Greater target or lure variability? An exploration on the effects of stimulus types and memory paradigms

In recognition memory, the variance of the target distribution is almost universally found to be greater than that of the lure distribution. However, these estimates commonly come from long-term memory paradigms where words are used as stimuli. Two exceptions to this rule have found evidence for greater lure variability: a short-term memory task (Yotsumoto et al., Memory & Cognition, 36, 282-294 2008) and in an eyewitness memory paradigm (Wixted et al., Cognitive Psychology, 105, 81-114 2018). In the present work, we conducted a series of recognition memory experiments using different stimulus (faces vs. words) along with different paradigms (long-term vs. short-term paradigms) to evaluate whether either of these conditions would result in greater variability in lure items. Greater target variability was observed across stimulus types and memory paradigms. This suggests that factors other than stimuli and retention interval might be responsible for cases where variability is less for targets than lures.

The effects of non-diagnostic information on confidence and decision making

Many decision-making tasks are characterized by a combination of diagnostic and non-diagnostic information, yet models of responding and confidence almost exclusively focus on the contribution of diagnostic information (e.g., evidence associated with stimulus discriminability), largely ignoring the contribution of non-diagnostic information. An exception is Baranski and Petrusic's Journal of Experimental Psychology: Human Perception and Performance, 24(3), 929-945, (1998) doubt-scaling model, which predicts a negative relationship between non-diagnostic information and confidence, and between non-diagnostic information and accuracy. In two perceptual-choice tasks, we tested the effects of manipulating non-diagnostic information on confidence, accuracy and response time (RT). In Experiment 1, participants viewed a dynamic grid consisting of flashing blue, orange and white pixels and indicated whether the stimulus was predominantly blue or orange (using a response scale ranging from low-confidence blue to high-confidence orange), with the white pixels constituting non-diagnostic information. Increasing non-diagnostic information reduced both confidence and accuracy, generally slowed RTs, and led to an increase in the speed of errors. Experiment 2 replicated these results for a decision-only task, providing further support for the doubt-scaling model of confidence.

A unified account of simple and response-selective inhibition

Response inhibition is a key attribute of human executive control. Standard stop-signal tasks require countermanding a single response; the speed at which that response can be inhibited indexes the efficacy of the inhibitory control networks. However, more complex stopping tasks, where one or more components of a multi-component action are cancelled (i.e., response-selective stopping) cannot be explained by the independent-race model appropriate for the simple task (Logan and Cowan 1984). Healthy human participants (n=28; 10 male; 19-40 years) completed a response-selective stopping task where a 'go' stimulus required simultaneous (bimanual) button presses in response to left and right pointing green arrows. On a subset of trials (30%) one, or both, arrows turned red (constituting the stop signal) requiring that only the button-press(es) associated with red arrows be cancelled. Electromyographic recordings from both index fingers (first dorsal interosseous) permitted the assessment of both voluntary motor responses that resulted in overt button presses, and activity that was cancelled prior to an overt response (i.e., partial, or covert, responses). We propose a simultaneously inhibit and start (SIS) model that extends the independent race model and provides a highly accurate account of response-selective stopping data. Together with fine-grained EMG analysis, our model-based analysis offers converging evidence that the selective-stop signal simultaneously triggers a process that stops the bimanual response and triggers a new unimanual response corresponding to the green arrow. Our results require a reconceptualisation of response-selective stopping and offer a tractable framework for assessing such tasks in healthy and patient populations. Significance Statement Response inhibition is a key attribute of human executive control, frequently investigated using the stop-signal task. After initiating a motor response to a go signal, a stop signal occasionally appears at a delay, requiring cancellation of the response. This has been conceptualised as a 'race' between the go and stop processes, with the successful (or failed) cancellation determined by which process wins the race. Here we provide a novel computational model for a complex variation of the stop-signal task, where only one component of a multicomponent action needs to be cancelled. We provide compelling muscle activation data that support our model, providing a robust and plausible framework for studying these complex inhibition tasks in both healthy and pathological cohorts.

Joint Modelling of Latent Cognitive Mechanisms Shared Across Decision-Making Domains

Decision-making behavior is often understood using the framework of evidence accumulation models (EAMs). Nowadays, EAMs are applied to various domains of decision-making with the underlying assumption that the latent cognitive constructs proposed by EAMs are consistent across these domains. In this study, we investigate both the extent to which the parameters of EAMs are related between four different decision-making domains and across different time points. To that end, we make use of the novel joint modelling approach, that explicitly includes relationships between parameters, such as covariances or underlying factors, in one combined joint model. Consequently, this joint model also accounts for measurement error and uncertainty within the estimation of these relations. We found that EAM parameters were consistent between time points on three of the four decision-making tasks. For our between-task analysis, we constructed a joint model with a factor analysis on the parameters of the different tasks. Our two-factor joint model indicated that information processing ability was related between the different decision-making domains. However, other cognitive constructs such as the degree of response caution and urgency were only comparable on some domains.

A hybrid approach to dynamic cognitive psychometrics : Dynamic cognitive psychometrics

Dynamic cognitive psychometrics measures mental capacities based on the way behavior unfolds over time. It does so using models of psychological processes whose validity is grounded in research from experimental psychology and the neurosciences. However, these models can sometimes have undesirable measurement properties. We propose a "hybrid" modeling approach that achieves good measurement by blending process-based and descriptive components. We demonstrate the utility of this approach in the stop-signal paradigm, in which participants make a series of speeded choices, but occasionally are required to withhold their response when a "stop signal" occurs. The stop-signal paradigm is widely used to measure response inhibition based on a modeling framework that assumes a race between processes triggered by the choice and the stop stimuli. However, the key index of inhibition, the latency of the stop process (i.e., stop-signal reaction time), is not directly observable, and is poorly estimated when the choice and the stop runners are both modeled by psychologically realistic evidence-accumulation processes. We show that using a descriptive account of the stop process, while retaining a realistic account of the choice process, simultaneously enables good measurement of both stop-signal reaction time and the psychological factors that determine choice behavior. We show that this approach, when combined with hierarchical Bayesian estimation, is effective even in a complex choice task that requires participants to perform only a relatively modest number of test trials.

