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Vision Lab
Life Science II, Rm 219
Carbondale, IL 62901
618-453-3537
visionlabsiuc@gmail.com
Main Content
Most of our recent publications (plus a few in press or preparation) are listed below. In many cases, an abstract is available, and sometimes, an electronic preprint too. Please email us if you would like a copy of any final, published reprints.
Journal Articles
Schlesinger, M., Johnson, S.P., & Amso, D. (2014). Prediction-learning in infants as a mechanism for gaze control during object exploration. Frontiers in Perception Science, 5, 1-12. doi: 10.3389/fpsyg.2014.00441 View Abstract
Abstract: We are pursuing the hypothesis that visual exploration and learning in young infants is achieved by producing gaze-sample sequences that are sequentially predictable. Our recent analysis of infants' gaze patterns during image free-viewing (Schlesinger & Amso, 2013) provides support for this idea. In particular, this work demonstrates that infants' gaze samples are more easily learnable than those produced by adults, as well as those produced by three artificial-observer models. In the current study, we extend these findings to a well-studied object-perception task, by investigating 3-month-olds' gaze patterns as they view a moving, partially-occluded object. We first use infants' gaze data from this task to produce a set of corresponding center-of-gaze (COG) sequences. Next, we generate two simulated sets of COG samples, from image-saliency and random-gaze models, respectively. Finally, we generate learnability estimates for the three sets of COG samples by presenting each as a training set to an SRN. There are two key findings. First, as predicted, infants COG samples from the occluded-object task are learned by a pool of simple recurrent networks faster than the samples produced by the yoked, artificial-observer models. Second, we also find that resetting activity in the recurrent layer increases the network’s prediction errors, which further implicates the presence of temporal structure in infants’ COG sequences. We conclude by relating our findings to the role of image-saliency and prediction-learning during the development of object perception.
Abstract: We propose that free viewing of natural images in human infants can be understood and analyzed as the product of intrinsically-motivated visual exploration. We examined this idea by first generating five sets of center-of-gaze (COG) image samples, which were derived by presenting a series of natural images to groups of both real observers (i.e., 9-month-olds and adults) and artificial observers (i.e., an image-saliency model, an image-entropy model, and a random-gaze model). In order to assess the sequential learnability of the COG samples, we paired each group of samples with a simple recurrent network, which was trained to reproduce the corresponding sequence of COG samples. We then asked whether an intrinsically-motivated artificial agent would learn to identify the most successful network. In Simulation 1, the agent was rewarded for selecting the observer group and network with the lowest prediction errors, while in Simulation 2 the agent was rewarded for selecting the observer group and network with the largest rate of improvement. Our prediction was that if visual exploration in infants is intrinsically-motivated – and more specifically, the goal of exploration is to learn to produce sequentially-predictable gaze patterns – then the agent would show a preference for the COG samples produced by the infants over the other four observer groups. The results from both simulations supported our prediction. We conclude by highlighting the implications of our approach for understanding visual development in infants, and discussing how the model can be elaborated and improved.
Abstract: The present study investigated the enhancement effects of an external focus-of-attention (FOA) in the context of a manual-tracking task, in which participants tracked both visible and occluded targets. Three conditions were compared, which manipulated the distance of the FOA from the participant as well as the external/internal dimension. As expected, an external FOA resulted in lower tracking errors than an internal FOA. In addition, analyses of participants' movement patterns revealed a systematic shift toward higher-frequency movements in the external FOA condition, consistent with the idea that an external FOA exploits the natural movement dynamics available during skilled action. Finally, target visibility did not influence the effect of focused attention on tracking performance, which provides evidence for the proposal that the mechanisms that underlie FOA do not depend on directly on vision.
