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 cognitive processes underlying risky decision-making by manipulating time pressure. A recent fMRI study by De Martino et al. (2006) found greater activation of the amygdala when exhibiting framing effects suggesting that they may be driven by System 1. Because this system is characterized as being fast, we expect more pronounced framing effects under time pressure. In our experiment, we manipulated time pressure and accuracy and use a dynamic dual-process model to explain our results. The model we develop is a sequential sampling model in which the drift rates and boundaries vary in accordance with the thinking modes, frames, and time pressure

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.

In decisions from experience (DFE), people sample from two or more lotteries prior to making a consequential choice. Although existing models can account for how sampled experiences relate to choice, they don’t explain decisions about how to search (in particular, when to stop sampling information). We propose that both choice and search behavior in this context can be understood as a sequential sampling process whereby decision makers sequentially accumulate outcome information from each option to form a preference for one alternative over the other. We formalize this process in a new model, Choice from Accumulated Samples of Experience (CHASE). The model provides a good account of choice behavior and goes beyond existing models by explaining variations in sample size under different task conditions. This approach offers a process-level framework for understanding how interactions between the choice environment and properties of the decision maker give rise to decisions from experience.