Automated decision aids: When are they advisors and when do they take control of human decision making?

We applied a computational model to examine the extent to which participants used an automated decision aid as an advisor, as compared to a more autonomous trigger of responding, at varying levels of decision aid reliability. In an air traffic control conflict detection task, we found higher accuracy when the decision aid was correct, and more errors when the decision aid was incorrect, as compared to a manual condition (no decision aid). Responses that were correct despite incorrect automated advice were slower than matched manual responses. Decision aids set at lower reliability (75%) had smaller effects on choices and response times, and were subjectively trusted less, than decision aids set at higher reliability (95%). We fitted an evidence accumulation model to choices and response times to measure how information processing was affected by decision aid inputs. Participants primarily treated low-reliability decision aids as an advisor rather than directly accumulating evidence based on its advice. Participants directly accumulated evidence based upon the advice of high-reliability decision aids, consistent with granting decision aids more autonomous influence over decisions. Individual differences in the level of direct accumulation correlated with subjective trust, suggesting a cognitive mechanism by which trust impacts human decisions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Proactive cues facilitate faster action reprogramming, but not stopping, in a response-selective stop signal task

The ability to stop simple ongoing actions has been extensively studied using the stop signal task, but less is known about inhibition in more complex scenarios. Here we used a task requiring bimanual responses to go stimuli, but selective inhibition of only one of those responses following a stop signal. We assessed how proactive cues affect the nature of both the responding and stopping processes, and the well-documented stopping delay (interference effect) in the continuing action following successful stopping. In this task, estimates of the speed of inhibition based on a simple-stopping model are inappropriate, and have produced inconsistent findings about the effects of proactive control on motor inhibition. We instead used a multi-modal approach, based on improved methods of detecting and interpreting partial electromyographical responses and the recently proposed SIS (simultaneously inhibit and start) model of selective stopping behaviour. Our results provide clear and converging evidence that proactive cues reduce the stopping delay effect by slowing bimanual responses and speeding unimanual responses, with a negligible effect on the speed of the stopping process.

What mechanisms mediate prior probability effects on rapid-choice decision-making?

Rapid-choice decision-making is biased by prior probability of response alternatives. Conventionally, prior probability effects are assumed to selectively affect, response threshold, which determines the amount of evidence required to trigger a decision. However, there may also be effects on the rate at which evidence is accumulated and the time required for non-decision processes (e.g., response production). Healthy young (n = 21) and older (n = 20) adults completed a choice response-time task requiring left- or right-hand responses to imperative stimuli. Prior probability was manipulated using a warning stimulus that informed participants that a particular response was 70% likely (i.e., the imperative stimulus was either congruent or incongruent with the warning stimulus). In addition, prior probability was either fixed for blocks of trials (block-wise bias) or varied from trial-to-trial (trial-wise bias). Response time and accuracy data were analysed using the racing diffusion evidence-accumulation model to test the selective influence assumption. Response times for correct responses were slower on incongruent than congruent trials, and older adults' responses were slower, but more accurate, than young adults. Evidence-accumulation modelling favoured an effect of prior probability on both response thresholds and nondecision time. Overall, the current results cast doubt on the selective threshold influence assumption in the racing diffusion model.

Calibration of cognitive tests to address the reliability paradox for decision-conflict tasks

Standard, well-established cognitive tasks that produce reliable effects in group comparisons also lead to unreliable measurement when assessing individual differences. This reliability paradox has been demonstrated in decision-conflict tasks such as the Simon, Flanker, and Stroop tasks, which measure various aspects of cognitive control. We aim to address this paradox by implementing carefully calibrated versions of the standard tests with an additional manipulation to encourage processing of conflicting information, as well as combinations of standard tasks. Over five experiments, we show that a Flanker task and a combined Simon and Stroop task with the additional manipulation produced reliable estimates of individual differences in under 100 trials per task, which improves on the reliability seen in benchmark Flanker, Simon, and Stroop data. We make these tasks freely available and discuss both theoretical and applied implications regarding how the cognitive testing of individual differences is carried out.

Investigating Intra-Individual Networks of Response Inhibition and Interference Resolution using 7T MRI

Response inhibition and interference resolution are often considered subcomponents of an overarching inhibition system that utilizes the so-called cortico-basal-ganglia loop. Up until now, most previous functional magnetic resonance imaging (fMRI) literature has compared the two using between-subject designs, pooling data in the form of a meta-analysis or comparing different groups. Here, we investigate the overlap of activation patterns underlying response inhibition and interference resolution on a within-subject level, using ultra-high field MRI. In this model-based study, we furthered the functional analysis with cognitive modelling techniques to provide a more in-depth understanding of behaviour. We applied the stop-signal task and multi-source interference task to measure response inhibition and interference resolution, respectively. Our results lead us to conclude that these constructs are rooted in anatomically distinct brain areas and provide little evidence for spatial overlap. Across the two tasks, common BOLD responses were observed in the inferior frontal gyrus and anterior insula. Interference resolution relied more heavily on subcortical components, specifically nodes of the commonly referred to indirect and hyperdirect pathways, as well as the anterior cingulate cortex, and pre-supplementary motor area. Our data indicated that orbitofrontal cortex activation is specific to response inhibition. Our model-based approach provided evidence for the dissimilarity in behavioural dynamics between the two tasks. The current work exemplifies the importance of reducing inter-individual variance when comparing network patterns and the value of UHF-MRI for high resolution functional mapping.

