Educational Robotics Intervention to Foster Computational Thinking in Preschoolers: Effects of Children's Task Engagement.

Computational thinking (CT) is a broadly used term in education to refer to the cognitive processes underlying the application of computer science concepts and strategies of problem-solving. Recent literature has pointed out the value of children acquiring computational thinking skills (i.e., understanding and applying concepts, such as conditionals, iteration, or generalization), especially while learning STEM subjects. Robotics has been used as a tool to introduce computational thinking and STEM knowledge to children. As physical objects, robots have been proposed as developmentally appropriate for the early childhood setting, promoting motivation and allowing young learners to represent abstract ideas in a concrete setting. This study presents a novel educational robotics (ER) intervention using RoboTito, a robot programmable through tangible elements in its environment designed for kindergarteners. We used a quasi-experimental design with an active control group. In addition, we conducted a structured observation of the filmed material of the sessions to gather data on children's attention and motivation throughout the activities. Fifty-one children (male = 33; mean age = 66 months, SD = 5.49 months) attending level 5 (kindergarten) at a Uruguayan public school participated in the study. Children in our experimental condition participated in an intervention programming RoboTito using tangible elements, while children in our control condition played with the robot through sensory-motor activities using a remote control and did not engage in programming. Motivational and attentional factors were assessed through video-recorded sessions of the ER activities. Four trained observers blind to the experimental conditions participated in the coding. Children's interactions were assessed in four categories: task engagement, distractibility, oral participation, and objective fulfillment. Our results suggest children's task engagement mediated their gains in CT after the intervention; post-hoc Tukey contrasts revealed non-significant pre-test to post-test gains for the control and low engagement groups, and significant for the high engagement group. Overall, we conclude task engagement played a central role in children's learning gains and our robotics intervention was successful in promoting CT for engaged children. We discuss the practical implications of our results for early childhood education and developmentally appropriate ER targeted for young learners.

Redundancy between spectral and higher-order texture statistics for natural image segmentation.

Visual texture, defined by local image statistics, provides important information to the human visual system for perceptual segmentation. Second-order or spectral statistics (equivalent to the Fourier power spectrum) are a well-studied segmentation cue. However, the role of higher-order statistics (HOS) in segmentation remains unclear, particularly for natural images. Recent experiments indicate that, in peripheral vision, the HOS of the widely adopted Portilla-Simoncelli texture model are a weak segmentation cue compared to spectral statistics, despite the fact that both are necessary to explain other perceptual phenomena and to support high-quality texture synthesis. Here we test whether this discrepancy reflects a property of natural image statistics. First, we observe that differences in spectral statistics across segments of natural images are redundant with differences in HOS. Second, using linear and nonlinear classifiers, we show that each set of statistics individually affords high performance in natural scenes and texture segmentation tasks, but combining spectral statistics and HOS produces relatively small improvements. Third, we find that HOS improve segmentation for a subset of images, although these images are difficult to identify. We also find that different subsets of HOS improve segmentation to a different extent, in agreement with previous physiological and perceptual work. These results show that the HOS add modestly to spectral statistics for natural image segmentation. We speculate that tuning to natural image statistics under resource constraints could explain the weak contribution of HOS to perceptual segmentation in human peripheral vision.

Flexible contextual modulation of naturalistic texture perception in peripheral vision.

Peripheral vision comprises most of our visual field, and is essential in guiding visual behavior. Its characteristic capabilities and limitations, which distinguish it from foveal vision, have been explained by the most influential theory of peripheral vision as the product of representing the visual input using summary statistics. Despite its success, this account may provide a limited understanding of peripheral vision, because it neglects processes of perceptual grouping and segmentation. To test this hypothesis, we studied how contextual modulation, namely the modulation of the perception of a stimulus by its surrounds, interacts with segmentation in human peripheral vision. We used naturalistic textures, which are directly related to summary-statistics representations. We show that segmentation cues affect contextual modulation, and that this is not captured by our implementation of the summary-statistics model. We then characterize the effects of different texture statistics on contextual modulation, providing guidance for extending the model, as well as for probing neural mechanisms of peripheral vision.

Sensory Flow as a Basis for a Novel Distance Cue in Freely Behaving Electric Fish.

