Capturing dynamic performance in a cognitive model: Estimating ACT-R memory parameters with the linear ballistic accumulator

The parameters governing our behaviour are in constant flux. Accurately capturing these dynamics in cognitive models poses a challenge to modellers. Here, we demonstrate a mapping of ACT-R's declarative memory onto the linear ballistic accumulator, a mathematical model describing a competition between evidence accumulation processes. We show that this mapping provides a method for inferring individual ACT-R parameters without requiring the modeller to build and fit an entire ACT-R model. We conduct a parameter recovery study to confirm that the LBA can recover ACT-R parameters from simulated data. Then, as a proof of concept, we use the LBA to estimate ACT-R parameters from an empirical data set. The resulting parameter estimates provide a cognitively meaningful explanation for observed differences in behaviour over time and between individuals.

Out of fright, out of mind: impaired memory for information negated during looming threat

People often need to update representations of information upon discovering them to be incorrect, a process that can be interrupted by competing cognitive demands. Because anxiety and stress can impair cognitive performance, we tested whether looming threat can similarly interfere with the process of updating representations of a statement’s truthfulness. On each trial, participants saw a face paired with a personality descriptor. Each pairing was followed by a signal indicating whether the pairing was “true”, or “false” (a negation of the truth of the statement), and this signal could be followed by a warning of imminent electric shock (i.e., the looming threat). As predicted, threat of shock left memory for “true” pairings intact, while impairing people’s ability to label negated pairings as untrue. Contrary to our predictions, the pattern of errors for pairings that were negated under threat suggested that these mistakes were at least partly attributable to participants forgetting that they saw the negated information at all (rather than being driven by miscategorization of the pairings as true). Consistent with this, linear ballistic accumulator modelling suggested that this impaired recognition stemmed from weaker memory traces rather than decisional processes. We suggest that arousal due to looming threat may interfere with executive processes important for resolving competition between mutually suppressive tags of whether representations in memory are “true” or “false”.

Patients with Ventromedial Prefrontal Lesions Show an Implicit Approach Bias to Angry Faces

Damage to the ventromedial PFC (VMPFC) can cause maladaptive social behavior, but the cognitive processes underlying these behavioral changes are still uncertain. Here, we tested whether patients with acquired VMPFC lesions show altered approach–avoidance tendencies to emotional facial expressions. Thirteen patients with focal VMPFC lesions and 31 age- and gender-matched healthy controls performed an implicit approach–avoidance task in which they either pushed or pulled a joystick depending on stimulus color. Whereas controls avoided angry faces, VMPFC patients displayed an incongruent response pattern characterized by both increased approach and reduced avoidance of angry facial expressions. The approach bias was stronger in patients with higher self-reported impulsivity and disinhibition and in those with larger lesions. We further used linear ballistic accumulator modeling to investigate latent parameters underlying approach–avoidance decisions. Controls displayed negative drift rates when approaching angry faces, whereas VMPFC lesions abolished this pattern. In addition, VMPFC patients had weaker response drifts than controls during avoidance. Finally, patients showed reduced drift rate variability and shorter nondecision times, indicating impulsive and rigid decision-making. Our findings thus suggest that VMPFC damage alters the pace of evidence accumulation in response to social signals, eliminating a default, protective avoidant bias and facilitating a dysfunctional approach behavior.

Examining Methods for Combining Speed and Accuracy in a Go/No-Go Vigilance Task

In many human performance tasks, researchers assess performance by measuring both accuracy and response time. A number of theoretical and practical approaches have been proposed to obtain a single performance value that combines these measures, with varying degrees of success. In this report, we examine data from a common paradigm used in applied human factors assessment: a go/no-go vigilance task (Smith et al., 2019). We examined whether 12 different measures of performance were sensitive to the vigilance decrement induced by the design, and also examined how the different measures were correlated. Results suggest that most combined measures were slight improvements over accuracy or response time alone, with the most sensitive and representative result coming from the Linear Ballistic Accumulator model. Practical lessons for applying these measures are discussed.

Same model, different conclusions: An identifiability issue in the linear ballistic accumulator model of decision-making

Evidence accumulation models (EAMs) – the dominant modelling framework for speeded decision-making – have become an important tool for model application. Model application involves using specific model to estimate parameter values that relate to different components of the cognitive process, and how these values differ over experimental conditions and/or between groups of participants. In this context, researchers are often agnostic to the specific theoretical assumptions made by different EAM variants, and simply desire a model that will provide them with an accurate measurement of the parameters that they are interested in. However, recent research has suggested that the two most commonly applied EAMs – the diffusion model and the linear ballistic accumulator (LBA) – come to fundamentally different conclusions when applied to the same empirical data. The current study provides an in-depth assessment of the measurement properties of the two models, as well as the mapping between, using two large scale simulation studies and a reanalysis of Evans (2020a). Importantly, the findings indicate that there is a major identifiability issue within the standard LBA, where differences in decision threshold between conditions are practically unidentifiable, which appears to be caused by a tradeoff between the threshold parameter and the overall drift rate across the different accumulators. While this issue can be remedied by placing some constraint on the overall drift rate across the different accumulators – such as constraining the average drift rate or the drift rate of one accumulator to have the same value in each condition – these constraints can qualitatively change the conclusions of the LBA regarding other constructs, such as non-decision time. Furthermore, all LBA variants considered in the current study still provide qualitatively different conclusions to the diffusion model. Importantly, the current findings suggest that researchers should not use the unconstrained version of the LBA for model application, and bring into question the conclusions of previous studies using the unconstrained LBA.

Think fast! The implications of emphasizing urgency in decision-making

Evidence accumulation models (EAMs) have become the dominant explanation of how the decision-making process operates, proposing that decisions are the result of a process of evidence accumulation. The primary use of EAMs has been as "measurement tools" of the underlying decision-making process, where researchers apply EAMs to empirical data to estimate participants' task ability (i.e., the "drift rate"), response caution (i.e., the "decision threshold"), and the time taken for other processes (i.e., the "non-decision time"), making EAMs a powerful tool for discriminating between competing psychological theories. Recent studies have brought into question the mapping between the latent parameters of EAMs and the theoretical constructs that they are thought to represent, showing that emphasizing urgent responding -- which intuitively should selectively influence decision threshold -- may also influence drift rate and/or non-decision time. However, these findings have been mixed, leading to differences in opinion between experts in the field. The current study aims to provide a more conclusive answer to the implications of emphasizing urgent responding, providing a re-analyse of 6 data sets from previous studies using two different EAMs -- the diffusion model and the linear ballistic accumulator (LBA) -- with state-of-the-art methods for model selection based inference. The findings display clear evidence for a difference in conclusions between the two models, with the diffusion model suggesting that decision threshold and non-decision time decrease when urgency is emphasized, and the LBA suggesting that decision threshold and drift rate decrease when urgency is emphasized. Furthermore, although these models disagree regarding whether non-decision time or drift rate decrease under urgency emphasis, both show clear evidence that emphasizing urgency does not selectively influence decision threshold. These findings suggest that researchers should revise their assumptions about certain experimental manipulations, the specification of certain EAMs, or perhaps both.