Dr Guy Hawkins

It is commonly assumed that a specific testing occasion (task, design, procedure, etc.) provides insights that generalize beyond that occasion. This assumption is infrequently carefully tested in data. We develop a statistically principled method to directly estimate the correlation between latent components of cognitive processing across tasks, contexts, and time. This method simultaneously estimates individual-participant parameters of a cognitive model at each testing occasion, group-level parameters representing across-participant parameter averages and variances, and across-task correlations. The approach provides a natural way to ¿borrow¿ strength across testing occasions, which can increase the precision of parameter estimates across all testing occasions. Two example applications demonstrate that the method is practical in standard designs. The examples, and a simulation study, also provide evidence about the reliability and validity of parameter estimates from the linear ballistic accumulator model. We conclude by highlighting the potential of the parameter-correlation method to provide an ¿assumption-light¿ tool for estimating the relatedness of cognitive processes across tasks, contexts, and time.

Transcranial direct current stimulation (tDCS) has been proposed to be able to modulate different cognitive functions. However, recent meta-analyses conclude that its efficacy is still in question. Recently, an increase in subjects¿ propensity to mind-wander has been reported as a consequence of anodal stimulation of the left dorsolateral prefrontal cortex (Axelrod et¿al., Proceedings of the National Academy of Sciences of the United States of America, 112, 2015). In addition, an independent group found a decrease in mind wandering after cathodal stimulation of the same region. These findings seem to indicate that high-level cognitive processes such as mind wandering can reliably be influenced by non-invasive brain stimulation. However, these previous studies used low sample sizes and are as such subject to concerns regarding the replicability of their findings. In this registered report, we implement a high-powered replication of Axelrod et¿al. (2015) finding that mind-wandering propensity can be increased by anodal tDCS. We used Bayesian statistics and a preregistered sequential-sampling design resulting in a total sample size of N¿=¿192 participants collected across three different laboratories. Our findings show support against a stimulation effect on self-reported mind-wandering scores. The effect was small, in the opposite direction as predicted and not reliably different from zero. Using a Bayes Factor specifically designed to test for replication success, we found strong evidence against a successful replication of the original study. Finally, even when combining data from both the original and replication studies, we could not find evidence for an effect of anodal stimulation. Our results underline the importance of designing studies with sufficient power to detect evidence for or against behavioural effects of non-invasive brain stimulation techniques, preferentially using robust Bayesian statistics in preregistered reports.

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.

An important feature of human cognition is the ability to flexibly and efficiently adapt behavior in response to continuously changing contextual demands. We leverage a large-scale dataset from Lumosity, an online cognitive-training platform, to investigate how cognitive processes involved in cued switching between tasks are affected by level of task practice across the adult lifespan. We develop a computational account of task switching that specifies the temporal dynamics of activating task-relevant representations and inhibiting task-irrelevant representations and how they vary with extended task practice across a number of age groups. Practice modulates the level of activation of the task-relevant representation and improves the rate at which this information becomes available, but has little effect on the task-irrelevant representation. While longterm practice improves performance across all age groups, it has a greater effect on older adults. Indeed, extensive task practice can make older individuals functionally similar to lesspracticed younger individuals, especially for cognitive measures that focus on the rate at which task-relevant information becomes available.

The study of non-human primates has been foundational in understanding the neural origins of human decision processes, yet the approach rests on the assumption that one can validly extrapolate from the animal to the human. In the context of decision making, this requires constancy across species in physiological and cognitive processes. The former cannot be experimentally validated and therefore remains assumed, and recent findings have called into question the latter: non-human primates become increasingly urgent as the time spent making a decision increases, but humans do not; from a normative perspective, monkeys are making closer-to-optimal decisions than humans. Rather than presuming species differences, here we test an alternative hypothesis: previously overlooked differences in methodological procedures from the two research traditions implicitly reinforced fundamentally different decision strategies across the two species. We show that when humans experience decision contexts matched to those experienced by non-human primates ¿ extensive task practice, or time-based penalties ¿ they display increasing levels of urgency as decision time grows longer, in precisely the same manner as non-human primates. Our findings indicate that previously observed differences in decision strategy between humans and non-human primates are eliminated when the decision environment is more closely matched across species, placing a constraint on the interpretation and mapping of neurophysiological results in non-human primates to humans when there are fundamental differences in the task design.

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation method that is frequently used to study cortical excitability changes and their impact on cognitive functions in humans. While most stimulators are capable of operating in double-blind mode, the amount of discomfort experienced during tDCS may break blinding. Therefore, specifically designed sham stimulation protocols are being used. The ¿fade-in, short-stimulation, fade-out¿ (FSF) protocol has been used in hundreds of studies and is commonly believed to be indistinguishable from real stimulation applied at 1 mA for 20¿min. We analysed subjective reports of 192 volunteers, who either received real tDCS (n¿=¿96) or FSF tDCS (n¿=¿96). Participants reported more discomfort for real tDCS and correctly guessed the condition above chance-level. These findings indicate that FSF does not ensure complete blinding and that better active sham protocols are needed.

