The Influence of Basic Cognitive Processes on Economic Decision Making

Decisions are commonly considered to be determined by an individual’s preferences. However, research has found that basic cognitive processes, such as number perception, memory limitations, and selective attention can substantially influence decision making. Distinguishing between the cognitive and the preferential aspects of behavior is important to understand and predict how people make judgments and decisions. In this thesis, the influence of numerical cognition, option complexity, and cognitive ability on decision making are investigated in two projects. Results from the first study revealed that option complexity can reduce the choice probability of a monetary lottery. Similarly, the study participants also valued complex options less than simple ones. This effect was especially pronounced for individuals with relatively lower cognitive ability. These results could be best explained by assuming that humans have a tendency to avoid the exertion of cognitive effort. In the second project, the results revealed that subjecting adult participants to unfamiliar place value systems leads to them making logarithmic-looking magnitude judgments in a ruler task. These results support the hypothesis that the compression (underestimation) in symbolic (e.g., 4) number perception is critically shaped by the place value system of decimal numbers, and therefore looks substantively different from compression in non-symbolic (e.g., dot clouds) number perception, which was found to be power-function shaped. This distinction contributes to the understanding of how numbers in decision making tasks are processed and evaluated. Together, these projects illuminate the potential of considering the influence of basic cognitive processes on decision making in describing, understanding, and predicting decision making behavior

Complexity aversion in risky choices and valuations: Moderators and possible causes

In the age of digitalization and globalization, an abundance of information is available, and our decision environments have become increasingly complex. However, it remains unclear under what circumstances complexity affects risk taking. In two experiments with monetary lotteries (one with a stratified national sample), we investigate behavioral effects and provide a cognitive explanation for the impact of complexity on risk taking. Results show that complexity, defined as the number of possible outcomes of a risky lottery, decreased the choice probability of an option but had a smaller and less consistent effect when evaluating lotteries independently. Importantly, choices of participants who spent more time looking at the complex option were less affected by complexity. A tendency to avoid cognitive effort can explain these effects, as the effort associated with evaluating the complex option can be sidestepped in choice tasks, but less so in valuation tasks. Further, the effect of complexity on valuations was influenced by individual differences in cognitive ability, such that people with higher cognitive ability showed less complexity aversion. Together, the results show that the impact of complexity on risk taking depends on both, decision format and individual differences and we discuss cognitive processes that could give rise to these effects

Valuation and estimation from experience

The processing of sequentially presented numerical information is a prerequisite for decisions from experience, where people learn about potential outcomes and their associated probabilities and then make choices between gambles. Little is known, however, about how people's preference for choosing a gamble is affected by how they perceive and process numerical information. To address this, we conducted a series of experiments wherein participants repeatedly sampled numbers from continuous outcome distributions. They were incentivized either to estimate the means of the numbers or to state their minimum selling prices to forgo a consequential draw from the distributions (i.e., the certainty equivalents or valuations). We found that participants valued distributions below their means, valued high-variance sequences lower than low-variance sequences, and valued left-skewed sequences lower than right-skewed sequences. Though less pronounced, similar patterns occurred in the mean estimation task where preferences should not play a role. These results are not consistent with prior findings in decision from experience such as the overweighting of high numbers and the underweighting of rare events. Rather, the qualitative effects, as well as the similarity of effects in valuation and estimation, are consistent with the assumption that people process numbers on a compressed mental number line in valuations from experience

Complexity Aversion in Risky Choices and Valuations: Moderators and Possible Causes

In the age of digitalization and globalization, an abundance of information is available, and our decision environments have become increasingly complex. However, it remains unclear under what circumstances complexity affects risk taking. In two experiments with monetary lotteries (one with a stratified national sample), we investigate behavioral effects and provide a cognitive explanation for the impact of complexity on risk taking. Results show that complexity, defined as the number of possible outcomes of a risky lottery, decreased the choice probability of an option but had a smaller and less consistent effect when evaluating lotteries independently. Importantly, choices of participants who spent more time looking at the complex option were less affected by complexity. A tendency to avoid cognitive effort can explain these effects, as the effort associated with evaluating the complex option can be sidestepped in choice tasks, but less so in valuation tasks. Further, the effect of complexity on valuations was influenced by individual differences in cognitive ability, such that people with higher cognitive ability showed less complexity aversion. Together, the results show that the impact of complexity on risk taking depends on both, decision environments and individual differences and we discuss cognitive processes that could give rise to these effects

Complexity Aversion in Risk Preferences

The project will investigate the influence of complexity on risk preferences

The Influence of the Place Value System on Symbolic Number Perception

Past research on numerical cognition has suggested that both symbolic and non-symbolic numbers are mapped onto the same compressed mental analogue representation. However, experiments using magnitude estimation tasks show logarithmic compression of symbolic numbers while the compression of non-symbolic numbers has a power-function shape. This warrants closer inspection of what differentiates the two processes. In this study, we hypothesized that estimates of symbolic numbers are systematically shaped by the format in which they are represented, namely, the place value system. To investigate this, we tested adults (N = 188) on a magnitude estimation task with unfamiliar base-26 and base-5 scales. Results reveal that adults showed systematic, logarithmic-looking underestimation on both scales, indicating that the place value system itself can cause the pattern. Additionally, the observed shape of participants’ estimates on both scales could be well explained with a simple model that assumed insufficient understanding of exponential growth (i.e., a characteristic of place value systems). Taken together, our results suggest that the discrepancy between symbolic and non-symbolic number compression can be explained by taking the effect of the place value system into account

Integrative modeling of hemodynamic changes and perfusion impairment in coronary microvascular disease

Introduction: Coronary microvascular disease is one of the responsible factors for cardiac perfusion impairment. Due to diagnostic and treatment challenges, this pathology (characterized by alterations to microvasculature local hemodynamics) represents a significant yet unsolved clinical problem.Methods: Due to the poor understanding of the onset and progression of this disease, we propose a new and noninvasive strategy to quantify in-vivo hemodynamic changes occurring in the microvasculature. Specifically, we here present a conceptual workflow that combines both in-vitro and in-silico modelling for the analysis of the hemodynamic alterations in the microvasculature.Results: First, we demonstrate a hybrid additive manufacturing process to fabricate circular cross-section, biocompatible fluidic networks in polytetrafluoroethylene. We then use these microfluidic devices and computational fluid dynamics to simulate different degrees of perfusion impairment.Discussion: Ultimately, we show that the developed workflow defines a robust platform for the multiscale analysis of multifactorial events occurring in coronary microvascular disease.ISSN:2296-418

Practicing theory building in a many modelers hackathon: A proof of concept

Scientific theories reflect some of humanity's greatest epistemic achievements. The best theories motivate us to search for discoveries, guide us towards successful interventions, and help us to explain and organize knowledge. Such theories require a high degree of specificity, and specifying them requires modeling skills. Unfortunately, in psychological science, theories are often not precise, and psychological scientists often lack the technical skills to formally specify existing theories. This problem raises the question: How can we promote formal theory development in psychology, where there are many content experts but few modelers? In this paper, we discuss one strategy for addressing this issue: a Many Modelers approach. Many Modelers consist of mixed teams of modelers and non-modelers that collaborate to create a formal theory of a phenomenon. We report a proof of concept of this approach, which we piloted as a three-hour hackathon at the SIPS 2021 conference. We find that (a) psychologists who have never developed a formal model can become excited about formal modeling and theorizing; (b) a division of labor in formal theorizing could be possible where only one or a few team members possess the prerequisite modeling expertise; and (c) first working prototypes of a theoretical model can be created in a short period of time