Wouter Boekel

Bias in the Brain: A Diffusion Model Analysis of Prior Probability and Potential Payoff

In perceptual decision-making, advance knowledge biases people toward choice alternatives that are more likely to be correct and more likely to be profitable. Accumulation-to-bound models provide two possible explanations for these effects: prior knowledge about the relative attractiveness of the alternatives at hand changes either the starting point of the decision process, or the rate of evidence accumulation. Here, we used model-based functional MRI to investigate whether these effects are similar for different types of prior knowledge, and whether there is a common neural substrate underlying bias in simple perceptual choices. We used two versions of the random-dot motion paradigm in which we manipulated bias by: (1) changing the prior likelihood of occurrence for two alternatives (“prior probability”) and (2) assigning a larger reward to one of two alternatives (“potential payoff”). Human subjects performed the task inside and outside a 3T MRI scanner. For each manipulation, bias was quantified by fitting the drift diffusion model to the behavioral data. Individual measurements of bias were then used in the imaging analyses to identify regions involved in biasing choice behavior. Behavioral results showed that subjects tended to make more and faster choices toward the alternative that was most probable or had the largest payoff. This effect was primarily due to a change in the starting point of the accumulation process. Imaging results showed that, at cue level, regions of the frontoparietal network are involved in changing the starting points in both manipulations, suggesting a common mechanism underlying the biasing effects of prior knowledge.

When the brain takes a break: a model-based analysis of mind wandering

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.

Action video games do not improve the speed of information processing in simple perceptual tasks.

Previous research suggests that playing action video games improves performance on sensory, perceptual, and attentional tasks. For instance, Green, Pouget, and Bavelier (2010) used the diffusion model to decompose data from a motion detection task and estimate the contribution of several underlying psychological processes. Their analysis indicated that playing action video games leads to faster information processing, reduced response caution, and no difference in motor responding. Because perceptual learning is generally thought to be highly context-specific, this transfer from gaming is surprising and warrants corroborative evidence from a large-scale training study. We conducted 2 experiments in which participants practiced either an action video game or a cognitive game in 5 separate, supervised sessions. Prior to each session and following the last session, participants performed a perceptual discrimination task. In the second experiment, we included a third condition in which no video games were played at all. Behavioral data and diffusion model parameters showed similar practice effects for the action gamers, the cognitive gamers, and the nongamers and suggest that, in contrast to earlier reports, playing action video games does not improve the speed of information processing in simple perceptual tasks.

A Neural Model of Mind Wandering

The role of the default-mode network (DMN) in the emergence of mind wandering and task-unrelated thought has been studied extensively. In parallel work, mind wandering has been associated with neuromodulation via the locus coeruleus (LC) norepinephrine (LC-NE) system. Here we propose a neural model that links the two systems in an integrative framework. The model attempts to explain how dynamic changes in brain systems give rise to the subjective experience of mind wandering. The model implies a neural and conceptual distinction between an off-focus state and an active mind-wandering state and provides a potential neural grounding for well-known cognitive theories of mind wandering. Finally, the proposed neural model of mind wandering generates precise, testable predictions at neural and behavioral levels.

The speed and accuracy of perceptual decisions in a random-tone pitch task

Research in perceptual decision making is dominated by paradigms that tap the visual system, such as the random-dot motion (RDM) paradigm. In this study, we investigated whether the behavioral signature of perceptual decisions in the auditory domain is similar to those observed in the visual domain. We developed an auditory version of the RDM task, in which tones correspond to dots and pitch corresponds to motion (the random-tone pitch task, RTP). In this task, participants have to decide quickly whether the pitch of a “sound cloud” of tones is moving up or down. Stimulus strength and speed–accuracy trade-off were manipulated. To describe the relationship between stimulus strength and performance, we fitted the proportional-rate diffusion model to the data. The results showed a close coupling between stimulus strength and the speed and accuracy of perceptual decisions in both tasks. Additionally, we fitted the full drift diffusion model (DDM) to the data and showed that three of the four participants had similar speed–accuracy trade-offs in both tasks. However, for the RTP task, drift rates were larger and nondecision times slower, suggesting that some DDM parameters might be dependent on stimulus modality (drift rate and nondecision time), whereas others might not be (decision bound). The results illustrate that the RTP task is suitable for investigating the dynamics of auditory perceptual choices. Future studies using the task might help to investigate modality-specific effects on decision making at both the behavioral and neuronal levels.