A cognitive process modeling framework for the ABCD study stop-signal task

The Adolescent Brain Cognitive Development (ABCD) Study is a longitudinal neuroimaging study of unprecedented scale that is in the process of following over 11,000 youth from middle childhood though age 20. However, a design feature of the study's stop-signal task violates "context independence", an assumption critical to current non-parametric methods for estimating stop-signal reaction time (SSRT), a key measure of inhibitory ability in the study. This has led some experts to call for the task to be changed and for previously collected data to be used with caution. We present a cognitive process modeling framework, the RDEX-ABCD model, that provides a parsimonious explanation for the impact of this design feature on "go" stimulus processing and successfully accounts for key behavioral trends in the ABCD data. Simulation studies using this model suggest that failing to account for the context independence violations in the ABCD design can lead to erroneous inferences in several realistic scenarios. However, we demonstrate that RDEX-ABCD effectively addresses these violations and can be used to accurately measure SSRT along with an array of additional mechanistic parameters of interest (e.g., attention to the stop signal, cognitive efficiency), advancing investigators' ability to draw valid and nuanced inferences from ABCD data. AVAILABILITY OF DATA AND MATERIALS: Data from the ABCD Study are available through the NIH Data Archive (NDA): nda.nih.gov/abcd. Code for all analyses featured in this study is openly available on the Open Science Framework (OSF): osf.io/2h8a7/.

Building a transdisciplinary expert consensus on the cognitive drivers of performance under pressure: An international multi-panel Delphi study

Introduction

The ability to perform optimally under pressure is critical across many occupations, including the military, first responders, and competitive sport. Despite recognition that such performance depends on a range of cognitive factors, how common these factors are across performance domains remains unclear. The current study sought to integrate existing knowledge in the performance field in the form of a transdisciplinary expert consensus on the cognitive mechanisms that underlie performance under pressure.

Methods

International experts were recruited from four performance domains [(i) Defense; (ii) Competitive Sport; (iii) Civilian High-stakes; and (iv) Performance Neuroscience]. Experts rated constructs from the Research Domain Criteria (RDoC) framework (and several expert-suggested constructs) across successive rounds, until all constructs reached consensus for inclusion or were eliminated. Finally, included constructs were ranked for their relative importance.

Results

Sixty-eight experts completed the first Delphi round, with 94% of experts retained by the end of the Delphi process. The following 10 constructs reached consensus across all four panels (in order of overall ranking): (1) Attention; (2) Cognitive Control-Performance Monitoring; (3) Arousal and Regulatory Systems-Arousal; (4) Cognitive Control-Goal Selection, Updating, Representation, and Maintenance; (5) Cognitive Control-Response Selection and Inhibition/Suppression; (6) Working memory-Flexible Updating; (7) Working memory-Active Maintenance; (8) Perception and Understanding of Self-Self-knowledge; (9) Working memory-Interference Control, and (10) Expert-suggested-Shifting.

Discussion

Our results identify a set of transdisciplinary neuroscience-informed constructs, validated through expert consensus. This expert consensus is critical to standardizing cognitive assessment and informing mechanism-targeted interventions in the broader field of human performance optimization.

Multifactorial structure of cognitive assessment tests in the UK Biobank: A combined exploratory factor and structural equation modeling analyses

Introduction

The UK Biobank cognitive assessment data has been a significant resource for researchers looking to investigate predictors and modifiers of cognitive abilities and associated health outcomes in the general population. Given the diverse nature of this data, researchers use different approaches - from the use of a single test to composing the general intelligence score, g, across the tests. We argue that both approaches are suboptimal - one being too specific and the other one too general - and suggest a novel multifactorial solution to represent cognitive abilities.

Methods

Using a combined Exploratory Factor (EFA) and Exploratory Structural Equation Modeling Analyses (ESEM) we developed a three-factor model to characterize an underlying structure of nine cognitive tests selected from the UK Biobank using a Cattell-Horn-Carroll framework. We first estimated a series of probable factor solutions using the maximum likelihood method of extraction. The best solution for the EFA-defined factor structure was then tested using the ESEM approach with the aim of confirming or disconfirming the decisions made.

Results

We determined that a three-factor model fits the UK Biobank cognitive assessment data best. Two of the three factors can be assigned to fluid reasoning (Gf) with a clear distinction between visuospatial reasoning and verbal-analytical reasoning. The third factor was identified as a processing speed (Gs) factor.

Discussion

This study characterizes cognitive assessment data in the UK Biobank and delivers an alternative view on its underlying structure, suggesting that the three factor model provides a more granular solution than g that can further be applied to study different facets of cognitive functioning in relation to health outcomes and to further progress examination of its biological underpinnings.

Comparing models of learning and relearning in large-scale cognitive training data sets

Practice in real-world settings exhibits many idiosyncracies of scheduling and duration that can only be roughly approximated by laboratory research. Here we investigate 39,157 individuals' performance on two cognitive games on the Lumosity platform over a span of 5 years. The large-scale nature of the data allows us to observe highly varied lengths of uncontrolled interruptions to practice and offers a unique view of learning in naturalistic settings. We enlist a suite of models that grow in the complexity of the mechanisms they postulate and conclude that long-term naturalistic learning is best described with a combination of long-term skill and task-set preparedness. We focus additionally on the nature and speed of relearning after breaks in practice and conclude that those components must operate interactively to produce the rapid relearning that is evident even at exceptionally long delays (over 2 years). Naturalistic learning over long time spans provides a strong test for the robustness of theoretical accounts of learning, and should be more broadly used in the learning sciences.

