered for publishing positive results of research studies. However, he also contests Lehrer's approach of applying conclusions on all fields of science, stating that in physics, chemistry or molecular biology, previous results are constantly repeated by others in order to progress in their own research. [20]

Criticism

One concern that some [21] have expressed is that Lehrer's article may further fuel people's skepticism about academic science. It was long believed that Lehrer's article originally hinted that academic science is not as rigid as people would like to believe. It is especially the article's ending that has upset many scientists and led to broad criticism of the article. Lehrer ends the article by saying: "Just because an idea is true doesn't mean it can be proved. And just because an idea can be proved doesn't mean it's true. When the experiments are done, we still have to choose what to believe." This has upset scientists in the scientific community. Many have written back to Lehrer and questioned his agenda. Some have characterized Lehrer's assertion as "absurd", while others claiming that Lehrer is trying to use publication bias as an excuse for not believing in anything. [21]

As an answer to the many comments Lehrer received upon publishing the article, Lehrer published a comment on his blog, The Frontal Cortex, [8] where he denied that he was implicitly questioning science and scientific methods in any way. In the same blog comment, Lehrer stated that he was not questioning fundamental scientific theories such as the theory of evolution by natural selection and global warming by calling them "two of the most robust and widely tested theories of modern science".

A further clarification was published as a follow-up note in The New Yorker. [8] In this note, entitled "More Thoughts on the Decline Effect", Lehrer tries mainly to answer the critics by giving examples where scientific research has both failed and succeeded. As an example, Lehrer uses Richard Feynman's commencement speech at Caltech in 1974 as a starting point. In his commencement speech, Feynman used Robert Millikan's and Harvey Fletcher's oil drop experiment to measure the charge of an electron to illustrate how selective reporting can bias scientific results. On the other hand, Feynman finds solace in the fact that other scientists will repeat other scientists' experiments and hence, the truth will win out in the end.

Lehrer once again uses the follow-up note to deny that his original intention was to support people denying well verified scientific theories such as natural selection and climate change. Instead, he wishes that "we'd spend more time considering the value of second-generation antipsychotics or the verity of the latest gene-association study". In the other parts of the follow-up note, Lehrer briefly discusses some of the creative feedback he has received in order to reduce publication bias. He does not give explicit support to any specific idea. The follow-up article ends with Lehrer once again stating that the decline effect is a problem in today's science, but that science will eventually find a tool to deal with the problem.

See also

Related Research Articles

<span class="mw-page-title-main">Parapsychology</span> Study of paranormal and psychic phenomena

Parapsychology is the study of alleged psychic phenomena and other paranormal claims, for example, those related to near-death experiences, synchronicity, apparitional experiences, etc. Criticized as being a pseudoscience, the majority of mainstream scientists reject it. Parapsychology has also been criticised by mainstream critics for claims by many of its practitioners that their studies are plausible despite a lack of convincing evidence after more than a century of research for the existence of any psychic phenomena.

Pathological science is an area of research where "people are tricked into false results ... by subjective effects, wishful thinking or threshold interactions." The term was first used by Irving Langmuir, Nobel Prize-winning chemist, during a 1953 colloquium at the Knolls Research Laboratory. Langmuir said a pathological science is an area of research that simply will not "go away"—long after it was given up on as "false" by the majority of scientists in the field. He called pathological science "the science of things that aren't so."

<span class="mw-page-title-main">Scientific method</span> Interplay between observation, experiment and theory in science

The scientific method is an empirical method for acquiring knowledge that has characterized the development of science since at least the 17th century It involves careful observation, applying rigorous skepticism about what is observed, given that cognitive assumptions can distort how one interprets the observation. It involves formulating hypotheses, via induction, based on such observations; the testability of hypotheses, experimental and the measurement-based statistical testing of deductions drawn from the hypotheses; and refinement of the hypotheses based on the experimental findings. These are principles of the scientific method, as distinguished from a definitive series of steps applicable to all scientific enterprises.

Reproducibility, closely related to replicability and repeatability, is a major principle underpinning the scientific method. For the findings of a study to be reproducible means that results obtained by an experiment or an observational study or in a statistical analysis of a data set should be achieved again with a high degree of reliability when the study is replicated. There are different kinds of replication but typically replication studies involve different researchers using the same methodology. Only after one or several such successful replications should a result be recognized as scientific knowledge.

Confirmation bias is the tendency to search for, interpret, favor, and recall information in a way that confirms or supports one's prior beliefs or values. People display this bias when they select information that supports their views, ignoring contrary information, or when they interpret ambiguous evidence as supporting their existing attitudes. The effect is strongest for desired outcomes, for emotionally charged issues, and for deeply entrenched beliefs. Confirmation bias is insuperable for most people, but they can manage it, for example, by education and training in critical thinking skills.

