Rate Of Evidence Accumulation Research Articles

Diffusing the bilingual lexicon: Task-based and lexical components of language switch costs

Bilingual speakers show a response time (RT) cost when switching between languages. These costs could reflect the organization of language in a shared bilingual lexicon (Grainger, Midgley, & Holcomb, 2010) or a domain general cognitive processing cost (Green & Abutalebi, 2013). To test these accounts, we analysed RT distributions of bilingual (Spanish-English) performance on generalized lexical decision (GLD) tasks using Ratcliff (1978) diffusion model. Experiment 1 revealed that language switches decrease the rate of evidence accumulation (drift rate) and slow the cognitive processes that occur prior to decision-making (non-decision time). Experiment 2 showed that the anticipation of language switches did not change these effects. The results suggest that language switch costs originate from a combination of at least two loci: lexical access and a task-specific decision process.

Modulation of the word frequency effect in recognition memory after an unrelated lexical decision task

• Lexical decision modulates the WFE in a subsequent unrelated recognition task. • An increase in hit rates and false alarms for high frequency words was found in RC. • A decrease in false alarms for the low frequency words was also found in RC. • Changes in drift rate and response criterion of the LBA model explain the WFE. • Previous processing history affects memory processing is subsequent tasks. The natural language frequency of a word is known to influence the ability to perform recognition judgments based on either semantic or episodic memory, an effect commonly known as the word frequency effect (WFE). For episodic recognition specifically, the WFE presents a mirrored pattern with higher hit rates and lower false alarm rates for low frequency words compared to high frequency words. Interestingly, the use of certain study tasks such as judgements of concreteness has been shown to reduce or even abolish the hit rate advantage of low frequency words. In the present study we tested the hypothesis that prior exposure to an unrelated lexical decision task leads to a modulation of the WFE in a subsequent episodic recognition test. Results showed an increase in hit rates and false alarm rates for high frequency words and a decrease in false alarm rates for low-frequency words. Analysis of behaviour with the linear ballistic accumulator model furthermore revealed that this effect was driven by changes in the rate of evidence accumulation of the decision process and in the response threshold. We discuss the relevance of these findings for current theories of memory.

Cognitive workload measurement and modeling under divided attention.

Motorists often engage in secondary tasks unrelated to driving that increase cognitive workload, resulting in fatal crashes and injuries. An International Standards Organization method for measuring a driver's cognitive workload, the detection response task (DRT), correlates well with driving outcomes, but investigation of its putative theoretical basis in terms of finite attention capacity remains limited. We address this knowledge gap using evidence-accumulation modeling of simple and choice versions of the DRT in a driving scenario. Our experiments demonstrate how dual-task load affects the parameters of evidence-accumulation models. We found that the cognitive workload induced by a secondary task (counting backward by 3s) reduced the rate of evidence accumulation, consistent with rates being sensitive to limited-capacity attention. We also found a compensatory increase in the amount of evidence required for a response and a small speeding in the time for nondecision processes. The International Standards Organization version of the DRT was found to be most sensitive to cognitive workload. A Wald-distributed evidence-accumulation model augmented with a parameter measuring response omissions provided a parsimonious measure of the underlying causes of cognitive workload in this task. This work demonstrates that evidence-accumulation modeling can accurately represent data produced by cognitive workload measurements, reproduce the data through simulation, and provide supporting evidence for the cognitive processes underlying cognitive workload. Our results provide converging evidence that the DRT method is sensitive to dynamic fluctuations in limited-capacity attention. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

The dissociation of temporal processing behavior in concussion patients: Stable motor and dynamic perceptual timing