Winner Takes All! What Are Race Models, and Why and How Should Psychologists Use Them?

Interest in the processes that mediate between stimuli and responses is at the heart of most modern psychology and neuroscience. These processes cannot be directly measured but instead must be inferred from observed responses. Race models, through their ability to account for both response choices and response times, have been a key enabler of such inferences. Examples of such models appeared contemporaneously with the cognitive revolution, and since then have become increasingly prominent and elaborated, so that psychologists now have a powerful array of race models at their disposal. We showcase the state of the art for race models and describe why and how they are used.

An app-enhanced cognitive fitness training program for athletes: The rationale and validation protocol

The core dimensions of cognitive fitness, such as attention and cognitive control, are emerging through a transdisciplinary expert consensus on what has been termed the Cognitive Fitness Framework (CF2). These dimensions represent key drivers of cognitive performance under pressure across many occupations, from first responders to sport, performing arts and the military. The constructs forming the building blocks of CF2 come from the RDoC framework, an initiative of the US National Institute of Mental Health (NIMH) aimed at identifying the cognitive processes underlying normal and abnormal behavior. Similar to physical conditioning, cognitive fitness can be improved with deliberate practice. This paper reports the development of a prototype cognitive fitness training program for competitive athletes and the protocol for its evaluation. The program is focused on primary cognitive capacities and subtending skills for adjusting training rhythms and enhancing readiness for competition. The project is driven by the Australian Psychological Society's College of Sport & Exercise Psychology and includes the development of a Cognitive Gym program for a smartphone app-enhanced implementation. Its key building blocks are training protocols (drills) connected by a periodized training plan. A website with background supporting resources has also been developed as part of the project. National-level training squads will participate in a three-week pilot evaluation protocol, assessing the program's efficacy and usability through gamified cognitive assessment of participants' training gains and coaching staff evaluations, respectively. Both near and far transfer of training effects will be examined.

A unified theory of discrete and continuous responding

Understanding the cognitive processes underlying choice requires theories that can disentangle the representation of stimuli from the processes that map these representations onto observed responses. We develop a dynamic theory of how stimuli are mapped onto discrete (choice) and onto continuous response scales. It proposes that the mapping from a stimulus to an internal representation and then to an evidence accumulation process is accomplished using multiple reference points or "anchors." Evidence is accumulated until a threshold amount for a particular response is obtained, with the relative balance of support for each anchor at that time determining the response. We tested this multiple anchored accumulation theory (MAAT) using the results of two experiments requiring discrete or continuous responses to line length and color stimuli. We manipulated the number of options for discrete responses, the number of different stimuli, and the similarity among them, and compared the outcomes to continuous response conditions. We show that MAAT accounts for several key phenomena: more accurate, faster, and more skewed distributions of responses near the ends of a response scale; lower accuracy and slower responses as the number of discrete choice options increases; and longer response times and lower accuracy when alternative responses are more similar to the target response. Our empirical and modeling results suggest that discrete and continuous response tasks can share a common evidence representation, and that the decision process is sensitive to the perceived similarity among the response options. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Dynamic workload measurement and modeling: Driving and conversing

Tillman et al. (2017) used evidence-accumulation modeling to ascertain the effects of a conversation (either with a passenger or on a hands-free cell phone) on a drivers' mental workload. They found that a concurrent conversation increased the response threshold but did not alter the rate of evidence accumulation. However, this earlier research collapsed across speaking and listening components of a natural conversation, potentially masking any dynamic fluctuations associated with this dual-task combination. In the present study, a unique implementation of the detection response task was used to simultaneously measure the demands on the driver and the nondriver when they were speaking or when they were listening. We found that the natural ebb and flow of a conversation altered both the rate of evidence accumulation and the response threshold for drivers and nondrivers alike. The dynamic fluctuations in cognitive workload observed with this novel method illustrate how quickly the parameters of cognition are altered by real-time task demands. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

OSARI, an Open-Source Anticipated Response Inhibition Task

The stop-signal paradigm has become ubiquitous in investigations of inhibitory control. Tasks inspired by the paradigm, referred to as stop-signal tasks, require participants to make responses on go trials and to inhibit those responses when presented with a stop-signal on stop trials. Currently, the most popular version of the stop-signal task is the 'choice-reaction' variant, where participants make choice responses, but must inhibit those responses when presented with a stop-signal. An alternative to the choice-reaction variant of the stop-signal task is the 'anticipated response inhibition' task. In anticipated response inhibition tasks, participants are required to make a planned response that coincides with a predictably timed event (such as lifting a finger from a computer key to stop a filling bar at a predefined target). Anticipated response inhibition tasks have some advantages over the more traditional choice-reaction stop-signal tasks and are becoming increasingly popular. However, currently, there are no openly available versions of the anticipated response inhibition task, limiting potential uptake. Here, we present an open-source, free, and ready-to-use version of the anticipated response inhibition task, which we refer to as the OSARI (the Open-Source Anticipated Response Inhibition) task.

