Conjoint Professor Andrew Heathcote

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.

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.

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.

Evidence accumulation models (EAMs) are a class of computational cognitive model used to understand the latent cognitive processes that underlie human decisions and response times (RTs). They have seen widespread application in cognitive psychology and neuroscience. However, historically, the application of these models was limited to simple decision tasks. Recently, researchers have applied these models to gain insight into the cognitive processes that underlie observed behaviour in applied domains, such as air-traffic control (ATC), driving, forensic and medical image discrimination, and maritime surveillance. Here, we discuss how this modelling approach helps researchers understand how the cognitive system adapts to task demands and interventions, such as task automation. We also discuss future directions and argue for wider adoption of cognitive modelling in Human Factors research.

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.

van Doorn et al. (2021) outlined various questions that arise when conducting Bayesian model comparison for mixed effects models. Seven response articles offered their own perspective on the preferred setup for mixed model comparison, on the most appropriate specification of prior distributions, and on the desirability of default recommendations. This article presents a round-table discussion that aims to clarify outstanding issues, explore common ground, and outline practical considerations for any researcher wishing to conduct a Bayesian mixed effects model comparison.

The ¿marginality principle¿ for linear regression models states that when a higher order term is included, its constituent terms must also be included. The target article relies on this principle for the fixed-effects part of linear mixed models of ANOVA designs and considers the implication that if extended to combined fixed-and-random-effects models, model selection tests specific to some fixed-effects ANOVA terms are not possible. We review the basis for this principle for fixed-effects models and delineate its limits. We then consider its extension to combined fixed-and-random-effects models. We conclude that we have been unable to find in the literature, including the target article, and have ourselves been unable to construct any satisfactory argument against the use of incomplete ANOVA models. The only basis we could find requires one to assume that it is not possible to test point-null hypotheses, something we disagree with, and which we believe is incompatible with the Bayesian model-selection methods that are the basis of the target article.

It has traditionally been assumed that responding after an error is slowed because participants try to improve their accuracy by increasing the amount of evidence required for subsequent decisions. However, recent work suggests a more varied picture of post-error effects, with instances of post-error speeding, and decreases or no change in accuracy. Further, the causal role of errors in these effects has been questioned due to confounds from slow fluctuations in attention caused by factors such as fatigue and boredom. In recognition memory tasks, we investigated both post-error speeding associated with instructions emphasising fast responding and post-error slowing associated with instructions emphasising the accuracy of responding. In order to identify the causes of post-error effects, we fit this data with evidence accumulation models using a method of measuring post-error effects that is robust to confounds from slow fluctuations. When the response-to-stimulus interval between trials was short, there were no post-error effect on accuracy and speeding and slowing were caused by differences in non-decision time (i.e. the time to encode choice stimuli and generate responses). In contrast, when the interval was longer, due to participants providing a confidence rating for their choice, there were also effects on the rate of evidence accumulation and the amount of evidence required for a decision. We discuss the implications of our methods and results for post-error effect research.

The stop-signal paradigm is a popular procedure to investigate response inhibition¿the ability to stop ongoing responses. It consists of a choice response time (RT) task that is occasionally interrupted by a stop stimulus signaling participants to withhold their response. Performance in the stop-signal paradigm is often formalized as race between a set of go runners triggered by the choice stimulus and a stop runner triggered by the stop signal. We investigated whether evidence-accumulation processes, which have been widely used in choice RT analysis, can serve as the runners in the stop-signal race model and support the estimation of psychologically meaningful parameters. We examined two types of the evidence-accumulation architectures: the racing Wald model (Logan et al. 2014) and a novel proposal based on the lognormal race (Heathcote and Love 2012). Using a series of simulation studies and fits to empirical data, we found that these models are not measurement models in the sense that the data-generating parameters cannot be recovered in realistic experimental designs.

Recognition memory models posit that false alarm rates increase as the global similarity between the probe cue and the contents of memory is increased. Global similarity predictions have been commonly tested using category length designs where it has been found that false alarm rates increase as the number of studied items from a common category is increased. In this work, we explored global similarity predictions within unstructured lists of words using representations from the BEAGLE model (Jones & Mewhort, 2007). BEAGLE differs from traditional semantic space models in that it contains two types of representations: item vectors, which encode unordered co-occurrence, and order vectors, in which words are similar to the extent to which they are share neighboring words in the same relative positions. Global similarity among item and order vectors was regressed onto drift rates in the diffusion decision model (DDM: Ratcliff, 1978), which unifies both response times and accuracy. We implemented this model in a hierarchical Bayesian framework across seven datasets with lists composed of unrelated words. Results indicated clear deficits due to global similarity among item vectors, but only a minimal impact of global similarity among the order vectors. We also found evidence for a linear relationship between global similarity and drift rate and did not find any evidence that global similarity differentially affected performance in speed vs. accuracy emphasis conditions. In addition, we found that global semantic similarity could only partially account for the word frequency effect, suggesting that other factors besides semantic similarity may be responsible.