Temporal processing is an integral aspect of human cognition and perception. Recent studies have suggested that patients suffering from concussion exhibit a deficit in temporal processing, characterized by poor performance on a variety of timing tasks. However, the majority of studies focusing on temporal processing deficits in concussion have focused on visual timing mechanisms. As temporal processing may be dominant for auditory-based processing, and so less susceptible to noise, we investigated patients with TBI and compared them to normal healthy controls on a battery of temporal processing tasks, including paced finger tapping and temporal bisection with sub-second intervals. The results of our investigation found that traumatic brain patients were unimpaired on the paced finger tapping task, suggesting that temporal processing deficits do not extend into motor timing and rhythmicity domain. In the temporal bisection task, TBI patients maintained precision but had a significantly higher bisection point, characterized by a greater propensity to judge stimuli as “short” and were significantly slower than controls. Analysis with a drift-diffusion model of perceptual decision-making revealed that TBI patients were specifically impaired in evidence accumulation, suggesting a smaller signal to noise ratio. Specifically, it demonstrated that patients had higher decision threshold and slower drift rates for accumulating evidence in order to arrive at a decision. Patients had to surmount higher evidence thresholds to reach a decision and were slower than controls in their rate of evidence accumulation. These results suggest specific deficits in temporal perceptual decision-making may predict the neural temporal pathways that may be compromised or unaffected, paving the way for designing targeted therapies to address these impairments.

Reward-driven changes in striatal pathway competition shape evidence evaluation in decision-making.

Cortico-basal-ganglia-thalamic (CBGT) networks are critical for adaptive decision-making, yet how changes to circuit-level properties impact cognitive algorithms remains unclear. Here we explore how dopaminergic plasticity at corticostriatal synapses alters competition between striatal pathways, impacting the evidence accumulation process during decision-making. Spike-timing dependent plasticity simulations showed that dopaminergic feedback based on rewards modified the ratio of direct and indirect corticostriatal weights within opposing action channels. Using the learned weight ratios in a full spiking CBGT network model, we simulated neural dynamics and decision outcomes in a reward-driven decision task and fit them with a drift diffusion model. Fits revealed that the rate of evidence accumulation varied with inter-channel differences in direct pathway activity while boundary height varied with overall indirect pathway activity. This multi-level modeling approach demonstrates how complementary learning and decision computations can emerge from corticostriatal plasticity.

A method, framework, and tutorial for efficiently simulating models of decision-making

Evidence accumulation models (EAMs) have become the dominant models of rapid decision-making. Several variants of these models have been proposed, ranging from the simple linear ballistic accumulator (LBA) to the more complex leaky-competing accumulator (LCA), and further extensions that include time-varying rates of evidence accumulation or decision thresholds. Although applications of the simpler variants have been widespread, applications of the more complex models have been fewer, largely due to their intractable likelihood function and the computational cost of mass simulation. Here, I present a framework for efficiently fitting complex EAMs, which uses a new, efficient method of simulating these models. I find that the majority of simulation time is taken up by random number generation (RNG) from the normal distribution, needed for the stochastic noise of the differential equation. To reduce this inefficiency, I propose using the well-known concept within computer science of “look-up tables” (LUTs) as an approximation to the inverse cumulative density function (iCDF) method of RNG, which I call “LUT-iCDF”. I show that when using an appropriately sized LUT, simulations using LUT-iCDF closely match those from the standard RNG method in R. My framework, which I provide a detailed tutorial on how to implement, includes C code for 12 different variants of EAMs using the LUT-iCDF method, and should make the implementation of complex EAMs easier and faster.

The grammaticality asymmetry in agreement attraction reflects response bias: Experimental and modeling evidence

Memory access mechanisms such as cue-based retrieval have come to dominate theories of the processing of linguistic dependencies such as subject-verb agreement. One phenomenon that has been regarded as demonstrating the role of such mechanisms is the grammaticality asymmetry in agreement attraction, which is the observation that nouns other than the grammatical controller of agreement can influence the computation of subject-verb agreement in ungrammatical, but not grammatical, sentences. This asymmetry is most often accounted for via the dynamics of retrieval interference. We challenge this interpretation, arguing that the asymmetry largely reflects response bias. Three forced-choice judgment experiments show that neutralizing response bias results in a decrease in the size of the grammaticality asymmetry, or its elimination altogether. Together with the response time patterns in these experiments, this result favors an account that attributes attraction effects to a continuous and equivocal representation of number, rather than to the dynamics of retrieval interference. We implement a model of grammaticality judgments that links a continuous representation of number to the rate of evidence accumulation in a diffusion process. This model accounts for the presence or absence of the grammaticality asymmetry through shifts in the decisional starting point (i.e. response bias), and highlights the importance of monitoring for response bias effects in judgment tasks.

