Reinforcement learning models of error-driven learning and sequential-sampling models of decision making have provided significant insight into the neural basis of a variety of cognitive processes. Until recently, model-based cognitive neuroscience research using both frameworks has evolved separately and independently. Recent efforts have illustrated the complementary nature of both modelling traditions and showed how they can be integrated into a unified theoretical framework, explaining trial-by-trial dependencies in choice behavior as well as response time distributions. Here, we review a theoretical background of integrating the two classes of models, and review recent empirical efforts towards this goal. We furthermore argue that the integration of both modelling traditions provides mutual benefits for both fields, and highlight promises of this approach for cognitive modelling and model-based cognitive neuroscience.
Evidence accumulation models (EAMs) are powerful tools for making sense of human and animal decision-making behaviour. EAMs have generated significant theoretical advances in psychology, behavioural economics, and cognitive neuroscience, and are increasingly used as a measurement tool in clinical research and other applied settings. Obtaining valid and reliable inferences from EAMs depends on knowing how to establish a close match between model assumptions and features of the task/data to which the model is applied. However, this knowledge is rarely articulated in the EAM literature, leaving beginners to rely on the private advice of mentors and colleagues, and on inefficient trial-and-error learning. In this article, we provide practical guidance for designing tasks appropriate for EAMs, for relating experimental manipulations to EAM parameters, for planning appropriate sample sizes, and for preparing data and conducting an EAM analysis. Our advice is based on prior methodological studies and the authors’ substantial collective experience with EAMs. By encouraging good task design practices, and warning of potential pitfalls, we hope to improve the quality and trustworthiness of future EAM research and applications.
Lee et al. (2019) make several practical recommendations for replicable and useful cognitive modeling. They also point out that the ultimate test of the usefulness of a cognitive model is its ability to solve practical problems. Solution-oriented modeling requires engaging practitioners who understand the relevant applied domain but may lack extensive modeling expertise. In this commentary, we argue that for cognitive modeling to reach practitioners there is a pressing need to move beyond providing the bare minimum information required for reproducibility, and instead aim for an improved standard of transparency and reproducibility in cognitive modeling research. We discuss several mechanisms by which reproducible research can foster engagement with applied practitioners. Notably, reproducible materials provide a starting point for practitioners to experiment with cognitive models and evaluate whether they are suitable for their domain of expertise. This is essential because solving complex problems requires exploring a range of modeling approaches, and there may not be time to implement each possible approach from the ground up. Several specific recommendations for best practice are provided, including the application of containerization technologies. We also note the broader benefits of adopting gold standard reproducible practices within the field.
Performing deferred actions in the future relies upon Prospective Memory (PM).Often, PM demands arise in complex dynamic tasks.Not only can PM be challenging in such environments, the processes required for PM may affect the performance of other tasks.To adapt to PM demands in such environments, humans may use a range of strategies, including flexible allocation of cognitive resources and cognitive control mechanisms.We sought to understand such mechanisms by using the Prospective Memory Decision Control (Strickland et al., 2018) model to provide a comprehensive, quantitative account of dual task performance in a complex dynamic environment (a simulated air traffic control conflict detection task).We found that PM demands encouraged proactive control over ongoing task decisions, but that this control was reduced at high time pressure to facilitate fast responding.We found reactive inhibitory control over ongoing task processes when PM targets were encountered, and that time pressure and PM demand both affect the attentional system, increasing the amount of cognitive resources available.However, as demands exceeded the capacity limit of the cognitive system, resources were reallocated (shared) between the ongoing and PM tasks.As the ongoing task used more resources to compensate for additional time pressure demands, it drained resources that would have otherwise been available for PM task processing.This study provides the first detailed quantitative understanding of how attentional resources and cognitive control mechanisms support PM and ongoing task performance in complex dynamic environments.
Response inhibition, the cancellation of planned movement, is essential for everyday motor control. Extensive fMRI and brain stimulation research provides evidence for the crucial role of a number of cortical and subcortical regions in response inhibition, including the subthalamic nucleus (STN), pre-supplementary motor area (preSMA), and the inferior frontal gyrus (IFG). Current models assume that these regions operate as a network, with action cancellation originating in the cortical areas and then executed rapidly via the subcortex. Response inhibition slows in older age, a change that has been attributed to deterioration or changes in the connectivity and integrity of this network. However, previous research has mainly used whole-brain approaches when investigating changes in structural connectivity across the lifespan, or have used simpler measures to investigate structural ageing. Here, we used high-resolution quantitative and diffusion MRI to extensively examine the anatomical changes that occur in this network across the lifespan. We found substantial changes in iron concentration in these tracts, increases in the apparent diffusion coefficient, and some evidence for demyelination. Conversely, we found very little evidence for age-related anatomical changes in the regions themselves. We propose that some of the functional changes observed in these regions in older adult populations (e.g., increased BOLD recruitment) are a reflection of alterations to the connectivity between the regions, rather than localised regional change.
Humans increasingly use automated decision aids. However, environmental uncertainty means that automated advice can be incorrect, creating the potential for humans to act on incorrect advice or to disregard correct advice. We present a quantitative model of the cognitive process by which humans use automation when deciding whether aircraft would violate requirements for minimum separation. The model closely fitted the performance of 24 participants, who each made 2,400 conflict-detection decisions (conflict vs. nonconflict), either manually (with no assistance) or with the assistance of 90% reliable automation. When the decision aid was correct, conflict-detection accuracy improved, but when the decision aid was incorrect, accuracy and response time were impaired. The model indicated that participants integrated advice into their decision process by inhibiting evidence accumulation toward the task response that was incongruent with that advice, thereby ensuring that decisions could not be made solely on automated advice without first sampling information from the task environment.
Working memory (WM) refers to a set of processes that makes task-relevant information accessible to higher-level cognitive processes. Recent work suggests WM is supported by a variety of information gating, updating, and removal processes, which ensure only task-relevant information occupies WM. Current neurocomputational theory suggests WM gating is accomplished via 'go/no-go' signalling in basal ganglia-thalamus-prefrontal cortex pathways, but is less clear about other subprocesses and brain structures known to play a role in WM. We review recent efforts to identify the neural basis of WM subprocesses using the recently developed reference-back task as a benchmark measure of WM subprocesses. Targets for future research using the methods of model-based cognitive neuroscience and novel extensions to the reference-back task are suggested.
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