Editorial

During its 128 years of operation, Psychological Review has exerted a powerful and consistent influence on the field under its long-term sponsor, the American Psychological Association (APA). Notwithstanding changes in ownership, it has always been what it is now-the flagship of the Association and the field. Since its inception, the journal has focused on theoretical analyses (e.g., systematic evaluations of alternative theories) and/or developments (e.g., the generation of novel theories) in the psychological sciences. Thus, the objectives of any incoming editor and editorial board remain steadfast: (a) to maintain and enhance the standing of Psychological Review in the field and (b) to correspondingly align its scope, content, and operations with any changes in the Association, the field of psychology in particular, and science and society in general. The journal's new senior editorial team is excited to navigate Psychological Review through the ever-changing landscape of psychology at this time of multiple challenges, referred to by the United Nations Secretary General António Guterres as "the greatest cascade of crises in our lifetime." Although we are initiating a number of changes, we will do our best to maintain Psychological Review's excellence. This will involve our capacity to reflect on and disseminate new theoretical developments, enriched and inspired by current trends in science in general and in psychological science in particular, while maintaining an overarching commitment to advancing the field through the incorporation of diverse perspectives. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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.

A general architecture for modeling the dynamics of goal-directed motivation and decision-making

We present a unified model of the dynamics of goal-directed motivation and decision-making. The model-referred to as the GOAL architecture-provides a quantitative framework for integrating theories of goal pursuit and for relating their predictions to different types of data. The GOAL architecture proposes that motivation changes over time according to three gradients that capture the effects of the distance to the goal (i.e., the progress remaining), the time to the deadline, and the rate of progress required to achieve the goal. This enables the integration and comparison of six theoretical perspectives that make different predictions about how these dynamics unfold when pursuing approach and avoidance goals. Hierarchical Bayesian modeling was used to analyze data from three experiments which manipulate distance to goal, time to deadline, and goal type (approach vs. avoidance), and data from the naturalistic context of professional basketball. The results show that people rely on the distance and rate gradients, and to a lesser degree the time gradient, when making resource allocation decisions during goal pursuit, although the relative influence of the gradients depends on the goal type. We also demonstrate how the GOAL architecture can be used to answer questions about the influence of goal importance. Our findings suggest that goal pursuit unfolds in a complex manner that cannot be accounted for by any one previous theoretical perspective, but that is well-characterized by our unified framework. This research highlights the importance of theoretical integration for understanding motivation and decision-making during goal pursuit. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Real-time prediction of short-timescale fluctuations in cognitive workload

Human operators often experience large fluctuations in cognitive workload over seconds timescales that can lead to sub-optimal performance, ranging from overload to neglect. Adaptive automation could potentially address this issue, but to do so it needs to be aware of real-time changes in operators' spare cognitive capacity, so it can provide help in times of peak demand and take advantage of troughs to elicit operator engagement. However, it is unclear whether rapid changes in task demands are reflected in similarly rapid fluctuations in spare capacity, and if so what aspects of responses to those demands are predictive of the current level of spare capacity. We used the ISO standard detection response task (DRT) to measure cognitive workload approximately every 4 s in a demanding task requiring monitoring and refueling of a fleet of simulated unmanned aerial vehicles (UAVs). We showed that the DRT provided a valid measure that can detect differences in workload due to changes in the number of UAVs. We used cross-validation to assess whether measures related to task performance immediately preceding the DRT could predict detection performance as a proxy for cognitive workload. Although the simple occurrence of task events had weak predictive ability, composite measures that tapped operators' situational awareness with respect to fuel levels were much more effective. We conclude that cognitive workload does vary rapidly as a function of recent task events, and that real-time predictive models of operators' cognitive workload provide a potential avenue for automation to adapt without an ongoing need for intrusive workload measurements.

Racing against the clock: Evidence-based versus time-based decisions

Classical dynamic theories of decision making assume that responses are triggered by accumulating a threshold amount of information. Recently, there has been a growing appreciation that the passage of time also plays a role in triggering responses. We propose that decision processes are composed of 2 diffusive accumulation mechanisms-1 evidence-based and 1 time-based-that compete in an independent race architecture. We show that this timed racing diffusion model (TRDM) provides a unified, comprehensive, and quantitatively accurate explanation of key decision phenomena-including the effects of implicit and explicit deadlines and the relative speed of correct and error responses under speed-accuracy trade-offs-without requiring additional mechanisms that have been criticized as being ad hoc in theoretical motivation and difficult to estimate, such as trial-to-trial variability parameters, collapsing thresholds, or urgency signals. In contrast, our addition is grounded in a widely validated account of time-estimation performance, enabling the same mechanism to simultaneously account for interval estimation and decision making with an explicit deadline. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Target learning in event-based prospective memory

Event-based prospective memory (PM) tasks require individuals to remember to perform a previously planned action when they encounter a specific event. Often, the natural environments in which PM tasks occur are embedded are constantly changing, requiring humans to adapt by learning. We examine one such adaptation by integrating PM target learning with the prospective memory decision control (PMDC) cognitive model. We apply this augmented model to an experiment that manipulated exposure to PM targets, comparing a single-target PM condition where the target was well learned from the outset, to a multiple-target PM condition with less initial PM target exposure, allowing us to examine the effect of continued target learning opportunities. Single-target PM accuracy was near ceiling whereas multiple-target PM accuracy was initially poorer but improved throughout the course of the experiment. PM response times were longer for the multiple- compared with single-target PM task but this difference also decreased over time. The model indicated that PM trial evidence accumulation rates, and the inhibition of competing responses, were initially higher for single compared to multiple PM targets, but that this difference decreased over time due to the learning of multiple-targets over the target repetitions. These outcomes provide insight into how the processes underlying event-based PM can dynamically evolve over time, and a modeling framework to further investigate the effect of learning on event-based PM decision processes. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Spatial Attention and Saccade Preparation Both Independently Contribute to the Discrimination of Oblique Orientations