Humans strategically shift decision bias by flexibly adjusting sensory evidence accumulation.

Decision bias is traditionally conceptualized as an internal reference against which sensory evidence is compared. Instead, we show that individuals implement decision bias by shifting the rate of sensory evidence accumulation toward a decision bound. Participants performed a target detection task while we recorded EEG. We experimentally manipulated participants' decision criterion for reporting targets using different stimulus-response reward contingencies, inducing either a liberal or a conservative bias. Drift diffusion modeling revealed that a liberal strategy biased sensory evidence accumulation toward target-present choices. Moreover, a liberal bias resulted in stronger midfrontal pre-stimulus 2-6 Hz (theta) power and suppression of pre-stimulus 8-12 Hz (alpha) power in posterior cortex. Alpha suppression in turn was linked to the output activity in visual cortex, as expressed through 59-100 Hz (gamma) power. These findings show that observers can intentionally control cortical excitability to strategically bias evidence accumulation toward the decision bound that maximizes reward.

Can we learn to learn? The influence of procedural working-memory training on rapid instructed-task-learning.

Humans have the unique ability to efficiently execute instructions that were never practiced beforehand. In this Rapid Instructed-Task-Learning, not-yet-executed novel rules are presumably held in procedural working-memory (WM), which is assumed to hold stimulus-to-response bindings. In this study, we employed a computerized-cognitive training protocol targeting procedural WM to test this assumption and to examine whether the ability to rapidly learn novel rules can itself be learned. 175 participants were randomly assigned to one of three groups: procedural WM training (involving task-switching and N-back elements, all with novel rules; Shahar and Meiran in PLoS One 10(3):e0119992, 2015), active-control training (adaptive visual-search task), and no-contact control. We examined participants' rapid instructed-task-learning abilities before and after training, by administrating 55 novel choice tasks, and measuring their performance in the first two trials (where participants had no practice). While all participants showed shorter reaction-times in post vs. pretest, only participants in the procedural WM training group did not demonstrate an increased error rate at posttest. Evidence accumulation modelling suggested that this result stems from a reduction in decision threshold (the amount of evidence that needs to be gathered to reach a decision), which was more pronounced in the control groups; possibly accompanied by an increased drift-rate (the rate of evidence accumulation) only for the training group. Implication are discussed.

Having More Choices Changes How Human Observers Weight Stable Sensory Evidence.

Decision-making becomes slower when more choices are available. Existing models attribute this slowing to poor sensory processing, to attenuated rates of sensory evidence accumulation, or to increases in the amount of evidence required before committing to a decision (a higher decision threshold). However, studies have not isolated the effects of having more choices on sensory and decision-related processes from changes in task difficulty and divided attention. Here, we controlled task difficulty while independently manipulating the distribution of attention and the number of choices available to male and female human observers. We used EEG to measure steady-state visually evoked potentials (SSVEPs) and a frontal late positive deflection (LPD), EEG markers of sensory and postsensory decision-related processes, respectively. We found that dividing attention decreased SSVEP and LPD amplitudes, consistent with dampened sensory responses and slower rates of evidence accumulation, respectively. In contrast, having more choices did not alter SSVEP amplitude and led to a larger LPD. These results suggest that having more options largely spares early sensory processing and slows down decision-making via a selective increase in decision thresholds.SIGNIFICANCE STATEMENT When more choices are available, decision-making becomes slower. We tested whether this phenomenon is due to poor sensory processing, to reduced rates of evidence accumulation, or to increases in the amount of evidence required before committing to a decision (a higher decision threshold). We measured choice modulations of sensory and decision-related neural responses using EEG. We also minimized potential confounds from changes in the distribution of attention and task difficulty, which often covary with having more choices. Dividing attention reduced the activity levels of both sensory and decision-related responses. However, having more choices did not change sensory processing and led to larger decision-related responses. These results suggest that having more choices spares sensory processing and selectively increases decision thresholds.

