Improving the prospective prediction of a near-term suicide attempt in veterans at risk for suicide, using a go/no-go task.

BACKGROUND: Neurocognitive testing may advance the goal of predicting near-term suicide risk. The current study examined whether performance on a Go/No-go (GNG) task, and computational modeling to extract latent cognitive variables, could enhance prediction of suicide attempts within next 90 days, among individuals at high-risk for suicide. METHOD: 136 Veterans at high-risk for suicide previously completed a computer-based GNG task requiring rapid responding (Go) to target stimuli, while withholding responses (No-go) to infrequent foil stimuli; behavioral variables included false alarms to foils (failure to inhibit) and missed responses to targets. We conducted a secondary analysis of these data, with outcomes defined as actual suicide attempt (ASA), other suicide-related event (OtherSE) such as interrupted/aborted attempt or preparatory behavior, or neither (noSE), within 90-days after GNG testing, to examine whether GNG variables could improve ASA prediction over standard clinical variables. A computational model (linear ballistic accumulator, LBA) was also applied, to elucidate cognitive mechanisms underlying group differences. RESULTS: On GNG, increased miss rate selectively predicted ASA, while increased false alarm rate predicted OtherSE (without ASA) within the 90-day follow-up window. In LBA modeling, ASA (but not OtherSE) was associated with decreases in decisional efficiency to targets, suggesting differences in the evidence accumulation process were specifically associated with upcoming ASA. CONCLUSIONS: These findings suggest that GNG may improve prediction of near-term suicide risk, with distinct behavioral patterns in those who will attempt suicide within the next 90 days. Computational modeling suggests qualitative differences in cognition in individuals at near-term risk of suicide attempt.

Linear Ballistic Accumulator Item Response Theory Model for Multidimensional Multiple-Alternative Forced-Choice Measurement of Personality.

There has been a growing interest in psychological measurements that use the multiple-alternative forced-choice (MAFC) response format for its resistance to response biases. Although several models have been proposed for the data obtained from such measurements, none have succeeded in incorporating the response time information. Given that currently, many psychological measurements are performed via computers, it would be beneficial to develop a joint model involving an MAFC item response and response time. The present study proposes the first model that combines a cognitive process model that underlies the observed response time and the forced-choice item response model. Specifically, the proposed model is based on the linear ballistic accumulator model of response time, which is substantially extended by reformulating its parameters so as to incorporate the MAFC item responses. The model parameters are estimated by the Markov chain Monte Carlo (MCMC) algorithm. A simulation study confirmed that the proposed approach could appropriately recover the parameters. Two empirical applications are reported to demonstrate the use of the proposed model and compare it with existing models. The results showed that the proposed model could be a useful tool for jointly modeling the MAFC item responses and response times.

Inhibitory Cognitive Control Allows Automated Advice to Improve Accuracy While Minimizing Misuse.

Humans increasingly use automated decision aids. However, environmental uncertainty means that automated advice can be incorrect, creating the potential for humans to act on incorrect advice or to disregard correct advice. We present a quantitative model of the cognitive process by which humans use automation when deciding whether aircraft would violate requirements for minimum separation. The model closely fitted the performance of 24 participants, who each made 2,400 conflict-detection decisions (conflict vs. nonconflict), either manually (with no assistance) or with the assistance of 90% reliable automation. When the decision aid was correct, conflict-detection accuracy improved, but when the decision aid was incorrect, accuracy and response time were impaired. The model indicated that participants integrated advice into their decision process by inhibiting evidence accumulation toward the task response that was incongruent with that advice, thereby ensuring that decisions could not be made solely on automated advice without first sampling information from the task environment.

How do recall requirements affect decision-making in free recall initiation? A linear ballistic accumulator approach.

Models of free recall describe free recall initiation as a decision-making process in which items compete to be retrieved. Recently, Osth and Farrell (Psychological Review, 126, 578-609, 2019) applied evidence accumulation models to complete RT distributions and serial positions of participants' first recalls in free recall, which resulted in some novel conclusions about primacy and recency effects. Specifically, the results of the modeling favored an account in which primacy was due to reinstatement of the start-of-the-list, and recency was found to be exponential in shape. In this work, we examine what happens when participants are given alternative recall instructions. Prior work has demonstrated weaker primacy and greater recency when fewer items are required to report (Ward & Tan, Memory & Cognition, 2019), and a key question is whether this change in instructions qualitatively changes the nature of the recall process, or merely changes the parameters of the recall competition. We conducted an experiment where participants studied six- or 12-item lists and were post-cued as to whether to retrieve a single item, or as many items as possible. Subsequently, we applied LBA models with various assumptions about primacy and recency, implemented using hierarchical Bayesian techniques. While greater recency was observed when only one item was required for output, the model selection did not suggest that there were qualitative differences between the two conditions. Specifically, start-of-list reinstatement and exponential recency functions were favored in both conditions.

