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A key assumption of models of human cognition is that there is variability in information processing. Evidence accumulation models (EAMs) commonly assume 2 broad variabilities in information processing: within-trial variability, which is thought to reflect moment-to-moment fluctuations in perceptual processes, and between-trial variability, which is thought to reflect variability in slower-changing processes like attention, or systematic variability between the stimuli on different trials. Recently, Ratcliff, Voskuilen, and McKoon (2018) claimed to "provide direct evidence that external noise is, in fact, required to explain the data from five simple two-choice decision tasks" (p. 33), suggesting that at least some portion of the between-trial variability in information processing is due to "noise." However, we argue that Ratcliff et al. (2018) failed to distinguish between 2 different potential sources of between-trial variability: random (i.e., "external noise") and systematic (e.g., item effects). Contrary to the claims of Ratcliff et al. (2018), we show that "external noise" is not required to explain their findings, as the same trends of data can be produced when only item effects are present. Furthermore, we contend that the concept of "noise" within cognitive models merely serves as a convenience parameter for sources of variability that we know exist but are unable to account for. Therefore, we question the usefulness of experiments aimed at testing the general existence of "random" variability and instead suggest that future research should attempt to replace the random variability terms within cognitive models with actual explanations of the process. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Psychological experiments often yield data that are hierarchically structured. A number of popular shortcut strategies in cognitive modeling do not properly accommodate this structure and can result in biased conclusions. To gauge the severity of these biases, we conducted a simulation study for a two-group experiment. We first considered a modeling strategy that ignores the hierarchical data structure. In line with theoretical results, our simulations showed that Bayesian and frequentist methods that rely on this strategy are biased towards the null hypothesis. Secondly, we considered a modeling strategy that takes a two-step approach by first obtaining participant-level estimates from a hierarchical cognitive model and subsequently using these estimates in a follow-up statistical test. Methods that rely on this strategy are biased towards the alternative hypothesis. Only hierarchical models of the multilevel data lead to correct conclusions. Our results are particularly relevant for the use of hierarchical Bayesian parameter estimates in cognitive modeling.

An important tool in the advancement of cognitive science are quantitative models that represent different cognitive variables in terms of model parameters. To evaluate such models, their parameters are typically tested for relationships with behavioral and physiological variables that are thought to reflect specific cognitive processes. However, many models do not come equipped with the statistical framework needed to relate model parameters to covariates. Instead, researchers often revert to classifying participants into groups depending on their values on the covariates, and subsequently comparing the estimated model parameters between these groups. Here we develop a comprehensive solution to the covariate problem in the form of a Bayesian regression framework. Our framework can be easily added to existing cognitive models and allows researchers to quantify the evidential support for relationships between covariates and model parameters using Bayes factors. Moreover, we present a simulation study that demonstrates the superiority of the Bayesian regression framework to the conventional classification-based approach.

A recent 'crisis of confidence' has emerged in the empirical sciences. Several studies have suggested that questionable research practices (QRPs) such as optional stopping and selective publication may be relatively widespread. These QRPs can result in a high proportion of false-positive findings, decreasing the reliability and replicability of research output. A potential solution is to register experiments prior to data acquisition and analysis. In this study we attempted to replicate studies that relate brain structure to behavior and cognition. These structural brain-behavior (SBB) correlations occasionally receive much attention in science and in the media. Given the impact of these studies, it is important to investigate their replicability. Here, we attempt to replicate five SBB correlation studies comprising a total of 17 effects. To prevent the impact of QRPs we employed a preregistered, purely confirmatory replication approach. For all but one of the 17 findings under scrutiny, confirmatory Bayesian hypothesis tests indicated evidence in favor of the null hypothesis ranging from anecdotal (Bayes factor < 3) to strong (Bayes factor > 10). In several studies, effect size estimates were substantially lower than in the original studies. To our knowledge, this is the first multi-study confirmatory replication of SBB correlations. With this study, we hope to encourage other researchers to undertake similar replication attempts., (Copyright © 2015. Published by Elsevier Ltd.)

Scientists who study cognition infer underlying processes either by observing behavior (e.g., response times, percentage correct) or by observing neural activity (e.g., the BOLD response). These two types of observations have traditionally supported two separate lines of study. The first is led by cognitive modelers, who rely on behavior alone to support their computational theories. The second is led by cognitive neuroimagers, who rely on statistical models to link patterns of neural activity to experimental manipulations, often without any attempt to make a direct connection to an explicit computational theory. Here we present a flexible Bayesian framework for combining neural and cognitive models. Joining neuroimaging and computational modeling in a single hierarchical framework allows the neural data to influence the parameters of the cognitive model and allows behavioral data, even in the absence of neural data, to constrain the neural model. Critically, our Bayesian approach can reveal interactions between behavioral and neural parameters, and hence between neural activity and cognitive mechanisms. We demonstrate the utility of our approach with applications to simulated fMRI data with a recognition model and to diffusion-weighted imaging data with a response time model of perceptual choice., (Copyright © 2013 Elsevier Inc. All rights reserved.)

