Category Archives: Cognitive Sciences

Example of both bottom-up and top-down processes that are integrated in a solution for the recognition of shapes

May 4, 2015 11:25 ,

Ching L. Teo, Cornelia Fermüller, and Yiannis Aloimonos, A Gestaltist approach to contour-based object recognition: Combining bottom-up and top-down cues, The International Journal of Robotics Research April 2015 34: 627-652, first published on March 25, 2015, DOI: 10.1177/0278364914558493.

This paper proposes a method for detecting generic classes of objects from their representative contours that can be used by a robot with vision to find objects in cluttered environments. The approach uses a mid-level image operator to group edges into contours which likely correspond to object boundaries. This mid-level operator is used in two ways, bottom-up on simple edges and top-down incorporating object shape information, thus acting as the intermediary between low-level and high-level information. First, the mid-level operator, called the image torque, is applied to simple edges to extract likely fixation locations of objects. Using the operator’s output, a novel contour-based descriptor is created that extends the shape context descriptor to include boundary ownership information and accounts for rotation. This descriptor is then used in a multi-scale matching approach to modulate the torque operator towards the target, so it indicates its location and size. Unlike other approaches that use edges directly to guide the independent edge grouping and matching processes for recognition, both of these steps are effectively combined using the proposed method. We evaluate the performance of our approach using four diverse datasets containing a variety of object categories in clutter, occlusion and viewpoint changes. Compared with current state-of-the-art approaches, our approach is able to detect the target with fewer false alarms in most object categories. The performance is further improved when we exploit depth information available from the Kinect RGB-Depth sensor by imposing depth consistency when applying the image torque.

Posted in: Computer vision, Psycho-physiological bases of engineering , Tagged:

Study of the explanation of probability and reasoning in the human mind through mental models, probability logic and classical logic

May 4, 2015 09:55 ,

P.N. Johnson-Laird, Sangeet S. Khemlani, Geoffrey P. Goodwin, Logic, probability, and human reasoning, Trends in Cognitive Sciences, Volume 19, Issue 4, April 2015, Pages 201-214, ISSN 1364-6613, DOI: 10.1016/j.tics.2015.02.006.

This review addresses the long-standing puzzle of how logic and probability fit together in human reasoning. Many cognitive scientists argue that conventional logic cannot underlie deductions, because it never requires valid conclusions to be withdrawn – not even if they are false; it treats conditional assertions implausibly; and it yields many vapid, although valid, conclusions. A new paradigm of probability logic allows conclusions to be withdrawn and treats conditionals more plausibly, although it does not address the problem of vapidity. The theory of mental models solves all of these problems. It explains how people reason about probabilities and postulates that the machinery for reasoning is itself probabilistic. Recent investigations accordingly suggest a way to integrate probability and deduction.

Posted in: Artificial Intelligence, Mathematical logic, Probability and statistics, Psycho-physiological bases of engineering , Tagged: ,

Neurological evidences of the hierarchical arrangement of the process of motor skill learning

May 4, 2015 09:51 ,

Jörn Diedrichsen, Katja Kornysheva, Motor skill learning between selection and execution, Trends in Cognitive Sciences, Volume 19, Issue 4, April 2015, Pages 227-233, ISSN 1364-6613, DOI: 10.1016/j.tics.2015.02.003.

Learning motor skills evolves from the effortful selection of single movement elements to their combined fast and accurate production. We review recent trends in the study of skill learning which suggest a hierarchical organization of the representations that underlie such expert performance, with premotor areas encoding short sequential movement elements (chunks) or particular component features (timing/spatial organization). This hierarchical representation allows the system to utilize elements of well-learned skills in a flexible manner. One neural correlate of skill development is the emergence of specialized neural circuits that can produce the required elements in a stable and invariant fashion. We discuss the challenges in detecting these changes with fMRI.

Posted in: Psycho-physiological bases of engineering , Tagged: ,

Novelty detection as a way for enhancing learning capabilities of a robot, and a brief but interesting survey of motivational theories and their difference with attention

April 24, 2015 12:46 ,

Y. Gatsoulis, T.M. McGinnity, Intrinsically motivated learning systems based on biologically-inspired novelty detection, Robotics and Autonomous Systems, Volume 68, June 2015, Pages 12-20, ISSN 0921-8890, DOI: 10.1016/j.robot.2015.02.006.

Intrinsic motivations play an important role in human learning, particularly in the early stages of childhood development, and ideas from this research field have influenced robotic learning and adaptability. In this paper we investigate one specific type of intrinsic motivation, that of novelty detection and we discuss the reasons that make it a powerful facility for continuous learning. We formulate and present one original type of biologically inspired novelty detection architecture and implement it on a robotic system engaged in a perceptual classification task. The results of real-world robot experiments we conducted show how this original architecture conforms to behavioural observations and demonstrate its effectiveness in terms of focusing the system’s attention in areas that are potential for effective learning.