Abstract: We recently proposed a multi-channel, image-filtering model for simulating the development of visual selective attention in young infants (Schlesinger, Amso, and Johnson, 2007). The model not only captures the performance of 3-month-olds’ on a visual search task, but also implicates two cortical regions that may play a role in the development of visual selective attention. In the current simulation study, we used the same model to simulate 3-month-olds’ performance on a second measure, the perceptual unity task. Two parameters in the model—corresponding to areas in the occipital and parietal cortices—were systematically varied while the gaze patterns produced by the model were recorded and subsequently analyzed. Three key findings emerged from the simulation study. First, the model successfully replicated the performance of 3-month-olds on the unity perception task. Second, the model also helps to explain the improved performance of 2-month-olds when the size of the occluder in the unity perception task is reduced. Third, in contrast to our previous simulation results, variation in only one of the two cortical regions simulated (i.e., recurrent activity in posterior parietal cortex) resulted in a performance pattern that matched 3-month-olds. These findings provide additional support for our hypothesis that the development of perceptual completion in early infancy is promoted by progressive improvements in visual selective attention and oculomotor skill.
Abstract: Does modeling matter? We address this question by providing a broad survey of the computational models of cognitive development that have been proposed and studied over the last three decades. Our overview is divided into three parts. First, we begin by noting both the advantages and limitations of computational models. We then describe four key dimensions across which models of development can be organized and classified. With this taxonomy in hand, we focus in the third section on how the modeling enterprise has evolved over time. In particular, we separate the timeline into three overlapping historical “waves”, and highlight how each wave of models has not only been shaped by developmental theory and behavioral research, but in return also provided valuable insights and innovations to the study of cognitive development.
Abstract: We present the broad outlines of a roadmap toward human-level artificial general intelligence (henceforth, AGI). We begin by discussing AGI in general, adopting a pragmatic goal for its attainment and a necessary foundation of characteristics and requirements. An initial capability landscape will be presented, drawing on major themes from developmental psychology and illuminated by mathematical, physiological, and information-processsing perspectives. The challenge of identifying appropriate tasks and environments for measuring AGI will be addressed, and seven scenarios will be presented as milestones suggesting a roadmap across the AGI landscape along with directions for future research and collaboration.
Abstract: Recent work by Amso and Johnson (2006) implicates the role of visual selective attention in the development of perceptual completion during early infancy. In the current paper, we extend this finding by simulating the performance of 3-month-olds on a visual search task, using a multi-channel, image-filtering model of early visual processing. Model parameters were systematically varied to simulate developmental change in three neural components of visual selective attention: degree of oculomotor noise, growth of horizontal connections in visual cortex, and duration of recurrent processing in parietal cortex. While two of the three components—horizontal connections and recurrent parietal processing—are each able to account for the visual search performance of 3-month-olds, recurrent parietal processing also suggests a coherent pattern of developmental change in visual selective attention during early infancy. We conclude by highlighting plausible neural mechanisms for modulating recurrent parietal activity, including the development of feedback from prefrontal cortex.
Abstract: The capacity for infants to form mental representations of hidden or occluded objects can be decomposed into two tasks: one process that identifies salient objects and a second complementary process that identifies salient locations. This functional decomposition is supported by the distinction between dorsal and ventral extrastriate visual processing in the primate visual system. This approach is illustrated by presenting an eye-movement model that incorporates both dorsal and ventral processing streams and by using the model to simulate infants’ reactions to possible and impossible events from an infant looking-time study (R. Baillargeon, “Representing the existence and the location of hidden objects: object permanence in 6- and 8-month-old infants”, Cognition, 23, pp. 21–41, 1986.). As expected, the model highlights how the dorsal system is sensitive to the location of a key feature in these events (i.e. the location of an obstacle), whereas the ventral system responds equivalently to the possible and impossible events. These results are used to help explain infants’reactions in looking-time studies.
Abstract: The emerging field of Evolutionary Computation (EC), inspired by neo-Darwinian principles (e.g. natural selection, mutation, etc.), offers developmental psychologists a wide array of mathematical tools for simulating ontogenetic processes. In this brief review, I begin by highlighting three of the approaches that EC researchers employ (Artificial Life, evolutionary robotics and comparative stochastic optimization). I then focus on the advantages of using comparative stochastic optimization as a method for studying development. As a concrete example, I illustrate the design and implementation of an EC model that simulates the development of reaching in young infants.Schlesinger, M., & Parisi, D. (Eds.). (2004). Beyond backprop: Emerging trends in connectionist models of development. [Special section]. Developmental Science, 7, 131-132. Includes contributions from Sylvain Sirois, Yuko Munakata, Jason Pflaffy, Maartje Raijmakers, Peter Molenaar, and David Klahr.