<span class="mw-page-title-main">Experiment</span> Scientific procedure performed to validate a hypothesis

An experiment is a procedure carried out to support or refute a hypothesis, or determine the efficacy or likelihood of something previously untried. Experiments provide insight into cause-and-effect by demonstrating what outcome occurs when a particular factor is manipulated. Experiments vary greatly in goal and scale but always rely on repeatable procedure and logical analysis of the results. There also exist natural experimental studies.

<span class="mw-page-title-main">Meta-analysis</span> Statistical method that summarizes data from multiple sources

A meta-analysis is a statistical analysis that combines the results of multiple scientific studies. Meta-analyses can be performed when there are multiple scientific studies addressing the same question, with each individual study reporting measurements that are expected to have some degree of error. The aim then is to use approaches from statistics to derive a pooled estimate closest to the unknown common truth based on how this error is perceived. It is thus a basic methodology of metascience. Meta-analytic results are considered the most trustworthy source of evidence by the evidence-based medicine literature.

In a blind or blinded experiment, information which may influence the participants of the experiment is withheld until after the experiment is complete. Good blinding can reduce or eliminate experimental biases that arise from a participants' expectations, observer's effect on the participants, observer bias, confirmation bias, and other sources. A blind can be imposed on any participant of an experiment, including subjects, researchers, technicians, data analysts, and evaluators. In some cases, while blinding would be useful, it is impossible or unethical. For example, it is not possible to blind a patient to their treatment in a physical therapy intervention. A good clinical protocol ensures that blinding is as effective as possible within ethical and practical constraints.

In published academic research, publication bias occurs when the outcome of an experiment or research study biases the decision to publish or otherwise distribute it. Publishing only results that show a significant finding disturbs the balance of findings in favor of positive results. The study of publication bias is an important topic in metascience.

In science, experimenter's regress refers to a loop of dependence between theory and evidence. In order to judge whether evidence is erroneous we must rely on theory-based expectations, and to judge the value of competing theories we rely on evidence. Cognitive bias affects experiments, and experiments determine which theory is valid. This issue is particularly important in new fields of science where there is no community consensus regarding the relative values of various competing theories, and where sources of experimental error are not well known.

The "ceiling effect" is one type of scale attenuation effect; the other scale attenuation effect is the "floor effect". The ceiling effect is observed when an independent variable no longer has an effect on a dependent variable, or the level above which variance in an independent variable is no longer measurable. The specific application varies slightly in differentiating between two areas of use for this term: pharmacological or statistical. An example of use in the first area, a ceiling effect in treatment, is pain relief by some kinds of analgesic drugs, which have no further effect on pain above a particular dosage level. An example of use in the second area, a ceiling effect in data-gathering, is a survey that groups all respondents into income categories, not distinguishing incomes of respondents above the highest level measured in the survey instrument. The maximum income level able to be reported creates a "ceiling" that results in measurement inaccuracy, as the dependent variable range is not inclusive of the true values above that point. The ceiling effect can occur any time a measure involves a set range in which a normal distribution predicts multiple scores at or above the maximum value for the dependent variable.

<span class="mw-page-title-main">John Ioannidis</span> American scientist (born 1965)

John P. A. Ioannidis is a Greek-American physician-scientist, writer and Stanford University professor who has made contributions to evidence-based medicine, epidemiology, and clinical research. Ioannidis studies scientific research itself, meta-research primarily in clinical medicine and the social sciences.

<span class="mw-page-title-main">Why Most Published Research Findings Are False</span> 2005 essay written by John Ioannidis

"Why Most Published Research Findings Are False" is a 2005 essay written by John Ioannidis, a professor at the Stanford School of Medicine, and published in PLOS Medicine. It is considered foundational to the field of metascience.

<span class="mw-page-title-main">Criticism of science</span> Critical observation of science

Criticism of science addresses problems within science in order to improve science as a whole and its role in society. Criticisms come from philosophy, from social movements like feminism, and from within science itself.

Funding bias, also known as sponsorship bias, funding outcome bias, funding publication bias, and funding effect, refers to the tendency of a scientific study to support the interests of the study's financial sponsor. This phenomenon is recognized sufficiently that researchers undertake studies to examine bias in past published studies. Funding bias has been associated, in particular, with research into chemical toxicity, tobacco, and pharmaceutical drugs. It is an instance of experimenter's bias.

White hat bias (WHB) is a purported "bias leading to the distortion of information in the service of what may be perceived to be righteous ends", which consist of both cherry picking the evidence and publication bias. Public health researchers David Allison and Mark Cope first discussed this bias in a 2010 paper and explained the motivation behind it in terms of "righteous zeal, indignation toward certain aspects of industry", and other factors.

Invalid science consists of scientific claims based on experiments that cannot be reproduced or that are contradicted by experiments that can be reproduced. Recent analyses indicate that the proportion of retracted claims in the scientific literature is steadily increasing. The number of retractions has grown tenfold over the past decade, but they still make up approximately 0.2% of the 1.4m papers published annually in scholarly journals.

