Rajan Bhattacharyya

Latent structure in random sequences drives neural learning toward a rational bias

Significance The human mind has a unique capacity to find order in chaos. The way the neocortex integrates information over time enables the mind to capture rich statistical structures embedded in random sequences. We show that a biologically motivated neural network model reacts to not only how often a pattern occurs (mean time) but also when a pattern is first encountered (waiting time). This behavior naturally produces the alternation bias in the gambler’s fallacy and provides a neural grounding for the Bayesian models of human behavior in randomness judgments. Our findings support a rational account for human probabilistic reasoning and a unifying perspective that connects the implicit learning without instruction with the generalization under structured and expressive rules. People generally fail to produce random sequences by overusing alternating patterns and avoiding repeating ones—the gambler’s fallacy bias. We can explain the neural basis of this bias in terms of a biologically motivated neural model that learns from errors in predicting what will happen next. Through mere exposure to random sequences over time, the model naturally develops a representation that is biased toward alternation, because of its sensitivity to some surprisingly rich statistical structure that emerges in these random sequences. Furthermore, the model directly produces the best-fitting bias-gain parameter for an existing Bayesian model, by which we obtain an accurate fit to the human data in random sequence production. These results show that our seemingly irrational, biased view of randomness can be understood instead as the perfectly reasonable response of an effective learning mechanism to subtle statistical structure embedded in random sequences.

Model of the interactions between neuromodulators and prefrontal cortex during a resource allocation task

Neuromodulators such as dopamine (DA), serotonin (5-HT), and acetylcholine (ACh) are crucial to the representations of reward, cost, and attention respectively. Recent experiments suggest that the reward and cost of actions are also partially represented in orbitofrontal and anterior cingulate cortices in that order. Previous models of action selection with neuromodulatory systems have not extensively considered prefrontal contributions to action selection. Here, we extend these models of action selection to include prefrontal structures in a resource allocation task. The model adapts to its environment, modulating its aggressiveness based on its successes. Selective lesions demonstrate how neuromodulatory and prefrontal areas drive learning and performance of strategy selection.

Using precise word timing information improves decoding accuracy in a multiband-accelerated multimodal reading experiment

ABSTRACT The blood-oxygen-level-dependent (BOLD) signal measured in functional magnetic resonance imaging (fMRI) experiments is generally regarded as sluggish and poorly suited for probing neural function at the rapid timescales involved in sentence comprehension. However, recent studies have shown the value of acquiring data with very short repetition times (TRs), not merely in terms of improvements in contrast to noise ratio (CNR) through averaging, but also in terms of additional fine-grained temporal information. Using multiband-accelerated fMRI, we achieved whole-brain scans at 3-mm resolution with a TR of just 500 ms at both 3T and 7T field strengths. By taking advantage of word timing information, we found that word decoding accuracy across two separate sets of scan sessions improved significantly, with better overall performance at 7T than at 3T. The effect of TR was also investigated; we found that substantial word timing information can be extracted using fast TRs, with diminishing benefits beyond TRs of 1000 ms.

A bio-inspired kinematic controller for obstacle avoidance during reaching tasks with real robots

This paper describes a redundant robot arm that is capable of learning to reach for targets in space while avoiding obstacles in a self-organized fashion. Self-generated movement commands that activate correlated visual, spatial and motor information are used to learn forward and inverse kinematic control models while moving in obstacle free space using the direction-to-rotation transform (DIRECT). The DIRECT based kinematic controller is fault tolerant and can handle a wide range of perturbations such as joint locking and the use of tools despite not experiencing them during learning. We have developed a DIRECT-based reactive obstacle avoidance controller (DIRECT-ROAC) that enables the redundant robot arm to avoid obstacles in environments with simple obstacle configurations. However, certain configurations of obstacles in the environment prevent the robot from reaching the target with purely reactive obstacle avoidance. To address this complexity, we model a self-organized process of mental rehearsals of movements inspired by human and animal experiments on reaching to generate plans for movement execution using DIRECT-ROAC in complex environments. These mental rehearsals or plans are self generated by utilizing perceptual information in the form of via-points extracted from attentional shrouds around obstacles in its environment. Computer simulations show that the proposed novel controller is successful in avoiding obstacles in environments with complex obstacle configurations.