The sensory input that an animal receives is directly linked to its motor activity. Behavior thus enables animals to influence their sensory input, a concept referred to as active sensing. How such behavior can serve as a scaffold for generating sensory information is of general scientific interest. In this article, we investigate how behavior can shape sensory information by using some unique features of the sensorimotor system of the weakly electric fish. Based on quantitative behavioral characterizations and computational reconstruction of sensory input, we show how electrosensory flow is actively created during highly patterned, spontaneous behavior in Gnathonemus petersii. The spatiotemporal structure of the sensory input provides information for the computation of a novel distance cue, which allows for a continuous estimation of distance. This has significant advantages over previously known nondynamic distance estimators as determined from electric image blur. Our investigation of the sensorimotor interactions in pulsatile electrolocation shows, for the first time, that the electrosensory flow contains behaviorally relevant information accessible only through active behavior. As patterned sensory behaviors are a shared feature of (active) sensory systems, our results have general implications for the understanding of (active) sensing, with the proposed sensory flow-based measure being potentially pertinent to a broad range of sensory modalities. SIGNIFICANCE STATEMENT: Acquisition of sensory information depends on motion, as either an animal or its sensors move. Behavior can thus actively influence the sensory flow; and in this way, behavior can be seen as a manifestation of the brain's integrative functions. The properties of the active pulsatile electrolocation system in Gnathonemus petersii allow for the sensory input to be computationally reconstructed, enabling us to link the informational content of spatiotemporal sensory dynamics to behavior. Our study reveals a novel sensory cue for estimating depth that is actively generated by the fishes' behavior. The physical and behavioral similarities between electrolocation and other active sensory systems suggest that this may be a mechanism shared by (active) sensory systems.

Modeling latency code processing in the electric sense: from the biological template to its VLSI implementation.

Understanding the coding of sensory information under the temporal constraints of natural behavior is not yet well resolved. There is a growing consensus that spike timing or latency coding can maximally exploit the timing of neural events to make fast computing elements and that such mechanisms are essential to information processing functions in the brain. The electric sense of mormyrid fish provides a convenient biological model where this coding scheme can be studied. The sensory input is a physically ordered spatial pattern of current densities, which is coded in the precise timing of primary afferent spikes. The neural circuits of the processing pathway are well known and the system exhibits the best known illustration of corollary discharge, which provides the reference to decoding the sensory afferent latency pattern. A theoretical model has been constructed from available electrophysiological and neuroanatomical data to integrate the principal traits of the neural processing structure and to study sensory interaction with motor-command-driven corollary discharge signals. This has been used to explore neural coding strategies at successive stages in the network and to examine the simulated network capacity to reproduce output neuron responses. The model shows that the network has the ability to resolve primary afferent spike timing differences in the sub-millisecond range, and that this depends on the coincidence of sensory and corollary discharge-driven gating signals. In the integrative and output stages of the network, corollary discharge sets up a proactive background filter, providing temporally structured excitation and inhibition within the network whose balance is then modulated locally by sensory input. This complements the initial gating mechanism and contributes to amplification of the input pattern of latencies, conferring network hyperacuity. These mechanisms give the system a robust capacity to extract behaviorally meaningful features of the electric image with high sensitivity over a broad working range. Since the network largely depends on spike timing, we finally discuss its suitability for implementation in robotic applications based on neuromorphic hardware.

Computational modeling of electric imaging in weakly electric fish: insights for physiology, behavior and evolution.

Weakly electric fish can sense electric signals produced by other animals whether they are conspecifics, preys or predators. These signals, sensed by passive electroreception, sustain electrocommunication, mating and agonistic behavior. Weakly electric fish can also generate a weak electrical discharge with which they can actively sense the animate and inanimate objects in their surroundings. Understanding both sensory modalities depends on our knowledge of how pre-receptorial electric images are formed and how movements modify them during behavior. The inability of effectively measuring pre-receptorial fields at the level of the skin contrasts with the amount of knowledge on electric fields and the availability of computational methods for estimating them. In this work we review past work on modeling of electric organ discharge and electric images, showing the usefulness of these methods to calculate the field and providing a brief explanation of their principles. In addition, we focus on recent work demonstrating the potential of electric image modeling and what the method has to offer for experimentalists studying sensory physiology, behavior and evolution.

Motor patterns during active electrosensory acquisition.

Motor patterns displayed during active electrosensory acquisition of information seem to be an essential part of a sensory strategy by which weakly electric fish actively generate and shape sensory flow. These active sensing strategies are expected to adaptively optimize ongoing behavior with respect to either motor efficiency or sensory information gained. The tight link between the motor domain and sensory perception in active electrolocation make weakly electric fish like Gnathonemus petersii an ideal system for studying sensory-motor interactions in the form of active sensing strategies. Analyzing the movements and electric signals of solitary fish during unrestrained exploration of objects in the dark, we here present the first formal quantification of motor patterns used by fish during electrolocation. Based on a cluster analysis of the kinematic values we categorized the basic units of motion. These were then analyzed for their associative grouping to identify and extract short coherent chains of behavior. This enabled the description of sensory behavior on different levels of complexity: from single movements, over short behaviors to more complex behavioral sequences during which the kinematics alter between different behaviors. We present detailed data for three classified patterns and provide evidence that these can be considered as motor components of active sensing strategies. In accordance with the idea of active sensing strategies, we found categorical motor patterns to be modified by the sensory context. In addition these motor patterns were linked with changes in the temporal sampling in form of differing electric organ discharge frequencies and differing spatial distributions. The ability to detect such strategies quantitatively will allow future research to investigate the impact of such behaviors on sensing.

