Posts Tagged: modeling
Paper: FFTrees: An R toolbox to create, visualize, and evaluate FFTs
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…
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 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
Donald D. Hoffmann (1998), p. XII
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.
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.
Paper: Social influence and collective opinion formation
Mark Twain, Christian Science (1907, Book 1, Ch. 5)
Mehdi Moussaïd, Juliane E. Kämmer, Pantelis P. Analytis, Hansjörg Neth
Social influence and the collective dynamics of opinion formation
Abstract: Social influence is the process by which individuals adapt their opinion, revise their beliefs, or change their behavior as a result of social interactions with other people. In our strongly interconnected society, social influence plays a prominent role in many self-organized phenomena such as herding in cultural markets, the spread of ideas and innovations, and the amplification of fears during epidemics. Yet, the mechanisms of opinion formation remain poorly understood, and existing physics-based models lack systematic empirical validation. Here, we report two controlled experiments showing how participants answering factual questions revise their initial judgments after being exposed to the opinion and confidence level of others.
Paper: Melioration as rational choice
Melioration (…) is the dynamic process controlling allocation of time across response alternatives.
Herrnstein & Vaughan (1980). Melioration and behavioral allocation, p. 143+172
Chris R. Sims, Hansjörg Neth, Robert A. Jacobs, Wayne D. Gray
Melioration as rational choice: Sequential decision making in uncertain environments
Abstract: Melioration — defined as choosing a lesser, local gain over a greater longer term gain — is a behavioral tendency that people and pigeons share. As such, the empirical occurrence of meliorating behavior has frequently been interpreted as evidence that the mechanisms of human choice violate the norms of economic rationality. In some environments, the relationship between actions and outcomes is known. In this case, the rationality of choice behavior can be evaluated in terms of how successfully it maximizes utility given knowledge of the environmental contingencies. In most complex environments, however, the relationship between actions and future outcomes is uncertain and must be learned from experience. When the difficulty of this learning challenge is taken into account, it is not evident that melioration represents suboptimal choice behavior.