Cognitive Science
Meta-Decision Making in Evaluative Processing
Current research in cognitive science conceptualizes meta-decision making as a form of cognitive control that determines the speed of processing, the degree of information seeking, and the quality of information integration. Here, we address the working hypothesis that meta-decisions would strategically allocate the amount of mental effort as a function of two levels of opportunity cost analysis: 1) anticipated gains relative to alternative options, and 2) anticipated gains relative to preexisting biases. Critically, we aim to establish how and when the consideration of multidimensional opportunity costs gives direction to meta-decision making.
Typical models of decision-making in psychology and neuroscience focus on two-alternative forced-choice (2AFC) paradigms that elicit a categorical type of information processing, which leaves no room for doubt, and provides no opportunity for seeking other options. For instance, Signal Detection Theory (Green & Swets, 1966) operates with a criterion that divides two response categories (signal present or absent), similar to statistical tests that impose a categorical decision on a continuous dimension (for a comprehensive review, see Lauwereyns, The Anatomy of Bias: How Neural Circuits Weigh the Options, MIT Press, 2010).
Meta-Decision Making in Evaluative Processing
Current research in cognitive science conceptualizes meta-decision making as a form of cognitive control that determines the speed of processing, the degree of information seeking, and the quality of information integration. Here, we address the working hypothesis that meta-decisions would strategically allocate the amount of mental effort as a function of two levels of opportunity cost analysis: 1) anticipated gains relative to alternative options, and 2) anticipated gains relative to preexisting biases. Critically, we aim to establish how and when the consideration of multidimensional opportunity costs gives direction to meta-decision making.
Typical models of decision-making in psychology and neuroscience focus on two-alternative forced-choice (2AFC) paradigms that elicit a categorical type of information processing, which leaves no room for doubt, and provides no opportunity for seeking other options. For instance, Signal Detection Theory (Green & Swets, 1966) operates with a criterion that divides two response categories (signal present or absent), similar to statistical tests that impose a categorical decision on a continuous dimension (for a comprehensive review, see Lauwereyns, The Anatomy of Bias: How Neural Circuits Weigh the Options, MIT Press, 2010).
However, in real life, decision-making frequently occurs in more fluid situations, in which the choice for one option may be pitched, not as a 2AFC problem, but as a decision problem in a more complex context, in which individuals may “decide not to decide” (Dror & Langenburg, 2018, J. Forensic Sci.) or “decide how to decide” (Boureau et al., 2015, Trends in Cogn. Sci.). This is particularly true for value-based decision-making, with respect to items that do not afford an obvious “right” or “wrong” answer, but involve subjective preferences or personal opinions.
Accordingly, many real-life decision situations give rise to non-linear decision-making, similar to foraging, in which the decision does not operate on a closed set of two alternatives, but rather on an open set of multiple options in an undefined sequence. For instance, during shopping, individuals may consider buying an item (e.g., a novel by Haruki Murakami), then decide to leave it for the time being, approach an entirely different item (e.g., a pink polo shirt), finally to come back to the first item after a chaotic itinerancy. In such foraging situations, key factors that drive the decision-making include the estimated opportunity costs, not only in terms of potential gains (Pirrone et al., 2018, Decision), but also in terms of time (Otto & Daw, 2018, Neuropsychologia), and effort (Shenhav et al., 2017, Annu. Rev. Neurosci.).
Accordingly, many real-life decision situations give rise to non-linear decision-making, similar to foraging, in which the decision does not operate on a closed set of two alternatives, but rather on an open set of multiple options in an undefined sequence. For instance, during shopping, individuals may consider buying an item (e.g., a novel by Haruki Murakami), then decide to leave it for the time being, approach an entirely different item (e.g., a pink polo shirt), finally to come back to the first item after a chaotic itinerancy. In such foraging situations, key factors that drive the decision-making include the estimated opportunity costs, not only in terms of potential gains (Pirrone et al., 2018, Decision), but also in terms of time (Otto & Daw, 2018, Neuropsychologia), and effort (Shenhav et al., 2017, Annu. Rev. Neurosci.).
It appears that the approach to value-based decision-making varies in terms of the time and effort devoted to information processing. As a corollary, the time and effort would determine the level of processing, in accordance with the classic notion by Craik and Lockhart (1972, J. Verb. Learn. & Verb. Behav.). Recent research has suggested that “meta-decision making” reflects a type of cognitive control that determines how individuals invest time and effort in processing relevant information (Boureau et al., 2015, Trends in Cogn. Sci.). This leads us to the key scientific question that comprises the core of the current research project: How does the consideration of different types of opportunity costs give direction to meta-decision making?
Bioethics
Empirical Bioethics: Toward a Fair Framing of Answer Space
The aim of this project, or set of projects, is to change the field of bioethics by creating an empirical research approach that merges the tools of cognitive science with the questions of bioethics. Critically, the issues in bioethics require us to make life-changing decisions about values and priorities in health, healthcare, well-being, and welfare, for humans as well as animals. However, the traditional approach to bioethics pays little or no attention to the cognitive and neural mechanisms that underlie decision-making. At best, the field has borrowed a few ready-made concepts about cognitive biases to exploit these through "nudging" - a form of libertarian paternalism (cf. Thaler & Sunstein, 2008).
In this project, we address the cognitive and neural mechanisms of decision-making as a dual-use type of knowledge. Aware of the potential dangers of exploitation, we aim to understand how the phrasing of questions changes the thinking toward answers, not through pre-conceived nudging, but through thorough empirical investigation of the variation and change in thought as a function of informational context. For this, it is necessary to chart the multidimensionality of ethical thinking, and to examine how the different factors are integrated and weighted at the most microscopic level of decision-making: within the minds of decision makers.
How do people weigh autonomy versus happiness versus fairness? How does this change as a function of attention, urgency, empathy, salience, and other states or frames of mind, as well more stable traits and factors, including religious affiliation, political orientation, and other aspects of identity? For a proper understanding of the cognitive dynamics, it is necessary to drastically reorient the approach to bioethics as an empirical enterprise, in which debate proceeds from relevant data on what - and how - people think.
Our point of departure is the question how the framing of “answer space” (e.g., categorical versus continuous; forced choice versus free choice; “yes or no?” versus “which of these?”) alters the decision processes, and the desirability of outcomes. The objective is to create a more informed approach toward decision-making for bioethical issues, with a view to optimizing 1) the quality of information-processing, 2) the amount of information-seeking, and 3) the consideration of opportunity costs.