Cortico-subthalamic connection predicts individual differences in value-driven choice bias

It has been suggested that a connection between the STN and value-sensitive areas of the prefrontal cortex might mediate value-based actions in perceptual decision making. In this study, we first seek to quantify a structural connection between the STN and a cortical region that was associated with mechanisms underlying bias in choice behavior (vmPFC). Next, we tested whether individual differences in the probabilistic tract-strength of this connection were predictive for individual differences in the magnitude of bias in a perceptual decision-making task. Probabilistic tractography was used to measure the tract-strength between the STN and the vmPFC. Bias was quantified using an accumulation-to-bound model where a shift in the starting point of the accumulation of sensory evidence causes faster and more choices for an alternative that is more likely or more valuable. Results show that vmPFC is structurally connected with the STN and that the strength of this connection is predictive for choice bias towards an alternative that is more valuable, but not for choice bias towards an alternative that is more likely. These findings confirm the involvement of the cortico-subthalamic circuit in mechanisms underlying value-based actions in perceptual decision making.

Challenges in replicating brain-behavior correlations: Rejoinder to Kanai (2015) and Muhlert and Ridgway (2015).

In a recent study we attempted to replicate five studies that had previously reported a combined total of 17 significant structural brain behavior (SBB) correlations (Boekel et al., 2015). We preregistered our analysis plan and used confirmatory Bayesian hypothesis tests to quantify the evidence that our data provided for the presence or absence of the SBB correlations. For about half of the 17 SBB correlations that we set out to replicate the data suggested at least moderate evidence for their absence, and for 16 out of the 17 correlations the data produced no evidence for their presence. Subsequent exploratory analyses using Bayesian parameter estimation and a Bayesian replication test sketched a more nuanced perspective of the replication results. Nevertheless, our overall results suggest that confirmatory replication studies in the cognitive neurosciences deserve a more prominent role. Our confirmatory replication has attracted two commentaries from researchers who are skeptical about our results. In “Failed replications, contributing factors and careful interpretations”, Muhlert and Ridgway (2015) critique our replication attempt for having low sample size and incomplete correction for nuisance variables. In addition, they note that there are differences in the VBM processing pipelines used by the original authors and those used in the replication attempt, and suggest that these differences may have contributed towards the discrepant results. In “Open questions in conducting confirmatory replication studies”, Kanai (2015) also critiques our replication approach and points out that our confirmatory ROI approach may underestimate the SBB correlations. In addition, Kanai feels that the process of refereeing a preregistered study demands clearer guidelines. We wish to thank the discussants for their interesting suggestions and constructive comments. At the moment little guidance exists with respect to the design and interpretation of purely confirmatory replication studies in the cognitive neurosciences, and we hope this discussion can help stimulate the development of common goals and guidelines. Below, we discuss the key concerns raised in the commentaries. We also suggest ways in which future replication studies can take into account the issues raised by these commentaries.

A Neural Model of Mind Wandering: (Trends in Cognitive Sciences 20, 570–578, 2016)

Due to an oversight in the preparation of this Opinion article, the authors inadvertently used the term ‘parietal cingulate cortex’ instead of ‘posterior cingulate cortex’ in the second paragraph of the main text and the caption of Figure 1. The phrasing has been corrected in the article online. The corrected sentences from the second paragraph and the figure caption are also shown below.‘The DMN is one of the most widely studied intrinsic connectivity networks (ICNs) [5] and includes nodes such as the medial prefrontal cortex (mPFC), the posterior cingulate cortex (PCC), the precuneus, and both angular gyri.’‘In these states the transmodal hub nodes of the default-mode network (DMN), the posterior cingulate cortex (PCC) and the medial prefrontal cortex (mPFC) (red), are connected to few networks involved in performing the task; for example, the dorsal attention network (DAN) (blue) during the on-task state and the medial temporal lobe (MTL) subsystem of the DMN (green) during the mind-wandering state.’

Corrigendum to “A purely confirmatory replication study of structural brain-behavior correlations” [Cortex 66 (2015) 115–133]

In our previous study, we reported a purely confirmatory replication study of structural brain-behavior correlations (Boekel et al., 2015). 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. We now discovered a mistake in the post-processing pipeline of our diffusion-weighted imaging (DWI) data analyses originally included in this replication study. This led us to recalculate and correct five of the 17 originally reported brain-behavior correlations that were based on DWI data. In