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Mushtaq, F.

Publications and source records attributed to Mushtaq, F..

2 recordsLinked to original sources

Neural Signatures of Prediction Errors in a Decision-making Task are Modulated by Action Execution Failures

Decisions must be implemented through actions, and actions are prone to error. As such, when an expected outcome is not obtained, an individual should not only be sensitive to whether the choice itself was suboptimal, but also whether the action required to indicate that choice was executed successfully. The intelligent assignment of credit to action execution versus action selection has clear ecological utility for the learner. To explore this scenario, we used a modified version of a classic reinforcement learning task in which feedback indicated if negative prediction errors were, or were not, associated with execution errors. Using fMRI, we asked if prediction error computations in the human striatum, a key substrate in reinforcement learning and decision making, are modulated when a failure in action execution results in the negative outcome. Participants were more tolerant of non-rewarded outcomes when these resulted from execution errors versus when execution was successful but the reward was withheld. Consistent with this behavior, a model-driven analysis of neural activity revealed an attenuation of the signal associated with negative reward prediction error in the striatum following execution failures. These results converge with other lines of evidence suggesting that prediction errors in the mesostriatal dopamine system integrate high-level information during the evaluation of instantaneous reward outcomes.

neuroscience

Studying Human Behaviour with Virtual Reality: The Unity Experiment Framework

Virtual Reality systems offer a powerful tool for human behaviour research. The ability to create three-dimensional visual scenes and measure responses to the visual stimuli enables the behavioural researcher to test hypotheses in a manner and scale that were previously unfeasible. For example, a researcher wanting to understand interceptive timing behaviour might wish to violate Newtonian mechanics, so objects move in novel 3D trajectories. The same researcher may wish to collect such data with hundreds of participants outside the laboratory, and the use of a VR headset makes this a realistic proposition. The difficulty facing the researcher is that sophisticated 3D graphics engines (e.g. Unity) have been created for game designers rather than behavioural scientists. In order to overcome this barrier, we have created a set of tools and programming syntaxes that allow logical encoding of the common experimental features required by the behavioural scientist. The Unity Experiment Framework (UXF) allows the researcher to readily implement several forms of data collection, and provides researchers with the ability to easily modify independent variables. UXF does not offer any stimulus presentation features, so the full power of the Unity game engine can be exploited. We use a case study experiment, measuring postural sway in response to an oscillating virtual room, to show how UXF can replicate and advance upon behavioural research paradigms. We show that UXF can simplify and speed up development of VR experiments created in commercial gaming software and facilitate the efficient acquisition of large quantities of behavioural research data.

neuroscience