Computing Bayes factors for evidence-accumulation models using Warp-III bridge sampling.

Over the last decade, the Bayesian estimation of evidence-accumulation models has gained popularity, largely due to the advantages afforded by the Bayesian hierarchical framework. Despite recent advances in the Bayesian estimation of evidence-accumulation models, model comparison continues to rely on suboptimal procedures, such as posterior parameter inference and model selection criteria known to favor overly complex models. In this paper, we advocate model comparison for evidence-accumulation models based on the Bayes factor obtained via Warp-III bridge sampling. We demonstrate, using the linear ballistic accumulator (LBA), that Warp-III sampling provides a powerful and flexible approach that can be applied to both nested and non-nested model comparisons, even in complex and high-dimensional hierarchical instantiations of the LBA. We provide an easy-to-use software implementation of the Warp-III sampler and outline a series of recommendations aimed at facilitating the use of Warp-III sampling in practical applications.

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.

Bayesian inference with Stan: A tutorial on adding custom distributions.

When evaluating cognitive models based on fits to observed data (or, really, any model that has free parameters), parameter estimation is critically important. Traditional techniques like hill climbing by minimizing or maximizing a fit statistic often result in point estimates. Bayesian approaches instead estimate parameters as posterior probability distributions, and thus naturally account for the uncertainty associated with parameter estimation; Bayesian approaches also offer powerful and principled methods for model comparison. Although software applications such as WinBUGS (Lunn, Thomas, Best, & Spiegelhalter, Statistics and Computing, 10, 325-337, 2000) and JAGS (Plummer, 2003) provide "turnkey"-style packages for Bayesian inference, they can be inefficient when dealing with models whose parameters are correlated, which is often the case for cognitive models, and they can impose significant technical barriers to adding custom distributions, which is often necessary when implementing cognitive models within a Bayesian framework. A recently developed software package called Stan (Stan Development Team, 2015) can solve both problems, as well as provide a turnkey solution to Bayesian inference. We present a tutorial on how to use Stan and how to add custom distributions to it, with an example using the linear ballistic accumulator model (Brown & Heathcote, Cognitive Psychology, 57, 153-178. doi: 10.1016/j.cogpsych.2007.12.002 , 2008).

Why more is better: Simultaneous modeling of EEG, fMRI, and behavioral data.

The need to test a growing number of theories in cognitive science has led to increased interest in inferential methods that integrate multiple data modalities. In this manuscript, we show how a method for integrating three data modalities within a single framework provides (1) more detailed descriptions of cognitive processes and (2) more accurate predictions of unobserved data than less integrative methods. Specifically, we show how combining either EEG and fMRI with a behavioral model can perform substantially better than a behavioral-data-only model in both generative and predictive modeling analyses. We then show how a trivariate model - a model including EEG, fMRI, and behavioral data - outperforms bivariate models in both generative and predictive modeling analyses. Together, these results suggest that within an appropriate modeling framework, more data can be used to better constrain cognitive theory, and to generate more accurate predictions for behavioral and neural data.

The impact of MRI scanner environment on perceptual decision-making.

Despite the widespread use of functional magnetic resonance imaging (fMRI), few studies have addressed scanner effects on performance. The studies that have examined this question show a wide variety of results. In this article we report analyses of three experiments in which participants performed a perceptual decision-making task both in a traditional setting as well as inside an MRI scanner. The results consistently show that response times increase inside the scanner. Error rates also increase, but to a lesser extent. To reveal the underlying mechanisms that drive the behavioral changes when performing a task inside the MRI scanner, the data were analyzed using the linear ballistic accumulator model of decision-making. These analyses show that, in the scanner, participants exhibit a slow down of the motor component of the response and have less attentional focus on the task. However, the balance between focus and motor slowing depends on the specific task requirements.

The neural basis of value accumulation in intertemporal choice.

Making intertemporal choices (choosing between rewards available at different points in time) requires determining and comparing the subjective values of available rewards. Several studies have found converging evidence identifying the neural systems that encode subjective value in intertemporal choice. However, the neural mechanisms responsible for the process that produces intertemporal decisions on the basis of subjective values have not been investigated. Using model-based and connectivity analyses of functional magnetic resonance imaging data, we investigated the neural mechanisms underlying the value-accumulation process by which subjective value guides intertemporal decisions. Our results show that the dorsomedial frontal cortex, bilateral posterior parietal cortex, and bilateral lateral prefrontal cortex are all involved in the accumulation of subjective value for the purpose of action selection. Our findings establish a mechanistic framework for understanding frontoparietal contributions to intertemporal choice and suggest that value-accumulation processes in the frontoparietal cortex may be a general mechanism for value-based choice.