Single Trial Plasticity in Evidence Accumulation Underlies Rapid Recalibration to Asynchronous Audiovisual Speech

Asynchronous arrival of audiovisual information at the peripheral sensory organs is a ubiquitous property of signals in the natural environment due to differences in the propagation time of light and sound. As these cues are constantly changing their distance from the observer, rapid adaptation to asynchronies is crucial for their appropriate integration. We investigated the neural basis of rapid recalibration to asynchronous audiovisual speech in humans using a combination of psychophysics, drift diffusion modeling, and electroencephalography (EEG). Consistent with previous reports, we found that perception of audiovisual temporal synchrony depends on the temporal ordering of the previous trial. Drift diffusion modelling indicated that this recalibration effect is well accounted for by changes in the rate of evidence accumulation (i.e. drift rate). Neural responses as indexed via evoked potentials were similarly found to vary based on the temporal ordering of the previous trial. Within and across subject correlations indicated that the observed changes in drift rate and the modulation of evoked potential magnitude were related. These results indicate that the rate and direction of evidence accumulation are affected by immediate sensory history and that these changes contribute to single trial recalibration to audiovisual temporal asynchrony.

Stochastic resonance enhances the rate of evidence accumulation during combined brain stimulation and perceptual decision-making.

Perceptual decision-making relies on the gradual accumulation of noisy sensory evidence. It is often assumed that such decisions are degraded by adding noise to a stimulus, or to the neural systems involved in the decision making process itself. But it has been suggested that adding an optimal amount of noise can, under appropriate conditions, enhance the quality of subthreshold signals in nonlinear systems, a phenomenon known as stochastic resonance. Here we asked whether perceptual decisions made by human observers obey these stochastic resonance principles, by adding noise directly to the visual cortex using transcranial random noise stimulation (tRNS) while participants judged the direction of coherent motion in random-dot kinematograms presented at the fovea. We found that adding tRNS bilaterally to visual cortex enhanced decision-making when stimuli were just below perceptual threshold, but not when they were well below or above threshold. We modelled the data under a drift diffusion framework, and showed that bilateral tRNS selectively increased the drift rate parameter, which indexes the rate of evidence accumulation. Our study is the first to provide causal evidence that perceptual decision-making is susceptible to a stochastic resonance effect induced by tRNS, and to show that this effect arises from selective enhancement of the rate of evidence accumulation for sub-threshold sensory events.

Interpersonal touch enhances cognitive control: A neurophysiological investigation.

Touch is central to mammalian communication, socialization, and wellbeing. Despite this prominence, interpersonal touch is relatively understudied. In this preregistered investigation, we assessed the influence of interpersonal touch on the subjective, neural, and behavioral correlates of cognitive control. Forty-five romantic couples were recruited (N = 90; dating >6 months), and one partner performed an inhibitory control task while electroencephalography was recorded to assess neural performance monitoring. Interpersonal touch was provided by the second partner and was manipulated between experimental blocks. A within-subject repeated-measures design was used to maximize statistical power, with our sample size providing 80% power for even small effect sizes (ds > .25). Results indicated that participants were not only happier when receiving touch, but also showed increased neural processing of mistakes. Further exploratory cognitive modeling using indirect effects tests and drift diffusion models of decision making revealed that touch was indirectly associated with both improved inhibitory control and increased rates of evidence accumulation (drift rate) through its influence on neural monitoring. Thus, beyond regulating emotion and stress, interpersonal touch appears to enhance the neurocognitive processes underling flexible goal-directed behavior. (PsycINFO Database Record

Alertness and cognitive control: Testing the early onset hypothesis.

