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.

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.

Human-like machines: Transparency and comprehensibility.

Artificial 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.

On theory integration: Toward developing affective components within cognitive architectures.

In The Cognitive-Emotional Brain, Pessoa (2013) suggests that cognition and emotion should not be considered separately. We agree with this and argue that cognitive architectures can provide steady ground for this kind of theory integration and for investigating interactions among underlying cognitive processes. We briefly explore how affective components can be implemented and how neuroimaging measures can help validate models and influence theory development.

Fermian guesstimation can boost the wisdom-of-the-inner-crowd.

How can people's ability to make accurate estimations be boosted? Psychological research on the wisdom-of-the-inner-crowd suggests that people's judgments improve when they use a simple consider-the-opposite-strategy, dubbed-inspired by Enlightenment philosopher Hegel-dialectical-bootstrapping: A person generates a first estimate (thesis), then rejects it and generates another one (anti-thesis), and finally integrates both (synthesis). Yet, the wisdom-of-the-inner-crowd-phenomenon comes with controversy concerning its measurement, robustness, and moderators. We (1) introduce a novel class of strategies to elicit the wisdom-of-the-inner-crowd. These strategies root in physics, where Nobel-laureate Enrico Fermi used back-of-the-envelope guesstimation, for instance, when assessing the explosive yield of the first tested nuclear bomb. Fermian strategies prescribe decomposing an estimation problem into subtasks, solving the subtasks separately, and ultimately integrating those solutions into a final estimate. In an experiment using a new task-environment, we find (2) that a similarity-based Fermian-strategy boosts the wisdom-of-the-inner-crowd even more than consider-the-opposite does, (3) that the provision of a memory aid differentially affects those two strategies' performance, and (4) that data trimming matters. Moreover, and for the first time, we document (5) overprecision in wisdom-of-the-inner-crowd estimations. Finally, we (6) replicate previous results, including that the collective intelligence of two persons still outperforms asking oneself twice.

Processes models, environmental analyses, and cognitive architectures: quo vadis quantum probability theory?

A lot of research in cognition and decision making suffers from a lack of formalism. The quantum probability program could help to improve this situation, but we wonder whether it would provide even more added value if its presumed focus on outcome models were complemented by process models that are, ideally, informed by ecological analyses and integrated into cognitive architectures.

Decision making in a human population living sustainably.

The Tiwi people of northern Australia have managed natural resources continuously for 6000-8000 years. Tiwi management objectives and outcomes may reflect how they gather information about the environment. We qualitatively analyzed Tiwi documents and management techniques to examine the relation between the social and physical environment of decision makers and their decision-making strategies. We hypothesized that principles of bounded rationality, namely, the use of efficient rules to navigate complex decision problems, explain how Tiwi managers use simple decision strategies (i.e., heuristics) to make robust decisions. Tiwi natural resource managers reduced complexity in decision making through a process that gathers incomplete and uncertain information to quickly guide decisions toward effective outcomes. They used management feedback to validate decisions through an information loop that resulted in long-term sustainability of environmental use. We examined the Tiwi decision-making processes relative to management of barramundi (Lates calcarifer) fisheries and contrasted their management with the state government's management of barramundi. Decisions that enhanced the status of individual people and their attainment of aspiration levels resulted in reliable resource availability for Tiwi consumers. Different decision processes adopted by the state for management of barramundi may not secure similarly sustainable outcomes.

Good judgments do not require complex cognition.

What cognitive capabilities allow Homo sapiens to successfully bet on the stock market, to catch balls in baseball games, to accurately predict the outcomes of political elections, or to correctly decide whether a patient needs to be allocated to the coronary care unit? It is a widespread belief in psychology and beyond that complex judgment tasks require complex solutions. Countering this common intuition, in this article, we argue that in an uncertain world actually the opposite is true: Humans do not need complex cognitive strategies to make good inferences, estimations, and other judgments; rather, it is the very simplicity and robustness of our cognitive repertoire that makes Homo sapiens a capable decision maker.

Reflections of the social environment in chimpanzee memory: applying rational analysis beyond humans.

In cognitive science, the rational analysis framework allows modelling of how physical and social environments impose information-processing demands onto cognitive systems. In humans, for example, past social contact among individuals predicts their future contact with linear and power functions. These features of the human environment constrain the optimal way to remember information and probably shape how memory records are retained and retrieved. We offer a primer on how biologists can apply rational analysis to study animal behaviour. Using chimpanzees (Pan troglodytes) as a case study, we modelled 19 years of observational data on their social contact patterns. Much like humans, the frequency of past encounters in chimpanzees linearly predicted future encounters, and the recency of past encounters predicted future encounters with a power function. Consistent with the rational analyses carried out for human memory, these findings suggest that chimpanzee memory performance should reflect those environmental regularities. In re-analysing existing chimpanzee memory data, we found that chimpanzee memory patterns mirrored their social contact patterns. Our findings hint that human and chimpanzee memory systems may have evolved to solve similar information-processing problems. Overall, rational analysis offers novel theoretical and methodological avenues for the comparative study of cognition.

Once and for all--how people change strategy to ignore irrelevant information in visual tasks.