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.

Do we use the same information to decide what we like and what we do not like? Best-worst scaling-where respondents select their most and their least preferred option from a set of options-is an efficient method for obtaining information of direct relevance to this question. Many best-worst scaling applications use multinomial logit (MNL) models to predict such best and worst choice data, explicitly or implicitly assuming that best and worst choices are driven by the same parameters for utility information. Some recent literature, however, has criticized this common practice as an overly simplistic representation of the choice process. We tested this assumption by applying three MNL-type models of increasing complexity in their parameterization to the stated best-worst choices from a total of 1,200 individuals drawn from five data sets. Our Bayesian latent mixture modeling found clear evidence that the same utility parameters drive individuals' best and worst choices, although usually with an additional scale parameter leading to more variable worst choices. These conclusions also held for stated best-worst choices of the same individuals for the same alternatives after a 6-, 12-, and 18-month delay. We argue that the conclusion of several recent papers that best and worst choices are driven by different utility information or reflect different decision processes is based on inadequate data and/or data analyses.

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.

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.

Reasoning and inference are well-studied aspects of basic cognition that have been explained as statistically optimal Bayesian inference. Using a simplified experimental design, we conducted quantitative comparisons between Bayesian inference and human inference at the level of individuals. In 3 experiments, with more than 13,000 participants, we asked people for prior and posterior inferences about the probability that 1 of 2 coins would generate certain outcomes. Most participants' inferences were inconsistent with Bayes' rule. Only in the simplest version of the task did the majority of participants adhere to Bayes' rule, but even in that case, there was a significant proportion that failed to do so. The current results highlight the importance of close quantitative comparisons between Bayesian inference and human data at the individual-subject level when evaluating models of cognition.

Functional neuroimaging data indicate the dorsal striatum is engaged when people are required to vary the cautiousness of their decisions, by emphasizing the speed or accuracy of responding in laboratory-based decision tasks. However, the functional contribution of the striatum to decision making is unknown. In the current study we tested patients with focal ischemic lesions of the dorsal striatum and matched non-lesion control participants on a speed-accuracy tradeoff (SAT) task. Analysis using a computational model of response selection in a competitive and time-pressured context indicated that the decisions of patients with striatal lesions were less cautious than those of matched controls. This deficit was most prominent when the accuracy of decisions was emphasized. The results are consistent with the hypothesis that the striatum plays an important role in strategically setting response caution, an essential function for flexible behavior.

For decades sequential sampling models have successfully accounted for human and monkey decision-making, relying on the standard assumption that decision makers maintain a pre-set decision standard throughout the decision process. Based on the theoretical argument of reward rate maximization, some authors have recently suggested that decision makers become increasingly impatient as time passes and therefore lower their decision standard. Indeed, a number of studies show that computational models with an impatience component provide a good fit to human and monkey decision behavior. However, many of these studies lack quantitative model comparisons and systematic manipulations of rewards. Moreover, the often-cited evidence from single-cell recordings is not unequivocal and complimentary data from human subjects is largely missing. We conclude that, despite some enthusiastic calls for the abandonment of the standard model, the idea of an impatience component has yet to be fully established; we suggest a number of recently developed tools that will help bring the debate to a conclusive settlement.

For nearly 50 years, the dominant account of decision-making holds that noisy information is accumulated until a fixed threshold is crossed. This account has been tested extensively against behavioral and neurophysiological data for decisions about consumer goods, perceptual stimuli, eyewitness testimony, memories, and dozens of other paradigms, with no systematic misfit between model and data. Recently, the standard model has been challenged by alternative accounts that assume that less evidence is required to trigger a decision as time passes. Such ¿collapsing boundaries¿ or ¿urgency signals¿ have gained popularity in some theoretical accounts of neurophysiology. Nevertheless, evidence in favor of these models is mixed, with support coming from only a narrow range of decision paradigms compared with a long history of support from dozens of paradigms for the standard theory. We conducted the first large-scale analysis of data from humans and nonhuman primates across three distinct paradigms using powerful model-selection methods to compare evidence for fixed versus collapsing bounds. Overall, we identified evidence in favor of the standard model with fixed decision boundaries. We further found that evidence for static or dynamic response boundaries may depend on specific paradigms or procedures, such as the extent of task practice. We conclude that the difficulty of selecting between collapsing and fixed bounds models has received insufficient attention in previous research, calling into question some previous results.