Project acronym AgeConsolidate
Project The Missing Link of Episodic Memory Decline in Aging: The Role of Inefficient Systems Consolidation
Researcher (PI) Anders Martin FJELL
Host Institution (HI) UNIVERSITETET I OSLO
Call Details Consolidator Grant (CoG), SH4, ERC-2016-COG
Summary Which brain mechanisms are responsible for the faith of the memories we make with age, whether they wither or stay, and in what form? Episodic memory function does decline with age. While this decline can have multiple causes, research has focused almost entirely on encoding and retrieval processes, largely ignoring a third critical process– consolidation. The objective of AgeConsolidate is to provide this missing link, by combining novel experimental cognitive paradigms with neuroimaging in a longitudinal large-scale attempt to directly test how age-related changes in consolidation processes in the brain impact episodic memory decline. The ambitious aims of the present proposal are two-fold:
(1) Use recent advances in memory consolidation theory to achieve an elaborate model of episodic memory deficits in aging
(2) Use aging as a model to uncover how structural and functional brain changes affect episodic memory consolidation in general
The novelty of the project lies in the synthesis of recent methodological advances and theoretical models for episodic memory consolidation to explain age-related decline, by employing a unique combination of a range of different techniques and approaches. This is ground-breaking, in that it aims at taking our understanding of the brain processes underlying episodic memory decline in aging to a new level, while at the same time advancing our theoretical understanding of how episodic memories are consolidated in the human brain. To obtain this outcome, I will test the main hypothesis of the project: Brain processes of episodic memory consolidation are less effective in older adults, and this can account for a significant portion of the episodic memory decline in aging. This will be answered by six secondary hypotheses, with 1-3 experiments or tasks designated to address each hypothesis, focusing on functional and structural MRI, positron emission tomography data and sleep experiments to target consolidation from different angles.
Summary
Which brain mechanisms are responsible for the faith of the memories we make with age, whether they wither or stay, and in what form? Episodic memory function does decline with age. While this decline can have multiple causes, research has focused almost entirely on encoding and retrieval processes, largely ignoring a third critical process– consolidation. The objective of AgeConsolidate is to provide this missing link, by combining novel experimental cognitive paradigms with neuroimaging in a longitudinal large-scale attempt to directly test how age-related changes in consolidation processes in the brain impact episodic memory decline. The ambitious aims of the present proposal are two-fold:
(1) Use recent advances in memory consolidation theory to achieve an elaborate model of episodic memory deficits in aging
(2) Use aging as a model to uncover how structural and functional brain changes affect episodic memory consolidation in general
The novelty of the project lies in the synthesis of recent methodological advances and theoretical models for episodic memory consolidation to explain age-related decline, by employing a unique combination of a range of different techniques and approaches. This is ground-breaking, in that it aims at taking our understanding of the brain processes underlying episodic memory decline in aging to a new level, while at the same time advancing our theoretical understanding of how episodic memories are consolidated in the human brain. To obtain this outcome, I will test the main hypothesis of the project: Brain processes of episodic memory consolidation are less effective in older adults, and this can account for a significant portion of the episodic memory decline in aging. This will be answered by six secondary hypotheses, with 1-3 experiments or tasks designated to address each hypothesis, focusing on functional and structural MRI, positron emission tomography data and sleep experiments to target consolidation from different angles.
Max ERC Funding
1 999 482 €
Duration
Start date: 2017-05-01, End date: 2022-04-30
Project acronym AMORE
Project A distributional MOdel of Reference to Entities
Researcher (PI) Gemma BOLEDA TORRENT
Host Institution (HI) UNIVERSIDAD POMPEU FABRA
Call Details Starting Grant (StG), SH4, ERC-2016-STG
Summary "When I asked my seven-year-old daughter ""Who is the boy in your class who was also new in school last year, like you?"", she instantly replied ""Daniel"", using the descriptive content in my utterance to identify an entity in the real world and refer to it. The ability to use language to refer to reality is crucial for humans, and yet it is very difficult to model. AMORE breaks new ground in Computational Linguistics, Linguistics, and Artificial Intelligence by developing a model of linguistic reference to entities implemented as a computational system that can learn its own representations from data.
