Amitash Ojha

Modulation of resource allocation by intelligent individuals in linguistic, mathematical and visuo-spatial tasks

This study investigates two questions: first, how individuals with high-intelligence allocate cognitive resources while solving linguistic, mathematical and visuo-spatial tasks with varying degree of difficulty as compared to individuals with low intelligence? Second, how to distinguish between high and low intelligent individuals by analyzing pupil dilation and eye blink together? We measured the response time, error rates along with pupil dilation and eye blink rate that indicate resource allocation. We divided the whole processing into three stages namely: pre-stimuli (5s prior to stimuli onset), during stimuli and post stimuli (until 5s after the response) for better assessment of preparation and resource allocation strategies. Individuals with high intelligence showed greater task evoked pupil dilation, decreased eye blink with less response time and error rates during-stimuli stage (processing) of tough linguistic and visuo-spatial tasks but not during mathematical tasks. The finding suggests that individuals with high intelligence allocate more resources if the task demands are high else they allocate less resources. Greater pre-stimuli pupil dilation and increased eye blink of high intelligent individuals in all tasks indicated their attentiveness and preparedness. The result of our study shows that individuals with high intelligence are more attentive and flexible in terms of altering the resource allocation strategy according to task demand. Eye-blinks along with pupil dilation and other behavioral parameters can be reliably used to assess the intelligence of an individual and the analysis of pupil dilation and blink rate at pre-stimuli stage can be crucial in distinguishing individuals with varying intelligence.

A Study on Region of Interest of a Selective Attention Based on Gestalt Principles

We propose a computational model to extend the region of attention in a visual scene. We assume that the visual information that is collected through bottom-up process is integrated by various mechanisms of perception process which in result further decides the attention regions of the object to accurately determine the object. This cycle is known as perception-action cycle. In our study we try to quantify relation between initial attention region and surrounding regions using Gestalt principles.

Role of Gestalt Principles in Selecting Attention Areas for Object Recognition

Human attention plays an important role in human visual system. We assume that the Gestalt law is one of important factors to guide human selective attention. In this paper, we present a series of studies in which we hypothesized that regions of image that get more attention in an object recognition task, confirm to one or more gestalt principles and subconsciously attract human attention which eventually help in object recognition. In our study, we collected attention parts of images by analyzing eye movement of participants. Then we compared Gestalt scores of high attention parts with those of nonattended random parts. Our results suggest that continuity and symmetry of features attract human attention. We argue that an approach to analyze parts with high Gestalt scores can yield better than analyzing random parts of image in object recognition.

Classification of High and Low Intelligent Individuals Using Pupil and Eye Blink

A commonly used method to determine the intelligence of an individual is a group test. It checks accuracy and response time while they solve a series of problems. However, it takes long time and is often inaccurate if the difficulty level of problems is high or the number of problems is too small. Therefore, there is an urgent need to find an objective, readily available, fast and more reliable method to determine the intelligence level of individuals. In this paper, we propose an alternative method to distinguish between high and low intelligent individuals using pupillary response and eye blink pattern. Studies have shown that these measures indicate the cognitive state of an individual more accurately and objectively. Our experimental results show that the bio-signals between high and low intelligent individuals are significantly different and proposed method has good performance.

Human-Robot Interaction using Intention Recognition

Recognition of human intention is an important issue in human-robot interaction research and allows a robot to respond adequately according to humans wish. In this paper, we discuss how robots can infer human intention by learning affordance, a concept used to represent the relation between an agent and its environment. Learning of the robot, to understand human and its interaction with environment, is achieved within the framework of action-perception cycle. The action-perception cycle explains how an intelligent agent learns and enhances its ability continuously by interacting with its surrounding. The proposed intention recognition and recommendation system includes several key functions such as joint attention, object recognition, affordance model, motion understanding module and so on. The experimental results show high successful recognition performance and the plausibility of the proposed system.

Pictogram Generator from Korean Sentences using Emoticon and Saliency Map

Picture is worth a thousand words. With changing life styles and technology advancement, visual or pictorial communication is preferred. We present a system to generate a pictogram for simple Korean sentences. The final pictogram integrates information about the object (about which something is said), the background (the environment) and the emotion of the user. The proposed system is divided into two parts. First is the registration part, which saves personal information and face image of the user. The second part searches corresponding images for words, downloads them and finally integrates all of them together to along with users emotion to generate a single pictogram.

Intelligence level and the allocation of resources for creative tasks : a pupillometry study

This pupillometry study examined the relationship between intelligence and creative cognition from the resource allocation perspective. It was hypothesized that, during a creative metaphor task, individuals with higher intelligence scores would have different resource allocation patterns than individuals with lower intelligence scores. The study also examined the influence of intelligence in language and visuo-spatial domains on the resource allocation mechanism of verbal and visual creativity. The results suggested that individuals with higher intelligence scores allocated more cognitive resources for creative tasks than those with lower intelligence scores but not for non-creative tasks. The findings of this study support the view that creativity requires allocation of several cognitive faculties and may share underlying cognitive and neural mechanisms with intelligence. Domain-specific intelligence did not seem to play a significant role in the same domain, as individuals with higher scores in both domains showed similar resource allocation patterns. However, individuals with higher intelligence scores in the visuo-spatial domain generated more creative metaphorical interpretations in both verbal and visual creative metaphor tasks suggesting its importance in creative cognition.

Concentration Monitoring with High Accuracy but Low Cost EEG Device

Concentration is an important part of our life especially during learning or thinking. Visually or auditory evoked concentration affects information processing in human brain. To understand the concentration process of humans, the underlying neural mechanism needs to be explored. EEG device is a promising device to understand underlying neural mechanism of various cognitive functions. In this paper, we propose an accurate concentration monitoring method using a low cost EEG device. Our low cost EEG device has two channel electrodes (FP1, FP2). Usually small channel EEG devices face filtering problem because commonly used filtering method, such as ICA, fails with less number of electrodes. In our work, we investigate effective filters for removing noises from raw data and suitable features for monitoring the concentration status with the low cost EEG device in real time. We collect EEG data from 10 participants for rest state with open eyes and concentration task state. For concentration task, Sudoku game is used. Using support vector machine, we successfully distinguish between rest state and concentration state over 88 % accuracy in real time.

Concentration Monitoring for Intelligent Tutoring System Based on Pupil and Eye-blink

Monitoring the concentration level of a learner is important to maximize the learning effect, giving proper feedback on tasks and to understand the performance of learners in tasks. In this paper, we propose a personal concentration level monitoring system when a user performs an online task on a computer by analyzing his/her pupillary response and eye-blinking pattern. We use low-priced web camera to detect eye blinking pattern and a portable eye tracker to detect pupillary response. Experimental results show good performance of the proposed concentration level monitoring system and suggest that it can be used for various real applications such as intelligent tutoring system, e-learning system, etc.