Previous research has revealed a peculiar interaction between alertness and cognitive control in selective-attention tasks: Congruency effects are larger on alert trials (on which an alerting cue is presented briefly in advance of the imperative stimulus) than on no-alert trials, despite shorter response times (RTs) on alert trials. One explanation for this finding is the early onset hypothesis, which is based on the assumptions that increased alertness shortens stimulus-encoding time and that cognitive control involves gradually focusing attention during a trial. The author tested the hypothesis in 3 experiments by manipulating alertness and stimulus quality (which were intended to shorten and lengthen stimulus-encoding time, respectively) in an arrow-based flanker task involving congruent and incongruent stimuli. Replicating past findings, the alerting manipulation led to shorter RTs but larger congruency effects on alert trials than on no-alert trials. The stimulus-quality manipulation led to longer RTs and larger congruency effects for degraded stimuli than for intact stimuli. These results provide mixed support for the early onset hypothesis, but the author discusses how data and theory might be reconciled if stimulus quality affects stimulus-encoding time and the rate of evidence accumulation in the decision process. (PsycINFO Database Record

Modeling 2-alternative forced-choice tasks: Accounting for both magnitude and difference effects

We present a model-based analysis of two-alternative forced-choice tasks in which two stimuli are presented side by side and subjects must make a comparative judgment (e.g., which stimulus is brighter). Stimuli can vary on two dimensions, the difference in strength of the two stimuli and the magnitude of each stimulus. Differences between the two stimuli produce typical RT and accuracy effects (i.e., subjects respond more quickly and more accurately when there is a larger difference between the two). However, the overall magnitude of the pair of stimuli also affects RT and accuracy. In the more common two-choice task, a single stimulus is presented and the stimulus varies on only one dimension. In this two-stimulus task, if the standard diffusion decision model is fit to the data with only drift rate (evidence accumulation rate) differing among conditions, the model cannot fit the data. However, if either of one of two variability parameters is allowed to change with stimulus magnitude, the model can fit the data. This results in two models that are extremely constrained with about one tenth of the number of parameters than there are data points while at the same time the models account for accuracy and correct and error RT distributions. While both of these versions of the diffusion model can account for the observed data, the model that allows across-trial variability in drift to vary might be preferred for theoretical reasons. The diffusion model fits are compared to the leaky competing accumulator model which did not perform as well.

Internal and external sources of variability in perceptual decision-making.

It is important to identify sources of variability in processing to understand decision-making in perception and cognition. There is a distinction between internal and external variability in processing, and double-pass experiments have been used to estimate their relative contributions. In these and our experiments, exact perceptual stimuli are repeated later in testing, and agreement on the 2 trials is examined to see if it is greater than chance. In recent research in modeling decision processes, some models implement only (internal) variability in the decision process whereas others explicitly represent multiple sources of variability. We describe 5 perceptual double-pass experiments that show greater than chance agreement, which is inconsistent with models that assume internal variability alone. Estimates of total trial-to-trial variability in the evidence accumulation (drift) rate (the decision-relevant stimulus information) were estimated from fits of the standard diffusion decision-making model to the data. The double-pass procedure provided estimates of how much of this total variability was systematic and dependent on the stimulus. These results provide the first behavioral evidence independent of model fits for trial-to-trial variability in drift rate in tasks used in examining perceptual decision-making. (PsycINFO Database Record