Many models of response time that base choices on the first evidence accumulator to win a race to threshold rely on statistical independence between accumulators to achieve mathematical tractability (e.g., Brown and Heathcote, 2008; Logan et al., 2014; Van Zandt et al., 2000). However, it is psychologically plausible that trial-to-trial fluctuations can cause both positive correlations (e.g., variability in arousal, attention or response caution that affect accumulators in the same way) and negative correlations (e.g., when evidence for each accumulator is computed relative to a criterion). We examine the effects of such correlations in a racing accumulator model that remains tractable when they are present, the log-normal race (LNR Heathcote and Love, 2012). We first show that correlations are hard to estimate in binary choice data, and that their presence does not noticeably improve model fit to lexical-decision data (Wagenmakers et al., 2008) that is well fit by an independent LNR model. Poor estimation is attributable to the fact that estimation of correlation requires information about the relationship between accumulator states but only the state of the winning accumulator is directly observed in binary choice. We then show that this problem is remedied when discrete confidence judgments are modeled by an extension of Vickers's (1979) ¿balance-of-evidence¿ hypothesis proposed by Reynolds et al. (submitted). In this ¿multiple-threshold race¿ model confidence is based on the state of the losing accumulator judged relative to one or more extra thresholds. We show that not only is correlation well estimated in a multiple-threshold log-normal race (MTLNR) model with as few as two confidence levels, but that it also resulted in clearly better fits to Ratcliff et al.¿s (1994) recognition memory data than an independent mode. We conclude that the MTLNR provides a mathematically tractable tool that is useful both for investigating correlations between accumulators and for modeling confidence judgments.

Psychological science has rightly become worried about questionable practices in experimental research, with a range of recent suggestions being made about remedies for this ¿replication crisis¿. To avoid similar problems in psychological-process modelling, Lee et al. (in review) propose ingenious adaptions of these remedies along with insightful new suggestions. Although in the main applauding of these developments, I question whether some of the lessons drawn from the replication crisis are applicable, particularly with respect to the confirmatory vs. exploratory dichotomy given the intrinsically explanatory nature of most psychological-process models.

Most data analyses rely on models. To complement statistical models, psychologists have developed cognitive models, which translate observed variables into psychologically interesting constructs. Response time models, in particular, assume that response time and accuracy are the observed expression of latent variables including 1) ease of processing, 2) response caution, 3) response bias, and 4) non-decision time. Inferences about these psychological factors, hinge upon the validity of the models¿ parameters. Here, we use a blinded, collaborative approach to assess the validity of such model-based inferences. Seventeen teams of researchers analyzed the same 14 data sets. In each of these two-condition data sets, we manipulated properties of participants¿ behavior in a two-alternative forced choice task. The contributing teams were blind to the manipulations, and had to infer what aspect of behavior was changed using their method of choice. The contributors chose to employ a variety of models, estimation methods, and inference procedures. Our results show that, although conclusions were similar across different methods, these "modeler¿s degrees of freedom" did affect their inferences. Interestingly, many of the simpler approaches yielded as robust and accurate inferences as the more complex methods. We recommend that, in general, cognitive models become a typical analysis tool for response time data. In particular, we argue that the simpler models and procedures are sufficient for standard experimental designs. We finish by outlining situations in which more complicated models and methods may be necessary, and discuss potential pitfalls when interpreting the output from response time models.

The ability to control behaviour is thought to rely at least partly on adequately suppressing impulsive responses to external stimuli. However, the evidence for a relationship between response inhibition ability and impulse control is weak and inconsistent. This study investigates the relationship between response inhibition and both self-report and behavioural measures of impulsivity as well as engagement in risky behaviours in a large community sample (N = 174) of healthy adolescents and young adults (15¿35 years). Using a stop-signal paradigm with a number parity go task, we implemented a novel hierarchical Bayesian model of response inhibition that estimates stop-signal reaction time (SSRT) as a distribution and also accounts for failures to react to the stop-signal (i.e., ¿trigger failure¿), and failure to react to the choice stimulus (i.e., ¿go failure¿ or omission errors). In line with previous studies, the model reduced estimates of SSRT by approximately 100 ms compared with traditional non-parametric SSRT estimation techniques. We found significant relationships between behavioural and self-report measures of impulsivity and traditionally estimated SSRT, that did not hold for the model-based SSRT estimates. Instead, behavioural impulsivity measures were correlated with rate of trigger failure. The relationship between trigger failure and impulsivity suggests that the former may index a higher order inhibition process, whereas SSRT may index a more automatic inhibition process. We suggest that the existence of distinct response inhibition processes that may be associated with different levels of cognitive control.

The sustained attention to response task (SART) has been the primary method of studying the phenomenon of mind wandering. We develop and experimentally test the first integrated cognitive process model that quantitatively explains all stationary features of behavioral performance in the SART. The model assumes that performance is generated by a competitive race between a stimulus-related decision process and a stimulus-unrelated rhythmic response process. We propose that the stimulus-unrelated process entrains to timing regularities in the task environment, and is unconditionally triggered as a habit or ¿insurance policy¿ to protect against the deleterious effects of mind wandering on ongoing task performance. For two SART experiments the model provided a quantitatively precise account of a range of previously reported trends in choice, response time and self-reported mind wandering data. It also accounted for three previously unidentified features of response time distributions that place critical constraints on cognitive models of performance in situations when people might engage in task-unrelated thoughts. Furthermore, the parameters of the rhythmic race model were meaningfully associated with participants¿ self-reported distraction, even though the model was never informed by these data. In a validation test, we disrupted the latent rhythmic component with a manipulation of inter-trial-interval variability, and showed that the architecture of the model provided insight into its counter-intuitive effect. We conclude that performance in the presence of mind wandering can be conceived as a competitive latent decision vs. rhythmic response process. We discuss how the rhythmic race model is not restricted to the study of distraction or mind wandering; it is applicable to any domain requiring repetitive responding where evidence accumulation is assumed to be an underlying principle of behavior.