Posted in: Psycho-physiological bases of engineering, Robotics , Tagged: , ,

Reinforcement learning used for an adaptive attention mechanism, and integrated in an architecture with both top-down and bottom-up vision processing

April 24, 2015 11:26 ,

Ognibene, D.; Baldassare, G., Ecological Active Vision: Four Bioinspired Principles to Integrate Bottom–Up and Adaptive Top–Down Attention Tested With a Simple Camera-Arm Robot, Autonomous Mental Development, IEEE Transactions on , vol.7, no.1, pp.3,25, March 2015. DOI: 10.1109/TAMD.2014.2341351.

Vision gives primates a wealth of information useful to manipulate the environment, but at the same time it can easily overwhelm their computational resources. Active vision is a key solution found by nature to solve this problem: a limited fovea actively displaced in space to collect only relevant information. Here we highlight that in ecological conditions this solution encounters four problems: 1) the agent needs to learn where to look based on its goals; 2) manipulation causes learning feedback in areas of space possibly outside the attention focus; 3) good visual actions are needed to guide manipulation actions, but only these can generate learning feedback; and 4) a limited fovea causes aliasing problems. We then propose a computational architecture (“BITPIC”) to overcome the four problems, integrating four bioinspired key ingredients: 1) reinforcement-learning fovea-based top-down attention; 2) a strong vision-manipulation coupling; 3) bottom-up periphery-based attention; and 4) a novel action-oriented memory. The system is tested with a simple simulated camera-arm robot solving a class of search-and-reach tasks involving color-blob “objects.” The results show that the architecture solves the problems, and hence the tasks, very efficiently, and highlight how the architecture principles can contribute to a full exploitation of the advantages of active vision in ecological conditions.

Posted in: Applications of reinforcement learning to robots, Computer vision, Psycho-physiological bases of engineering , Tagged: , , , ,

Survey of Hierarchical Task Planning

March 11, 2015 15:28 ,

Ilche Georgievski, Marco Aiello, 2015, HTN planning: Overview, comparison, and beyond, Artificial Intelligence, Volume 222, May 2015, Pages 124-156, ISSN 0004-3702, DOI: 10.1016/j.artint.2015.02.002.

Hierarchies are one of the most common structures used to understand and conceptualise the world. Within the field of Artificial Intelligence (AI) planning, which deals with the automation of world-relevant problems, Hierarchical Task Network (HTN) planning is the branch that represents and handles hierarchies. In particular, the requirement for rich domain knowledge to characterise the world enables HTN planning to be very useful, and also to perform well. However, the history of almost 40 years obfuscates the current understanding of HTN planning in terms of accomplishments, planning models, similarities and differences among hierarchical planners, and its current and objective image. On top of these issues, the ability of hierarchical planning to truly cope with the requirements of real-world applications has been often questioned. As a remedy, we propose a framework-based approach where we first provide a basis for defining different formal models of hierarchical planning, and define two models that comprise a large portion of HTN planners. Second, we provide a set of concepts that helps in interpreting HTN planners from the aspect of their search space. Then, we analyse and compare the planners based on a variety of properties organised in five segments, namely domain authoring, expressiveness, competence, computation and applicability. Furthermore, we select Web service composition as a real-world and current application, and classify and compare the approaches that employ HTN planning to solve the problem of service composition. Finally, we conclude with our findings and present directions for future work. In summary, we provide a novel and comprehensive viewpoint on a core AI planning technique.

Posted in: Artificial Intelligence , Tagged:

On the process of the brain for detecting similarities, with a proposal for its structure and its timing

March 11, 2015 15:20 ,

Qingfei Chen, Xiuling Liang, Peng Li, Chun Ye, Fuhong Li, Yi Lei, Hong Li, 2015, The processing of perceptual similarity with different features or spatial relations as revealed by P2/P300 amplitude, International Journal of Psychophysiology, Volume 95, Issue 3, March 2015, Pages 379-387, ISSN 0167-8760, DOI: 10.1016/j.ijpsycho.2015.01.009.

Visual features such as “color” and spatial relations such as “above” or “beside” have complex effects on similarity and difference judgments. We examined the relative impact of features and spatial relations on similarity and difference judgments via ERPs in an S1–S2 paradigm. Subjects were required to compare a remembered geometric shape (S1) with a second one (S2), and made a “high” or “low” judgment of either similarity or difference in separate blocks of trials. We found three main differences that suggest that the processing of features and spatial relations engages distinct neural processes. The first difference is a P2 effect in fronto-central regions which is sensitive to the presence of a feature difference. The second difference is a P300 in centro-parietal regions that is larger for difference judgments than for similarity judgments. Finally, the P300 effect elicited by feature differences was larger relative to spatial relation differences. These results supported the view that similarity judgments involve structural alignment rather than simple feature and relation matches, and furthermore, indicate the similarity judgment could be divided into three phases: feature or relation comparison (P2), structural alignment (P3 at 300–400 ms), and categorization (P3 at 450–550 ms).

