Most models of pricing embody a static, deterministic theoryof value where the monetary amount people assign to an itemis computed as a fixed function of its attributes. Preference re-versals — where prices assigned to gambles conflict with pref-erence orders elicited through binary choices – indicate thatthe response processes going into value assessments are impor-tant. In this paper, we additionally show that price responsesare sensitive to time pressure, suggesting a dynamic underlyingcognitive process. We also show that the elicited price distribu-tions can possess strong positive or negative skew, indicatingthat diverging information is used to generate buying versusselling and certainty equivalent prices. We develop a computa-tional cognitive model that predicts these continuous distribu-tions of price responses and how they change over time, show-ing that it can account for the major dynamic and distributionalproperties of prices and decisions.

Traditionally, models of the decision-making process have fo-cused on the case where a decision-maker must choose be-tween two alternatives. The most successful of these, sequen-tial sampling models, have been extended from the binary caseto account for choices and response times between multiplealternatives. In this paper, I present a geometric representa-tion of diffusion and accumulator models of multiple-choicedecisions, and show how these can be analyzed as Markovprocesses on lattices. I then introduce psychological relation-ships between choice alternatives and show how this impactsthe sequential sampling process. I conclude with two examplesshowing how one can predict distributions of responses on acontinuum as well as response times by incorporating psycho-logical representations into a multi-dimensional random walkdiffusion process.

Understanding how people rate their confidence is critical for the characterization of a wide range of perceptual, memory, motor and cognitive processes. To enable the continued exploration of these processes, we created a large database of confidence studies spanning a broad set of paradigms, participant populations and fields of study. The data from each study are structured in a common, easy-to-use format that can be easily imported and analysed using multiple software packages. Each dataset is accompanied by an explanation regarding the nature of the collected data. At the time of publication, the Confidence Database (which is available at https://osf.io/s46pr/) contained 145 datasets with data from more than 8,700 participants and almost 4 million trials. The database will remain open for new submissions indefinitely and is expected to continue to grow. Here we show the usefulness of this large collection of datasets in four different analyses that provide precise estimations of several foundational confidence-related effects.