We examined how people respond to time pressure factors in a complex, multistimulus environment. In Study 1, we manipulated time pressure by varying information load via stimulus complexity and the number of stimuli. In Study 2, we replaced the complexity manipulation with deadline-that is, the time available to classify stimuli presented within a trial. We identified several ways that people can adapt to time pressure: increasing the rate of information processing via effort or arousal, changing strategy by lowering response caution, and adjusting response bias. We tested these mechanisms using the linear ballistic accumulator model of choice and response time (Brown & Heathcote, 2008). Whereas stimulus complexity influenced the quality of choice information, the number of stimuli influenced response caution, and deadline pressures caused a failure of encoding that was only partially compensated for by increased effort or arousal. Our results reveal that, rather than having a common response, people adapt, and fail to adapt, to the different time pressure factors in different ways.

MicroRNAs (miRNAs) within the ventral and dorsal striatum have been shown to regulate addiction-relevant behaviours. However, it is unclear how cocaine experience alone can alter the expression of addiction-relevant miRNAs within striatal subregions. Further, it is not known whether differential expression of miRNAs in the striatum contributes to individual differences in addiction vulnerability. We first examined the effect of cocaine self-administration on the expression of miR-101b, miR-137, miR-212 and miR-132 in nucleus accumbens core and nucleus accumbens shell (NAcSh), as well as dorsomedial striatum and dorsolateral striatum (DLS). We then examined the expression of these same miRNAs in striatal subregions of animals identified as being ¿addiction-prone¿, either immediately following self-administration training or following extinction and relapse testing. Cocaine self-administration was associated with changes in miRNA expression in a regionally discrete manner within the striatum, with the most marked changes occurring in the nucleus accumbens core. When we examined the miRNA profile of addiction-prone rats following self-administration, we observed increased levels of miR-212 in the dorsomedial striatum. After extinction and relapse testing, addiction-prone rats showed significant increases in the expression of miR-101b, miR-137, miR-212 and miR-132 in NAcSh, and miR-137 in the DLS. This study identifies temporally specific changes in miRNA expression consistent with the engagement of distinct striatal subregions across the course of the addiction cycle. Increased dysregulation of miRNA expression in NAcSh and DLS at late stages of the addiction cycle may underlie habitual drug seeking, and may therefore aid in the identification of targets designed to treat addiction.

A robust finding in recognition memory is that performance declines monotonically across test trials. Despite the prevalence of this decline, there is a lack of consensus on the mechanism responsible. Three hypotheses have been put forward: (1) interference is caused by learning of test items (2) the test items cause a shift in the context representation used to cue memory and (3) participants change their speed-accuracy thresholds through the course of testing. We implemented all three possibilities in a combined model of recognition memory and decision making, which inherits the memory retrieval elements of the Osth and Dennis (2015) model and uses the diffusion decision model (DDM: Ratcliff, 1978) to generate choice and response times. We applied the model to four datasets that represent three challenges, the findings that: (1) the number of test items plays a larger role in determining performance than the number of studied items, (2) performance decreases less for strong items than weak items in pure lists but not in mixed lists, and (3) lexical decision trials interspersed between recognition test trials do not increase the rate at which performance declines. Analysis of the model's parameter estimates suggests that item interference plays a weak role in explaining the effects of recognition testing, while context drift plays a very large role. These results are consistent with prior work showing a weak role for item noise in recognition memory and that retrieval is a strong cause of context change in episodic memory.

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.

Event-based prospective memory (PM) requires remembering to perform intended deferred actions when particular stimuli or events are encountered in the future. We propose a detailed process theory within Braver's (2012) proactive and reactive framework of the way control is maintained over the competing demands of prospective memory decisions and decisions associated with ongoing task activities. The theory is instantiated in a quantitative "Prospective Memory Decision Control" (PMDC) architecture, which uses linear ballistic evidence accumulation (Brown & Heathcote, 2008) to model both PM and ongoing decision processes. Prospective control is exerted via decision thresholds, as in Heathcote, Loft, and Remington's (2015) "Delay Theory" of the impact of PM demands on ongoing-task decisions. However, PMDC goes beyond Delay Theory by simultaneously accounting for both PM task decisions and ongoing task decisions. We use Bayesian estimation to apply PMDC to experiments manipulating PM target focality (i.e., the extent to which the ongoing task directs attention to the features of PM targets processed at encoding) and the relative importance of the PM task. As well as confirming Delay Theory's proactive control of ongoing task thresholds, the comprehensive account provided by PMDC allowed us to detect both proactive control of the PM accumulator threshold and reactive control of the relative rates of the PM and ongoing-task evidence accumulation processes. We discuss potential extensions of PMDC to account for other factors that may be prevalent in real-world PM, such as failures of memory retrieval.

Neuroscientific theories of attention-deficit/hyperactivity disorder (ADHD) alternately posit that cognitive aberrations in the disorder are due to acute attentional lapses, slowed neural processing, or reduced signal-to-noise ratios. However, they make similar predictions about behavioral summary statistics (response times [RTs] and accuracy), hindering the field's ability to produce strong and specific tests of these theories. The current study uses the linear ballistic accumulator (LBA; Brown & Heathcote, 2008), a mathematical model of choice RT tasks, to distinguish between competing theory predictions. Children with ADHD (n = 80) and age-matched controls (n = 32) completed a numerosity discrimination paradigm at 2 levels of difficulty, and RT data were fit to the LBA model to test theoretical predictions. Individuals with ADHD displayed slowed processing of evidence for correct responses (signal) relative to their peers but comparable processing of evidence for error responses (noise) and between-trial variability in processing (performance lapses). The findings are inconsistent with accounts that posit an increased incidence of attentional lapses in the disorder and provide partial support for those that posit slowed neural processing and lower signal-to-noise ratios. Results also highlight the utility of well-developed cognitive models for distinguishing between the predictions of etiological theories of psychopathology.