Ignoring irrelevant visual information aids efficient interaction with task environments. We studied how people, after practice, start to ignore the irrelevant aspects of stimuli. For this we focused on how information reduction transfers to rarely practised and novel stimuli. In Experiment 1, we compared competing mathematical models on how people cease to fixate on irrelevant parts of stimuli. Information reduction occurred at the same rate for frequent, infrequent, and novel stimuli. Once acquired with some stimuli, it was applied to all. In Experiment 2, simplification of task processing also occurred in a once-for-all manner when spatial regularities were ruled out so that people could not rely on learning which screen position is irrelevant. Apparently, changes in eye movements were an effect of a once-for-all strategy change rather than a cause of it. Overall, the results suggest that participants incidentally acquired knowledge about regularities in the task material and then decided to voluntarily apply it for efficient task processing. Such decisions should be incorporated into accounts of information reduction and other theories of strategy change in skill acquisition.

Strategy selection: An introduction to the modeling challenge.

Modeling the mechanisms that determine how humans and other agents choose among different behavioral and cognitive processes-be they strategies, routines, actions, or operators-represents a paramount theoretical stumbling block across disciplines, ranging from the cognitive and decision sciences to economics, biology, and machine learning. By using the cognitive and decision sciences as a case study, we provide an introduction to what is also known as the strategy selection problem. First, we explain why many researchers assume humans and other animals to come equipped with a repertoire of behavioral and cognitive processes. Second, we expose three descriptive, predictive, and prescriptive challenges that are common to all disciplines which aim to model the choice among these processes. Third, we give an overview of different approaches to strategy selection. These include cost-benefit, ecological, learning, memory, unified, connectionist, sequential sampling, and maximization approaches. We conclude by pointing to opportunities for future research and by stressing that the selection problem is far from being resolved. WIREs Cogn Sci 2014, 5:39-59. doi: 10.1002/wcs.1265 For further resources related to this article, please visit the WIREs website.

Transferring control demands across incidental learning tasks - stronger sequence usage in serial reaction task after shortcut option in letter string checking.

After incidentally learning about a hidden regularity, participants can either continue to solve the task as instructed or, alternatively, apply a shortcut. Past research suggests that the amount of conflict implied by adopting a shortcut seems to bias the decision for vs. against continuing instruction-coherent task processing. We explored whether this decision might transfer from one incidental learning task to the next. Theories that conceptualize strategy change in incidental learning as a learning-plus-decision phenomenon suggest that high demands to adhere to instruction-coherent task processing in Task 1 will impede shortcut usage in Task 2, whereas low control demands will foster it. We sequentially applied two established incidental learning tasks differing in stimuli, responses and hidden regularity (the alphabet verification task followed by the serial reaction task, SRT). While some participants experienced a complete redundancy in the task material of the alphabet verification task (low demands to adhere to instructions), for others the redundancy was only partial. Thus, shortcut application would have led to errors (high demands to follow instructions). The low control demand condition showed the strongest usage of the fixed and repeating sequence of responses in the SRT. The transfer results are in line with the learning-plus-decision view of strategy change in incidental learning, rather than with resource theories of self-control.

Heuristic decision making in medicine.

Can less information be more helpful when it comes to making medical decisions? Contrary to the common intuition that more information is always better, the use of heuristics can help both physicians and patients to make sound decisions. Heuristics are simple decision strategies that ignore part of the available information, basing decisions on only a few relevant predictors. We discuss: (i) how doctors and patients use heuristics; and (ii) when heuristics outperform information-greedy methods, such as regressions in medical diagnosis. Furthermore, we outline those features of heuristics that make them useful in health care settings. These features include their surprising accuracy, transparency, and wide accessibility, as well as the low costs and little time required to employ them. We close by explaining one of the statistical reasons why heuristics are accurate, and by pointing to psychiatry as one area for future research on heuristics in health care.

Cognitive niches: an ecological model of strategy selection.

How do people select among different strategies to accomplish a given task? Across disciplines, the strategy selection problem represents a major challenge. We propose a quantitative model that predicts how selection emerges through the interplay among strategies, cognitive capacities, and the environment. This interplay carves out for each strategy a cognitive niche, that is, a limited number of situations in which the strategy can be applied, simplifying strategy selection. To illustrate our proposal, we consider selection in the context of 2 theories: the simple heuristics framework and the ACT-R (adaptive control of thought-rational) architecture of cognition. From the heuristics framework, we adopt the thesis that people make decisions by selecting from a repertoire of simple decision strategies that exploit regularities in the environment and draw on cognitive capacities, such as memory and time perception. ACT-R provides a quantitative theory of how these capacities adapt to the environment. In 14 simulations and 10 experiments, we consider the choice between strategies that operate on the accessibility of memories and those that depend on elaborate knowledge about the world. Based on Internet statistics, our model quantitatively predicts people's familiarity with and knowledge of real-world objects, the distributional characteristics of the associated speed of memory retrieval, and the cognitive niches of classic decision strategies, including those of the fluency, recognition, integration, lexicographic, and sequential-sampling heuristics. In doing so, the model specifies when people will be able to apply different strategies and how accurate, fast, and effortless people's decisions will be.

From recognition to decisions: extending and testing recognition-based models for multialternative inference.

The recognition heuristic is a noncompensatory strategy for inferring which of two alternatives, one recognized and the other not, scores higher on a criterion. According to it, such inferences are based solely on recognition. We generalize this heuristic to tasks with multiple alternatives, proposing a model of how people identify the consideration sets from which they make their final decisions. In doing so, we address concerns about the heuristic's adequacy as a model of behavior: Past experiments have led several authors to conclude that there is no evidence for a noncompensatory use of recognition but clear evidence that recognition is integrated with other information. Surprisingly, however, in no study was this competing hypothesis--the compensatory integration of recognition--formally specified as a computational model. In four studies, we specify five competing models, conducting eight model comparisons. In these model comparisons, the recognition heuristic emerges as the best predictor of people's inferences.