Positive versus negative thinking: computational phenotyping of motivational attitude

The standard approach in cognitive neuroscience is to search for commonalities across participants, with the aim of identifying key brain mechanisms that fulfill specific functions. In this research project, on the contrary, the aim is to identify personal ways of controlling the behavior. The search for systematic differences between participants will be applied to the core set of functions investigated in the host team: motivation, decision, evaluation. The central intuition is that people may strive for either maximizing positive outcomes or minimizing negative outcomes. This dissociation has been observed in the domain of reinforcement learning (e.g., Pessiglione et al., Nature 2006), with participants under different dopamine-related drugs being better at obtaining rewards versus avoiding punishments. Yet the divide between positive and negative thinking might be more profound and could be extended to different cognitive domains. To test this idea, the proposal is to develop a new behavioral task, based on a memory test already programmed in the lab. In each mini-block of the task, participants try to memorize as many items as they can, from a specific category. They are then tested using multiple-choice questionnaires and provided with feedback on their performance. After each mini-block, they are asked to rate their mood (from very high to very low). At the end of each block, they are also asked to choose on which category they would like to be tested during a final block, for which there would be a financial payoff proportional to their performance. Across mini-blocks (hence, categories), the number of questions and the number of choices per question will be varied. This means that, while the minimal performance is always zero, the maximal possible performance will change (with the number of questions), as well as chance-level performance (with the number of choices). The feedback given to participants will always be the number of correct responses that they provided. However, this same information may have different effects, depending on whether the participant implements positive self-evaluation (difference from minimal or chance-level performance) or negative self-evaluation (difference from maximal possible performance). Personal attitude will be phenotyped by fitting a computational model to the different types of behavioral responses made by the participant (mood rating, effort invested in the next mini-block, and choice between mini-blocks). This project has applications in the domain of education and management. It could be pursued in a PhD internship that would aim at 1) elucidating the brain mechanisms underlying positive versus negative thinking and 2) specifying the bias towards positive versus negative thinking imposed by various pathological conditions.