For many years the Diffusion Decision Model (DDM) has successfully accounted for behavioral data from a wide range of domains. Important contributors to the DDM's success are the across-trial variability parameters, which allow the model to account for the various shapes of response time distributions encountered in practice. However, several researchers have pointed out that estimating the variability parameters can be a challenging task. Moreover, the numerous fitting methods for the DDM each come with their own associated problems and solutions. This often leaves users in a difficult position. In this collaborative project we invited researchers from the DDM community to apply their various fitting methods to simulated data and provide advice and expert guidance on estimating the DDM's across-trial variability parameters using these methods. Our study establishes a comprehensive reference resource and describes methods that can help to overcome the challenges associated with estimating the DDM's across-trial variability parameters.

The mirror effect ¿ a phenomenon whereby a manipulation produces opposite effects on hit and false alarm rates ¿ is benchmark regularity of recognition memory. A likelihood ratio decision process, basing recognition on the relative likelihood that a stimulus is a target or a lure, naturally predicts the mirror effect, and so has been widely adopted in quantitative models of recognition memory. Glanzer, Hilford, and Maloney (2009) demonstrated that likelihood ratio models, assuming Gaussian memory strength, are also capable of explaining regularities observed in receiver-operating characteristics (ROCs), such as greater target than lure variance. Despite its central place in theorising about recognition memory, however, this class of models has not been tested using response time (RT) distributions. In this article, we develop a linear approximation to the likelihood ratio transformation, which we show predicts the same regularities as the exact transformation. This development enabled us to develop a tractable model of recognition-memory RT based on the diffusion decision model (DDM), with inputs (drift rates) provided by an approximate likelihood ratio transformation. We compared this ¿LR-DDM¿ to a standard DDM where all targets and lures receive their own drift rate parameters. Both were implemented as hierarchical Bayesian models and applied to four datasets. Model selection taking into account parsimony favored the LR-DDM, which requires fewer parameters than the standard DDM but still fits the data well. These results support log-likelihood based models as providing an elegant explanation of the regularities of recognition memory, not only in terms of choices made but also in terms of the times it takes to make them.

Recently, Veksler, Myers, and Gluck (2015) proposed model flexibility analysis as a method that "aids model evaluation by providing a metric for gauging the persuasiveness of a given fit" (p. 755) Model flexibility analysis measures the complexity of a model in terms of the proportion of all possible data patterns it can predict. We show that this measure does not provide a reliable way to gauge complexity, which prevents model flexibility analysis from fulfilling either of the 2 aims outlined by Veksler et al. (2015): absolute and relative model evaluation. We also show that model flexibility analysis can even fail to correctly quantify complexity in the most clear cut case, with nested models. We advocate for the use of well-established techniques with these characteristics, such as Bayes factors, normalized maximum likelihood, or cross-validation, and against the use of model flexibility analysis. In the discussion, we explore 2 issues relevant to the area of model evaluation: the completeness of current model selection methods and the philosophical debate of absolute versus relative model evaluation.

We develop a broad theoretical framework for modelling difficult perceptual information integration tasks under different decision rules. The framework allows us to compare coactive architectures, which combine information before it enters the decision process, with parallel architectures, where logical rules combine independent decisions made about each perceptual source. For both architectures we test the novel hypothesis that participants break the decision rules on some trials, making a response based on only one stimulus even though task instructions require them to consider both. Our models take account of not only the decisions made but also the distribution of the time that it takes to make them, providing an account of speed-accuracy tradeoffs and response biases occurring when one response is required more often than another. We also test a second novel hypothesis, that the nature of the decision rule changes the evidence on which choices are based. We apply the models to data from a perceptual integration task with near threshold stimuli under two different decision rules. The coactive architecture was clearly rejected in favor of logical-rules. The logical-rule models were shown to provide an accurate account of all aspects of the data, but only when they allow for response bias and the possibility for subjects to break those rules. We discuss how our framework can be applied more broadly, and its relationship to Townsend and Nozawa's (1995) Systems-Factorial Technology.

The question of cognitive architecture-how cognitive processes are temporally organized-has arisen in many areas of psychology. This question has proved difficult to answer, with many proposed solutions turning out to be spurious. Systems factorial technology (Townsend & Nozawa, 1995) provided the first rigorous empirical and analytical method of identifying cognitive architecture, using the survivor interaction contrast (SIC) to determine when people are using multiple sources of information in parallel or in series. Although the SIC is based on rigorous nonparametric mathematical modeling of response time distributions, for many years inference about cognitive architecture has relied solely on visual assessment. Houpt and Townsend (2012) recently introduced null hypothesis significance tests, and here we develop both parametric and nonparametric (encompassing prior) Bayesian inference. We show that the Bayesian approaches can have considerable advantages.

