We explore intuitive monitoring and control within a Brunswikian framework where intuition refers to probabilistic processes rooted in perception and inductive experience, while analysis involves deterministic processing of facts and symbols, in turn implying operational criteria in terms of different response distributions. In the context of the bat-and-ball problem, we test the claim that intuition uses contextual cues to monitor the analytic calculations. Across two experiments, we show that participants' responses fall into three categories: (i) the normative algorithm, (ii) a simple response bias, and (iii) responses where contextual cues are taken into consideration by switching the cost of the bat and the ball when such a shift is intuitively plausible. The results suggest that, although it may be difficult to elicit intuitive control in a maths quiz like the bat-and-ball problem, the choice of analytical calculation is affected by the intuitive plausibility of the output in the predicted direction.

Arriving at a clinical diagnosis using diagnostic criteria is fundamental to mental health practice. Here we argue that high diagnostic quality can only be achieved by aligning diagnostic frameworks with the cognitive capabilities of clinicians, which may require redefining not only the diagnostic procedures but also the criteria themselves.

In psychological research, noise is often considered a nuisance that obscures rather than contributes information. This simplification overlooks that noise can be informative and that by exploring the nature of the noise one can often draw additional conclusions concerning the underlying psychological processes. It is arguably only in recent years that the mainstream of researchers has taken this idea to heart and demonstrated that it can lead to breakthroughs in the understanding of human behavior. The aim of this special section is to showcase some of the ways in which systematic exploration of noise can be achieved and how it can enrich psychological research. In this introductory article, we introduce the idea of treating noise as endogenous as opposed to exogenous to the theoretical and statistical models of psychological phenomena. We then contribute a historical review of the role of noise in psychological research, including discussions of previous endogenous treatments of noise in the literature. As an illustration, we describe our own research on the precise/not precise model and show how noise distributions can be used to delineate analytic and intuitive modes of reasoning. Finally, we briefly introduce the other contributions to this special section.

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Cognitive models that assume that judgments are based on processes of sampling from memory have a long history in psychology and take a variety of forms, but the exact cognitive interpretations of them differ, are unclear, or remain elusive. Using the Precise/Not Precise (PNP) model (Sundh et al., 2021) we have revived an old approach to intuition and analyses, originally proposed by Egon Brunswik (1956). The model is based on the distinction between analytic algorithms that usually yield the same exact output and approximate intuitive algorithms that are rarely far off the mark but are inevitably perturbed by a random noise. The PNP model distinguishes intuitive and analytic processes depending on the error distributions around the model estimates. By combining the PNP model with specific cognitive algorithms, one can determine if analytic or intuitive cognitive processes implement the cognitive algorithms. In this chapter, we argue that also the memory sampling processes observed in multiple-cue judgments, characterized by good fit of the Generalized Context Model (Nosofsky, 2015), come in two different forms: one that involves analytic application of root-memorized individual exemplars and one that involves a noisy similarity-based inference about the likely criterion. We demonstrate that different parameterizations of the Generalized Context Model naturally imply response distributions that realize the distinction implied by the PNP model. With data from multiple-cue judgment, we show how the PNP model identifies, not only intuitive and analytic rule-based processes, but also processes of memory sampling with the empirical hallmarks of intuition and analysis.

When people use rule-based integration of abstracted cues to make multiple-cue judgments they tend to default to linear additive integration of the cues, which may interfere with efficient learning in non-additive tasks. We hypothesize that this effect becomes especially pronounced when cues are presented numerically rather than verbally, because numbers elicit expectations about a task with a simple numerical solution that can be appropriately addressed by linear and additive integration. This predicts that, relative to a verbal format, a numerical format should be advantageous for learning in additive tasks, but detrimental for learning in nonadditive tasks. In two experiments, we find support for the hypothesis that a verbal format can improve learning in non-additive tasks. The division-of-labor between cognitive processes observed in previous research (Juslin et al., 2008), with cue abstraction in additive tasks and exemplar memory in non-additive tasks, was only present in conditions with numeric information and may therefore in part be driven by the use of numeric formats. This illustrates how surface characteristic of stimuli can elicit different priors about the nature of the variables and the generative model that produced the cues and the criterion. We fitted cue-abstraction and exemplar algorithms by PNP-modeling (Sundh et al., 2021). At the end of training both cue abstraction and exemplar memory processes primarily involved exact analytic processes marred by occasional error, rather than the noisy and approximate intuitive processes typically assumed in previous studies - specifically, cue abstraction was primarily implemented by number crunching and exemplar memory by rote memorization.