The extent to which the preparation of an eye movement and spatial attention both independently influence performance within the same task has long been debated. In a recent study that combined computational modelling with a dual-task, both saccade preparation and spatial cueing were revealed to separately contribute to the discrimination of targets oriented along the cardinal axis (horizontal and vertical). However, it remains to be seen whether and to what degree the same holds true when different perceptual stimuli are used. In the present study, we combined evidence accumulation modelling with a dual-task paradigm to assess the extent to which both saccade preparation and spatial attention contribute to the discrimination of full contrast targets oriented along the oblique axis (diagonal). The results revealed a separate and quantifiable contribution of both types of orienting to discrimination performance. Comparison of the magnitude of these effects to those obtained for cardinal orientation discrimination revealed the influence of saccade preparation and spatial attention to be six times smaller for oblique orientations. Importantly, the results revealed a separate and quantifiable contribution of both saccade preparation and spatial attention regardless of perceptual stimuli or stimulus contrast.

A new model of decision processing in instrumental learning tasks

Learning and decision-making are interactive processes, yet cognitive modeling of error-driven learning and decision-making have largely evolved separately. Recently, evidence accumulation models (EAMs) of decision-making and reinforcement learning (RL) models of error-driven learning have been combined into joint RL-EAMs that can in principle address these interactions. However, we show that the most commonly used combination, based on the diffusion decision model (DDM) for binary choice, consistently fails to capture crucial aspects of response times observed during reinforcement learning. We propose a new RL-EAM based on an advantage racing diffusion (ARD) framework for choices among two or more options that not only addresses this problem but captures stimulus difficulty, speed-accuracy trade-off, and stimulus-response-mapping reversal effects. The RL-ARD avoids fundamental limitations imposed by the DDM on addressing effects of absolute values of choices, as well as extensions beyond binary choice, and provides a computationally tractable basis for wider applications.

Systematic Parameter Reviews in Cognitive Modeling: Towards a Robust and Cumulative Characterization of Psychological Processes in the Diffusion Decision Model

Parametric cognitive models are increasingly popular tools for analyzing data obtained from psychological experiments. One of the main goals of such models is to formalize psychological theories using parameters that represent distinct psychological processes. We argue that systematic quantitative reviews of parameter estimates can make an important contribution to robust and cumulative cognitive modeling. Parameter reviews can benefit model development and model assessment by providing valuable information about the expected parameter space, and can facilitate the more efficient design of experiments. Importantly, parameter reviews provide crucial-if not indispensable-information for the specification of informative prior distributions in Bayesian cognitive modeling. From the Bayesian perspective, prior distributions are an integral part of a model, reflecting cumulative theoretical knowledge about plausible values of the model's parameters (Lee, 2018). In this paper we illustrate how systematic parameter reviews can be implemented to generate informed prior distributions for the Diffusion Decision Model (DDM; Ratcliff and McKoon, 2008), the most widely used model of speeded decision making. We surveyed the published literature on empirical applications of the DDM, extracted the reported parameter estimates, and synthesized this information in the form of prior distributions. Our parameter review establishes a comprehensive reference resource for plausible DDM parameter values in various experimental paradigms that can guide future applications of the model. Based on the challenges we faced during the parameter review, we formulate a set of general and DDM-specific suggestions aiming to increase reproducibility and the information gained from the review process.

Establishing the separable contributions of spatial attention and saccade preparation across tasks with varying acuity demands

One common way to investigate the relationship between eye movements and attention is to pair the cueing paradigm with a saccadic dual-task. Here eye movements are directed to one location in the visual field, while a spatial cue simultaneously directs attention to the same or a different location. The magnitude of the cueing effect is then compared between trials where gaze is maintained at fixation and trials where eye movements are prepared. As these comparisons typically occur across blocked single and dual-task conditions, it is difficult to address possible confounds due to changes in response caution. In this paper we use evidence accumulation modeling to remove this confound and extract a measure of orienting that can be used to quantify and compare the influence of spatial attention across four different manipulations of eye movements: 2 that require fixation and 2 that require saccade preparation. The results demonstrate that the magnitude of the cueing effect is similar regardless of eye movement condition or perceptual task. The perceptual benefit associated with preparing a saccade, in contrast, was found to vary by perceptual task. Taken together these results establish that spatial attention and saccade preparation are separable and, we suggest, mediated by distinct underlying mechanisms. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Urgency, leakage, and the relative nature of information processing in decision-making

Over the last decade, there has been a robust debate in decision neuroscience and psychology about what mechanism governs the time course of decision-making. Historically, the most prominent hypothesis is that neural architectures accumulate information over time until some threshold is met, the so-called Evidence Accumulation hypothesis. However, most applications of this theory rely on simplifying assumptions, belying a number of potential complexities. Is changing stimulus information perceived and processed in an independent manner or is there a relative component? Does urgency play a role? What about evidence leakage? Although the latter questions have been the subject of recent investigations, most studies to date have been piecemeal in nature, addressing one aspect of the decision process or another. Here we develop a modeling framework, an extension of the Urgency Gating Model, in conjunction with a changing information experimental paradigm to simultaneously probe these aspects of the decision process. Using state-of-the-art Bayesian methods to perform parameter-based inference, we find that (a) information processing is relative with early information influencing the perception of late information, (b) time varying urgency and evidence accumulation are of roughly equal strength in the decision process, and (c) leakage is present with a time scale of ∼200-250 ms. We also show that these effects can only be identified in a changing information paradigm. To our knowledge, this is the first comprehensive study to utilize a changing information paradigm to jointly and quantitatively estimate the temporal dynamics of human decision-making. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

