Jan K. Woike

Correlates of diagnostic accuracy in patients with nonspecific complaints

Objective. To investigate diagnostic accuracy in patient histories involving nonspecific complaints and the extent to which characteristics of physicians and structural properties of patient histories are associated with accuracy. Methods. Six histories of patients presenting to the emergency department (ED) with nonspecific complaints were provided to 112 physicians: 36 ED physicians, 50 internists, and 26 family practitioners. Physicians listed the 3 most likely diagnoses for each history and indicated which cue(s) they considered crucial. Four weeks later, a subset of 20 physicians diagnosed the same 6 histories again. For each history, experts had previously determined the correct diagnoses and the diagnostic cues. Results. Accuracy ranged from 14% to 64% correct diagnoses (correct diagnosis listed as the most likely) and from 29% to 87% correct differential diagnoses (correct diagnosis listed in the differential). Acute care physicians (ED physicians and internists) included the correct diagnosis in the differential in, on average, 3.4 histories, relative to 2.6 for the family practitioners (P = 0.001, d = .75). Diagnostic performance was fairly reliable (r = .61, P < 0.001). Clinical experience was negatively correlated with diagnostic accuracy (r = –.25, P = 0.008). Two structural properties of patient histories—cue consensus and cue substitutability—were significantly associated with diagnostic accuracy, whereas case difficulty was not. Finally, prevalence of diagnosis also proved significantly correlated with accuracy. Conclusions. Average diagnostic accuracy in cases with nonspecific complaints far exceeds chance performance, and accuracy varies with medical specialty. Analyzing cue properties in patient histories can help shed light on determinants of diagnostic performance and thus suggest ways to enhance physicians’ ability to accurately diagnose cases with nonspecific complaints.

Integrating and testing natural frequencies, naïve Bayes, and fast-and-frugal trees.

This article relates natural frequency representations of cue-criterion relationships to fast-and-frugal heuristics for inferences based on multiple cues. In the conceptual part of this work, three approaches to classification are compared to one another: The first uses a natural Bayesian classification scheme, based on profile memorization and natural frequencies. The second is based on naïve Bayes, a heuristic that assumes conditional independence between cues (given the criterion). The third approach is to construct fast-and-frugal classification trees, which can be conceived as pruned versions of diagnostic natural frequency trees. Fast-and-frugal trees can be described as lexicographic classifiers but can also be related to another fundamental class of models, namely linear models. Linear classifiers with fixed thresholds and noncompensatory weights coincide with fast-and-frugal trees—not as processes but in their output. Various heuristic principles for tree construction are proposed. In the second, empirical part of this article, the classification performance of the three approaches when making inferences under uncertainty (i.e., out of sample) is evaluated in 11 medical data sets in terms of Receiver Operating Characteristics (ROC) diagrams and predictive accuracy. Results show that the two heuristic approaches, naïve Bayes and fast-and-frugal trees, generally outperform the model that is normative when fitting known data, namely classification based on natural frequencies (or, equivalently, profile memorization). The success of fast-and-frugal trees is grounded in their ecological rationality: Their construction principles can exploit the structure of information in the data sets. Finally, implications, applications, limitations, and possible extensions of this work are discussed.

Description and experience: How experimental investors learn about booms and busts affects their financial risk taking.

A few years ago, the world experienced the most severe economic crisis since the Great Depression. According to the depression baby hypothesis, people who live through such macroeconomic shocks take less financial risk in their future lives (e.g., lower stock market participation). This hypothesis has previously been tested against survey data. Here, we tested it in a simulated experimental stock market (based on the Spanish stock index, IBEX-35), varying both the length of historical data available to participants (including or excluding a macroeconomic shock) and the mode of learning about macroeconomic events (through sequential experience or symbolic descriptions). Investors who learned about the market from personal experience took less financial risk than did those who learned from graphs, thus echoing the description-experience gap observed in risky choice. In a second experiment, we reversed the market, turning the crisis into a boom. The description-experience gap persisted, with investors who experienced the boom taking more risk than those who did not. The results of a third experiment suggest that the observed gap is not driven by a wealth effect, and modeling suggests that the description-experience gap is explained by the fact that participants who learn from experience are more risk averse after a negative shock. Our findings highlight the crucial role of the mode of learning for financial risk taking and, by extension, in the legally required provision of financial advice.

Framing the debate on human-like framing effects in bonobos and chimpanzees: a comment on Krupenye et al. (2015).

Recently, Krupenye, Rosati & Hare (KRH henceforth) reported that bonobos and chimpanzees show ‘human-like framing effects’ in a food choice task [[1][1]]. Chimpanzees and bonobos could choose between a ‘framed’ option of fruit and an alternative option of peanuts (matched in expected value