Game on:  Leveraging Gamification in Cognitive and Computational Neuroscience

Title: Developing gamified decision-making battery for longitudinal assessment of patients with affective disorders

Abstract: Affective disorders are characterised by dynamic transitions bewteen different affective states characterizing periods with low, neutral, or elated mood. Fluctuations in the functioning of the dopaminergic system known to regulate reinforcement learning and incentive motivation are likely to play an important role in these dynamics. We hypothesised that reinforcement learning ability and incentive motivation reflect dissociable mechanisms that can be independently quantified. We have developed a battery of decision-making tasks to quantify these mechanisms that we have validated online. Some of the tasks have been newly designed and some of them have been taken from available repositories online. Our data show that performance in all tasks is comparable to the performance in anonical versions of these tasks and show satisfactory levels of test-retest reliability. Moreover, we show that we can reliably quantify reinforcement learning ability and incentive motivation using the battery. This battery will be suitable for longitudinal assessment of patients with affective disorders.

/

Title: Towards large-scale neuroimaging of naturalistic gameplay

Abstract: A key bottleneck in drawing comparisons between artificial neural networks and human brain representations is the limited quantity of data typically collected in cognitive neuroscience experiments. Recent arguments advocate far richer data collection (10s-100s of hours per participant) in smaller numbers of individual participants. The success of this approach has already been seen in studies of vision, object representation, and language. Yet currently, few large-scale datasets exist for tasks in which participants generate rich, naturalistic behaviour. I will discuss a proposal to collect large-scale behavioural and magnetoencaphalography data while participants learn a naturalistic task: learning to play video games from scratch. I will discuss recent work that has led up to this proposal, candidate games that may make for useful testbeds for such a dataset, and potential benchmarks against which to evaluate behavioural and neural data.

/

/

/

Title: Free lunch: continuous (trial-free) paradigms yield fast and reliable neural readouts of belief updating

Abstract: The holy grail for neurocognitive experiments is a task that is intuitive to play, fast to complete, and yet provides meaningful and reliable readouts of a relevant cognitive process and its neural correlates. In reality, researchers often face trade-offs, such as between tasks that are artificially structured and removed from every-day experience, but highly controlled, and those that are more naturalistic, but also more difficult to analyse. Or between measuring reliable signals while subjecting participants to long and exhausting measurements, versus shorter recordings that yield less reliable readouts but keep participants happy. Here we present an example for achieving both (i.e., a free lunch). N=30 participants played a gamified continuous (trial-free) predictive inference task on two occasions (1 week apart) while we recorded their EEG. Combining the novel task design with an analysis approach based on deconvolutional GLMs, we were able to measure EEG signatures of belief updating with high test-retest reliability in as little as 6min of task performance. These signatures reflected how participants adapted their belief updating strategies to changes in stimulus volatility and noise. Our task is ideally suited to study belief updating in clinical contexts and with interventional designs.

Title: From Trials to Play: Enhancing Neuropsychology Experiments with Gamification

Abstract:  Reinforcement learning (RL) models decision-making by training agents through trial and error, where actions yield rewards that reinforce behavior. In experimental neuropsychology, RL algorithms are used to simulate aspects of human cognition and motivation—such as goal-directed control and approach-avoidance behavior—yet they require validation through rigorous experiments like sequential decision-making, reversal learning, and go-no-go tasks. Traditional experiments typically rely on repetitive cycles and monetary incentives, often overlooking the importance of an engaging testing environment. By transforming the experimental environment into a more immersive and enjoyable experience, gamification can potentially reduce participant fatigue, improve data quality, and ultimately yield deeper insights into human decision-making processes. 

We proposed a gamification framework for RL-based neuropsychology experiments designed to enhance participant engagement, improve study design, and facilitate the retrieval of meaningful results. Developed through an interdisciplinary, user-centered approach involving neuropsychologists and computer scientists, our framework integrates interactive, game-like elements to motivate participants more effectively than conventional methods. 

Link to ACM paper: https://shorturl.at/7ymY7