<span class="mw-page-title-main">Replication crisis</span> Observed inability to reproduce scientific studies

The replication crisis is an ongoing methodological crisis in which the results of many scientific studies are difficult or impossible to reproduce. Because the reproducibility of empirical results is an essential part of the scientific method, such failures undermine the credibility of theories building on them and potentially call into question substantial parts of scientific knowledge.

The Reproducibility Project is a series of crowdsourced collaborations aiming to reproduce published scientific studies, finding high rates of results which could not be replicated. It has resulted in two major initiatives focusing on the fields of psychology and cancer biology. The project has brought attention to the replication crisis, and has contributed to shifts in scientific culture and publishing practices to address it.

<span class="mw-page-title-main">Brian Nosek</span> American social psychologist

Brian Arthur Nosek is an American social-cognitive psychologist, professor of psychology at the University of Virginia, and the co-founder and director of the Center for Open Science. He also co-founded the Society for the Improvement of Psychological Science and Project Implicit. He has been on the faculty of the University of Virginia since 2002.

References

  1. ↑ Cronbach, L. J. (1975). "Beyond the two disciplines of scientific psychology". American Psychologist. 30 (2): 116–127. doi:10.1037/h0076829.
  2. 1 2 3 4 5 Lehrer J (2010). "The Truth Wears Off". The New Yorker.
  3. ↑ Schooler JW, Engstler-Schooler TY (January 1990). "Verbal overshadowing of visual memories: some things are better left unsaid". Cognitive Psychology. 22 (1): 36–71. doi:10.1016/0010-0285(90)90003-m. PMID   2295225. S2CID   6044806.
  4. ↑ Chin JM, Schooler JW (2009). "Why do words hurt? content, process, and criterion shift accounts of verbal overshadowing". European Journal of Cognitive Psychology. 20 (3): 396–413. doi:10.1080/09541440701728623. S2CID   53396737.
  5. 1 2 Schooler J (February 2011). "Unpublished results hide the decline effect". Nature. 470 (7335): 437. Bibcode:2011Natur.470..437S. doi: 10.1038/470437a . PMID   21350443.
  6. ↑ "Brian Nosek". Project Implicit.
  7. ↑ Mooneyham BW, Franklin MS, Mrazek MD, Schooler JW (2012). "Modernizing Science: Comments on Nosek and Bar-Anan (2012)". Psychological Inquiry. 23 (3): 281–284. doi:10.1080/1047840X.2012.705246. S2CID   144248740.
  8. 1 2 3 4 Lehrer J (2010-12-09). "The Mysterious Decline Effect". Wired.
  9. 1 2 3 Clements JC, Sundin J, Clark TD, Jutfelt F (February 2022). "Meta-analysis reveals an extreme "decline effect" in the impacts of ocean acidification on fish behavior". PLOS Biology. 20 (2): e3001511. doi: 10.1371/journal.pbio.3001511 . PMC   8812914 . PMID   35113875.
  10. ↑ Paulos JA (2010). "The decline effect and why scientific 'Truth' so often turns out wrong". ABC News.
  11. ↑ Ioannidis JP (August 2005). "Why most published research findings are false". PLOS Medicine. 2 (8): e124. doi: 10.1371/journal.pmed.0020124 . PMC   1182327 . PMID   16060722.
  12. ↑ Ioannidis JP (July 2005). "Contradicted and initially stronger effects in highly cited clinical research". JAMA. 294 (2): 218–228. doi: 10.1001/jama.294.2.218 . PMID   16014596.
  13. ↑ Gorski D (2010). "The "decline effect": Is it a real decline or just science correcting itself?". Science-based Medicine.
  14. ↑ Gladstone B (2012). "The 'Decline Effect' and Scientific Truth". NPR On The Media. Archived from the original on 2012-07-04.
  15. ↑ Ryman G (2012). "What We Found". In Dozois G (ed.). The Year's Best Science Fiction. New York, NY: St. Martin's Publishing Group. pp. 309–329. ISBN   978-1-250-00354-6.
  16. ↑ Addelman M (2012). "Ryman wins one of world's top science fiction prizes". University of Manchester.
  17. ↑ Enserink M (May 2021). "Sea of doubts". Science. 372 (6542): 560–565. Bibcode:2021Sci...372..560E. doi:10.1126/science.372.6542.560. PMID   33958459. S2CID   233984942.
  18. ↑ Novella S (2010). "The Decline Effect". Neurologicablog.
  19. ↑ Myers PZ (2010). "Science is not dead". Scienceblogs. Archived from the original on 2011-03-03.
  20. ↑ Coyne JA (2010). "Why Evolution is True". Wordpress.
  21. 1 2 Horgan J (2010). "The truth we'll doubt: Does the decline effect mean that all science is "truthy"?". Scientific America.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.