A dynamic model of deciding not to choose

We propose a dynamic theory of decisions not to choose which of 2 options is correct. Such "do not-know" judgments are of theoretical and practical importance in domains ranging from comparative psychology, psychophysics, episodic memory, and metacognition to applied areas including educational testing and eyewitness testimony. However, no previous theory has provided a detailed quantitative account of the time it takes to make both definitive and do not-know responses and their relative frequencies. We tested our theory, the multiple threshold race (MTR), in 1 recognition memory experiment where participants had to pick a previously studied target out of 2 similar faces and another where targets and lures were tested 1 at a time. In both experiments we manipulated similarity through face morphing. High similarity made decisions difficult, encouraging do not-know responses. We also tested the MTR's ability to account for other manipulations that aimed to affect the speed and probability of do not-know responses, including increasing penalties for making an error (with no penalty for a do not-know response) and emphasizing either response speed or accuracy. We found that there were marked individual differences in do not-know use, and that the MTR was able to account for the intricate pattern of effects associated with our manipulations, both on average and in terms of individual differences. We discuss how estimates of MTR's parameters illuminate the psychological mechanisms that govern the interplay between definitive and do not-know responding. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Using evidence accumulation modeling to quantify the relative contributions of spatial attention and saccade preparation in perceptual tasks

A typical way to investigate the relationship between spatial attention and the programming of an eye movement is with a dual-task. Here, participants simultaneously make an eye movement in 1 direction and discriminate a target at the same or a different location. Results of these tasks consistently find that performance is best at the goal of an upcoming eye movement. It is less clear, however, the extent to which spatial attention can shift independently of the programmed saccade. In this article, for the first time, we use an evidence accumulation model to examine this longstanding question. Specifically, across 2 studies, we quantify the relative contributions of spatial attention and saccade preparation in a perceptual dual-task. Our results establish that there is a unique and measurable effect of spatial attention away from the saccade goal, and, interestingly, that the relative magnitude of this effect varies by cue type. There is a larger influence of spatial attention when a peripheral rather than a central cue is employed. We suggest that these results support the claim that each form of orienting is mediated by a distinct underlying mechanism. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

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.

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.

Accumulating advantages: A new conceptualization of rapid multiple choice

Independent racing evidence-accumulator models have proven fruitful in advancing understanding of rapid decisions, mainly in the case of binary choice, where they can be relatively easily estimated and are known to account for a range of benchmark phenomena. Typically, such models assume a one-to-one mapping between accumulators and responses. We explore an alternative independent-race framework where more than one accumulator can be associated with each response, and where a response is triggered when a sufficient number of accumulators associated with that response reach their thresholds. Each accumulator is primarily driven by the difference in evidence supporting one versus another response (i.e., that response's "advantage"), with secondary inputs corresponding to the total evidence for both responses and a constant term. We use Brown and Heathcote's (2008) linear ballistic accumulator (LBA) to instantiate the framework in a mathematically tractable measurement model (i.e., a model whose parameters can be successfully recovered from data). We show this "advantage LBA" model provides a detailed quantitative account of a variety of benchmark binary and multiple choice phenomena that traditional independent accumulator models struggle with; in binary choice the effects of additive versus multiplicative changes to input values, and in multiple choice the effects of manipulations of the strength of lure (i.e., nontarget) stimuli and Hick's law. We conclude that the advantage LBA provides a tractable new avenue for understanding the dynamics of decisions among multiple choices. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Assessing Theoretical Conclusions With Blinded Inference to Investigate a Potential Inference Crisis

Scientific advances across a range of disciplines hinge on the ability to make inferences about unobservable theoretical entities on the basis of empirical data patterns. Accurate inferences rely on both discovering valid, replicable data patterns and accurately interpreting those patterns in terms of their implications for theoretical constructs. The replication crisis in science has led to widespread efforts to improve the reliability of research findings, but comparatively little attention has been devoted to the validity of inferences based on those findings. Using an example from cognitive psychology, we demonstrate a blinded-inference paradigm for assessing the quality of theoretical inferences from data. Our results reveal substantial variability in experts’ judgments on the very same data, hinting at a possible inference crisis.

Modeling the effects of methylphenidate on interference and evidence accumulation processes using the conflict linear ballistic accumulator

RATIONALE

Although methylphenidate and other stimulants have been demonstrated to improve task performance across a variety of domains, a computationally rigorous account of how these drugs alter cognitive processing remains elusive. Recent applications of mathematical models of cognitive processing and electrophysiological methods to this question have suggested that stimulants improve the integrity of evidence accumulation processes for relevant choices, potentially through catecholaminergic modulation of neural signal-to-noise ratios. However, this nascent line of work has thus far been limited to simple perceptual tasks and has largely omitted more complex conflict paradigms that contain experimental manipulations of specific top-down interference resolution processes.

OBJECTIVES AND METHODS

To address this gap, this study applied the conflict linear ballistic accumulator (LBA), a newly proposed model designed for conflict tasks, to data from healthy adults who performed the Multi-Source Interference Task (MSIT) after acute methylphenidate or placebo challenge.

RESULTS

Model-based analyses revealed that methylphenidate improved performance by reducing individuals' response thresholds and by enhancing evidence accumulation processes across all task conditions, either by improving the quality of evidence or by reducing variability in accumulation processes. In contrast, the drug did not reduce bottom-up interference or selectively facilitate top-down interference resolution processes probed by the experimental conflict manipulation.