We used Bayesian cognitive modelling to identify the underlying causes of apparent inhibitory deficits in the stop-signal paradigm. The analysis was applied to stop-signal data reported by Badcock et al. (Psychological Medicine 32: 87-297, 2002) and Hughes et al. (Biological Psychology 89: 220-231, 2012), where schizophrenia patients and control participants made rapid choice responses, but on some trials were signalled to stop their ongoing response. Previous research has assumed an inhibitory deficit in schizophrenia, because estimates of the mean time taken to react to the stop signal are longer in patients than controls. We showed that these longer estimates are partly due to failing to react to the stop signal (¿trigger failures¿) and partly due to a slower initiation of inhibition, implicating a failure of attention rather than a deficit in the inhibitory process itself. Correlations between the probability of trigger failures and event-related potentials reported by Hughes et al. are interpreted as supporting the attentional account of inhibitory deficits. Our results, and those of Matzke et al. (2016), who report that controls also display a substantial although lower trigger-failure rate, indicate that attentional factors need to be taken into account when interpreting results from the stop-signal paradigm.

Cognitive load from secondary tasks is a source of distraction causing injuries and fatalities on the roadway. The Detection Response Task (DRT) is an international standard for assessing cognitive load on drivers¿ attention that can be performed as a secondary task with little to no measurable effect on the primary driving task. We investigated whether decrements in DRT performance were related to the rate of information processing, levels of response caution, or the non-decision processing of drivers. We had pairs of participants take part in the DRT while performing a simulated driving task, manipulated cognitive load via the conversation between driver and passenger, and observed associated slowing in DRT response time. Fits of the single-bound diffusion model indicated that slowing was mediated by an increase in response caution. We propose the novel hypothesis that, rather than the DRT¿s sensitivity to cognitive load being a direct result of a loss of information processing capacity to other tasks, it is an indirect result of a general tendency to be more cautious when making responses in more demanding situations.

The relative amount of variability in memory strength for targets vs. lures in recognition memory is commonly measured using the receiver operating characteristic (ROC) procedure, in which participants are given either a bias manipulation or are instructed to give confidence ratings to probe items. A near universal finding is that targets have higher variability than lures. Ratcliff and Starns (2009) questioned the conclusions of the ROC procedure by demonstrating that accounting for decision noise within a response time model yields different conclusions about relative memory evidence than the ROC procedure yields. In an attempt to better understand the source of the discrepancy, we applied models that include different sources of decision noise, including both the diffusion decision model (DDM) and the linear ballistic accumulator (LBA) model, which either include or lack within-trial noise in evidence accumulation, and compared their estimates of the ratio of standard deviations to those from ROC analysis. Each method produced dramatically different estimates of the relative variability of target items, with the LBA even indicating equal variance in some cases. This stands in contrast to prior work suggesting that the DDM and LBA produce largely similar estimates of relevant model parameters, such as drift rate, boundary separation, and nondecision time. Parameter validation using data from Starns's (2014) numerosity discrimination data demonstrated that only the DDM was able to correctly reproduce the evidence ratios in the data. These results suggest that the DDM may be providing a more accurate account of lure-to-target variability evidence ratios in recognition memory.

Response inhibition is frequently investigated using the stop-signal paradigm, where participants perform a two-choice response time task that is occasionally interrupted by a stop signal instructing them to withhold their response. Stop-signal performance is formalized as a race between a go and a stop process. If the go process wins, the response is executed; if the stop process wins, the response is inhibited. Successful inhibition requires fast stop responses and a high probability of triggering the stop process. Existing methods allow for the estimation of the latency of the stop response, but are unable to identify deficiencies in triggering the stop process. We introduce a Bayesian model that addresses this limitation and enables researchers to simultaneously estimate the probability of trigger failures and the entire distribution of stopping latencies. We demonstrate that trigger failures are clearly present in two previous studies, and that ignoring them distorts estimates of stopping latencies. The parameter estimation routine is implemented in the BEESTS software (Matzke et al., Front. Quantitative Psych. Measurement, 4, 918; 2013a) and is available at http://dora.erbe-matzke.com/software.html.

The lexical-decision task is among the most commonly used paradigms in psycholinguistics. In both the signal-detection theory and Diffusion Decision Model (DDM; Ratcliff, Gomez, & McKoon, Psychological Review, 111, 159¿182, 2004) frameworks, lexical-decisions are based on a continuous source of word-likeness evidence for both words and non-words. The Retrieving Effectively from Memory model of Lexical-Decision (REM¿LD; Wagenmakers et al., Cognitive Psychology, 48(3), 332¿367, 2004) provides a comprehensive explanation of lexical-decision data and makes the prediction that word-likeness evidence is more variable for words than non-words and that higher frequency words are more variable than lower frequency words. To test these predictions, we analyzed five lexical-decision data sets with the DDM. For all data sets, drift-rate variability changed across word frequency and non-word conditions. For the most part, REM¿LD¿s predictions about the ordering of evidence variability across stimuli in the lexical-decision task were confirmed.