A response time model of the three-choice Mnemonic Similarity Task provides stable, mechanistically interpretable individual-difference measures.

Introduction: The Mnemonic Similarity Task (MST) is a widely used measure of individual tendency to discern small differences between remembered and presently presented stimuli. Significant work has established this measure as a reliable index of neurological and cognitive dysfunction and decline. However, questions remain about the neural and psychological mechanisms that support performance in the task. Methods: Here, we provide new insights into these questions by fitting seven previously-collected MST datasets (total N = 519), adapting a three-choice evidence accumulation model (the Linear Ballistic Accumulator). The model decomposes choices into automatic and deliberative components. Results: We show that these decomposed processes both contribute to the standard measure of behavior in this task, as well as capturing individual variation in this measure across the lifespan. We also exploit a delayed test/re-test manipulation in one of the experiments to show that model parameters exhibit improved stability, relative to the standard metric, across a 1 week delay. Finally, we apply the model to a resting-state fMRI dataset, finding that only the deliberative component corresponds to off-task co-activation in networks associated with long-term, episodic memory. Discussion: Taken together, these findings establish a novel mechanistic decomposition of MST behavior and help to constrain theories about the cognitive processes that support performance in the task.

Items Outperform Adjectives in a Computational Model of Binary Semantic Classification.

Semantic memory encompasses one's knowledge about the world. Distributional semantic models, which construct vector spaces with embedded words, are a proposed framework for understanding the representational structure of human semantic knowledge. Unlike some classic semantic models, distributional semantic models lack a mechanism for specifying the properties of concepts, which raises questions regarding their utility for a general theory of semantic knowledge. Here, we develop a computational model of a binary semantic classification task, in which participants judged target words for the referent's size or animacy. We created a family of models, evaluating multiple distributional semantic models, and mechanisms for performing the classification. The most successful model constructed two composite representations for each extreme of the decision axis (e.g., one averaging together representations of characteristically big things and another of characteristically small things). Next, the target item was compared to each composite representation, allowing the model to classify more than 1,500 words with human-range performance and to predict response times. We propose that when making a decision on a binary semantic classification task, humans use task prompts to retrieve instances representative of the extremes on that semantic dimension and compare the probe to those instances. This proposal is consistent with the principles of the instance theory of semantic memory.

Integrating word-form representations with global similarity computation in recognition memory.

In recognition memory, retrieval is thought to occur by computing the global similarity of the probe to each of the studied items. However, to date, very few global similarity models have employed perceptual representations of words despite the fact that false recognition errors for perceptually similar words have consistently been observed. In this work, we integrate representations of letter strings from the reading literature with global similarity models. Specifically, we employed models of absolute letter position (slot codes and overlap models) and relative letter position (closed and open bigrams). Each of the representations was used to construct a global similarity model that made contact with responses and RTs at the individual word level using the linear ballistic accumulator (LBA) model (Brown & Heathcote Cognitive Psychology, 57 , 153-178, 2008). Relative position models were favored in three of the four datasets and parameter estimates suggested additional influence of the initial letters in the words. When semantic representations from the word2vec model were incorporated into the models, results indicated that orthographic representations were almost equally consequential as semantic representations in determining inter-item similarity and false recognition errors, which undermines previous suggestions that long-term memory is primarily driven by semantic representations. The model was able to modestly capture individual word variability in the false alarm rates, but there were limitations in capturing variability in the hit rates that suggest that the underlying representations require extension.

Listening efficiency in adult cochlear-implant users compared with normally-hearing controls at ecologically relevant signal-to-noise ratios.