People generally slow down after they make an error, a phenomenon that is more pronounced for older individuals than it is for young individuals. Here, we examine the origin of this age-related difference in posterror slowing (PES) by applying the diffusion model to data from young and older participants performing a random dot motion task and a lexical decision task. Results show that the PES effects on response time and accuracy were qualitatively different for young and older participants. A diffusion model analysis revealed that following an error, older participants became more cautious, processed information less effectively, and spent more time on irrelevant processes. This pattern was evident in both the random dot motion task and the lexical decision task. For young participants, in contrast, the origin of the PES effect depended on the task that was performed: In the random dot motion task, the PES effect was due to time spent on irrelevant processes; in the lexical decision task, the PES effect was due to increased caution and decreased effectiveness in information processing. Overall, PES effects were much larger in the lexical decision task than in the random dot motion task. These findings indicate that PES originates from the interplay of different psychological processes whose contribution depends on both task settings and individual differences.

Many theories of human cognition postulate that people are equipped with a repertoire of strategies to solve the tasks they face. This theoretical framework of a cognitive toolbox provides a plausible account of intra- and interindividual differences in human behavior. Unfortunately, it is often unclear how to rigorously test the toolbox framework. How can a toolbox model be quantitatively specified? How can the number of toolbox strategies be limited to prevent uncontrolled strategy sprawl? How can a toolbox model be formally tested against alternative theories? The authors show how these challenges can be met by using Bayesian inference techniques. By means of parameter recovery simulations and the analysis of empirical data across a variety of domains (i.e., judgment and decision making, children's cognitive development, function learning, and perceptual categorization), the authors illustrate how Bayesian inference techniques allow toolbox models to be quantitatively specified, strategy sprawl to be contained, and toolbox models to be rigorously tested against competing theories. The authors demonstrate that their approach applies at the individual level but can also be generalized to the group level with hierarchical Bayesian procedures. The suggested Bayesian inference techniques represent a theoretical and methodological advancement for toolbox theories of cognition and behavior.

After more than 15 years of study, the 1/f noise or complex-systems approach to cognitive science has delivered promises of progress, colorful verbiage, and statistical analyses of phenomena whose relevance for cognition remains unclear. What the complex-systems approach has arguably failed to deliver are concrete insights about how people perceive, think, decide, and act. Without formal models that implement the proposed abstract concepts, the complex-systems approach to cognitive science runs the danger of becoming a philosophical exercise in futility. The complex-systems approach can be informative and innovative, but only if it is implemented as a formal model that allows concrete prediction, falsification, and comparison against more traditional approaches., (Copyright © 2011 Cognitive Science Society, Inc.)

Cognitive neuroscientists study how the brain implements particular cognitive processes such as perception, learning, and decision-making. Traditional approaches in which experiments are designed to target a specific cognitive process have been supplemented by two recent innovations. First, formal cognitive models can decompose observed behavioral data into multiple latent cognitive processes, allowing brain measurements to be associated with a particular cognitive process more precisely and more confidently. Second, cognitive neuroscience can provide additional data to inform the development of formal cognitive models, providing greater constraint than behavioral data alone. We argue that these fields are mutually dependent; not only can models guide neuroscientific endeavors, but understanding neural mechanisms can provide key insights into formal models of cognition., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

A growing number of researchers use descriptive distributions such as the ex-Gaussian and the shifted Wald to summarize response time data for speeded two-choice tasks. Some of these researchers also assume that the parameters of these distributions uniquely correspond to specific cognitive processes. We studied the validity of this cognitive interpretation by relating the parameters of the ex-Gaussian and shifted Wald distributions to those of the Ratcliff diffusion model, a successful model whose parameters have well-established cognitive interpretations. In a simulation study, we fitted the ex-Gaussian and shifted Wald distributions to data generated from the diffusion model by systematically varying its parameters across a wide range of plausible values. In an empirical study, the two descriptive distributions were fitted to published data that featured manipulations of task difficulty, response caution, and a priori bias. The results clearly demonstrate that the ex-Gaussian and shifted Wald parameters do not correspond uniquely to parameters of the diffusion model. We conclude that researchers should resist the temptation to interpret changes in the ex-Gaussian and shifted Wald parameters in terms of cognitive processes. Supporting materials may be downloaded from http://pbr.psychonomic-journals.org/content/supplemental.