Event-based prospective memory (PM) tasks require participants to substitute an atypical PM response for an ongoing task response when presented with PM targets. Responses to ongoing tasks are often slower with the addition of PM demands ("PM costs"). Prominent PM theories attribute costs to capacity-sharing between the ongoing and PM tasks, which reduces the rate of processing of the ongoing task. We modeled PM costs using the Linear Ballistic Accumulator and the Diffusion Decision Model in a lexical-decision task with nonfocal PM targets defined by semantic categories. Previous decision modeling, which attributed costs to changes in caution rather than rate of processing (Heathcote et al., 2015; Horn & Bayen, 2015), could be criticized on the grounds that the PM tasks included did not sufficiently promote capacity-sharing. Our semantic PM task was potentially more dependent on lexical decision resources than previous tasks (Marsh, Hicks, & Cook, 2005), yet costs were again driven by changes in threshold and not by changes in processing speed (drift rate). Costs resulting from a single target focal PM task were also driven by threshold changes. The increased thresholds underlying nonfocal and focal costs were larger for word trials than nonword trials. As PM targets were always words, this suggests that threshold increases are used to extend the time available for retrieval on PM trials. Under nonfocal conditions, but not focal conditions, the nonword threshold also increased. Thus, it seems that only nonfocal instructions cause a global threshold increase because of greater perceived task complexity.

State-trace methods have recently been advocated for exploring the latent dimensionality of psychological processes. These methods rely on assessing the monotonicity of a set of responses embedded within a state-space. Prince et al. (2012) proposed Bayes factors for state-trace analysis, allowing the assessment of the evidence for monotonicity within individuals. Under the assumption that the population is homogeneous, these Bayes factors can be combined across participants to produce a "group" Bayes factor comparing the monotone hypothesis to the non-monotone hypothesis. However, combining information across individuals without assuming homogeneity is problematic due to the nonparametric nature of state-trace analysis. We introduce group-level Bayes factors that can be used to assess the evidence that the population is homogeneous vs. heterogeneous, and demonstrate their utility using data from a visual change-detection task. Additionally, we describe new computational methods for rapidly computing individual-level Bayes factors.

Although post-error slowing and the ¿hot hand¿ (streaks of good performance) are both types of sequential dependencies arising from the differential influence of success and failure, they have not previously been studied together. We bring together these two streams of research in a task where difficulty can be controlled by participants delaying their decisions, and where responses required a degree deliberation, and so are relatively slow. We compared performance of unpaid participants against paid participants who were rewarded differentially, with higher reward for better performance. In contrast to most previous results, we found no post-error slowing for paid or unpaid participants. For the unpaid group, we found post-error speeding and a hot hand, even though the hot hand is typically considered a fallacy. Our results suggest that the effect of success and failure on subsequent performance may differ substantially with task characteristics and demands. We also found payment affected post-error performance; financially rewarding successful performance led to a more cautious approach following errors, whereas unrewarded performance led to recklessness following errors.

Progress in understanding recognition memory has been hampered by confounding among effects associated with the study position, test position and study-test lag factors that are intrinsic to the widely used study-test list paradigm. For example, the list-length effect - once considered a robust benchmark phenomenon - is now known to be either weak or absent when confounding effects associated with these factors are controlled. We investigate two effects of recent theoretical interest - item-context facilitation (occurring when items studied together are tested together) and test-position interference (with performance decreasing over a sequence of test trials) - and one effect of long-standing interest - decreasing performance as study-test lag increases. Traditional analyses of our experiment, whose design affords control over a range of confounds and allows us to disentangle the three effects, affirms all three as fundamental causes of systematic variability in recognition accuracy. These conclusions are strengthened and expanded by model-based analyses of recognition confidence and random item effects that also take into account non-systematic sources of variability.

Although first-impressions are known to impact decision-making and to have prolonged effects on reasoning, it is less well known that the same type of rapidly formed assumptions can explain biases in automatic relevance filtering outside of deliberate behavior. This paper features two studies in which participants have been asked to ignore sequences of sound while focusing attention on a silent movie. The sequences consisted of blocks, each with a high-probability repetition interrupted by rare acoustic deviations (i.e., a sound of different pitch or duration). The probabilities of the two different sounds alternated across the concatenated blocks within the sequence (i.e., short-to-long and long-to-short). The sound probabilities are rapidly and automatically learned for each block and a perceptual inference is formed predicting the most likely characteristics of the upcoming sound. Deviations elicit a prediction-error signal known as mismatch negativity (MMN). Computational models of MMN generally assume that its elicitation is governed by transition statistics that define what sound attributes are most likely to follow the current sound. MMN amplitude reflects prediction confidence, which is derived from the stability of the current transition statistics. However, our prior research showed that MMN amplitude is modulated by a strong first-impression bias that outweighs transition statistics. Here we test the hypothesis that this bias can be attributed to assumptions about predictable vs. unpredictable nature of each tone within the first encountered context, which is weighted by the stability of that context. The results of Study 1 show that this bias is initially prevented if there is no 1:1 mapping between sound attributes and probability, but it returns once the auditory system determines which properties provide the highest predictive value. The results of Study 2 show that confidence in the first-impression bias drops if assumptions about the temporal stability of the transition-statistics are violated. Both studies provide compelling evidence that the auditory system extrapolates patterns on multiple timescales to adjust its response to prediction-errors, while profoundly distorting the effects of transition-statistics by the assumptions formed on the basis of first-impressions.

Evidence accumulation models transform observed choices and associated response times into psychologically meaningful constructs such as the strength of evidence and the degree of caution. Standard versions of these models were developed for rapid (~1 s) choices about simple stimuli, and have recently been elaborated to some degree to address more complex stimuli and response methods. However, these elaborations can be difficult to use with designs and measurements typically encountered in complex applied settings. We test the applicability of 2 standard accumulation models-the diffusion (Ratcliff & McKoon, 2008) and the linear ballistic accumulation (LBA) (Brown & Heathcote, 2008)-to data from a task representative of many applied situations: the detection of heterogeneous multiattribute targets in a simulated unmanned aerial vehicle (UAV) operator task. Despite responses taking more than 2 s and complications added by realistic features, such as a complex target classification rule, interruptions from a simultaneous UAV navigation task, and time pressured choices about several concurrently present potential targets, these models performed well descriptively. They also provided a coherent psychological explanation of the effects of decision uncertainty and workload manipulations. Our results support the wider application of standard evidence accumulation models to applied decision-making settings.

