Project acronym COGTOM
Project Cognitive tomography of mental representations
Researcher (PI) Máté Miklós LENGYEL
Host Institution (HI) KOZEP-EUROPAI EGYETEM
Call Details Consolidator Grant (CoG), SH4, ERC-2016-COG
Summary 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.
Summary
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.
Max ERC Funding
1 179 462 €
Duration
Start date: 2017-05-01, End date: 2022-04-30
Project acronym ELWar
Project Electoral Legacies of War: Political Competition in Postwar Southeast Europe
Researcher (PI) Josip GLAURDIC
Host Institution (HI) UNIVERSITE DU LUXEMBOURG
Call Details Starting Grant (StG), SH2, ERC-2016-STG
Summary We know remarkably little about the impact of war on political competition in postwar societies in spite of the fact that postwar elections have garnered tremendous interest from researchers in a variety of fields. That interest, however, has been limited to establishing the relationship between electoral democratization and the incidence of conflict. Voters’ and parties’ electoral behaviour after the immediate post‐conflict period have remained largely neglected by researchers. The proposed project will fill this gap in our understanding of electoral legacies of war by analysing the evolution of political competition over the course of more than two decades in the six postwar states of Southeast Europe: Bosnia-Herzegovina, Croatia, Kosovo, Macedonia, Montenegro, and Serbia. Organised around three thematic areas/levels of analysis – voters, parties, communities – the project will lead to a series of important contributions. Through a combination of public opinion research, oral histories, and the innovative method of matching of individual census entries, the project will answer to which extent postwar elections are decided by voters’ experiences and perceptions of the ended conflict, as opposed to their considerations of the parties’ peacetime economic platforms and performance in office. In-depth study of party documents and platforms, party relations with the organisations of the postwar civil sector, as well as interviews with party officials and activists will shed light on the influence of war on electoral strategies, policy preferences, and recruitment methods of postwar political parties. And a combination of large-N research on the level of the region’s municipalities and a set of paired comparisons of several communities in the different postwar communities in the region will help expose the mechanisms through which war becomes embedded into postwar political competition and thus continues to exert its influence even decades after the violence has ended.
Summary
We know remarkably little about the impact of war on political competition in postwar societies in spite of the fact that postwar elections have garnered tremendous interest from researchers in a variety of fields. That interest, however, has been limited to establishing the relationship between electoral democratization and the incidence of conflict. Voters’ and parties’ electoral behaviour after the immediate post‐conflict period have remained largely neglected by researchers. The proposed project will fill this gap in our understanding of electoral legacies of war by analysing the evolution of political competition over the course of more than two decades in the six postwar states of Southeast Europe: Bosnia-Herzegovina, Croatia, Kosovo, Macedonia, Montenegro, and Serbia. Organised around three thematic areas/levels of analysis – voters, parties, communities – the project will lead to a series of important contributions. Through a combination of public opinion research, oral histories, and the innovative method of matching of individual census entries, the project will answer to which extent postwar elections are decided by voters’ experiences and perceptions of the ended conflict, as opposed to their considerations of the parties’ peacetime economic platforms and performance in office. In-depth study of party documents and platforms, party relations with the organisations of the postwar civil sector, as well as interviews with party officials and activists will shed light on the influence of war on electoral strategies, policy preferences, and recruitment methods of postwar political parties. And a combination of large-N research on the level of the region’s municipalities and a set of paired comparisons of several communities in the different postwar communities in the region will help expose the mechanisms through which war becomes embedded into postwar political competition and thus continues to exert its influence even decades after the violence has ended.
Max ERC Funding
1 499 788 €
Duration
Start date: 2017-04-01, End date: 2022-03-31
Project acronym PARTNERS
Project Tracking and evaluating social relations and potential partners in infancy
Researcher (PI) Gergely Csibra
Host Institution (HI) KOZEP-EUROPAI EGYETEM
Call Details Advanced Grant (AdG), SH4, ERC-2016-ADG
Summary In order to navigate the social world, children must understand how social interactions unfold in their society. While many recent studies have investigated how children evaluate the roles that people play in everyday interactions and what inferences they draw from their observations, to date there is no unifying account for the conceptual repertoire and computational mechanisms used by infants to analyse their social environment. Taking a new theoretical perspective on this topic, we plan to study whether and how human infants and young children are able to infer the social relations that underlie observed interactions. The theoretical background of this approach is based on the combination of two proposals: (1) that actions are analysed in terms of the costs and benefits they produce to the actors and others affected, and (2) Alan Fiske’s theory, according to which human social relations could be classified into basic elementary forms. Using a variety of behavioural and neuroimaging techniques, we intend to investigate whether children infer the specific social relation that the intentional structure and the cost-benefit outcome of an observed interaction could reveal. More specifically, while resource transfer events (e.g., giving, taking) alter the distribution of goods among participants, they may also cue certain types of underlying relations that would ensure that all parties benefit, directly or indirectly, from the exchange on the long run (e.g., by reciprocity). We aim to establish whether drawing inferences to social relations enjoys the priority in the infant mind over attribution of social dispositions, whether infants predict the outcome of new, previously unobserved interactions, what information children use to choose partners for cooperative tasks, and how they track individuals across social contexts. This research will also provide a new perspective on the development of moral psychology by extending its domain from actions to social interactions.
