Dynamic models of choice.

Parameter estimation in evidence-accumulation models of choice response times is demanding of both the data and the user. We outline how to fit evidence-accumulation models using the flexible, open-source, R-based Dynamic Models of Choice (DMC) software. DMC provides a hands-on introduction to the Bayesian implementation of two popular evidence-accumulation models: the diffusion decision model (DDM) and the linear ballistic accumulator (LBA). It enables individual and hierarchical estimation, as well as assessment of the quality of a model's parameter estimates and descriptive accuracy. First, we introduce the basic concepts of Bayesian parameter estimation, guiding the reader through a simple DDM analysis. We then illustrate the challenges of fitting evidence-accumulation models using a set of LBA analyses. We emphasize best practices in modeling and discuss the importance of parameter- and model-recovery simulations, exploring the strengths and weaknesses of models in different experimental designs and parameter regions. We also demonstrate how DMC can be used to model complex cognitive processes, using as an example a race model of the stop-signal paradigm, which is used to measure inhibitory ability. We illustrate the flexibility of DMC by extending this model to account for mixtures of cognitive processes resulting from attention failures. We then guide the reader through the practical details of a Bayesian hierarchical analysis, from specifying priors to obtaining posterior distributions that encapsulate what has been learned from the data. Finally, we illustrate how the Bayesian approach leads to a quantitatively cumulative science, showing how to use posterior distributions to specify priors that can be used to inform the analysis of future experiments.

A dynamic model of deciding not to choose.

We propose a dynamic theory of decisions not to choose which of 2 options is correct. Such "do not-know" judgments are of theoretical and practical importance in domains ranging from comparative psychology, psychophysics, episodic memory, and metacognition to applied areas including educational testing and eyewitness testimony. However, no previous theory has provided a detailed quantitative account of the time it takes to make both definitive and do not-know responses and their relative frequencies. We tested our theory, the multiple threshold race (MTR), in 1 recognition memory experiment where participants had to pick a previously studied target out of 2 similar faces and another where targets and lures were tested 1 at a time. In both experiments we manipulated similarity through face morphing. High similarity made decisions difficult, encouraging do not-know responses. We also tested the MTR's ability to account for other manipulations that aimed to affect the speed and probability of do not-know responses, including increasing penalties for making an error (with no penalty for a do not-know response) and emphasizing either response speed or accuracy. We found that there were marked individual differences in do not-know use, and that the MTR was able to account for the intricate pattern of effects associated with our manipulations, both on average and in terms of individual differences. We discuss how estimates of MTR's parameters illuminate the psychological mechanisms that govern the interplay between definitive and do not-know responding. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Equally flexible and optimal response bias in older compared to younger adults.

Base-rate neglect is a failure to sufficiently bias decisions toward a priori more likely options. Given cognitive and neurocognitive model-based evidence indicating that, in speeded choice tasks, (a) age-related slowing is associated with higher and less flexible overall evidence thresholds (response caution) and (b) gains in speed and accuracy in relation to base-rate bias require flexible control of choice-specific evidence thresholds (response bias), it was hypothesized that base-rate neglect might increase with age due to compromised flexibility, and so optimality, of response bias. We administered a computer-based perceptual discrimination task to 20 healthy older (63-78 years) and 20 younger (18-28 years) adults where base-rate direction was either variable or constant over trials and so required more or less flexible bias control. Using an evidence accumulation model of response times and accuracy (specifically, the Linear Ballistic Accumulator model; Brown & Heathcote, 2008), age-related slowing was attributable to higher response caution, and gains in speed and accuracy per base-rate bias were attributable to response bias. Both age groups were less biased than required to achieve optimal accuracy, and more so when base-rate direction changed frequently. However, bias was closer to optimal among older than younger participants, especially when base-rate direction was constant. We conclude that older participants performed better than younger participants because of their greater emphasis on accuracy, and that, by making greater absolute and equivalent relative adjustments of evidence thresholds in relation to base-rate bias, flexibility of bias control is at most only slightly compromised with age. (PsycINFO Database Record (c) 2019 APA, all rights reserved).