This interdisciplinary project builds on two complementary semantic traditions: 1) Formal semantics, a symbolic approach that can delimit and track linguistic referents, but does not adequately match them with the descriptive content of linguistic expressions; 2) Distributional semantics, which can handle descriptive content but does not associate it to individuated referents. AMORE synthesizes the two approaches into a unified, scalable model of reference that operates with individuated referents and links them to referential expressions characterized by rich descriptive content. The model is a distributed (neural network) version of a formal semantic framework that is furthermore able to integrate perceptual (visual) and linguistic information about entities. We test it extensively in referential tasks that require matching noun phrases (“the Medicine student”, “the white cat”) with entity representations extracted from text and images.
AMORE advances our scientific understanding of language and its computational modeling, and contributes to the far-reaching debate between symbolic and distributed approaches to cognition with an integrative proposal. I am in a privileged position to carry out this integration, since I have contributed top research in both distributional and formal semantics.
"
Summary
"When I asked my seven-year-old daughter ""Who is the boy in your class who was also new in school last year, like you?"", she instantly replied ""Daniel"", using the descriptive content in my utterance to identify an entity in the real world and refer to it. The ability to use language to refer to reality is crucial for humans, and yet it is very difficult to model. AMORE breaks new ground in Computational Linguistics, Linguistics, and Artificial Intelligence by developing a model of linguistic reference to entities implemented as a computational system that can learn its own representations from data.
This interdisciplinary project builds on two complementary semantic traditions: 1) Formal semantics, a symbolic approach that can delimit and track linguistic referents, but does not adequately match them with the descriptive content of linguistic expressions; 2) Distributional semantics, which can handle descriptive content but does not associate it to individuated referents. AMORE synthesizes the two approaches into a unified, scalable model of reference that operates with individuated referents and links them to referential expressions characterized by rich descriptive content. The model is a distributed (neural network) version of a formal semantic framework that is furthermore able to integrate perceptual (visual) and linguistic information about entities. We test it extensively in referential tasks that require matching noun phrases (“the Medicine student”, “the white cat”) with entity representations extracted from text and images.
AMORE advances our scientific understanding of language and its computational modeling, and contributes to the far-reaching debate between symbolic and distributed approaches to cognition with an integrative proposal. I am in a privileged position to carry out this integration, since I have contributed top research in both distributional and formal semantics.
"
Max ERC Funding
1 499 805 €
Duration
Start date: 2017-02-01, End date: 2022-01-31
Project acronym Becoming Social
Project Social Interaction Perception and the Social Brain Across Typical and Atypical Development
Researcher (PI) Kami KOLDEWYN
Host Institution (HI) BANGOR UNIVERSITY
Call Details Starting Grant (StG), SH4, ERC-2016-STG
Summary Social interactions are multifaceted and subtle, yet we can almost instantaneously discern if two people are cooperating or competing, flirting or fighting, or helping or hindering each other. Surprisingly, the development and brain basis of this remarkable ability has remained largely unexplored. At the same time, understanding how we develop the ability to process and use social information from other people is widely recognized as a core challenge facing developmental cognitive neuroscience. The Becoming Social project meets this challenge by proposing the most complete investigation to date of the development of the behavioural and neurobiological systems that support complex social perception. To achieve this, we first systematically map how the social interactions we observe are coded in the brain by testing typical adults. Next, we investigate developmental change both behaviourally and neurally during a key stage in social development in typically developing children. Finally, we explore whether social interaction perception is clinically relevant by investigating it developmentally in autism spectrum disorder. The Becoming Social project is expected to lead to a novel conception of the neurocognitive architecture supporting the perception of social interactions. In addition, neuroimaging and behavioural tasks measured longitudinally during development will allow us to determine how individual differences in brain and behaviour are causally related to real-world social ability and social learning. The planned studies as well as those generated during the project will enable the Becoming Social team to become a world-leading group bridging social cognition, neuroscience and developmental psychology.