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

A Simon effect occurs when the irrelevant spatial attributes of a stimulus conflict with choice responses based on non-spatial stimulus attributes. Many theories of the Simon effect assume that activation from task-irrelevant spatial attributes becomes available before the activation from taskrelevant attributes. We refer to this as the time-difference account. Other theories follow a magnitude-difference account, assuming activation from relevant and irrelevant attributes becomes available at the same time, but with the activation from irrelevant attributes initially being stronger. To distinguish these two accounts, we incorporated the responsesignal procedure into the reach-to-touch paradigm to map out the emergence of the Simon effect. We also used a carefully calibrated neutral condition to reveal differences in the initial onset of the influence of relevant and irrelevant information. Our results establish that irrelevant spatial information becomes available earlier than relevant non-spatial information. This finding is consistent with the time-difference account and inconsistent with the magnitude-difference account. However, we did find a magnitude effect, in the form of reduced interference from irrelevant information, for the second of a sequence of two incongruent trials.

In the real world, decision making processes must be able to integrate non-stationary information that changes systematically while the decision is in progress. Although theories of decision making have traditionally been applied to paradigms with stationary information, non-stationary stimuli are now of increasing theoretical interest. We use a random-dot motion paradigm along with cognitive modeling to investigate how the decision process is updated when a stimulus changes. Participants viewed a cloud of moving dots, where the motion switched directions midway through some trials, and were asked to determine the direction of motion. Behavioral results revealed a strong delay effect: after presentation of the initial motion direction there is a substantial time delay before the changed motion information is integrated into the decision process. To further investigate the underlying changes in the decision process, we developed a Piecewise Linear Ballistic Accumulator model (PLBA). The PLBA is efficient to simulate, enabling it to be fit to participant choice and response-time distribution data in a hierarchal modeling framework using a non-parametric approximate Bayesian algorithm. Consistent with behavioral results, PLBA fits confirmed the presence of a long delay between presentation and integration of new stimulus information, but did not support increased response caution in reaction to the change. We also found the decision process was not veridical, as symmetric stimulus change had an asymmetric effect on the rate of evidence accumulation. Thus, the perceptual decision process was slow to react to, and underestimated, new contrary motion information.

Event-based prospective memory (PM) requires a deferred action to be performed when a target event is encountered in the future. Individuals are often slower to perform a concurrent ongoing task when they have PM task requirements relative to performing the ongoing task in isolation. Theories differ in their detailed interpretations of this PM cost, but all assume that the PM task shares limited-capacity resources with the ongoing task. In what was interpreted as support of this core assumption, diffusion model fits reported by Boywitt and Rummel (2012) and Horn, Bayen, and Smith (2011) indicated that PM demands reduced the rate of accumulation of evidence about ongoing task choices. We revaluate this support by fitting both the diffusion and linear ballistic accumulator (Brown & Heathcote, 2008) models to these same data sets and 2 new data sets better suited to model fitting. There was little effect of PM demands on evidence accumulation rates, but PM demands consistently increased the evidence required for ongoing task response selection (response thresholds). A further analysis of data reported by Lourenço, White, and Maylor (2013) found that participants differentially adjusted their response thresholds to slow responses associated with stimuli potentially containing PM targets. These findings are consistent with a delay theory account of costs, which contends that individuals slow ongoing task responses to allow more time for PM response selection to occur. Our results call for a fundamental reevaluation of current capacity-sharing theories of PM cost that until now have dominated the PM literature.

Trueblood, Brown, and Heathcote (2014) developed a new model, called the multiattribute linear ballistic accumulator (MLBA), to explain contextual preference reversals in multialternative choice. MLBA was shown to provide good accounts of human behavior through both qualitative analyses and quantitative fitting of choice data. Tsetsos, Chater, and Usher (2015) investigated the ability of MLBA to simultaneously capture 3 prominent context effects (attraction, compromise, and similarity). They concluded that MLBA must set a "fine balance" of competing forces to account for all 3 effects simultaneously and that its predictions are sensitive to the position of the stimuli in the attribute space. Through a new experiment, we show that the 3 effects are very fragile and that only a small subset of people shows all 3 simultaneously. Thus, the predictions that Tsetsos et al. generated from the MLBA model turn out to match closely real data in a new experiment. Support for these predictions provides strong evidence for the MLBA. A corollary is that a model that can "robustly" capture all 3 effects simultaneously is not necessarily a good model. Rather, a good model captures patterns found in human data, but cannot accommodate patterns that are not found.

We examined the role of dual-task interference in working memory using a novel dual two-back task that requires a redundant-target response (i.e., a response that neither the auditory nor the visual stimulus occurred two back versus a response that one or both occurred two back) on every trial. Comparisons with performance on single two-back trials (i.e., with only auditory or only visual stimuli) showed that dual-task demands reduced both speed and accuracy. Our task design enabled a novel application of Townsend and Nozawa¿s (Journal of Mathematical Psychology 39: 321¿359, 1995) workload capacity measure, which revealed that the decrement in dual two-back performance was mediated by the sharing of a limited amount of processing capacity. Relative to most other single and dual n-back tasks, performance measures for our task were more reliable, due to the use of a small stimulus set that induced a high and constant level of proactive interference. For a version of our dual two-back task that minimized response bias, accuracy was also more strongly correlated with complex span than has been found for most other single and dual n-back tasks.

