Advisory Board

Joydeep “Joy” Bhattacharya

The Scientific American article What Are We Thinking When We (Try to) Solve Problems? said

Aha! Eureka! Bingo! “By George, I think she’s got it!” Everyone knows what it’s like to finally figure out a seemingly impossible problem. But what on Earth is happening in the brain while we’re driving toward mental pay dirt? Researchers eager to find out have long been on the hunt, knowing that such information could one day provide priceless clues in uncovering and fixing faulty neural systems believed to be behind some mental illnesses and learning disabilities.
“This insight is at the core of human intelligence … this is a key cognitive function that the human can boast to have,” says Joydeep Bhattacharya, an assistant professor in Goldsmiths’s psychology department. “We’re interested [in finding out] whether — there is a sudden change that takes place or something that changes gradually [that] we’re not consciously aware of,” he says. The researchers believed they could pin down brain signals that would enable them to predict whether a person could solve a particular problem or not.
In many cases, the subjects hit a wall, or what researchers refer to as a “mental impasse”. If the participants arrived at this point, they could press a button for a clue to help them untangle a problem. Bhattacharya says blocks correlated with strong gamma rhythms (a pattern of brain wave activity associated with selective attention) in the parietal cortex, a region in the upper rear of the brain that has been implicated in integrating information coming from the senses. The research team noticed an interesting phenomenon taking place in the brains of participants given hints: The clues were less likely to help if subjects had an especially high gamma rhythm pattern. The reason, Bhattacharya speculates, is that these participants were, in essence, locked into an inflexible way of thinking and less able to free their minds, and thereby unable to restructure the problem before them.
“If there’s excessive attention, it somehow creates mental fixation,” he notes. “Your brain is not in a receptive condition.”

