ORCID Profile
0000-0001-8201-5123
Current Organisation
University of Tasmania
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Publisher: Elsevier BV
Date: 06-2020
Publisher: American Psychological Association (APA)
Date: 09-2019
DOI: 10.1037/PAG0000339
Abstract: 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).
Publisher: American Psychological Association (APA)
Date: 2021
DOI: 10.1037/XGE0000770
Publisher: Elsevier BV
Date: 12-2018
DOI: 10.1016/J.IJPSYCHO.2018.10.010
Abstract: Fear conditioning and extinction is a construct integral to understanding trauma-, stress- and anxiety-related disorders. In the laboratory, associative learning paradigms that pair aversive with neutral stimuli are used as analogues to real-life fear learning. These studies use physiological indices, such as skin conductance, to sensitively measure rates and intensity of learning and extinction. In this review, we discuss some of the potential limitations in interpreting and analysing physiological data during the acquisition or extinction of conditioned fear. We argue that the utmost attention should be paid to the development of modelling approaches of physiological data in associative learning paradigms, by illustrating the lack of replicability and interpretability of results in current methods. We also show that statistical significance may be easily achieved in this paradigm without more stringent data and data analysis reporting requirements, leaving this particular field vulnerable to misleading conclusions. This review is written so that issues and potential solutions are accessible to researchers without mathematical training. We conclude the review with some suggestions that all laboratories should be able to implement, including visualising the full data set in publications and adopting modelling, or at least regression-based, approaches.
Publisher: Springer Science and Business Media LLC
Date: 29-06-2019
DOI: 10.3758/S13428-018-1067-Y
Abstract: 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 in idual 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 ex le 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.
No related grants have been discovered for Angus Reynolds.