Is numerical information always beneficial? Verbal and numerical cue-integration in additive and non-additive tasks.

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 non-additive 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.

Cognitiva Speciebus: Towards a Linnaean Approach to Cognition.

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.

Precise/not precise (PNP): A Brunswikian model that uses judgment error distributions to identify cognitive processes.

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.