The ability to play chess is generally assumed to depend on two types of processes: slow processes such as search, and fast processes such as pattern recognition. It has been argued that an increase in time pressure during a game selectively hinders the ability to engage in slow processes. Here we study the effect of time pressure on expert chess performance in order to test the hypothesis that compared to weak players, strong players depend relatively heavily on fast processes. In the first study we examine the performance of players of various strengths at an online chess server, for games played under different time controls. In a second study we examine the effect of time controls on performance in world championship matches. Both studies consistently show that skill differences between players become less predictive of the game outcome as the time controls are tightened. This result indicates that slow processes are at least as important for strong players as they are for weak players. Our findings pose a challenge for current theorizing in the field of expertise and chess.

Researchers in psychology are paying increasing attention to temporal correlations in performance on cognitive tasks. Recently, Thornton and Gilden (2005) introduced a spectral method for analyzing psychological time series; in particular, this method is tailored to distinguish transient serial correlations from the persistent correlations characterized by 1/f noise. Thornton and Gilden applied their method to word-naming data to support the claimed ubiquity of 1/f noise in psychological time series. We argue that a previously presented method for distinguishing transient and persistent correlations (e.g., Wagenmakers, Farrell, and Ratcliff, 2004) compares favorably with the new method presented by Thornton and Gilden. We apply Thornton and Gilden's method to time series from a range of cognitive tasks and show that 1/f noise is not a ubiquitous property of psychological time series. Finally, we assess the theoretical developments in this area and argue that the development of well-specified models of the principles or mechanisms of human cognition giving rise to 1/f noise is long overdue.

Recently, G. C. Van Orden, J. G. Holden, and M. T. Turvey (2003) proposed to abandon the conventional framework of cognitive psychology in favor of the framework of nonlinear dynamical systems theory. Van Orden et al. presented evidence that "purposive behavior originates in self-organized criticality" (p. 333). Here, the authors show that Van Orden et al.'s analyses do not test their hypotheses. Further, the authors argue that a confirmation of Van Orden et al.'s hypotheses would not have constituted firm evidence in support of their framework. Finally, the absence of a specific model for how self-organized criticality produces the observed behavior makes it very difficult to derive testable predictions. The authors conclude that the proposed paradigm shift is presently unwarranted., (((c) 2005 APA, all rights reserved).)

Recent analyses of serial correlations in cognitive tasks have provided preliminary evidence of the presence of a particular form of long-range serial dependence known as 1/f noise. It has been argued that long-range dependence has been largely ignored in mainstream cognitive psychology even though it accounts for a substantial proportion of variability in behavior (see, e.g., Gilden, 1997, 2001). In this article, we discuss the defining characteristics of long-range dependence and argue that claims about its presence need to be evaluated by testing against the alternative hypothesis of short-range dependence. For the data from three experiments, we accomplish such tests with autoregressive fractionally integrated moving-average time series modeling. We find that long-range serial dependence in these experiments can be explained by any of several mechanisms, including mixtures of a small number of short-range processes.

Learning the statistical structure of the environment is crucial for adaptive behavior. Humans and nonhuman decision-makers seem to track such structure through a process of probabilistic inference, which enables predictions about behaviorally relevant events. Deviations from such predictions cause surprise, which in turn helps improve inference. Surprise about the timing of behaviorally relevant sensory events drives phasic responses of neuromodulatory brainstem systems, which project to the cerebral cortex. Here, we developed a computational model-based magnetoencephalography (MEG) approach for mapping the resulting cortical transients across space, time, and frequency, in the human brain (N = 28,17 female). We used a Bayesian ideal observer model to learn the statistics of the timing of changes in a simple visual detection task. This model yielded quantitative trial-by-trial estimates of temporal surprise. The model-based surprise variable predicted trial-by-trial variations in reaction time more strongly than the externally observable interval timings alone. Trial-by-trial variations in surprise were negatively correlated with the power of cortical population activity measured with MEG. This surprise-related power suppression occurred transiently around the behavioral response, specifically in the beta frequency band. It peaked in parietal and prefrontal cortices, remote from the motor cortical suppression of beta power related to overt report (button press) of change detection. Our results indicate that surprise about sensory event timing transiently suppresses ongoing beta-band oscillations in association cortex. This transient suppression of frontal beta-band oscillations might reflect an active reset triggered by surprise, and is in line with the idea that beta-oscillations help maintain cognitive sets. [ABSTRACT FROM AUTHOR]

The probabilistic approach to human reasoning is exemplified by the information gain model for the Wason card selection task. Although the model is elegant and original, severed key aspects of the model warrant further discussion, particularly those concerning the scope of the task and the choice process of individuals. [ABSTRACT FROM AUTHOR]