Modeling and Aiding Intuition: Introduction to the Commentary Section

This section of JARMAC includes a series of commentaries on articles published in the September, 2015, special issue of JARMAC: "Modeling and aiding intuition in organizational decision making" (Marewski & Hoffrage, 2015). The commentaries focus on research programs such as naturalistic decision making, heuristics-and-biases, ACT-R, and CLARION. They feature topics ranging from evolution to decision styles. In this introduction, we provide a brief overview of those contributions, alongside with concluding words on this project of pulling together multiple and very different strands of research on intuition

Human-like machines: Transparency and comprehensibility [Commentary]

AbstractArtificial intelligence algorithms seek inspiration from human cognitive systems in areas where humans outperform machines. But on what level should algorithms try to approximate human cognition? We argue that human-like machines should be designed to make decisions in transparent and comprehensible ways, which can be achieved by accurately mirroring human cognitive processes.</jats:p

Broadening the Scope of Recognition Memory

Within the literature of psychological and decision sciences, there is a critical difference in the way recognition is defined and studied experimentally. To address this difference, the current experiment examines and attempts to disentangle the influence of two recognition judgment sources (from within an experiment and from an individual’s prior life experiences) upon two different recognition judgments. By presenting participants with a set of related stimuli that vary naturally in environmental occurrence and by manipulating exposure within an experimental context, this experiment allows for a broader and more ecologically valid assessment of recognition memory. Contrasting with the typical word-frequency effect, the results reveal an overall bias to judge high-frequency items as studied on an episodic recognition test. Additionally, the results underscore the role of context by showing that a single study exposure increases the probability that individuals will judge stimuli as presented outside the laboratory

Architectural process models of decision making: Towards a model database

We present the project aimed at creating a database of detailed architectural process models of memory-based decision models. Those models are implemented in the cognitive architecture ACT-R. In creating this database, we have identified commonalities and differences of various decision models in the literature. The model database can provide insights into the interrelation among decision models and can be used in future research to address debates on inferences from memory, which are hard to resolve without specifying the processing steps at the level of precision that a cognitive architecture provides

Narratives, environments, and decision-making: A fascinating narrative, but one to be completed.

I encourage Johnson et al. to ground Conviction Narrative Theory in more detail in foundational, earlier decision-making research - first and foremost in Herbert Simon's work. Moreover, I wonder if and how further reflections about narratives could aid tackling two interrelated grand challenges of the decision sciences: To describe decision-making environments; to understand how people select among decision-strategies in environments

Heuristics as conceptual lens for understanding and studying the usage of bibliometrics in research evaluation

While bibliometrics are widely used for research evaluation purposes, a common theoretical framework for conceptually understanding, empirically studying, and effectively teaching its usage is lacking. In this paper, we outline such a framework: the fast-and-frugal heuristics research program, proposed originally in the context of the cognitive and decision sciences, lends itself particularly well for understanding and investigating the usage of bibliometrics in research evaluations. Such evaluations represent judgments under uncertainty in which typically not all possible options, their consequences, and those consequences' probabilities of occurring may be known. In these situations of incomplete information, candidate descriptive and prescriptive models of human behavior are heuristics. Heuristics are simple strategies that, by exploiting the structure of environments, can aid people to make smart decisions. Relying on heuristics does not mean trading off accuracy against effort: while reducing complexity, heuristics can yield better decisions than more information-greedy procedures in many decision environments. The prescriptive power of heuristics is documented in a cross-disciplinary literature, cutting across medicine, crime, business, sports, and other domains. We outline the fast-and-frugal heuristics research program, provide examples of past empirical work on heuristics outside the field of bibliometrics, explain why heuristics may be especially suitable for studying the usage of bibliometrics, and propose a corresponding conceptual framework.Comment: in press at Scientometric

How to model the neurocognitive dynamics of decision making: A methodological primer with ACT-R

Higher cognitive functions are the product of a dynamic interplay of perceptual, mnemonic, and other cognitive processes. Modeling the interplay of these processes and generating predictions about both behavioral and neural data can be achieved with cognitive architectures. However, such architectures are still used relatively rarely, likely because working with them comes with high entry-level barriers. To lower these barriers, we provide a methodological primer for modeling higher cognitive functions and their constituent cognitive subprocesses with arguably the most developed cognitive architecture today—ACT-R. We showcase a principled method of generating individual response time predictions, and demonstrate how neural data can be used to refine ACT-R models. To illustrate our approach, we develop a fully specified neurocognitive model of a prominent strategy for memory-based decisions—the take-the-best heuristic—modeling decision making as a dynamic interplay of perceptual, motor, and memory processes. This implementation allows us to predict the dynamics of behavior and the temporal and spatial patterns of brain activity. Moreover, we show that comparing the predictions for brain activity to empirical BOLD data allows us to differentiate competing ACT-R implementations of take the best