Introduction: Due to having to work with an impoverished auditory signal, cochlear-implant (CI) users may experience reduced speech intelligibility and/or increased listening effort in real-world listening situations, compared to their normally-hearing (NH) peers. These two challenges to perception may be usefully integrated in a measure of listening efficiency: conceptually, the amount of accuracy achieved for a certain amount of effort expended. Methods: We describe a novel approach to quantifying listening efficiency based on the rate of evidence accumulation toward a correct response in a linear ballistic accumulator (LBA) model of choice decision-making. Estimation of this objective measure within a hierarchical Bayesian framework confers further benefits, including full quantification of uncertainty in parameter estimates. We applied this approach to examine the speech-in-noise performance of a group of 24 CI users (M age: 60.3, range: 20-84 years) and a group of 25 approximately age-matched NH controls (M age: 55.8, range: 20-79 years). In a laboratory experiment, participants listened to reverberant target sentences in cafeteria noise at ecologically relevant signal-to-noise ratios (SNRs) of +20, +10, and +4 dB SNR. Individual differences in cognition and self-reported listening experiences were also characterised by means of cognitive tests and hearing questionnaires. Results: At the group level, the CI group showed much lower listening efficiency than the NH group, even in favourable acoustic conditions. At the individual level, within the CI group (but not the NH group), higher listening efficiency was associated with better cognition (i.e., working-memory and linguistic-closure) and with more positive self-reported listening experiences, both in the laboratory and in daily life. Discussion: We argue that listening efficiency, measured using the approach described here, is: (i) conceptually well-motivated, in that it is theoretically impervious to differences in how individuals approach the speed-accuracy trade-off that is inherent to all perceptual decision making; and (ii) of practical utility, in that it is sensitive to differences in task demand, and to differences between groups, even when speech intelligibility remains at or near ceiling level. Further research is needed to explore the sensitivity and practical utility of this metric across diverse listening situations.

tDCS augments decision-making efficiency in an intensity dependent manner: A training study.

The application of transcranial direct current stimulation (tDCS) to the prefrontal cortex has the potential to improve performance more than cognitive training alone. Such stimulation-induced performance enhancements can generalize beyond trained tasks, leading to benefits for untrained tasks/processes. We have shown evidence that stimulation intensity has non-linear effects on augmenting cognitive training outcomes. However, it is currently unclear how stimulation intensity augments cognitive processing to impact training and transfer effects. Here, we applied decision-making modelling via the linear ballistic accumulator framework to understand what aspects of cognitive processes underlying speeded single-/dual-task decision-making performance change with tDCS intensity. One hundred and twenty-three participants were split into four groups: sham, 0.7 mA, 1.0 mA and 2.0 mA stimulation intensities. Participants completed four training sessions whilst tDCS was delivered. The 0.7 mA & 1.0 mA intensities provided the greatest benefit for performance (increased decision-making efficiency as measured by drift rates) on the trained task - more than sham or 2.0 mA stimulation. The latent decision components integrated both accuracy and reaction times to estimate performance more broadly. We see an inverted u-shaped function of stimulation intensity and cognitive performance in the trained-on task, where either no stimulation or too much stimulation is sub-optimal for performance. By contrast, 1.0 mA and 2.0 mA intensities led to increased drift rates in an untrained (transfer) single task. In sum, tDCS intensity non-linearly modulates cognitive processes related to decision-making efficiency.

Capturing Dynamic Performance in a Cognitive Model: Estimating ACT-R Memory Parameters With the Linear Ballistic Accumulator.

The parameters governing our behavior are in constant flux. Accurately capturing these dynamics in cognitive models poses a challenge to modelers. Here, we demonstrate a mapping of ACT-R's declarative memory onto the linear ballistic accumulator (LBA), 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 modeler to build and fit an entire ACT-R model. Existing parameter estimation methods for the LBA can be used, instead of the computationally expensive parameter sweeps that are traditionally done. 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 dataset. The resulting parameter estimates provide a cognitively meaningful explanation for observed differences in behavior over time and between individuals. In addition, we find that the mapping between ACT-R and LBA lends a more concrete interpretation to ACT-R's latency factor parameter, namely as a measure of response caution. This work contributes to a growing movement towards integrating formal modeling approaches in cognitive science.

Task-general efficiency of evidence accumulation as a computationally-defined neurocognitive trait: Implications for clinical neuroscience.

Quantifying individual differences in higher-order cognitive functions is a foundational area of cognitive science that also has profound implications for research on psychopathology. For the last two decades, the dominant approach in these fields has been to attempt to fractionate higher-order functions into hypothesized components (e.g., "inhibition", "updating") through a combination of experimental manipulation and factor analysis. However, the putative constructs obtained through this paradigm have recently been met with substantial criticism on both theoretical and empirical grounds. Concurrently, an alternative approach has emerged focusing on parameters of formal computational models of cognition that have been developed in mathematical psychology. These models posit biologically plausible and experimentally validated explanations of the data-generating process for cognitive tasks, allowing them to be used to measure the latent mechanisms that underlie performance. One of the primary insights provided by recent applications of such models is that individual and clinical differences in performance on a wide variety of cognitive tasks, ranging from simple choice tasks to complex executive paradigms, are largely driven by efficiency of evidence accumulation (EEA), a computational mechanism defined by sequential sampling models. This review assembles evidence for the hypothesis that EEA is a central individual difference dimension that explains neurocognitive deficits in multiple clinical disorders and identifies ways in which in this insight can advance clinical neuroscience research. We propose that recognition of EEA as a major driver of neurocognitive differences will allow the field to make clearer inferences about cognitive abnormalities in psychopathology and their links to neurobiology.

