“Human achievement is lower when there are nonlinearities in the ecology.” (What has Brunswik’s Lens Model Taught?).
This post is based on the paper: “The Neural Basis of Risky Choice with Affective Outcomes,” written by Renata S. Suter, Thorsten Pachur, Ralph Hertwig, Tor Endestad, and Guido Biele
that appeared in PLOS ONE journal.pone.0122475 April 1, 2015. The paper is similar to one discussed in the post Affect Gap that included Pachur and Hertwig although that paper did not use fMRI. Suter et al note that both normative and many descriptive theories of decision making under risk have typically investigated choices involving relatively affect-poor, monetary outcomes. This paper compared choice in relatively affect-poor, monetary lottery problems with choice in relatively affect-rich medical decision problems.
The paper is notable in that it not only examined behavioral differences between affect rich and affect poor risky choice, but also watched the brains of the people making the decisions with fMRI. The researchers assert that the traditional notion of a mechanism that assumes sensitivity to outcome and probability information and expectation maximization may not hold when options elicit relatively high levels of affect. Instead, qualitatively different strategies may be used in affect-rich versus affect-poor decisions. This is not much of a leap.
In order to examine the neural underpinnings of cognitive processing in affect-rich and affect poor decisions, the researchers asked participants to make choices between two options with relatively affect-rich outcomes (drugs that cause a side effect with some probability) as well as between two options with relatively affect-poor outcomes (lotteries that incur monetary losses with some probability). The monetary losses were matched to each individual’s subjective monetary evaluation of the side effects, permitting a within-subject comparison between affect-rich and affect-poor choices in otherwise monetarily equivalent problems. This was cleverly done. Specifically, participants were first asked to indicate the amount of money they considered equivalent to specific nonmonetary outcomes (here: side effects; Fig 1A). The monetary amounts indicated (willingness-to-pay; WTP) were then used to construct individualized lotteries in which either a side effect (affect-rich problem) or a monetary loss (affect-poor problem) occurred with some probability. For example, consider a participant who specified a WTP of $18 to avoid insomnia and $50 to avoid depression. In the affect-rich problem, she would be presented with a choice between drug A, leading to insomnia
with a probability of 15% (no side effects otherwise), and drug B, leading to depression
with a probability of 5% (no side effects otherwise). In the corresponding affect-poor problem,
she would be presented with a choice between lottery A, leading to a loss of $18 with a probability of 15% (nothing otherwise), and lottery B, leading to a loss of $50 with a probability of 5% (nothing otherwise). This paradigm allowed the authors to compare the decision mechanisms underlying affect-rich versus affect-poor risky choice on the basis of lottery problems that were equivalent in monetary terms (Fig 1A and 1B).
To assess whether experiencing a side effect was rated as evoking stronger negative affect than losing the equivalent monetary amount, Suter et al analyzed the ratings. Fig 2. presents them.
Were the differences in affect associated with different choices? Their findings showed that, despite the monetary equivalence between affect-rich and affect-poor problems, people reversed their preferences between the corresponding problems in 46.07% of cases, on average. To examine the cognitive mechanisms underlying affect-rich and affect-poor choices, the researchers modeled them using Cumulative Prospect Theory (CPT). On average, CPT based on individually fitted parameters correctly described participants’ choices in 82.45% of affect-rich choices and in 90.42% of affect-poor choices. Modeling individuals’ choices using CPT, they found affect-rich choice was best described by a substantially more strongly curved weighting function than affect-poor choice, signaling that the psychological impact of probability information is diminished in the context of emotionally laden outcomes. Participants seemed to avoid the option associated with the worse side effects, irrespective of their probabilities, and therefore often ended up choosing the option with the lower expected value.
The neural testing was complicated and used extensive computational modeling analysis. Neuroimaging analyses further supported the hypothesis that choices between affect-rich options are based on qualitatively different cognitive processes than choices between affect poor options; the two triggered qualitatively different brain circuits. Affect-rich problems engage more affective processing, as indicated by stronger activation in the amygdala. The results suggested that affect-poor choice is based on calculative processes, whereas affect-rich choice involves emotional processing and autobiographical memories. When a choice elicits strong emotions, decision makers seem to focus instead on the potential outcomes and the memories attached to them.