Summary
Social interactions are multifaceted and subtle, yet we can almost instantaneously discern if two people are cooperating or competing, flirting or fighting, or helping or hindering each other. Surprisingly, the development and brain basis of this remarkable ability has remained largely unexplored. At the same time, understanding how we develop the ability to process and use social information from other people is widely recognized as a core challenge facing developmental cognitive neuroscience. The Becoming Social project meets this challenge by proposing the most complete investigation to date of the development of the behavioural and neurobiological systems that support complex social perception. To achieve this, we first systematically map how the social interactions we observe are coded in the brain by testing typical adults. Next, we investigate developmental change both behaviourally and neurally during a key stage in social development in typically developing children. Finally, we explore whether social interaction perception is clinically relevant by investigating it developmentally in autism spectrum disorder. The Becoming Social project is expected to lead to a novel conception of the neurocognitive architecture supporting the perception of social interactions. In addition, neuroimaging and behavioural tasks measured longitudinally during development will allow us to determine how individual differences in brain and behaviour are causally related to real-world social ability and social learning. The planned studies as well as those generated during the project will enable the Becoming Social team to become a world-leading group bridging social cognition, neuroscience and developmental psychology.
Max ERC Funding
1 500 000 €
Duration
Start date: 2017-04-01, End date: 2022-03-31
Project acronym BRAINBELIEFS
Project Proving or improving yourself: longitudinal effects of ability beliefs on neural feedback processing and school outcomes
Researcher (PI) Nienke VAN ATTEVELDT
Host Institution (HI) STICHTING VU
Call Details Starting Grant (StG), SH4, ERC-2016-STG
Summary To successfully complete secondary education, persistent learning behavior is essential. Why are some adolescents more resilient to setbacks at school than others? In addition to actual ability, students’ implicit beliefs about the nature of their abilities have major impact on their motivation and achievements. Ability beliefs range from viewing abilities as “entities” that cannot be improved much by effort (entity beliefs), to believing that they are incremental with effort and time (incremental beliefs). Importantly, ability beliefs shape which goals a student pursues at school; proving themselves (performance goals) or improving themselves (learning goals). The central aims of the proposal are to unravel 1) the underlying processing mechanisms of how beliefs and goals shape resilience to setbacks at school and 2) how to influence these mechanisms to stimulate persistent learning behavior.
Functional brain research, including my own, has revealed the profound top-down influence of goals on selective information processing. Goals may thus determine which learning-related information is attended. Project 1 jointly investigates the essential psychological and neurobiological processes to unravel the longitudinal effects of beliefs and goals on how the brain prioritizes information during learning, and how this relates to school outcomes. Project 2 reveals how to influence this interplay with the aim to long-lastingly stimulate persistent learning behavior. I will move beyond existing approaches by introducing a novel intervention in which students experience their own learning-related brain activity and its malleability.
The results will demonstrate how ability beliefs and goals shape functional brain development and school outcomes during adolescence, and how we can optimally stimulate this interplay. The research has high scientific impact as it bridges multiple disciplines and thereby provides a strong impulse to the emerging field of educational neuroscience.
Summary
To successfully complete secondary education, persistent learning behavior is essential. Why are some adolescents more resilient to setbacks at school than others? In addition to actual ability, students’ implicit beliefs about the nature of their abilities have major impact on their motivation and achievements. Ability beliefs range from viewing abilities as “entities” that cannot be improved much by effort (entity beliefs), to believing that they are incremental with effort and time (incremental beliefs). Importantly, ability beliefs shape which goals a student pursues at school; proving themselves (performance goals) or improving themselves (learning goals). The central aims of the proposal are to unravel 1) the underlying processing mechanisms of how beliefs and goals shape resilience to setbacks at school and 2) how to influence these mechanisms to stimulate persistent learning behavior.
Functional brain research, including my own, has revealed the profound top-down influence of goals on selective information processing. Goals may thus determine which learning-related information is attended. Project 1 jointly investigates the essential psychological and neurobiological processes to unravel the longitudinal effects of beliefs and goals on how the brain prioritizes information during learning, and how this relates to school outcomes. Project 2 reveals how to influence this interplay with the aim to long-lastingly stimulate persistent learning behavior. I will move beyond existing approaches by introducing a novel intervention in which students experience their own learning-related brain activity and its malleability.
