Posts in Category: psychology

the study of (mostly human) sensation & perception, cognition (learning, knowledge representation, thinking & reasoning) and behavior (action, judgment, choice & decision making, expertise and skilled performance)

riskyr: A toolbox for rendering risk literacy more transparent

Solving a problem simply means representing it
so as to make the solution transparent.

Simon, H.A. (1996). The Sciences of the Artificial



Hansjörg Neth, Felix Gaisbauer, Nico Gradwohl, Wolfgang Gaissmaier

riskyr: A toolbox for rendering risk literacy more transparent

Example of a riskyr prism plot.

Abstract:  Risk-related information — like the prevalence of conditions and the sensitivity and specificity of diagnostic tests or treatment decisions — can be expressed in terms of probabilities or frequencies. By providing a toolbox of methods and metrics, riskyr computes, translates, and displays risk-related information in a variety of ways. Offering multiple complementary perspectives on the interplay between key parameters renders teaching and training of risk literacy more transparent.

Paper: FFTrees: An R toolbox to create, visualize, and evaluate FFTs

If a decision tree that measures up very well on the performance criterion
is nevertheless totally incomprehensible to a human expert, can it
be described as knowledge? Under the common-sense definition of this term
as material that might be assimilated and used by human beings, it is not…

J. Ross Quinlan (1987), p. 498





Nathaniel Phillips, Hansjörg Neth, Jan Woike, Wolfgang Gaissmaier

FFTrees: A toolbox to create, visualize, and evaluate fast-and-frugal decision trees

An example of an FFT (created by FFTrees) predicting heard disease.

An example of an FFT predicting the risk of having heart disease.

Abstract:  Fast-and-frugal trees (FFTs) are simple algorithms that facilitate efficient and accurate decisions based on limited information. But despite their successful use in many applied domains, there is no widely available toolbox that allows anyone to easily create, visualize, and evaluate FFTs. We fill this gap by introducing the R package FFTrees.

Paper: Visual working memory resources as item activation

To understand visual intelligence is to understand, in large part, who we are.
Donald D. Hoffmann (1998), p. XII


The body’s movements at this time scale provide an essential link between processes underlying elemental perceptual events
and those involved in symbol manipulation and the organization of complex behaviors.
Ballard et al. (1997), p. 723


Bella Z. Veksler, Rachel Boyd, Christopher W. Myers, Glenn Gunzelmann, Hansjörg Neth, Wayne D. Gray

Visual working memory resources are best characterized as dynamic, quantifiable mnemonic traces

An example stimulus used in the paradigm of repeated serial search.

An example stimulus used in the paradigm of repeated serial search.

Abstract:  Visual working memory (VWM) is a construct hypothesized to store a small amount of accurate perceptual information that can be brought to bear on a task.  Much research concerns the construct’s capacity and the precision of the information stored.  Two prominent theories of VWM representation have emerged: slot-based and continuous-resource mechanisms.  Prior modeling work suggests that a continuous resource that varies over trials with variable capacity and a potential to make localization errors best accounts for the empirical data.  Questions remain regarding the variability in VWM capacity and precision.  Using a novel eye-tracking paradigm, we demonstrate that VWM facilitates search and exhibits effects of fixation frequency and recency, particularly for prior targets.  Whereas slot-based memory models cannot account for the human data, a novel continuous-resource model does capture the behavioral and eye tracking data, and identifies the relevant resource as item activation.

Artikel: Die Intelligenz einfacher Entscheidungsregeln

Mors certa, vita incerta.
(Philip K. Dick, 1968)


Wolfgang Gaissmaier, Hansjörg Neth

Die Intelligenz einfacher Entscheidungsregeln in einer ungewissen Welt

Fazit:  Unsere Welt mag sicher oder unsicher sein, aber wird immer eine ungewisse bleiben. Wir sollten uns von der Illusion ihrer umfassenden Berechen- und Kontrollierbarkeit verabschieden, ohne deswegen in Angststarre zu verfallen.  Denn gute Entscheidungen sind dennoch möglich und beruhen auf einer angemessenen Einschätzung unserer Ausgangslage:  Je berechenbarer eine Situation ist („Risiko“), desto mehr brauchen wir statistisches Denken und komplexe Modelle; je unberechenbarer eine Situation ist („Ungewissheit“), desto mehr brauchen wir einfache Heuristiken, einschlägige Erfahrung und Vertrauen auf Intuition (vgl. Abbildung 1).  Dabei handelt es sich bei Risiko und Ungewissheit um Pole eines Kontinuums, so dass es sich bei den meisten Situationen um einen Zwischenzustand handeln dürfte. Die Kunst des guten Entscheidens besteht darin, zu wissen, wo auf diesem Kontinuum wir uns befinden, um das jeweils passende Entscheidungswerkzeug geschickt auszuwählen und gezielt zum Einsatz zu bringen.  Und sie erfordert den Mut, Entscheidungen nicht zu verschieben oder zu vermeiden, sondern sie beherzt zu treffen und die Verantwortung für ihre Konsequenzen zu tragen.