Summary
In order to navigate the social world, children must understand how social interactions unfold in their society. While many recent studies have investigated how children evaluate the roles that people play in everyday interactions and what inferences they draw from their observations, to date there is no unifying account for the conceptual repertoire and computational mechanisms used by infants to analyse their social environment. Taking a new theoretical perspective on this topic, we plan to study whether and how human infants and young children are able to infer the social relations that underlie observed interactions. The theoretical background of this approach is based on the combination of two proposals: (1) that actions are analysed in terms of the costs and benefits they produce to the actors and others affected, and (2) Alan Fiske’s theory, according to which human social relations could be classified into basic elementary forms. Using a variety of behavioural and neuroimaging techniques, we intend to investigate whether children infer the specific social relation that the intentional structure and the cost-benefit outcome of an observed interaction could reveal. More specifically, while resource transfer events (e.g., giving, taking) alter the distribution of goods among participants, they may also cue certain types of underlying relations that would ensure that all parties benefit, directly or indirectly, from the exchange on the long run (e.g., by reciprocity). We aim to establish whether drawing inferences to social relations enjoys the priority in the infant mind over attribution of social dispositions, whether infants predict the outcome of new, previously unobserved interactions, what information children use to choose partners for cooperative tasks, and how they track individuals across social contexts. This research will also provide a new perspective on the development of moral psychology by extending its domain from actions to social interactions.
Max ERC Funding
2 498 748 €
Duration
Start date: 2018-01-01, End date: 2022-12-31
Project acronym POLBUSNETWORKS
Project Political and Business Networks
Researcher (PI) Adam Gyorgy SZEIDL
Host Institution (HI) KOZEP-EUROPAI EGYETEM
Call Details Consolidator Grant (CoG), SH1, ERC-2016-COG
Summary I explore the role of networks involving firms---networks between firms and politicians, and networks between firms and firms--for economic outcomes. I focus on two broad questions. (1) What are the determinants and implications of political favor networks? Although political favoritism affects allocations in many countries, its mechanisms are not well understood. I develop a new model based on the idea of trust embedded in favor networks, and test in Hungarian data the model's implications about how political centralization shapes favoritism. I also quantify the welfare cost of misallocation created by favoritism. My results help understand how political institutions shape economic outcomes. (2) What is the impact of supply chain networks on firm performance? Inputs from suppliers account for the majority of firm sales, yet we know little about the contribution of suppliers to firm performance. I use a field experiment in China to measure the causal effect of suppliers and the underlying mechanisms. I then use observational data from Hungary to quantify the contribution of differences in suppliers to differences in firm performance. The results help understand how supply chains and their misallocation shape firm and aggregate productivity. My approach to both of these questions emphasizes the role of trust embedded in firm networks and how these trust-based networks create misallocation of resources. A key component of this research is the development of original datasets. Beyond documenting and explaining facts, I also hope to obtain policy implications on reducing favoritism and improving firm performance in developing countries.
Summary
I explore the role of networks involving firms---networks between firms and politicians, and networks between firms and firms--for economic outcomes. I focus on two broad questions. (1) What are the determinants and implications of political favor networks? Although political favoritism affects allocations in many countries, its mechanisms are not well understood. I develop a new model based on the idea of trust embedded in favor networks, and test in Hungarian data the model's implications about how political centralization shapes favoritism. I also quantify the welfare cost of misallocation created by favoritism. My results help understand how political institutions shape economic outcomes. (2) What is the impact of supply chain networks on firm performance? Inputs from suppliers account for the majority of firm sales, yet we know little about the contribution of suppliers to firm performance. I use a field experiment in China to measure the causal effect of suppliers and the underlying mechanisms. I then use observational data from Hungary to quantify the contribution of differences in suppliers to differences in firm performance. The results help understand how supply chains and their misallocation shape firm and aggregate productivity. My approach to both of these questions emphasizes the role of trust embedded in firm networks and how these trust-based networks create misallocation of resources. A key component of this research is the development of original datasets. Beyond documenting and explaining facts, I also hope to obtain policy implications on reducing favoritism and improving firm performance in developing countries.
Max ERC Funding
1 833 214 €
Duration
Start date: 2017-05-01, End date: 2022-04-30