Investigating the effects of ongoing-task bias on prospective memory.

Event-based prospective memory (PM) refers to the cognitive processes required to perform a planned action upon encountering a future event. Event-based PM studies engage participants in an ongoing task (e.g., lexical decision-making) with an instruction to make an alternative PM response to certain items (e.g., items containing "tor"). The Prospective Memory Decision Control (PMDC) model, which provides a quantitative process account of ongoing-task and PM decisions, proposes that PM and ongoing-task processes compete in a race to threshold. We use PMDC to test whether, as proposed by the Delay Theory of PM costs, PM can be improved by biasing decision-making against a specific ongoing-task choice, so that the PM process is more likely to win the race. We manipulated bias in a lexical decision task with an accompanying PM intention. In one condition, a bias was induced against deciding items were words, and in another, a bias was induced against deciding items were non-words. The bias manipulation had little effect on PM accuracy but did affect the types of ongoing-task responses made on missed PM trials. PMDC fit the observed data well and verified that the bias manipulation had the intended effect on ongoing-task processes. Furthermore, although simulations from PMDC could produce an improvement in PM accuracy due to ongoing-task bias, this required implausible parameter values. These results illustrate the importance of understanding event-based PM in terms of a comprehensive model of the processes that interact to determine all aspects of task performance.

Model-based assessment and neural correlates of spatial memory deficits in mild cognitive impairment.

Mild cognitive impairment (MCI) is characterized by subjective and objective memory impairments within the context of generally intact everyday functioning. Such memory deficits are typically thought to arise from medial temporal lobe dysfunction; however, differences in memory task performance can arise from a variety of altered processes (e.g., strategy adjustments) rather than, or in addition to, "pure" memory deficits. To address this problem, we applied the linear ballistic accumulator (LBA: Brown and Heathcote, 2008) model to data from individuals with MCI (n = 18) and healthy older adults (HOA; n = 16) who performed an object-location association memory retrieval task during functional magnetic resonance imaging (fMRI). The primary goals were to 1) assess between-group differences in model parameters indexing processes of interest (memory sensitivity, accumulation speed, caution and time spent on peripheral perceptual and motor processes) and 2) determine whether differences in model-based metrics were consistent with fMRI data. The LBA provided evidence that, relative to the HOA group, those with MCI displayed lower sensitivity (i.e., difficulty discriminating targets from lures), suggestive of memory impairment, and displayed higher evidence accumulation speed and greater caution, suggestive of increased arousal and strategic changes in this group, although these changes had little impact on MCI-related accuracy differences. Consistent with these findings, fMRI revealed reduced activation in brain regions previously linked to evidence accumulation and to the implementation of caution reductions in the MCI group. Findings suggest that multiple cognitive mechanisms differ during memory retrieval in MCI, and that these mechanisms may explain neuroimaging alterations outside of the medial temporal lobes.

A Note on Decomposition of Sources of Variability in Perceptual Decision-making.

Information processing underlying human perceptual decision-making is inherently noisy and identifying sources of this noise is important to understand processing. Ratcliff, Voskuilen, and McKoon (2018) examined results from five experiments using a double-pass procedure in which stimuli were repeated typically a hundred trials later. Greater than chance agreement between repeated tests provided evidence for trial-to-trial variability from external sources of noise. They applied the diffusion model to estimate the quality of evidence driving the decision process (drift rate) and the variability (standard deviation) in drift rate across trials. This variability can be decomposed into random (internal) and systematic (external) components by comparing the double-pass accuracy and agreement with the model predictions. In this note, we provide an additional analysis of the double-pass experiments using the linear ballistic accumulator (LBA) model. The LBA model does not have within-trial variability and thus it captures all variability in processing with its across-trial variability parameters. The LBA analysis of the double-pass data provides model-based evidence of external variability in a decision process, which is consistent with Ratcliff et al.'s result. This demonstrates that across-trial variability is required to model perceptual decision-making. The LBA model provides measures of systematic and random variability as the diffusion model did. But due to the lack of within-trial variability, the LBA model estimated the random component as a larger proportion of across-trial total variability than did the diffusion model.