Joydeep “Joy” Bhattacharya, Ph.D. is Reader, Department of Psychology, Goldsmiths, University of London.
Joy researches Bio-Signal Processing including the following areas:
I. Assessment of Synchronization
There has been no doubt that different brain regions do communicate, a fact which is often generally termed as synchronization or synchrony, neither is there much of arguments against the claim that the strength of such communication (or interdependency) is dynamic in nature. But the problem of assessing the strength of the communication between two distant brain regions is not a trivial task. In addition, the possible nonlinear properties of the brain pose further constraints to this problem. He has been actively involved in developing and applying new algorithms based on nonlinear dynamical system theory to large scale macroscopic signals like EEG or MEG.
His relevant papers include Predictability Improvement as an Asymmetrical Measure of Interdependence in Bivariate Time Series, Effective Detection of Coupling in Short and Noisy Bivariate Data, Long-Range Synchrony in the Gamma Band: Role in Music Perception, and Universality in the brain while listening to music.
II. Combining Methodologies of Synchronization
After the twin emergences of high performance computing and nonlinear dynamical system theory, there have been initial flurries of theoretical interest which were later then applied to investigate the strength, weak or strong, and nature, linear or nonlinear, of interdependencies between multiple brain regions. Additionally in the linear modelling domain, there is renewed interest in finding casual or directional information from neural data. However, there is very little effort in presenting all these available techniques in a common framework; as a result, confusing and contradictory results appeared out of less careful applications of the theories. Thus, it will be extremely useful, as well as informative, to the end users, as well as to the system neuroscientists, if the available theoretical methods are presented in one complete article without any bias towards any particular method. Recently, he has undertaken, exactly, this approach while concentrating on the problem of detecting synchronization in EEG/MEG signals.
His relevant papers include Nonlinear multivariate analysis of neurophysiological signals and Assessment of Neuronal Synchronization.
III. Modelling of complex Systems
The human cerebral cortex consists of approximately 1011 neurons linked with 1015 synapses, forming an enormously complex network with spatial heterogeneous patterns ranging in scale from local microcircuits to cortico-cortical and cortico-thalamic pathways extending across the entire brain. Two important features are: (i) the highly nonlinear, nonstationary, and adaptive nature of the neuronal elements, (ii) massively parallel patterns of interconnections whose characteristics can fluctuate across multiple time scales in behaviorally significant ways. Through the careful applications of (nonlinear) complexity theory, he is interested in understanding the functioning of this massive network by studying the dynamics over multiple spatial scales from microscopic single neuron to macroscopic global activity.
IV. Time Series Analysis
Our brains are never at rest: fluctuations and irregularities dominate the activities over multiple spatial scales ranging from microscopic single neuron to macroscopic summed activity over a large region of cortex. Traditionally all these fluctuations are considered as random or stochastic variations without any coherent structure. But I believe that underlying this apparent random fluctuation, there is a spatiotemporally non-random structure which can only be unearthed by sophisticated time series analysis. Additionally, the advanced techniques of time series analysis help to extract novel information from a wide spectrum of neural data recorded under various conditions.
His relevant papers include Long-range Temporal Correlations in the Spontaneous Spiking of Neurons in the Hippocampal-Amygdala Complex of Humans and Nonlinear Dynamics of Evoked Neuromagnetic Responses Signifies Potential Defensive Mechanisms Against Photosensitivity.
V. Information Theory & Causality
While studying complex systems, it is important not only to detect synchronized states, but also to identify causal (“who drives whom”) relationships between concerned (sub) systems. For example, in physiological systems, the problem of detecting causality is extremely relevant, such as, whether heart drives respiration or vice-versa in cardiorespiratory synchronization, how the information flow from one cortical region to another one forms a distributed cortical network. The knowledge of information-theoretic measures (i.e. mutual information, conditional entropy) is essential for the analysis of information flow between two systems or between constituent subsystems of a complex system. However, the estimation of these measures from a set of finite samples is not a trivial task. Presently he is engaged with the topic of reliable estimation of informational theoretic measures from short noisy time series.
Joy also researches Cognitive Neuroscience including the following areas:
I. Decision Making
Making choices is a fundamental aspect of human life. In the real world we often have to choose between possible stimuli that lead to different outcomes. Some choices are made on preferential responses and some are based on selective responses. Behavioral studies indicate that there are distinct differences in terms of orientations between these two decisions, preferential and selective. He is interested in finding the underlying neural dynamics which cause these decisions. Further, he aims to predict from analyzing single trial data if and when the subject is going to take the decision.
His relevant papers include Assessment of Connectivity Patterns from Multivariate Time Series by Partial Directed Coherence.
II. Problem Solving
We need to solve problems at every step of our life. Some problems are learned through experiences, so their solution strategies are fairly automatized. Some other problems are rule based, so once the underlying rules are known, the problems, however complex they might be, can be solved by the application of sequential reasoning. But there is a vast domain of other problems which challenge our dominant mode of thinking: neither we can sketch any clear solution strategy nor can we apply our dominant mode or knowledge to the solution. As a result, we lurk around the solution in an endless loop without making any significant progress.
The breakthrough comes by a new insight into the problem and the solution emerges; this is often associated in literature as the eureka or aha moment or insight phenomenon. Despite its widespread reports, the brain mechanism underlying eureka phenomenon is poorly understood. What happens in the brain during that particular moment? Is that moment purely sudden as often reported by the solver or is there any (neural) precursor to it? Is insightful problem solving different from noninsightful problem solving, and how are the differences reflected in brain dynamics? Which are the brain networks responsible for eureka? And finally, can we predict whether and when, if at all, the solver will hit upon the eureka moment? He is most interested to answer these questions from a neurophysiological point of view.
III. Crossmodal interactions
The interaction and integration of information carried by different sensory channels are effortlessly done within the human brain and such integration of the information garnered through separate sensory modalities offers an immediate evolutionary advantage over an independent sensory processing mechanism by improving the detection performance, by eliminating disambiguates, and by enhancing the speed of detection of external stimuli. Although these behavioral responses have been well studied, very little is conclusively known about the nature and the dynamics of the cortical network responsible for such crossmodal interaction in human. The high temporal resolutions of EEG or MEG make them ideally suitable for investigating the time course of multisensory processing in the human brain. He has been currently investigating several experiments with the research aims of determining the nature, and the dynamical spatiotemporal progression of brain responses due to multisensory interaction.
His relevant papers include Sound-induced illusory flash perception: role of gamma band responses, Early modulation of visual cortex by sound: an MEG study, and MultisensoryInteraction (MSI) “When, where, and what?” As revealed by EEG & MEG.
IV. Proficiency in Music, Language, Art
How do perceive natural music? Do the musicians listen to music like other naive listeners? What are the neural correlates of perception of visual art? How does an artist draw an artwork on her mental canvas? What are the neurological concomitants of professional expertise in music and in art?
Philosophers and behavioral psychologists have investigated some of these questions over many decades but we have limited information about the functioning of the human brain while performing these extremely common tasks. One of the principal questions in this context is how the brain operates with simultaneous interaction between convergent (integrated) and divergent (differentiated) neuronal assemblies in a functionally effective and dynamical fashion. Over the years, he has been pursuing these issues by investigating functional cooperation between multiple brain regions and its possible relation with human expertise in music, in visual art during the perception of music, of visual art, and of creative imagery.
His relevant papers include Drawing on Mind’s Canvas: Differences in Cortical Integration Patterns Between Artists and Non-Artists, Phase synchrony analysis of EEG during music perception reveals changes in functional connectivity due to musical expertise, Shadows of artistry: cortical synchrony during perception and imagery of visual art, Musicians and the gamma band: a secret affair?, and Investigation of Topographical Connectivity Patterns During the Perception of Music, Art, & “Creative” Imagery.
Read the full list of his publications!
Joy earned his Bachelor of Engineering (Electronics & Telecomm. Eng.) at Bengal Engineering College, University of Calcutta in 1994 and his Doctor of Philosophy (Electronics & Electrical Communication Engineering), Indian Institute of Technology (IIT), Kharagpur, India in 2000.