Research
Fields & Labs
Research in the department
Memory and forgetting research, linguistic and social aspects of human memory
In the lab, we investigate the neuro-cognitive basis of episodic memory: the human ability to mentally travel through time and re-experience life events (e.g., a trip abroad or even a commute to work). Our research addresses, among other questions, the following: Why and how do we forget things we once remembered? What is the nature of the gap between the emotional experience during an event and when we recall it? Can memories be suppressed, and if so, how? How does memory get influenced by brain and cognitive processes prior to learning? How is the stored information in memory organized so that we can efficiently retrieve it? How early can linguistic differences be detected between people who will develop dementia and those who will not?
Additionally, we study the social aspects of human memory, for example: How and to what extent do we assess the accuracy of other people's memories? What are the social and personality factors that influence our ability to distinguish between memories and imaginations of things that never happened?
We conduct behavioral experiments in the lab (using computers, VR tools, or with a human experimenter), online experiments, and also use brain imaging technologies such as EEG and fMRI, eye-tracking equipment, and machine learning and natural language processing (NLP) tools. In some studies, we compare different populations (young and healthy participants, patients with memory impairments, older adults).
Publication list
tsadeh@bgu.ac.ilThe study of controlled cognitive processes, automaticity, as well as automatic and conscious cognitive aspects of human skill
In addition to behavioral research, my studies focus on several philosophical questions concerning the relationship between computation, information processing, and cognition. These include (a) the classification of physical computational processes, (b) the relationship between information and information processing and cognition, and (c) the nature of computational and representational explanations in cognitive neuroscience. Another area of philosophical inquiry is the nature and limitations of Bayesian explanations in cognitive and brain sciences. Another line of research that engages directly with the behavioral studies examines the interaction between factual (or semantic) knowledge (knowledge-that) and procedural knowledge (knowledge how), as well as the relationship between knowledge and skill.
Our lab primarily investigates the interaction between learning, skill acquisition, and automaticity in behavioral experiments, although we are also open to other areas of research guided by epistemic questions. Some of the questions we study include: (1) Can cognitive control be automated? If so, to what extent? (2) Does training improve cognitive control, as measured by reaction speed and accuracy? (3) Does the performance of automatic cognitive processes, such as reading words and numbers or recognizing faces, improve the performance of other cognitively controlled tasks (e.g., verbal and numerical classification tasks or gender classification of faces)? (4) Does such improvement extend to processing in different domains, or is it limited only to the same domain? (5) Can automatic effects (e.g., implicit bias or the Stroop effect) be suppressed over time, for example, after practicing the same task for several weeks? (6) How do instructions or feedback, whether written or verbal, affect the acquisition of a skill?
By addressing these questions, we hope to deepen our understanding of the complex relationship between computation and cognition as well as the development of human skill and procedural knowledge.
Lab website
Motor and cognitive control, motor learning, rehabilitation and recovery processes
We investigate the neural and cognitive basis of motor skill learning and motor action performance using behavioral experiments, brain imaging, and kinematic models of movement. In addition, we study how motor skills are impaired following damage to the central nervous system (such as stroke) and how these skills recover after a stroke and through rehabilitative treatments. We are also interested in the impact of cognitive decline in older age and following damage to the nervous system on the ability of participants to learn new motor tasks and on their ability to recover from injury.
Complex cognitive systems and multi-domain applications of artificial intelligence
Yair Neuman is currently working on three main projects: (1) Developing innovative AI technologies for text interpretation. (2) Automatic identification of norm violations in Chinese Mandarin (DARPA sponsored), and (3) Under the title of “Profound simplicity”, he is developing deep albeit simple scientific models for real-world challenges.
Facial expression research
The lab focuses on interdisciplinary research centered on facial expressions. The starting point is the assumption that facial expressions are composed of units of facial movements with semantic meaning, combined according to syntactic rules similar to spoken language. The lab combines cognitive and behavioral theories that describe the dynamic nature of human interaction, along with computational models such as natural language processing (NLP) frameworks, advanced machine learning techniques, and computer vision models (e.g., deep convolutional neural networks). Lab members and collaborators come from diverse backgrounds, including psychology, neuroscience, computer science, and machine learning.
Development of diagnostic tools, computational models, and brain technologies for psychiatric and neurological phenomena.
The research in the lab combines experimental work, including EEG measurements, with theoretical and computational work, including various mathematical analysis tools and machine learning and deep learning techniques. Data analysis and computational models integrate concepts from the fields of complex systems physics and nonlinear dynamics. Key research topics in the lab include the dynamics of the epileptic brain; sleep monitoring and sleep deprivation; brain-computer interfaces and their applications in clinical, civilian, and security contexts; measures of cognitive load and skill acquisition processes; monitoring of consciousness and awareness during anesthesia, in disorders of consciousness, and under the influence of psychedelic drugs. There are open positions for PhD students and postdocs.
Gaining Insight into Human Vision Through Deep Neural Network Modeling
In the Brains and Machines Lab at BGU, we investigate how humans work using two complementary approaches: (1) constructing neural network-based models that simulate high-order human vision, leveraging state-of-the-art tools and insights from the field of machine learning, and (2) conducting behavioral, eye-tracking, and neuroimaging experiments designed to empirically test the models' predictions. The lab's research program is part of the emerging subfield of cognitive computational neuroscience, which aims to develop mechanistic explanations of human cognition through building and testing neural network-based computational models.
Cognitive Sciences at Ben-Gurion University of the Negev
Ben-Gurion University of the Negev (“BGU") has a large community of researchers in cognitive and brain sciences and interfacing disciplines. About sixty senior faculty members are involved in the Master's and Doctorate degree programs.
The program's faculty members belong to different units of the university.