According to Suter et al, on a theoretical level, models assuming expectation maximization (and implementing the weighting of some function of outcome by some function of probability) may fail to accurately predict people’s choices in the context of emotionally laden outcomes. Instead, alternative modeling frameworks (e.g., simplifying, lexicographic cognitive strategies) may be more appropriate. On a practical level, the researchers suggest that to the extent that people show strongly attenuated sensitivity to probability information (or even neglect it altogether) in decisions with affect-rich outcomes, different decision aids may be required to help them make good choices. For instance, professionals who communicate risks, such as doctors or policy makers, may need to pay special attention to refocusing people’s attention on the probabilities of (health) risks by illustrating those risks visually.
This paper does not present things in ways that I have seen often. It focuses on the most compensatory analytic strategies like prospect theory and says that these strategies do not reflect how we make decisions that are emotionally laden. It suggests that simplifying lexicographic strategies may be more appropriate. Other studies that have used decision times and eye tracking instead of fMRI have made it clear that compensatory analytic strategies do not reflect actual decision making, although not as definitively. We also know it from our own experiences. However, from my understanding, this does not necessarily push us to lexicographic strategies. There are compensatory strategies like parallel constraint satisfaction that might also be the explanation. It may be that this is just part of the cognitive niches v. parallel constraint satisfaction or evidence accumulation decision models debate. Fuzzy trace theory is another candidate that is not a lexicographic strategy.
This post looks at signal detection theory (SDT) once again. Ken Hammond helped me see the power of signal detection as a descriptive theory (post Irreducible Uncertainty..) The last year of news with respect to fatal encounters between the police and the public has made me think of signal detection again as quite relevant. I should note that Ken Hammond died in May 2015 and I am looking for his last paper “Concepts from Aeronautical Engineering Can Lead to Advances in Social Psychology”. This post is based on a paper: “Signal Detection by Human Observers: A Cutoff Reinforcement Learning Model of Categorization Decisions Under Uncertainty,” written by Ido Erev that appeared in the Journal of the American Psychological Association, 1998, Vol. 105, No. 2, 280-298. This paper is important, but dated.
Many common activities involve binary categorization decisions under uncertainty. The police must try to distinguish between the individuals who can and want to harm the public and/or the police from others. A doctor has to decide whether or not he should do more tests to see if you may have cancer. According to Erev, the frequent performance of categorization decisions and the observation that they can have high survival value suggest that the cognitive processes that determine these decisions should be simple and adaptive. Thus, it could be hypothesized that one basic (simple and adaptive) model can be used to describe these processes within a wide set of situations.
This post looks at the medical/health component of decision making as addressed in Gerd Gigerenzer’s new book, Risk Saavy, How to Make Good Decisions. First, Gigerenzer has contributed greatly to improving health decision making. This blog includes three consecutive posts on the Statistics of Health Decision Making based on Gigerenzer’s work.
He points out both the weaknesses of screening tests and our understanding of the results. We have to overcome our tendency to see linear relationships when they are nonlinear. Doctors are no different. The classic problem is an imperfect screening test for a relatively rare disease. You cannot think in fractions or percentages. You must think in absolute frequencies. Breast cancer screening is one example. Generally, it can catch about 90% of breast cancers and only about 9% test positive who do not have breast cancer. So if you have a positive test, that means chances are you have breast cancer. No! You cannot let your intuition get involved especially when the disease is more rare than the test’s mistakes. If we assume that 10 out of 1000 women have breast cancer, then 90% or 9 will be detected, but about 90 of the 1000 women will test positive who do not have disease. Thus only 9 of the 99 who test positive actually have breast cancer. I know this, but give me a new disease or a slightly different scenario and let a month pass, I will still be tempted to shortcut the absolute frequencies and get it wrong.
This post is based on a paper by Rebecca Ferrer, William Klein, Jennifer Lerner, Valerie Reyna, and Dacher Keltner: “Emotions and Health Decison-Making, Extending the Appraisal Tendency Framework to Improve Health and Healthcare,” in Behavioral Economics and Public Health, 2014. I note that Valerie Reyna is one of the authors of fuzzy trace theory (see post Fuzzy Trace Theory-Meaning, Memory, Development and subsequent posts.) which I find interesting.
