Search

Research on intertemporal choice has suggested that decisionprocesses automatically favor immediate rewards. In thispaper, we use a drift diffusion model to conceptualize andempirically investigate the role of these biases. Our modelpermits automatic biases in the response process, automaticbiases in the evaluation process, as well as differentialweighting for monetary payoffs and time delays. We fit ourmodel to individual-level choice and response time data, andfind that automatic biases are prevalent in intertemporalchoice, but that the type, magnitude, and direction of thesebiases vary greatly across individuals. Our results pose newchallenges for theories of intertemporal choice behavior.

When choosing between multiple alternatives, people usuallydo not have ready-made preferences in their mind but ratherconstruct them on the go. The 2N-ary Choice Tree Model(Wollschlaeger & Diederich, 2012) proposes a preference con-struction process for N choice options from description, whichis based on attribute weights, differences between attribute val-ues, and noise. It is able to produce similarity, attraction,and compromise effects, which have become a benchmark formulti-alternative choice models, but also several other contextand reference point effects. Here, we present a new and math-ematically tractable version of the model – the Simple ChoiceTree Model – which also explains the above mentioned effectsand additionally accounts for the positive correlation betweenthe attraction and compromise effect, and the negative correla-tion between these two and the similarity effect as observed byBerkowitsch, Scheibehenne, and Rieskamp (2014).

This work investigates the intuitive and deliberative cognitiveprocesses underlying risky decision-making by manipulatingtime pressure. A recent fMRI study by De Martino et al.(2006) found greater activation of the amygdala whenexhibiting framing effects suggesting that they may be drivenby System 1. Because this system is characterized as beingfast, we expect more pronounced framing effects under timepressure. In our experiment, we manipulated time pressureand accuracy and use a dynamic dual-process model toexplain our results. The model we develop is a sequentialsampling model in which the drift rates and boundaries varyin accordance with the thinking modes, frames, and timepressure

In decisions from experience (DFE), people sample from twoor more lotteries prior to making a consequential choice. Althoughexisting models can account for how sampled experiencesrelate to choice, they don’t explain decisions abouthow to search (in particular, when to stop sampling information).We propose that both choice and search behavior inthis context can be understood as a sequential sampling processwhereby decision makers sequentially accumulate outcomeinformation from each option to form a preference forone alternative over the other. We formalize this process ina new model, Choice from Accumulated Samples of Experience(CHASE). The model provides a good account of choicebehavior and goes beyond existing models by explaining variationsin sample size under different task conditions. Thisapproach offers a process-level framework for understandinghow interactions between the choice environment and propertiesof the decision maker give rise to decisions from experience.

An inequality by J. O. Miller (1982) has become the standard tool to test the race model for redundant signals reaction times (RTs), as an alternative to a neural summation mechanism. It stipulates that the RT distribution function to redundant stimuli is never larger than the sum of the distribution functions for 2 single stimuli. When many different experimental conditions are to be compared, a numerical index of violation is very desirable. Widespread practice is to take a certain area with contours defined by the distribution functions for single and redundant stimuli. Here this area is shown to equal the difference between 2 mean RT values. This result provides an intuitive interpretation of the index and makes it amenable to simple statistical testing. An extension of this approach to 3 redundant signals is presented.