The results will demonstrate how ability beliefs and goals shape functional brain development and school outcomes during adolescence, and how we can optimally stimulate this interplay. The research has high scientific impact as it bridges multiple disciplines and thereby provides a strong impulse to the emerging field of educational neuroscience.
Max ERC Funding
1 597 291 €
Duration
Start date: 2017-03-01, End date: 2022-02-28
Project acronym BRAINCODES
Project Brain networks controlling social decisions
Researcher (PI) Christian Carl RUFF
Host Institution (HI) UNIVERSITAT ZURICH
Call Details Consolidator Grant (CoG), SH4, ERC-2016-COG
Summary Successful social interactions require social decision making, the ability to guide our actions in line with the goals and expectations of the people around us. Disordered social decision making – e.g., associated with criminal activity or psychiatric illnesses – poses significant financial and personal challenges to society. However, the brain mechanisms that enable us to control our social behavior are far from being understood. Here I will take decisive steps towards a causal understanding of these mechanisms by elucidating the role of functional interactions in the brain networks responsible for steering strategic, prosocial, and norm-compliant behavior. I will employ a unique multi-method approach that integrates computational modeling of social decisions with new combinations of multimodal neuroimaging and brain stimulation methods. Using EEG-fMRI, I will first identify spatio-temporal patterns of functional interactions between brain areas that correlate with social decision processes as identified by computational modeling of behavior in different economic games. In combined brain stimulation-fMRI studies, I will then attempt to affect – and in fact enhance – these social decision-making processes by modulating the identified brain network patterns with novel, targeted brain stimulation protocols and measuring the resulting effects on behavior and brain activity. Finally, I will examine whether the identified brain network mechanisms are indeed related to disturbed social decisions in two psychiatric illnesses characterized by maladaptive social behavior (post-traumatic stress disorder and autism spectrum disorder). My proposed work plan will generate a causal understanding of the brain network mechanisms that allow humans to control their social decisions, thereby elucidating a biological basis for individual differences in social behavior and paving the way for new perspectives on how disordered social behavior may be identified and hopefully remedied.
Summary
Successful social interactions require social decision making, the ability to guide our actions in line with the goals and expectations of the people around us. Disordered social decision making – e.g., associated with criminal activity or psychiatric illnesses – poses significant financial and personal challenges to society. However, the brain mechanisms that enable us to control our social behavior are far from being understood. Here I will take decisive steps towards a causal understanding of these mechanisms by elucidating the role of functional interactions in the brain networks responsible for steering strategic, prosocial, and norm-compliant behavior. I will employ a unique multi-method approach that integrates computational modeling of social decisions with new combinations of multimodal neuroimaging and brain stimulation methods. Using EEG-fMRI, I will first identify spatio-temporal patterns of functional interactions between brain areas that correlate with social decision processes as identified by computational modeling of behavior in different economic games. In combined brain stimulation-fMRI studies, I will then attempt to affect – and in fact enhance – these social decision-making processes by modulating the identified brain network patterns with novel, targeted brain stimulation protocols and measuring the resulting effects on behavior and brain activity. Finally, I will examine whether the identified brain network mechanisms are indeed related to disturbed social decisions in two psychiatric illnesses characterized by maladaptive social behavior (post-traumatic stress disorder and autism spectrum disorder). My proposed work plan will generate a causal understanding of the brain network mechanisms that allow humans to control their social decisions, thereby elucidating a biological basis for individual differences in social behavior and paving the way for new perspectives on how disordered social behavior may be identified and hopefully remedied.