The authors use the appraisal tendency framework (ATF) to predict how emotions may interact with situational factors to improve or degrade health-related decisions. The paper examines four categories of judgments and thought processes as related to health decisions: risk perception, valuation and reward-seeking, interpersonal attribution, and depth of information processing. They illustrate ways in which a better understanding of emotion can improve judgments and choices regarding health.
The ATF assumes that specific emotions give rise to corresponding cognitive and motivational processes that are related to the target of the emotion (i.e., the situation, person, or other stimulus that elicited the emotion). In contrast to theories that predict how broad mood states (positive or negative) may influence judgment and decision making, the ATF offers specific predictions for how discrete emotions will influence judgment and decision making (See Tables 1 and 2).
This post is based on the paper, “Fuzzy Trace Theory and Medical Decisions by Minors: Differences in Reasoning between Adolescents and Adults,” by Evan Wilhelms and Valerie Reyna that appeared in the June 2013, Journal of Medical Philosophy. This is an application of Fuzzy Trace Theory to the medical decision setting. The concept is more generally addressed in the first of three posts: FTT Meaning, Memory, and Development.
The mature minor exception allows adolescents under the age of 18 to make medical decisions and consent to procedures with equivalent authority of an adult. Although this was originally conceived to be applied in emergency situations in which parents are not available, it now according to Wilhelms and Reyna represents a blanket exception for those over the age of 14, so long as the benefits outweigh the risks and the adolescent is not otherwise deemed intellectually incapable. This expansion of rights has been used for easier access to abortion and contraceptives without parental consent, as well as the access to treatment for sexually transmitted infections, addictions, mental health problems and prenatal care. On occasion, this expanded legal standing of minors has been used to justify treatment refusal.
I lost my composure recently after reading David Brooks column “Beyond the Brain” in the June 17, 2013, New York Times. I tried to write a post in response to it, but it got too political and personal quick. I decided that is not what I want to do here. But this post is prompted by something personal.
In the spring of 2011, my then 57 year old sister went to her internal medicine doctor after experiencing a large amount of blood in her urine. She was referred to a urologist who did several tests and scans and found no issues. In August 2012 after experiencing back and leg pain, she was diagnosed with bladder cancer of the renal pelvis-Stage IV. She has been through chemotherapy, radiation, surgery, and now more chemotherapy. She has not seen the same urologist or his group since the diagnosis.
Gigerenzer says that we must teach risk literacy in medical school and statistical literacy to all in primary school. He and his colleagues go into considerable detail to say how this should be done. Teaching statistics early is not sufficient. It is also essential to represent probabilistic information in forms that the human mind can grasp. To this end, visual and hands-on material can enable a playful development of statistical thinking. For instance, tinker-cubes are lego-like units that first graders can use to represent simple events, to combine to represent joint events, and to count to determine conditional frequencies.
Gerd Gigerenzer has produced much work that is relevant and easy to understand for the laymen. Gigerenzer has defended the effectiveness and our abilities to use heuristics. He has also tirelessly promoted statistics education and transparent presentation of statistical information. This post tries to summarize some unique and interesting material with respect to the causes and consequences of our statistical illiteracy.
Gerd Gigerenzer along with Wolfgang Gaissmaier, Elke Kurz-Milcke, Lisa M. Schwartz, and Steven Woloshin suggest that the long opposition to health statistics can be traced back to the struggle between three 19th-century visions of the physician: artist, determinist, or statistician. They argue that these professional ideals go hand in hand with patients’ corresponding ideals, which even today determine the mixture of feelings about health statistics: The artist embodies paternalism and requests blind trust from the patient, the determinist strives for perfect knowledge of causes and invites the illusion of certainty in patients, and the statistician relies on facts rather than medical charisma, paving the way for shared decision making. The first two ideals effectively deter interest in health statistics.
This will be the first post of a three-part series. They will all be based on a 2008 monograph that appeared in Psychological Science in the Public Interest, entitled: “Helping Doctors and Patients Make Sense of Health Statistics.” Gerd Gigerenzer was the primary author. I have started posts on numeracy, especially health numeracy, based on journal articles, but they tended to be focused on the public’s generally poor numeracy skills. This always stopped me in my tracks, because it left out the doctors who have skills much like the public, and it left out the researchers, screening test providers, drug makers, and device makers who tend to have excellent skills. They sometimes use those skills to take advantage of the doctors, patients, and the public(for my purposes journalists are just part of the public). This monograph puts the pieces together.