The innovation systems framework facilitates a broad view on the actors, organizations and institutions involved in innovation processes, but innovation studies still suffer from an unbalanced focus on universities and firms, and a neglect of other organizational actors, among them research infrastructures. In this chapter, sociological theory of functional differentiation is put to use to analyze and conceptualize the role of infrastructures in innovation systems. With the help of a case study of the international Halden Reactor Project and its importance for the research, development and innovation activities in the Swedish nuclear energy industry, the chapter shows how research infrastructures can take enormously important roles in national and sectorial innovation systems, beyond what simple indicators can convey. On the basis of this analysis, the chapter argues that the specialized function of research infrastructures in innovation systems should be further acknowledged both in research and in policymaking.

In 1956, Brunswik proposed a definition of what he called intuitive and analytic cognitive processes, not in terms of verbally specified properties, but operationally based on the observable error distributions. In the decades since, the diagnostic value of error distributions has generally been overlooked, arguably because of a long tradition to consider the error as exogenous (and irrelevant) to the process. Based on Brunswik’s ideas, we develop the precise/not precise (PNP) model, using a mixture distribution to model the proportion of error-perturbed versus error-free executions of an algorithm, to determine if Brunswik’s claims can be replicated and extended. In Experiment 1, we demonstrate that the PNP model recovers Brunswik’s distinction between perceptual and conceptual tasks. In Experiment 2, we show that also in symbolic tasks that involve no perceptual noise, the PNP model identifies both types of processes based on the error distributions. In Experiment 3, we apply the PNP model to confirm the often-assumed “quasi-rational” nature of the rule-based processes involved in multiple-cue judgment. The results demonstrate that the PNP model reliably identifies the two cognitive processes proposed by Brunswik, and often recovers the parameters of the process more effectively than a standard regression model with homogeneous Gaussian error, suggesting that the standard Gaussian assumption incorrectly specifies the error distribution in many tasks. We discuss the untapped potentials of using error distributions to identify cognitive processes and how the PNP model relates to, and can enlighten, debates on intuition and analysis in dual-systems theories.

In 1956, Brunswik proposed a definition of what he called intuitive and analytic cognitive processes, not in terms of verbally specified properties, but operationally based on the observable error distributions. In the decades since, the diagnostic value of error distributions has generally been overlooked, arguably because of a long tradition to consider the error as exogenous (and irrelevant) to the process. Based on Brunswik’s ideas, we develop the precise/not precise (PNP) model, using a mixture distribution to model the proportion of error-perturbed versus error-free executions of an algorithm, to determine if Brunswik’s claims can be replicated and extended. In Experiment 1, we demonstrate that the PNP model recovers Brunswik’s distinction between perceptual and conceptual tasks. In Experiment 2, we show that also in symbolic tasks that involve no perceptual noise, the PNP model identifies both types of processes based on the error distributions. In Experiment 3, we apply the PNP model to confirm the often-assumed “quasi-rational” nature of the rule-based processes involved in multiple-cue judgment. The results demonstrate that the PNP model reliably identifies the two cognitive processes proposed by Brunswik, and often recovers the parameters of the process more effectively than a standard regression model with homogeneous Gaussian error, suggesting that the standard Gaussian assumption incorrectly specifies the error distribution in many tasks. We discuss the untapped potentials of using error distributions to identify cognitive processes and how the PNP model relates to, and can enlighten, debates on intuition and analysis in dual-systems theories.

Research points to the limitations of approaches to decision-making, that rest on general 'Newtonian principles' derived from unitary a priori conceptions of rationality. To understand how the mind exploits environments, we instead propose a process of more open-ended discovery and systematization in the mold of Linnaeus's famous taxonomy of plants.