How race affects evidence accumulation during the decision to shoot

The biasing role of stereotypes is a central theme in social cognition research. For example, to understand the role of race in police officers’ decisions to shoot, participants have been shown images of Black and White males and instructed to shoot only if the target is holding a gun. Findings show that Black targets are shot more frequently and more quickly than Whites. The decision to shoot has typically been modeled and understood as a signal detection process in which a sample of information is compared against a criterion, with the criterion set for Black targets being lower. We take a different approach, modeling the decision to shoot as a dynamic process in which evidence is accumulated over time until a threshold is reached. The model accounts for both the choice and response time data for both correct and incorrect decisions using a single set of parameters. Across four studies, this dynamic perspective revealed that the target’s race did not create an initial bias to shoot Black targets. Instead, race impacted the rate of evidence accumulation with evidence accumulating faster to shoot for Black targets. Some participants also tended to be more cautious with Black targets, setting higher decision thresholds. Besides providing a more cohesive and richer account of the decision to shoot or not, the dynamic model suggests interventions that may address the use of race information in decisions to shoot and a means to measure their effectiveness.

Parsing the neural signatures of reduced error detection in older age

Recent work has demonstrated that explicit error detection relies on a neural evidence accumulation process that can be traced in the human electroencephalogram (EEG). Here, we sought to establish the impact of natural aging on this process by recording EEG from young (18–35 years) and older adults (65–88 years) during the performance of a Go/No-Go paradigm in which participants were required to overtly signal their errors. Despite performing the task with equivalent accuracy, older adults reported substantially fewer errors, and the timing of their reports were both slower and more variable. These behavioral differences were linked to three key neurophysiological changes reflecting distinct parameters of the error detection decision process: a reduction in medial frontal delta/theta (2–7 Hz) activity, indicating diminished top-down input to the decision process; a slower rate of evidence accumulation as indexed by the rate of rise of a centro-parietal signal, known as the error positivity; and a higher motor execution threshold as indexed by lateralized beta-band (16–30 Hz) activity. Our data provide novel insight into how the natural aging process affects the neural underpinnings of error detection.

The Sustained Influence of an Error on Future Decision-Making.

Post-error slowing (PES) is consistently observed in decision-making tasks after negative feedback. Yet, findings are inconclusive as to whether PES supports performance accuracy. We addressed the role of PES by employing drift diffusion modeling which enabled us to investigate latent processes of reaction times and accuracy on a large-scale dataset (>5,800 participants) of a visual search experiment with emotional face stimuli. In our experiment, post-error trials were characterized by both adaptive and non-adaptive decision processes. An adaptive increase in participants’ response threshold was sustained over several trials post-error. Contrarily, an initial decrease in evidence accumulation rate, followed by an increase on the subsequent trials, indicates a momentary distraction of task-relevant attention and resulted in an initial accuracy drop. Higher values of decision threshold and evidence accumulation on the post-error trial were associated with higher accuracy on subsequent trials which further gives credence to these parameters’ role in post-error adaptation. Finally, the evidence accumulation rate post-error decreased when the error trial presented angry faces, a finding suggesting that the post-error decision can be influenced by the error context. In conclusion, we demonstrate that error-related response adaptations are multi-component processes that change dynamically over several trials post-error.

Bayesian analysis of the piecewise diffusion decision model.

Most past research on sequential sampling models of decision-making have assumed a time homogeneous process (i.e., parameters such as drift rates and boundaries are constant and do not change during the deliberation process). This has largely been due to the theoretical difficulty in testing and fitting more complex models. In recent years, the development of simulation-based modeling approaches matched with Bayesian fitting methodologies has opened the possibility of developing more complex models such as those with time-varying properties. In the present work, we discuss a piecewise variant of the well-studied diffusion decision model (termed pDDM) that allows evidence accumulation rates to change during the deliberation process. Given the complex, time-varying nature of this model, standard Bayesian parameter estimation methodologies cannot be used to fit the model. To overcome this, we apply a recently developed simulation-based, hierarchal Bayesian methodology called the probability density approximation (PDA) method. We provide an analysis of this methodology and present results of parameter recovery experiments to demonstrate the strengths and limitations of this approach. With those established, we fit pDDM to data from a perceptual experiment where information changes during the course of trials. This extensible modeling platform opens the possibility of applying sequential sampling models to a range of complex non-stationary decision tasks.