Hippocampal anatomy supports the use of context in object recognition: a computational model

The human hippocampus receives distinct signals via the lateral entorhinal cortex, typically associated with object features, and the medial entorhinal cortex, associated with spatial or contextual information. The existence of these distinct types of information calls for some means by which they can be managed in an appropriate way, by integrating them or keeping them separate as required to improve recognition. We hypothesize that several anatomical features of the hippocampus, including differentiation in connectivity between the superior/inferior blades of DG and the distal/proximal regions of CA3 and CA1, work together to play this information managing role. We construct a set of neural network models with these features and compare their recognition performance when given noisy or partial versions of contexts and their associated objects. We found that the anterior and posterior regions of the hippocampus naturally require different ratios of object and context input for optimal performance, due to the greater number of objects versus contexts. Additionally, we found that having separate processing regions in DG significantly aided recognition in situations where object inputs were degraded. However, split processing in both DG and CA3 resulted in performance tradeoffs, though the actual hippocampus may have ways of mitigating such losses.

Optimal detection of objects in images and videos using electroencephalography (EEG)

The Rapid Serial Visual Presentation (RSVP) protocol for EEG has recently been discovered as a useful tool for highthroughput filtering of images into simple target and nontarget categories [1]. This concept can be extended to the detection of objects and anomalies in images and videos that are of interest to the user (observer) in an applicationspecific context. For example, an image analyst looking for a moving vehicle in wide-area imagery will consider such an object to be target or Item Of Interest (IOI). The ordering of images in the RSVP sequence is expected to have an impact on the detection accuracy. In this paper, we describe an algorithm for learning the RSVP ordering that employs a user interaction step to maximize the detection accuracy while simultaneously minimizing false alarms. With user feedback, the algorithm learns the optimal balance of image distance metrics in order to closely emulate the humans own preference for image order. It then employs the fusion of various perceptual and bio-inspired image metrics to emulate the humans sequencing ability for groups of image chips, which are subsequently used in RSVP trials. Such a method can be employed in human-assisted threat assessment in which the system must scan a wide field of view and report any detections or anomalies to the landscape. In these instances, automated classification methods might fail. We will describe the algorithm and present preliminary results on real-world imagery.

A bio-inspired kinematic controller for obstacle avoidance during reaching tasks with redundant robots

This paper describes a redundant robot arm that is capable of learning to reach for targets in space while avoiding obstacles in a self-organized fashion. Self-generated movement commands that activate correlated visual, spatial and motor information are used to learn forward and inverse kinematic control models while moving in obstacle free space using the direction-to-rotation transform (DIRECT). The DIRECT based kinematic controller is fault tolerant and can handle a wide range of perturbations such as joint locking and the use of tools despite not experiencing them during learning. We have developed a DIRECT-based reactive obstacle avoidance controller (DIRECT-ROAC) that enables the redundant robot arm to avoid obstacles in environments with simple obstacle configurations. However, certain configurations of obstacles in the environment prevent the robot from reaching the target with purely reactive obstacle avoidance. To address this complexity, we model a self-organized process of mental rehearsals of movements inspired by human and animal experiments on reaching to generate plans for movement execution using DIRECT-ROAC in complex environments. These mental rehearsals or plans are self generated by utilizing perceptual information in the form of via-points extracted from attentional shrouds around obstacles in its environment. Computer simulations show that the proposed novel controller is successful in avoiding obstacles in environments with complex obstacle configurations.

The neural basis of decision-making during sensemaking: Implications for human-system interaction

We have created a high-fidelity model of 9 regions of the brain involved in making sense of complex and uncertain situations. Sense making is a proactive form of situation awareness requiring sifting through information of various types to form hypotheses about evolving situations. The MINDS model (Mirroring Intelligence in a Neural Description of Sensemaking) reveals the neural principles and cognitive tradeoffs that explain weaknesses in human reasoning and decision-making.

Adaptive recall in hippocampus

Complementary learning systems (CLS) theory describes how the hippocampal and cortical contributions to recognition memory are a direct result of their architectural and computational specializations. In this paper we model a further refinement of CLS that features separate handling of inputs from the dorsal and ventral posterior cortices, and present a possible mechanism for adaptive recall in hippocampus based on several research findings that have not previously been related to each other. This model suggests how we are able to recognize familiar objects in unfamiliar settings.

Does Neurotechnology Produce a Better Brain

Neurotechnologies in clinical applications can image the brain noninvasively, but they typically require surgical insertion to stimulate it. Although an increasingly popular alternative is to use noninvasive stimulation to enhance nervous system functions, questions about its effectiveness and ethical use remain unanswered.