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 is based on the paper: “The Affect Gap in Risky Choice: Affect-Rich Outcomes Attenuate Attention to Probability Information,” authored by Thorsten Pachur, Ralph Hertwig, and Roland Wolkewitz that appeared in Decision, 2013, Volume 1, No. 1, p 64-78. This is a continuation of the affect/ emotion theme. It is more of a valence based idea than Lerner’s Appraisal Tendency Framework. This is more thinking about emotion than actually experiencing it although the two can come together.
Often risky decisions involve outcomes that can create considerable emotional reactions. Should we travel by plane and tolerate a minimal risk of a fatal terrorist attack or take the car and run the risk of traffic jams and car accidents? How do people make such decisions? Decisions under risk typically obey the principle of the maximization of expectation.
The expectation expresses the average of an option’s outcomes, each weighted by its
probability. This, of course, underlies expected utility theory and cumulative prospect theory and these models do a good job in accounting for choices among relatively affect-poor
This post examines a couple of applications of Signal Detection Theory. Both are technically beyond me, but the similarities in the applications seem instructive. In both articles, SDT is used to evaluate questionnaire type screening tools. This is not a big surprise since it is where most of us first saw applications of statistical hypothesis testing and those false positives and false negatives. One paper looks at BRCA genetic risk screening and the other depression screening. In both cases, the screening instruments do not propose to be gold standards, but only introductory screening. It might be the type of screening that internal medicine doctors might do. In both cases, there is the idea that pure probability based instruments are ineffective, due to the biases that most people carry with them. One paper utilizes a fast and frugal decision tree(FFT) and the other three risk categories to provide the gist as in fuzzy trace theory(FTT). This gives us promotion of two similar acronyms: FFT and FTT.
This post examines two papers studying expertise and decision making. First, I will summarize the findings of each paper and then, at the end, discuss them.
Customs Officer Expertise
The first paper is “Expert intuitions: How to model the decision strategies of airport customs officers?” authored by Thorsten Pachur and Gianmarco Marinello. They asked Swiss airport customs officers in Zurich and Bern and a novice control group to decide which passengers (described on several cue dimensions) they would submit to a search. Additionally, participants estimated the validities of the different cues. Then the researchers modeled the decisions using compensatory strategies, which integrate many cues, and a non-compensatory heuristic, which relies on one-reason decision making. Their analysis of the decisions of airport customs officers suggests that experts prefer simple decision strategies that rely on few cues and go without integration, whereas novices tend to use compensatory strategies that integrate multiple cues. Among the compensatory strategies, weighted-additive provided the worst account of participants’ decisions. Pachur specifically notes that this contradicts the claim that much of people’s decision making is based on an automatic weighted integration process as postulated by Glöckner & Betsch.