© 2015 APA, all rights reserved). Shepard and Metzler's (1971) seminal mental-rotation task-which requires participants to decide if 1 object is a rotated version of another or its mirror image-has played a central role in the study of spatial cognition. We provide the first quantitative model of behavior in this task that is comprehensive in the sense of simultaneously providing an account of both error rates and the full distribution of response times. We used Brown and Heathcote's (2008) model of choice processing to separate out the contributions of mental rotation and decision stages. This model-based titration process was applied to data from a paradigm where converging evidence supported performance being based on rotation rather than other strategies. Stimuli were similar to Shepard and Metzler's block figures except a long major axis made rotation angle well defined for mirror stimuli, enabling comprehensive modeling of both mirror and normal responses. Results supported a mental rotation stage based on Larsen's (2014) model, where rotation takes a variable amount of time with a mean and variance that increase linearly with rotation angle. Differences in response threshold differences were largely responsible for mirror responses being slowed, and for errors increasing with rotation angle for some participants. (PsycINFO Database Record

The linear ballistic accumulator model is a theory of decision-making that has been used to analyse data from human and animal experiments. It represents decisions as a race between independent evidence accumulators, and has proven successful in a form assuming a normal distribution for accumulation ("drift") rates. However, this assumption has some limitations, including the corollary that some decision times are negative or undefined. We show that various drift rate distributions with strictly positive support can be substituted for the normal distribution without loss of analytic tractability, provided the candidate distribution has a closed-form expression for its mean when truncated to a closed interval. We illustrate the approach by developing three new linear ballistic accumulation variants, in which the normal distribution for drift rates is replaced by either the lognormal, Fréchet, or gamma distribution. We compare some properties of these new variants to the original normal-rate model.

Anodal transcranial direct current stimulation (tDCS) over the primary motor cortex (M1) has been proposed as a possible therapeutic rehabilitation technique for motor impairment. However, despite extensive investigation into the effects of anodal tDCS on motor output, there is little information on how anodal tDCS affects response processes. In this study, we used a cued go/nogo task with both directional and non-directional cues to assess the effects of anodal tDCS over the dominant (left) primary motor cortex on prepared and unprepared motor responses. Three experiments explored whether the effectiveness of tDCS varied with timing between stimulation and test. Healthy, right-handed young adults participated in a double-blind randomised controlled design with crossover of anodal tDCS and sham stimulation. In Experiment 1, twenty-four healthy young adults received anodal tDCS over dominant M1 at least 40 mins before task performance. In Experiment 2, eight participants received anodal tDCS directly before task performance. In Experiment 3, twenty participants received anodal tDCS during task performance. In all three experiments, participants responded faster to directional compared to non-directional cues and with their right hand. However, anodal tDCS had no effect on go/nogo task performance at any stimulation - test interval. Bayesian analysis confirmed that anodal stimulation had no effect on response speed. We conclude that anodal tDCS over M1 does not improve response speed of prepared or unprepared responses of young adults in a go/nogo task.

Repetitious patterns enable the auditory system to form prediction models specifying the most likely characteristics of subsequent sounds. Pattern deviations elicit mismatch negativity (MMN), the amplitude of which is modulated by the size of the deviation and confidence in the model. Todd et al. (Neuropsychologia 49:3399-3405, 2011; J Neurophysiol 109:99-105, 2013) demonstrated that a multi-timescale sequence reveals a bias that profoundly distorts the impact of local sound statistics on the MMN amplitude. Two sounds alternate roles as repetitious "standard" and rare "deviant" rapidly (every 0.8 min) or slowly (every 2.4 min). The bias manifests as larger MMN to the sound first encountered as deviant in slow compared to fast changing sequences, but no difference for the sound first encountered as a standard. We propose that the bias is due to how Bayesian priors shape filters of sound relevance. By examining the time-course of change in MMN amplitude we show that the bias manifests immediately after roles change but rapidly disappears thereafter. The bias was reflected in the response to deviant sounds only (not in response to standards), consistent with precision estimates extracted from second order patterns modulating gain differentially for the two sounds. Evoked responses to deviants suggest that pattern extraction and reactivation of priors can operate over tens of minutes or longer. Both MMN and deviant responses establish that: (1) priors are defined by the most proximally encountered probability distribution when one exists but; (2) when no prior exists, one is instantiated by sequence onset characteristics; and (3) priors require context interruption to be updated. © 2013 Springer Science+Business Media.

Mind wandering is an ubiquitous phenomenon in everyday life. In the cognitive neurosciences, mind wandering has been associated with several distinct neural processes, most notably increased activity in the default mode network (DMN), suppressed activity within the anti-correlated (task-positive) network (ACN), and changes in neuromodulation. By using an integrative multimodal approach combining machine-learning techniques with modeling of latent cognitive processes, we show that mind wandering in humans is characterized by inefficiencies in executive control (task-monitoring) processes. This failure is predicted by a single-trial signature of (co)activations in the DMN, ACN, and neuromodulation, and accompanied by a decreased rate of evidence accumulation and response thresholds in the cognitive model.