References of "Ansarinia, Morteza 50034722"
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See detailTowards a Computational Model of General Cognitive Control Using Artificial Intelligence, Experimental Psychology and Cognitive Neuroscience
Ansarinia, Morteza UL

Doctoral thesis (2023)

Cognitive control is essential to human cognitive functioning as it allows us to adapt and respond to a wide range of situations and environments. The possibility to enhance cognitive control in a way ... [more ▼]

Cognitive control is essential to human cognitive functioning as it allows us to adapt and respond to a wide range of situations and environments. The possibility to enhance cognitive control in a way that transfers to real life situations could greatly benefit individuals and society. However, the lack of a formal, quantitative definition of cognitive control has limited progress in developing effective cognitive control training programs. To address this issue, the first part of the thesis focuses on gaining clarity on what cognitive control is and how to measure it. This is accomplished through a large-scale text analysis that integrates cognitive control tasks and related constructs into a cohesive knowledge graph. This knowledge graph provides a more quantitative definition of cognitive control based on previous research, which can be used to guide future research. The second part of the thesis aims at furthering a computational understanding of cognitive control, in particular to study what features of the task (i.e., the environment) and what features of the cognitive system (i.e., the agent) determine cognitive control, its functioning, and generalization. The thesis first presents CogEnv, a virtual cognitive assessment environment where artificial agents (e.g., reinforcement learning agents) can be directly compared to humans in a variety of cognitive tests. It then presents CogPonder, a novel computational method for general cognitive control that is relevant for research on both humans and artificial agents. The proposed framework is a flexible, differentiable end-to-end deep learning model that separates the act of control from the controlled act, and can be trained to perform the same cognitive tests that are used in cognitive psychology to assess humans. Together, the proposed cognitive environment and agent architecture offer unique new opportunities to enable and accelerate the study of human and artificial agents in an interoperable framework. Research on training cognition with complex tasks, such as video games, may benefit from and contribute to the broad view of cognitive control. The final part of the thesis presents a profile of cognitive control and its generalization based on cognitive training studies, in particular how it may be improved by using action video game training. More specifically, we contrasted the brain connectivity profiles of people that are either habitual action video game players or do not play video games at all. We focused in particular on brain networks that have been associated with cognitive control. Our results show that cognitive control emerges from a distributed set of brain networks rather than individual specialized brain networks, supporting the view that action video gaming may have a broad, general impact of cognitive control. These results also have practical value for cognitive scientists studying cognitive control, as they imply that action video game training may offer new ways to test cognitive control theories in a causal way. Taken together, the current work explores a variety of approaches from within cognitive science disciplines to contribute in novel ways to the fascinating and long tradition of research on cognitive control. In the age of ubiquitous computing and large datasets, bridging the gap between behavior, brain, and computation has the potential to fundamentally transform our understanding of the human mind and inspire the development of intelligent artificial agents. [less ▲]

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See detailTemporary Self-Deprivation Can Impair Cognitive Control: Evidence From the Ramadan Fast
Salari Rad, Mostafa; Ansarinia, Morteza UL; Shafir, Eldar

in Personality and social psychology bulletin (2022), 49(3), 415--428

During Ramadan, people of Muslim faith fast by not eating or drinking between sunrise and sunset. This is likely to have physiological and psychological consequences for fasters, and societal and economic ... [more ▼]

During Ramadan, people of Muslim faith fast by not eating or drinking between sunrise and sunset. This is likely to have physiological and psychological consequences for fasters, and societal and economic impacts on the wider population. We investigate whether, during this voluntary and temporally limited fast, reminders of food can impair the fasters' reaction time and accuracy on a non-food-related test of cognitive control. Using a repeated measures design in a sample of Ramadan fasters (N = 190), we find that when food is made salient, fasters are slower and less accurate during Ramadan compared with after Ramadan. Control participants perform similarly across time. Furthermore, during Ramadan performances vary by how recently people had their last meal. Potential mechanisms are suggested, grounded in research on resource scarcity, commitment, and thought suppression, as well as the psychology of rituals and self-regulation, and implications for people who fast for religious or health reasons are discussed. [less ▲]

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See detailLinking Theories and Methods in Cognitive Sciences via Joint Embedding of the Scientific Literature: The Example of Cognitive Control
Ansarinia, Morteza UL; Schrater, Paul; Cardoso-Leite, Pedro UL

E-print/Working paper (2022)

Traditionally, theory and practice of Cognitive Control are linked via literature reviews by human domain experts. This approach, however, is inadequate to track the ever-growing literature. It may also ... [more ▼]

Traditionally, theory and practice of Cognitive Control are linked via literature reviews by human domain experts. This approach, however, is inadequate to track the ever-growing literature. It may also be biased, and yield redundancies and confusion. Here we present an alternative approach. We performed automated text analyses on a large body of scientific texts to create a joint representation of tasks and constructs. More specifically, 385,705 scientific abstracts were first mapped into an embedding space using a transformers-based language model. Document embeddings were then used to identify a task-construct graph embedding that grounds constructs on tasks and supports nuanced meaning of the constructs by taking advantage of constrained random walks in the graph. This joint task-construct graph embedding, can be queried to generate task batteries targeting specific constructs, may reveal knowledge gaps in the literature, and inspire new tasks and novel hypotheses. [less ▲]