Posted in: Psycho-physiological bases of engineering , Tagged:

On the role of emotions in cognition, in particular in cognitive control

March 11, 2015 15:14 ,

Michael Inzlicht, Bruce D. Bartholow, Jacob B. Hirsh, 2015, Emotional foundations of cognitive control, Trends in Cognitive Sciences, Volume 19, Issue 3, March 2015, Pages 126-132, DOI: 10.1016/j.tics.2015.01.004.

Often seen as the paragon of higher cognition, here we suggest that cognitive control is dependent on emotion. Rather than asking whether control is influenced by emotion, we ask whether control itself can be understood as an emotional process. Reviewing converging evidence from cybernetics, animal research, cognitive neuroscience, and social and personality psychology, we suggest that cognitive control is initiated when goal conflicts evoke phasic changes to emotional primitives that both focus attention on the presence of goal conflicts and energize conflict resolution to support goal-directed behavior. Critically, we propose that emotion is not an inert byproduct of conflict but is instrumental in recruiting control. Appreciating the emotional foundations of control leads to testable predictions that can spur future research.

Posted in: Cognitive sciences , Tagged: ,

On the not-so-domain-generic nature of statistical learning in the human brain

March 11, 2015 15:09 ,

Ram Frost, Blair C. Armstrong, Noam Siegelman, Morten H. Christiansen, 2015, Domain generality versus modality specificity: the paradox of statistical learning, Trends in Cognitive Sciences, Volume 19, Issue 3, March 2015, Pages 117-125, DOI: 10.1016/j.tics.2014.12.010.

Statistical learning (SL) is typically considered to be a domain-general mechanism by which cognitive systems discover the underlying distributional properties of the input. However, recent studies examining whether there are commonalities in the learning of distributional information across different domains or modalities consistently reveal modality and stimulus specificity. Therefore, important questions are how and why a hypothesized domain-general learning mechanism systematically produces such effects. Here, we offer a theoretical framework according to which SL is not a unitary mechanism, but a set of domain-general computational principles that operate in different modalities and, therefore, are subject to the specific constraints characteristic of their respective brain regions. This framework offers testable predictions and we discuss its computational and neurobiological plausibility.

Posted in: Machine learning, Psycho-physiological bases of engineering , Tagged: ,

Abstracting and representing tasks performed under Learning from Demonstration, using bayesian non-parametric time-series analysis (good review of both LfD and HMMs for time-series)

February 10, 2015 16:37 ,

Scott Niekum, Sarah Osentoski, George Konidaris, Sachin Chitta, Bhaskara Marthi, Andrew G. Barto (2015), Learning grounded finite-state representations from unstructured demonstrations, The International Journal of Robotics Research, vol. 34, pp. 131-157. DOI: 10.1177/0278364914554471

Robots exhibit flexible behavior largely in proportion to their degree of knowledge about the world. Such knowledge is often meticulously hand-coded for a narrow class of tasks, limiting the scope of possible robot competencies. Thus, the primary limiting factor of robot capabilities is often not the physical attributes of the robot, but the limited time and skill of expert programmers. One way to deal with the vast number of situations and environments that robots face outside the laboratory is to provide users with simple methods for programming robots that do not require the skill of an expert. For this reason, learning from demonstration (LfD) has become a popular alternative to traditional robot programming methods, aiming to provide a natural mechanism for quickly teaching robots. By simply showing a robot how to perform a task, users can easily demonstrate new tasks as needed, without any special knowledge about the robot. Unfortunately, LfD often yields little knowledge about the world, and thus lacks robust generalization capabilities, especially for complex, multi-step tasks. We present a series of algorithms that draw from recent advances in Bayesian non-parametric statistics and control theory to automatically detect and leverage repeated structure at multiple levels of abstraction in demonstration data. The discovery of repeated structure provides critical insights into task invariants, features of importance, high-level task structure, and appropriate skills for the task. This culminates in the discovery of a finite-state representation of the task, composed of grounded skills that are flexible and reusable, providing robust generalization and transfer in complex, multi-step robotic tasks. These algorithms are tested and evaluated using a PR2 mobile manipulator, showing success on several complex real-world tasks, such as furniture assembly.

Posted in: Learning from Demonstration , Tagged: , ,