Max ERC Funding
1 999 991 €
Duration
Start date: 2017-09-01, End date: 2022-08-31
Project acronym CALC
Project Computer-Assisted Language Comparison: Reconciling Computational and Classical Approaches in Historical Linguistics
Researcher (PI) Johann-Mattis LIST
Host Institution (HI) MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV
Call Details Starting Grant (StG), SH4, ERC-2016-STG
Summary By comparing the languages of the world, we gain invaluable insights into human prehistory, predating the appearance of written records by thousands of years. The traditional methods for language comparison are based on manual data inspection. With more and more data available, they reach their practical limits. Computer applications, however, are not capable of replacing experts' experience and intuition. In a situation where computers cannot replace experts and experts do not have enough time to analyse the massive amounts of data, a new framework, neither completely computer-driven, nor ignorant of the help computers provide, becomes urgent. Such frameworks are well-established in biology and translation, where computational tools cannot provide the accuracy needed to arrive at convincing results, but do assist humans to digest large data sets.
This project establishes a computer-assisted framework for historical linguistics. We pursue an interdisciplinary approach that adapts methods from computer science and bioinformatics for the use in historical linguistics. While purely computational approaches are common today, the project focuses on the communication between classical and computational linguists, developing interfaces that allow historical linguists to produce their data in machine readable formats while at the same time presenting the results of computational analyses in a transparent and human-readable way.
As a litmus test which proves the suitability of the new framework, the project will create an etymological database of Sino-Tibetan languages. The abundance of language contact and the peculiarity of complex processes of language change in which sporadic patterns of morphological change mask regular patterns of sound change make the Sino-Tibetan language family an ideal test case for a new overarching framework that combines the best of two worlds: the experience of experts
and the consistency of computational models.
Summary
By comparing the languages of the world, we gain invaluable insights into human prehistory, predating the appearance of written records by thousands of years. The traditional methods for language comparison are based on manual data inspection. With more and more data available, they reach their practical limits. Computer applications, however, are not capable of replacing experts' experience and intuition. In a situation where computers cannot replace experts and experts do not have enough time to analyse the massive amounts of data, a new framework, neither completely computer-driven, nor ignorant of the help computers provide, becomes urgent. Such frameworks are well-established in biology and translation, where computational tools cannot provide the accuracy needed to arrive at convincing results, but do assist humans to digest large data sets.
This project establishes a computer-assisted framework for historical linguistics. We pursue an interdisciplinary approach that adapts methods from computer science and bioinformatics for the use in historical linguistics. While purely computational approaches are common today, the project focuses on the communication between classical and computational linguists, developing interfaces that allow historical linguists to produce their data in machine readable formats while at the same time presenting the results of computational analyses in a transparent and human-readable way.
As a litmus test which proves the suitability of the new framework, the project will create an etymological database of Sino-Tibetan languages. The abundance of language contact and the peculiarity of complex processes of language change in which sporadic patterns of morphological change mask regular patterns of sound change make the Sino-Tibetan language family an ideal test case for a new overarching framework that combines the best of two worlds: the experience of experts
and the consistency of computational models.
Max ERC Funding
1 499 438 €
Duration
Start date: 2017-04-01, End date: 2022-03-31
Project acronym ChangeBehavNeuro
Project Novel Mechanism of Behavioural Change
Researcher (PI) Tom SCHONBERG
Host Institution (HI) TEL AVIV UNIVERSITY
Call Details Starting Grant (StG), SH4, ERC-2016-STG
Summary Understanding how values of different options that lead to choice are represented in the brain is a basic scientific question with far reaching implications. I recently showed that by the mere-association of a cue and a button press we could influence preferences of snack food items up to two months following a single training session lasting less than an hour. This novel behavioural change manipulation cannot be explained by any of the current learning theories, as external reinforcement was not used in the process, nor was the context of the decision changed. Current choice theories focus on goal directed behaviours where the value of the outcome guides choice, versus habit-based behaviours where an action is repeated up to the point that the value of the outcome no longer guides choice. However, in this novel task training via the involvement of low-level visual, auditory and motor mechanisms influenced high-level choice behaviour. Thus, the far-reaching goal of this project is to study the mechanism, by which low-level sensory, perceptual and motor neural processes underlie value representation and change in the human brain even in the absence of external reinforcement. I will use behavioural, eye-gaze and functional MRI experiments to test how low-level features influence the neural representation of value. I will then test how they interact with the known striatal representation of reinforced behavioural change, which has been the main focus of research thus far. Finally, I will address the basic question of dynamic neural plasticity and if neural signatures during training predict long term success of sustained behavioural change. This research aims at a paradigmatic shift in the field of learning and decision-making, leading to the development of novel interventions with potential societal impact of helping those suffering from health-injuring behaviours such as addictions, eating or mood disorders, all in need of a long lasting behavioural change.