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See detailCogEnv: A Reinforcement Learning Environment for Cognitive Tests
Ansarinia, Morteza UL; Clocher, Brice UL; Defossez, Aurélien et al

in 2022 Conference on Cognitive Computational Neuroscience (2022)

Understanding human cognition involves developing computational models that mimic and possibly explain behavior; these are models that “act” like humans and produce similar outputs when facing the same ... [more ▼]

Understanding human cognition involves developing computational models that mimic and possibly explain behavior; these are models that “act” like humans and produce similar outputs when facing the same inputs. To facilitate the development of such models and ultimately further our understanding of the human mind we created CogEnv: a reinforcement learning environment where artificial agents interact with and learn to perform cognitive tests and can then be directly compared to humans. By leveraging CogEnv, cognitive and AI scientists can join efforts to better understand human cognition: the relative performance profiles of human and artificial agents may provide new insights on the computational basis of human cognition and on what human-like abilities artificial agents may lack. [less ▲]

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See detailTraining Cognition with Video Games
Cardoso-Leite, Pedro UL; Ansarinia, Morteza UL; Schmück, Emmanuel UL et al

in Cohen Kadosh, Kathrin (Ed.) The Oxford Handbook of Developmental Cognitive Neuroscience (2021)

This chapter reviews the behavioral and neuroimaging scientific literature on the cognitive consequences of playing various genres of video games. The available research highlights that not all video ... [more ▼]

This chapter reviews the behavioral and neuroimaging scientific literature on the cognitive consequences of playing various genres of video games. The available research highlights that not all video games have similar cognitive impact; action video games as defined by first- and third-person shooter games have been associated with greater cognitive enhancement, especially when it comes to top-down attention, than puzzle or life-simulation games. This state of affairs suggests specific game mechanics need to be embodied in a video game for it to enhance cognition. These hypothesized game mechanics are reviewed; yet, the authors note that the advent of more complex, hybrid, video games poses new research challenges and call for a more systematic assessment of how specific video game mechanics relate to cognitive enhancement. [less ▲]

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See detailThe Structure of Behavioral Data
Defossez, Aurélien; Ansarinia, Morteza UL; Clocher, Brice UL et al

E-print/Working paper (2020)

For more than a century, scientists have been collecting behavioral data--an increasing fraction of which is now being publicly shared so other researchers can reuse them to replicate, integrate or extend ... [more ▼]

For more than a century, scientists have been collecting behavioral data--an increasing fraction of which is now being publicly shared so other researchers can reuse them to replicate, integrate or extend past results. Although behavioral data is fundamental to many scientific fields, there is currently no widely adopted standard for formatting, naming, organizing, describing or sharing such data. This lack of standardization is a major bottleneck for scientific progress. Not only does it prevent the effective reuse of data, it also affects how behavioral data in general are processed, as non-standard data calls for custom-made data analysis code and prevents the development of efficient tools. To address this problem, we develop the Behaverse Data Model (BDM), a standard for structuring behavioral data. Here we focus on major concepts in behavioral data, leaving further details and developments to the project's website (https://behaverse.github.io/data-model/). [less ▲]

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See detailA Formal Framework for Structured N-Back Stimuli Sequences
Ansarinia, Morteza UL; Mussack, Dominic UL; Schrater, Paul et al

Scientific Conference (2019, September 15)

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See detailA Multi-Objective Optimization Algorithm to Generate Unbiased Stimuli Sequences for Cognitive Tasks
Ansarinia, Morteza UL; Mussack, Dominic; Schrater, Paul et al

Poster (2019)

Cognitive scientists want to ensure that particular cognitive tasks target particular cognitive functions that can be mapped to stable neural markers. Numerous cognitive tasks, like the n-back, involve ... [more ▼]

Cognitive scientists want to ensure that particular cognitive tasks target particular cognitive functions that can be mapped to stable neural markers. Numerous cognitive tasks, like the n-back, involve generating sequence of trials which satisfy certain statistical properties.The common approach to generate these sequences however lacks a theoretical framework and induces unintentional structure in the sequences which affects both behavioral performance and might bias the people’s cognitive strategies when completing a task. For example, people might exploit local properties in a random sequence in their decision making process. We argue that optimized experimental design requires cognitive tasks to be served by stimulus sequence generators that satisfy multiple constraints, both at the global and at the local structures of the sequence and that these sequence properties need to be systematically incorporated in the behavioral data analysis pipeline. We then develop a framework to reformulate the sequence generation process as a compositional soft constraint satisfaction problem and offer a multi-objective, genetic-algorithm-based method to generate controlled sequences under behavioral and neural constraints. This approach provides a systematic and coherent framework to handle stimulus sequences which in turn will impact the insights that can be gained from the behavioral and neural data collected on people performing cognitive tasks using those sequences. [less ▲]

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