Ana M. Franco-Watkins

Psychological plausibility of the theory of probabilistic mental models and the fast and frugal heuristics.

The theory of probabilistic mental models (PMM; G. Gigerenzer, U. Hoffrage, & H. Kleinbölting, 1991) has had a major influence on the field of judgment and decision making, with the most recent important modifications to PMM theory being the identification of several fast and frugal heuristics (G. Gigerenzer & D. G. Goldstein, 1996). These heuristics were purported to provide psychologically plausible cognitive process models that describe a variety of judgment behavior. In this article, the authors evaluate the psychological plausibility of the assumptions upon which PMM were built and, consequently, the psychological plausibility of several of the fast and frugal heuristics. The authors argue that many of PMM theorys assumptions are questionable, given available data, and that fast and frugal heuristics are, in fact, psychologically implausible.

Taxing executive processes does not necessarily increase impulsive decision making.

A link has been established between impulsivity in real-world situations and impulsive decision making in laboratory tasks in brain-damaged patients and individuals with substance abuse. Whether or not this link exists for all individuals is less clear. We conducted an experiment to determine whether taxing central executive processes with a demanding cognitive load task results in impulsive decision making in a normal sample. Participants (n = 53) completed a delay discounting task under the presence (load condition) and absence (control condition) of a demanding generation task. Results indicated that taxing working memory is neither necessary nor sufficient to produce impulsive decision making; instead, the demanding generation task resulted in an increase in the number of inconsistent choices.

Decision moving window: using interactive eye tracking to examine decision processes

It has become increasingly more important for researchers to better capture the complexities of making a decision. To better measure cognitive processes such as attention during decision making, we introduce a new methodology: the decision moving window, which capitalizes on both mouse-tracing and eye-tracking methods. We demonstrate the effectiveness of this methodology in a probabilistic inferential decision task where we reliably measure attentional processing during decision making while allowing the person to determine how information is acquired. We outline the advantages of this methodological paradigm and how it can advance both decision-making research and the development of new metrics to capture cognitive processes in complex tasks.

Implications of cognitive load for hypothesis generation and probability judgment.

We tested the predictions of HyGene (Thomas et al., 2008) that both divided attention at encoding and judgment should affect the degree to which participants’ probability judgments violate the principle of additivity. In two experiments, we showed that divided attention during judgment leads to an increase in subadditivity, suggesting that the comparison process for probability judgments is capacity limited. Contrary to the predictions of HyGene, a third experiment revealed that divided attention during encoding leads to an increase in later probability judgment made under full attention. The effect of divided attention during encoding on judgment was completely mediated by the number of hypotheses participants generated, indicating that limitations in both encoding and recall can cascade into biases in judgments.

Comprehension of hazard communication: Effects of pictograms on safety data sheets and labels

INTRODUCTION The United Nations has proposed the Globally Harmonized System (GHS) of Classification and Labelling of Chemicals to make hazard communication more uniform and to improve comprehension. METHOD Two experiments were conducted to test whether the addition of hazard and precautionary pictograms to safety data sheets and product labels would improve the transfer of information to users compared to safety data sheets and product labels containing text only. Additionally, naïve users, workers, and experts were tested to determine any potential differences among users. RESULTS The effect of adding pictograms to safety data sheets and labels was statistically significant for some conditions, but was not significant across all conditions. One benefit of the addition of pictograms was that the time to respond to the survey questions decreased when the pictograms were present for both the SDS and the labels. GHS format SDS and labels do provide benefits to users, but the system will need further enhancements and modifications to continue to improve the effectiveness of hazard communication. IMPACT ON INDUSTRY The final rule to modify the HCS to include the Globally Harmonized System (GHS) for the Classification and Labelling of Chemicals announced by OSHA (2012b) will change the information content of every chemical SDS and label used in commerce. This study suggests that the inclusion of GHS hazard pictograms and precautionary pictograms to SDS and labels may benefit the user.

A Realistic Framework for Investigating Decision Making in the Brain With High Spatiotemporal Resolution Using Simultaneous EEG/fMRI and Joint ICA

Human decision making is a multidimensional construct, driven by a complex interplay between external factors, internal biases, and computational capacity constraints. Here, we propose a layered approach to experimental design in which multiple tasks—from simple to complex—with additional layers of complexity introduced at each stage are incorporated for investigating decision making. This is demonstrated using tasks involving intertemporal choice between immediate and future prospects. Previous functional magnetic resonance imaging (fMRI) and electroencephalographic (EEG) studies have separately investigated the spatial and temporal neural substrates, respectively, of specific factors underlying decision making. In contrast, we performed simultaneous acquisition of EEG/fMRI data and fusion of both modalities using joint independent component analysis such that: 1) the native temporal/spatial resolutions of either modality is not compromised and 2) fast temporal dynamics of decision making as well as involved deeper striatal structures can be characterized. We show that spatiotemporal neural substrates underlying our proposed complex intertemporal task simultaneously incorporating rewards, costs, and uncertainty of future outcomes can be predicted (using a linear model) from neural substrates of each of these factors, which were separately obtained by simpler tasks. This was not the case for spatial and temporal features obtained separately from fMRI and EEG, respectively. However, certain prefrontal activations in the complex task could not be predicted from activations in simpler tasks, indicating that the assumption of pure insertion has limited validity. Overall, our approach provides a realistic and novel framework for investigating the neural substrates of decision making with high spatiotemporal resolution.

Coherence-driven Reflective Equilibrium model of ethical decision-making

Instilling trust in autonomous systems requires having confidence that agents have decision-making mechanisms governed by ethical principles. The ability to resolve conflicts among moral principles, duties, obligations, and the consequences is a critical challenge in developing such mechanisms. We demonstrate how the Reflective Equilibrium Method and its implementation in terms of a parallel constraint satisfaction mechanism can help manage conflicts and simultaneously assess multiple principles in a context-sensitive manner. The proposed domain architecture and its implementation are used toward developing an Ethical Advisor for training purposes as well as for designing cognitive decision-making models. A high-level Domain-Specific Language is introduced to specify coherence-governed models of ethical decision-making, so that domain experts express concepts relevant to modeling ethical behavior instead of using general-purpose programming language constructs.

Computational models of ethical decision-making: A coherence-driven reflective equilibrium model

Abstract There are scientific and technical challenges that must be addressed in developing systems that interact with humans and work along with other agents in complex, dynamic, and uncertain environments where ethical concerns may arise. In such systems relationships between users and autonomous components will be driven as much by issues such as trust, responsibility, and acceptability, as technical ones such as planning and coordination. This paper provides a comprehensive review and classification of existing methods in machine ethics, resulting in delineation of specific challenges and issues. To address the identified challenges, we introduce a method that leverages the method of reflective equilibrium and the multi-coherence theory as a unifying constraint satisfaction framework to simultaneously assess multiple ethical principles and manage ethical conflicts in a context-sensitive manner.