Summary
Understanding how values of different options that lead to choice are represented in the brain is a basic scientific question with far reaching implications. I recently showed that by the mere-association of a cue and a button press we could influence preferences of snack food items up to two months following a single training session lasting less than an hour. This novel behavioural change manipulation cannot be explained by any of the current learning theories, as external reinforcement was not used in the process, nor was the context of the decision changed. Current choice theories focus on goal directed behaviours where the value of the outcome guides choice, versus habit-based behaviours where an action is repeated up to the point that the value of the outcome no longer guides choice. However, in this novel task training via the involvement of low-level visual, auditory and motor mechanisms influenced high-level choice behaviour. Thus, the far-reaching goal of this project is to study the mechanism, by which low-level sensory, perceptual and motor neural processes underlie value representation and change in the human brain even in the absence of external reinforcement. I will use behavioural, eye-gaze and functional MRI experiments to test how low-level features influence the neural representation of value. I will then test how they interact with the known striatal representation of reinforced behavioural change, which has been the main focus of research thus far. Finally, I will address the basic question of dynamic neural plasticity and if neural signatures during training predict long term success of sustained behavioural change. This research aims at a paradigmatic shift in the field of learning and decision-making, leading to the development of novel interventions with potential societal impact of helping those suffering from health-injuring behaviours such as addictions, eating or mood disorders, all in need of a long lasting behavioural change.
Max ERC Funding
1 500 000 €
Duration
Start date: 2017-01-01, End date: 2021-12-31
Project acronym CITIZINGLOBAL
Project Citizens, Institutions and Globalization
Researcher (PI) Giacomo Antonio Maria PONZETTO
Host Institution (HI) Centre de Recerca en Economia Internacional (CREI)
Call Details Starting Grant (StG), SH1, ERC-2016-STG
Summary Globalization has brought the world economy unprecedented prosperity, but it poses governance challenges. It needs governments to provide the infrastructure for global economic integration and to refrain from destructive protectionism; yet it can engender popular discontent and a crisis of democracy. My proposal will study when trade- and productivity-enhancing policies enjoy democratic support; why voters may support instead inefficient surplus-reducing policies; and how political structure reacts to globalization.
Part A studies the puzzling popularity of protectionism and how lobbies can raise it by manipulating information. It will study empirically if greater transparency causes lower trade barriers. It will introduce salience theory to political economics and argue that voters overweight concentrated losses and disregard diffuse benefits. It will show that lobbies can raise protection by channeling information to insiders and advertising the plight of displaced workers.
Part B studies inefficient infrastructure policy and the ensuing spatial misallocation of economic activity. It will show that voters’ unequal knowledge lets local residents capture national policy. They disregard nationwide positive externalities, so investment in major cities is insufficient, but also nationwide taxes, so spending in low-density areas is excessive. It will argue that the fundamental attribution error causes voter opposition to growth-enhancing policies and efficient incentive schemes like congestion pricing.
Part C studies how the size of countries and international unions adapts to expanding trade opportunities. It will focus on three forces: cultural diversity, economies of scale and scope in government, and trade-reducing border effects. It will show they explain increasing country size in the 19th century; the rise and fall of colonial empires; and the recent emergence of regional and global economic unions, accompanied by a peaceful increase in the number of countries.
Summary
Globalization has brought the world economy unprecedented prosperity, but it poses governance challenges. It needs governments to provide the infrastructure for global economic integration and to refrain from destructive protectionism; yet it can engender popular discontent and a crisis of democracy. My proposal will study when trade- and productivity-enhancing policies enjoy democratic support; why voters may support instead inefficient surplus-reducing policies; and how political structure reacts to globalization.
