Evidence-Accumulation Models: Applications I

Attentional control refers to the ability to maintain and implement a goal and goal-relevant information when facing distraction. So far, previous research has failed to substantiate strong evidence for a psychometric construct of attentional control. This has been argued to result from two methodological shortcomings: (a) the neglect of individual differences in speed-accuracy trade-offs when only speed or accuracy is used as dependent variable, and (b) the difficulty of isolating attentional control from measurement error. To overcome both issues, we combined hierarchical-Bayesian Wiener diffusion modeling with structural equation modeling. We re-analyzed five datasets, which included data from three to eight attentional-control tasks and from young and older adults. Overall, the results showed that even when accounting for speed-accuracy trade-offs and removing measurement error, measures of attentional control failed to correlate with each other and to load successfully on a latent variable. These findings emphasize the necessity of rethinking attentional control.

This is an in-person presentation on July 21, 2023 (09:00 ~ 09:20 UTC).

Attention has been shown to play a central role in decision-making and multi-alternative multiattribute choice. However, the role of attention has been elusive and characterized in different ways. In this project, we explore the role of attention by manipulating the salience of different options in a multi-alternative, multi-attribute choice display. We include two sets of trials. In one set of trials, there is a dominant option that is better on both attributes than the other alternatives. In the second set, we use attraction effect trials, where a target option dominates a decoy option but not a competitor. We observe that salience interacts with choice, where the salient option is selected more often, especially in quick decisions in both sets of trials. This suggests that salience plays an important role in the dynamics of multiattribute choice. We test different hypotheses for how salience-driven attention impacts preferences using an evidence accumulation modeling framework where the salient option is given an initial starting point boost or more attention is paid to comparisons with the salient option during deliberation.

This is an in-person presentation on July 21, 2023 (09:20 ~ 09:40 UTC).

The present study aims to replicate and extend the experiment conducted by Brunstein & Maier (2005) on the impact of performance feedback and the strength of the implicit achievement motive on task performance. Brunstein and Maier found that more achievement motivated individuals show a reduc- tion in mean RTs when they get bogus negative intraindividual performance feedback. The reduction in mean RTs is interpreted by the authors as enhanced effort. This feedback by achievement motive interaction effect is cited as one key finding of motive literature. However, the effect has not yet been replicated. In our study, participants complete an attention task akin to the d2-R task while receiving either positive or negative bogus intraindividual performance feedback. The study has two primary objectives: firstly, to replicate the feed- back by achievement motive interaction effect reported by Brunstein and Maier, and secondly, to gain a more detailed understanding of the cognitive processes involved using the diffusion model (Ratcliff, 1978). In addition to presenting the results from our replication study, we will show the results of a pre-study demon- strating the applicability of the diffusion model to the type of task employed by Brunstein and Maier. Overall, we argue that diffusion model analyses can help to gain a better understanding of the effects of achievement motive frustration.

This is an in-person presentation on July 21, 2023 (10:00 ~ 10:20 UTC).

I present results from four or five visual working memory (VWM) experiments in which subjects were briefly shown between 2 and 6 colored squares. They were then cued to recall the color of one of the squares and they responded by choosing the color on a continuous color wheel. The experiments provided response proportions and response time (RT) measures as a function of angle for the choices. Current VWM models for this task include discrete models that assume an item is either within working memory or not and resource models that assume that memory strength varies as a function of the number of items. Because these models do not include processes that allow them to account for RT data, we implemented them within the spatially continuous diffusion model (SCDM, Ratcliff, 2018) and use the experimental data to evaluate these combined models. In the SCDM, evidence retrieved from memory is represented as a spatially continuous normal distribution and this drives the decision process until a criterion (represented as a 1-D line) is reached, which produces a decision. Noise in the accumulation process is represented by continuous Gaussian process noise over spatial position. The models that fit best from the discrete and resource-based classes converged on a common model that had a guessing/zero evidence component (a zero-drift process) and that allowed the height of the normal memory- strength distribution to vary with number of items. The combination of choice and RT data allows models that were not identifiable based on choice data alone to be discriminated.

This is an in-person presentation on July 21, 2023 (10:20 ~ 10:40 UTC).