CONCLUSIONS

Enhancement of evidence accumulation is a biologically plausible and task-general mechanism of stimulant effects on cognition. Moreover, the assumption that methylphenidate's effects on behavior are only visible with complex executive tasks may be misguided.

Cognitive Modeling Suggests That Attentional Failures Drive Longer Stop-Signal Reaction Time Estimates in Attention Deficit/Hyperactivity Disorder

Mean stop-signal reaction time (SSRT) is frequently employed as a measure of response inhibition in cognitive neuroscience research on ADHD. However, this measurement model is limited by two factors which may bias SSRT estimation in this population: 1) excessive skew in "go" RT distributions, and 2) trigger failures, or instances in which individuals fail to trigger an inhibition process in response to the "stop" signal. We use a Bayesian parametric approach, which allows unbiased estimation of the shape of entire SSRT distributions and the probability of trigger failures, to clarify mechanisms of stop-signal task deficits in ADHD. Children with ADHD displayed greater positive skew than their peers in both "go" RT and SSRT distributions. However, they also displayed more frequent trigger failures, which appeared to drive ADHD-related stopping difficulties. Results suggest that stop-signal task performance in ADHD reflects impairments in early attentional processes, rather than inefficiency in the stop process.

Strategic attention and decision control support prospective memory in a complex dual-task environment

Human performance in complex multiple-task environments depends critically on the interplay between cognitive control and cognitive capacity. In this paper we propose a tractable computational model of how cognitive control and capacity influence the speed and accuracy of decisions made in the event-based prospective memory (PM) paradigm, and in doing so test a new quantitative formulation that measures two distinct components of cognitive capacity (gain and focus) that apply generally to choices among two or more options. Consistent with prior work, individuals used proactive control (increased ongoing task thresholds under PM load) and reactive control (inhibited ongoing task accumulation rates to PM items) to support PM performance. Individuals used cognitive gain to increase the amount of resources allocated to the ongoing task under time pressure and PM load. However, when demands exceeded the capacity limit, resources were reallocated (shared) between ongoing task and PM processes. Extending previous work, individuals used cognitive focus to control the quality of processing for the ongoing and PM tasks based on the particular demand and payoff structure of the environment (e.g., higher focus for higher priority tasks; lower focus under high time pressure and with PM load). Our model provides the first detailed quantitative understanding of cognitive gain and focus as they apply to evidence accumulation models, which - along with cognitive control mechanisms - support decision-making in complex multiple-task environments.

A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task

Response inhibition is essential for navigating everyday life. Its derailment is considered integral to numerous neurological and psychiatric disorders, and more generally, to a wide range of behavioral and health problems. Response-inhibition efficiency furthermore correlates with treatment outcome in some of these conditions. The stop-signal task is an essential tool to determine how quickly response inhibition is implemented. Despite its apparent simplicity, there are many features (ranging from task design to data analysis) that vary across studies in ways that can easily compromise the validity of the obtained results. Our goal is to facilitate a more accurate use of the stop-signal task. To this end, we provide 12 easy-to-implement consensus recommendations and point out the problems that can arise when they are not followed. Furthermore, we provide user-friendly open-source resources intended to inform statistical-power considerations, facilitate the correct implementation of the task, and assist in proper data analysis.

Cognitive control and capacity for prospective memory in complex dynamic environments

Performing deferred actions in the future relies upon Prospective Memory (PM). Often, PM demands arise in complex dynamic tasks. Not only can PM be challenging in such environments, the processes required for PM may affect the performance of other tasks. To adapt to PM demands in such environments, humans may use a range of strategies, including flexible allocation of cognitive resources and cognitive control mechanisms. We sought to understand such mechanisms by using the Prospective Memory Decision Control (Strickland, Loft, Remington, & Heathcote, 2018) model to provide a comprehensive, quantitative account of dual task performance in a complex dynamic environment (a simulated air traffic control conflict detection task). We found that PM demands encouraged proactive control over ongoing task decisions, but that this control was reduced at high time pressure to facilitate fast responding. We found reactive inhibitory control over ongoing task processes when PM targets were encountered, and that time pressure and PM demand both affect the attentional system, increasing the amount of cognitive resources available. However, as demands exceeded the capacity limit of the cognitive system, resources were reallocated (shared) between the ongoing and PM tasks. As the ongoing task used more resources to compensate for additional time pressure demands, it drained resources that would have otherwise been available for PM task processing. This study provides the first detailed quantitative understanding of how attentional resources and cognitive control mechanisms support PM and ongoing task performance in complex dynamic environments. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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).

Equally flexible and optimal response bias in older compared to younger adults

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).

Refining the law of practice

The "law of practice"-a simple nonlinear function describing the relationship between mean response time (RT) and practice-has provided a practically and theoretically useful way of quantifying the speed-up that characterizes skill acquisition. Early work favored a power law, but this was shown to be an artifact of biases caused by averaging over participants who are individually better described by an exponential law. However, both power and exponential functions make the strong assumption that the speedup always proceeds at a steadily decreasing rate, even though there are sometimes clear exceptions. We propose a new law that can both accommodate an initial delay resulting in a slower-faster-slower rate of learning, with either power or exponential forms as limiting cases, and which can account for not only mean RT but also the effect of practice on the entire distribution of RT. We evaluate this proposal with data from a broad array of tasks using hierarchical Bayesian modeling, which pools data across participants while minimizing averaging artifacts, and using inference procedures that take into account differences in flexibility among laws. In a clear majority of paradigms our results supported a delayed exponential law. (PsycINFO Database Record