Part A studies the puzzling popularity of protectionism and how lobbies can raise it by manipulating information. It will study empirically if greater transparency causes lower trade barriers. It will introduce salience theory to political economics and argue that voters overweight concentrated losses and disregard diffuse benefits. It will show that lobbies can raise protection by channeling information to insiders and advertising the plight of displaced workers.
Part B studies inefficient infrastructure policy and the ensuing spatial misallocation of economic activity. It will show that voters’ unequal knowledge lets local residents capture national policy. They disregard nationwide positive externalities, so investment in major cities is insufficient, but also nationwide taxes, so spending in low-density areas is excessive. It will argue that the fundamental attribution error causes voter opposition to growth-enhancing policies and efficient incentive schemes like congestion pricing.
Part C studies how the size of countries and international unions adapts to expanding trade opportunities. It will focus on three forces: cultural diversity, economies of scale and scope in government, and trade-reducing border effects. It will show they explain increasing country size in the 19th century; the rise and fall of colonial empires; and the recent emergence of regional and global economic unions, accompanied by a peaceful increase in the number of countries.
Max ERC Funding
960 000 €
Duration
Start date: 2017-01-01, End date: 2021-12-31
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 Connections
Project Oligopoly Markets and Networks
Researcher (PI) Andrea Galeotti
Host Institution (HI) LONDON BUSINESS SCHOOL
Call Details Consolidator Grant (CoG), SH1, ERC-2016-COG
Summary Via our connections we learn about new ideas, quality of products, new investment opportunities and job opportunities. We influence and are influenced by our circle of friends. Firms are interconnected in complex processes of production and distribution. A firm’s decisions in a supply chain depends on other firms’ choices in the same supply chain, as well as on firms' behaviour in competing chains. Research on networks in the last 20 years has provided a series of tolls to study a system of interconnected economic agents. This project will advance the state of the art by further developing new applications of networks to better understand modern oligopoly markets.
The project is organised into two sub-projects. In sub-project 1 networks will be used to model diffusion and adoption of network goods. Different consumers' network locations will summarise different consumers' level of influence. The objectives are to understand how firms incorporate information about consumers' influence in their marketing strategies—pricing strategy and product design. It will provide a rigorous framework to evaluate how the increasing ability of firms to gather information on consumers’ influence affects outcomes of markets with network effects. In sub-project 2 networks will be used to model how inputs—e.g., intermediary goods and patents—are combined to deliver final goods. Possible applications are supply chains, communication networks and networks of patents. The objectives are to study firms' strategic behaviour, like pricing and R&D investments, in a complex process of production and distribution, and to understand the basic network metrics that are useful to describe market power. This is particularly important to provide a guide to competition authorities and alike when they evaluate mergers in complex interconnected markets.
Summary
Via our connections we learn about new ideas, quality of products, new investment opportunities and job opportunities. We influence and are influenced by our circle of friends. Firms are interconnected in complex processes of production and distribution. A firm’s decisions in a supply chain depends on other firms’ choices in the same supply chain, as well as on firms' behaviour in competing chains. Research on networks in the last 20 years has provided a series of tolls to study a system of interconnected economic agents. This project will advance the state of the art by further developing new applications of networks to better understand modern oligopoly markets.
The project is organised into two sub-projects. In sub-project 1 networks will be used to model diffusion and adoption of network goods. Different consumers' network locations will summarise different consumers' level of influence. The objectives are to understand how firms incorporate information about consumers' influence in their marketing strategies—pricing strategy and product design. It will provide a rigorous framework to evaluate how the increasing ability of firms to gather information on consumers’ influence affects outcomes of markets with network effects. In sub-project 2 networks will be used to model how inputs—e.g., intermediary goods and patents—are combined to deliver final goods. Possible applications are supply chains, communication networks and networks of patents. The objectives are to study firms' strategic behaviour, like pricing and R&D investments, in a complex process of production and distribution, and to understand the basic network metrics that are useful to describe market power. This is particularly important to provide a guide to competition authorities and alike when they evaluate mergers in complex interconnected markets.
Max ERC Funding
829 000 €
Duration
Start date: 2017-06-01, End date: 2022-05-31