Sensory flow shaped by active sensing: sensorimotor strategies in electric fish.

Goal-directed behavior in most cases is composed of a sequential order of elementary motor patterns shaped by sensorimotor contingencies. The sensory information acquired thus is structured in both space and time. Here we review the role of motion during the generation of sensory flow focusing on how animals actively shape information by behavioral strategies. We use the well-studied examples of vision in insects and echolocation in bats to describe commonalities of sensory-related behavioral strategies across sensory systems, and evaluate what is currently known about comparable active sensing strategies in electroreception of electric fish. In this sensory system the sensors are dispersed across the animal's body and the carrier source emitting energy used for sensing, the electric organ, is moved while the animal moves. Thus ego-motions strongly influence sensory dynamics. We present, for the first time, data of electric flow during natural probing behavior in Gnathonemus petersii (Mormyridae), which provide evidence for this influence. These data reveal a complex interdependency between the physical input to the receptors and the animal's movements, posture and objects in its environment. Although research on spatiotemporal dynamics in electrolocation is still in its infancy, the emerging field of dynamical sensory systems analysis in electric fish is a promising approach to the study of the link between movement and acquisition of sensory information.

From static electric images to electric flow: towards dynamic perceptual cues in active electroreception.

Active electroreception is an ancestral trait found in many aquatic vertebrates and has evolved independently in two teleost lineages, the Gymnotiformes and the Mormyriformes. Unique to these so-called weakly electric fish is their ability to actively generate electrical currents in the water and sense the electrical properties of the environment. How natural behavior contributes to this sensory system has been of interest to neuroethologists since the pioneering works of Lissmann. Here we report on a mutual modeling and experimental study of the stimuli available during active electrolocation of Gnathonemus petersii (Mormyridae). We show the validity of the model (I) by demonstrating that localized spatial patterns of object induced modulations in the electric field (electric images) are comparable to experimentally mapped 2-dimensional electric images and (II) by replicating earlier key findings showing that a normalized metric of electric image width provides an unambiguous cue for distance estimation. We then show that electric images and the distance metric vary systematically when an object is moved along the trunk. These potential ambiguities with regard to localization lead us to a spatiotemporal analysis of electric images. We introduce a new temporal metric for distance estimation that is based on the normalized spatial properties of electrical images. Finally, based on a survey of exploratory behavior, we show how objects situated at the tail, a region previously neglected, cast global electric images that extend over the whole sensory epithelium of the animals.

Endogenous presynaptic nitric oxide supports an anterograde signaling in the central nervous system.

The source size and density determine the extent of nitric oxide (NO) diffusion which critically influences NO signaling. In the brain, NO released from postsynaptic somas following NMDA-mediated activation of neuronal nitric oxide synthase (nNOS) retrogradely affects smaller presynaptic targets. By contrast, in guinea pig trigeminal motor nucleus (TMN), NO is produced presynaptically by tiny and disperse nNOS-containing terminals that innervate large nNOS-negative motoneurons expressing the soluble guanylyl-cyclase (sGC); consequently, it is uncertain whether endogenous NO supports an anterograde signaling between pre-motor terminals and postsynaptic trigeminal motoneurons. In retrogradely labeled motoneurons, we indirectly monitored NO using triazolofluorescein (DAF-2T) fluorescence, and evaluated sGC activity by confocal cGMP immunofluorescence. Multiple fibers stimulation enhanced NO content and cGMP immunofluorescence into numerous nNOS-negative motoneurons; NOS inhibitors prevented depolarization-induced effects, whereas NO donors mimicked them. Enhance of cGMP immunofluorescence required extracellular Ca(2+), a nNOS-physiological activator, and was prevented by inhibiting sGC, silencing neuronal activity or impeding NO diffusion. In conclusion, NO released presynaptically from multiple cooperative tiny fibers attains concentrations sufficient to activate sGC in many motoneurons despite of the low source/target size ratio and source dispersion; thus, endogenous NO is an effective anterograde neuromodulator. By adjusting nNOS activation, presynaptic Ca(2+) might modulate the NO diffusion field in the TMN.

Spatial facilitation is involved in flash-lag effect.

The flash-lag effect (FLE) is the perceptual phenomenon in which a flash adjacent to a continuously moving object is perceived behind it. Horizontal propagation of activity could explain a shorter latency for moving than for flashed objects but, to our knowledge, no psychophysical data supporting this has been given. We show that two concurrent moving stimuli increase the FLE, presumably due to a latency decrease in movement perception. Our results support the idea that spatial facilitation along the trajectory of a moving object reduces movement perception delay and, therefore, sustains an involvement of latency differences in FLE generation.