ERC FUNDED PROJECTS

"This proposal outlines projects to develop robust and portable theories studying the impact of psychological phenomena in economic settings. The proposed work falls in three broad research agendas. My first main agenda is to formally model and economically apply a simple observation: that when people make decisions, they do not focus equally on all attributes of their available options, and overweight the attributes they focus on. I will build a set of portable models of focusing in attribute-based choice and risky choice based on the idea that a person focuses more on attributes in which her options differ more. I will also use the framework to develop novel, focus-based, theories of intertemporal choice and social preferences, as well as analyze the implications of focusing for product design, principal-agent relationships, and other economic questions. My second main agenda is to explore some implications for market outcomes, welfare, and policy of the possibility that consumers misperceive certain aspects of products. I will investigate the circumstances that facilitate the profitable deception of consumers; firms' incentives for ""innovating"" deceptive products, including novel financial products aimed at exploiting investors; how firms' ability to distinguish naive and sophisticated consumers affects the consequences of deception; whether learning on the part of consumers will help them to avoid making mistakes; and how regulators and other observers can detect consumer mistakes from market data. Two further projects apply the model of reference-dependent utility I have developed in earlier work to understand the pricing and advertising behavior of firms. I will also aim to disseminate some of my work, along with other cutting-edge research in psychology and economics, in a Journal of Economic Literature survey on ""Behavioral Contract Theory."""

1 275 448 €

Start date: 2012-11-01, End date: 2018-10-31

Internal models are fundamental to our understanding of how the mind constructs percepts, makes decisions, controls movements, and interacts with others. Yet, we lack principled quantitative methods to systematically estimate internal models from observable behaviour, and current approaches for discovering their mental representations remain heuristic and piecemeal. I propose to develop a set of novel 'doubly Bayesian' data analytical methods, using state-of-the-art Bayesian statistical and machine learning techniques to infer humans' internal models formalised as prior distributions in Bayesian models of cognition. This approach, cognitive tomography, takes a series of behavioural observations, each of which in itself may have very limited information content, and accumulates a detailed reconstruction of the internal model based on these observations. I also propose a set of stringent, quantifiable criteria which will be systematically applied at each step of the proposed work to rigorously assess the success of our approach. These methodological advances will allow us to track how the structured, task-general internal models that are so fundamental to humans' superior cognitive abilities, change over time as a result of decay, interference, and learning. We will apply cognitive tomography to a variety of experimental data sets, collected by our collaborators, in paradigms ranging from perceptual learning, through visual and motor structure learning, to social and concept learning. These analyses will allow us to conclusively and quantitatively test our central hypothesis that, rather than simply changing along a single 'memory strength' dimension, internal models typically change via complex and consistent patterns of transformations along multiple dimensions simultaneously. To facilitate the widespread use of our methods, we will release and support off-the-shelf usable implementations of our algorithms together with synthetic and real test data sets.

1 179 462 €

Start date: 2017-05-01, End date: 2022-04-30

Our ability to recognize an object amongst many others is one of the most important features of the human mind. However, object recognition requires tremendous computational effort, as we need to solve a complex and recursive environment with ease and proficiency. This challenging feat is dependent on the implementation of an effective organization of knowledge in the brain. In ContentMAP I will put forth a novel understanding of how object knowledge is organized in the brain, by proposing that this knowledge is topographically laid out in the cortical surface according to object-related dimensions that code for different types of representational content – I will call this contentotopic mapping. To study this fine-grain topography, I will use a combination of fMRI, behavioral, and neuromodulation approaches. I will first obtain patterns of neural and cognitive similarity between objects, and from these extract object-related dimensions using a dimensionality reduction technique. I will then parametrically manipulate these dimensions with an innovative use of a visual field mapping technique, and test how functional selectivity changes across the cortical surface according to an object’s score on a target dimension. Moreover, I will test the tuning function of these contentotopic maps. Finally, to mirror the complexity of implementing a high-dimensional manifold onto a 2D cortical sheet, I will aggregate the topographies for the different dimensions into a composite map, and develop an encoding model to predict neural signatures for each object. To sum up, ContentMAP will have a dramatic impact in the cognitive sciences by describing how the stuff of concepts is represented in the brain, and providing a complete description of how fine-grain representations and functional selectivity within high-level complex processes are topographically implemented.

1 816 004 €

Start date: 2019-02-01, End date: 2024-01-31

Human social interaction and learning depends on making the right inferences about other people’s thoughts, a process commonly called mentalizing, or Theory of Mind, a cognitive achievement which several decades of research concluded was reached at around age 4. The last 10 years has radically changed this view, and innovative new paradigms suggest that even preverbal infants can think about others’ minds. This new developmental data has created arguably one of the biggest puzzles in the history of developmental science: How can infants be mentalizing when years of research have shown that a) pre-schoolers fail at mentalizing tasks and b) mentalizing depends on the development of cognitive control, language, and brain maturation? The key issue is whether behaviour that looks like infant mentalizing really is mentalizing, or might infants’ success belie alternative processes? The most powerful strategy for resolving this puzzle is to look to brain activity. By applying the same methods and paradigms across infancy and early childhood, DEVOMIND will investigate whether infants’ success on mentalizing tasks recruits the same network of brain regions, and neural processes, that we know are involved in success in older children and adults. In the second half of the project, we will use our neural indicators of mentalizing to test a completely novel hypothesis in which infants’ success is possible because they have a limited ability to distinguish self from other. Although novel, this hypothesis deserves to be tested because it has the potential to explain both infants’ success and preschoolers’ failures under a single, unified theory. By bringing a neuroimaging approach to the puzzle of early mentalizing, DEVOMIND will allow us to move beyond the current impasse, and to generate a new theory of Theory of Mind.

1 761 190 €

Start date: 2018-02-01, End date: 2023-01-31