Abstract: Although computational models are playing an increasingly important role in developmental psychology, at least one lesson from robotics is still being learned: Modeling epigenetic processes often requires simulating an embodied, autonomous organism. This article first contrasts prevailing models of infant cognition with an agent-based approach. A series of infant studies by Baillargeon (1986; Baillargeon & DeVos, 1991) is described, and an eye-movement model is then used to simulate infants’ visual activity in this study. I conclude by describing three behavioral predictions of the eye-movement model and discussing the implications of this work for infant cognition research.
Abstract: Schlesinger (2003, Adaptive Behavior, 11: 97–107) recently proposed a model of eye movements as a tool for investigating infants’ visual expectations. In the present study, this gaze-direction model was evaluated by: (a) generating a set of predictions concerning how infants distribute their attention during possible and impossible events; and (b) testing these predictions in a replication of Baillargeon’s ‘car study’ (Baillargeon, 1986, Cognition, 23: 21–41, Baillargeon and DeVos, Child Development, 62: 1227–1246). We found that the model successfully predicts general features of infants’ gaze direction, but not specific differences obtained during the possible and impossible events. The implications of these results for infant cognition research and theory are discussed.
Abstract: Artificial Life is the study of all phenomena of the living world through their reproduction in artificial systems.We argue that Artificial Life models of evolution and development offer a new set of theoretical and methodological tools for investigating Piaget’s ideas. The concept of an Artificial Life Neural Network (ALNN) is first introduced, and contrasted with the study of other recent approaches to modeling development. We then illustrate how several key elements of Piaget’s theory of cognitive development (e.g., sensorimotor schemata, perception-action integration) can be investigated within the Artificial Life framework. We conclude by discussing possible new directions of Artificial Life research that will help to elaborate and extend Piaget’s developmental framework.
Abstract: The agent-based approach emphasizes the importance of learning through organism-environment interaction. This approach is part of a recent trend in computational models of learning and development toward studying autonomous organisms that are embedded in virtual or real environments. In this paper we introduce the concepts of online and offline sampling and highlight the role of online sampling in agent-based models. After comparing the strengths of each approach for modeling particular developmental phenomena and research questions, we describe a recent agent-based model of infant causal perception. We conclude by discussing some of the present limitations of agent-based models and suggesting how these challenges may be addressed.
Abstract: By the onset of reaching, young infants are already able to keep track of the position of their hand by using visual feedback from the target and proprioceptive feedback from the arm. How is this multimodal coordination achieved? We propose that infants learn to coordinate vision and proprioception by using tactile feedback from the target. In order to evaluate this hypothesis, we employ an evolutionary-based learning algorithm as a proxy for trial-and-error sensorimotor development in young infants. A series of simulation studies illustrate how touch: 1) helps coordinate vision and proprioception; 2) facilitates an efficient reaching strategy; and 3) promotes intermodal recalibration when the coordination is perturbed. We present two developmental predictions generated by the model and discuss the relative importance of visual and tactile feedback while learning to reach.
Abstract: Trial-and-error learning strategies play a central role in sensorimotor development during early infancy. However, learning to reach by trial-and-error normally requires a slow and laborious search through the space of possible movements. We propose a computational model of reaching based on the notion that early sensorimotor control is driven by the generation of exploratory movements, followed by the selection and maintenance of adaptive movement patterns. We find that, instead of exhaustively exploring the full search space of movement patterns, the model exploits several emergent constraints that limit the initial size of the movement search space. These constraints exploit both mechanical and kinematic properties of the reaching task. We relate these results to the development of reaching during infancy, and discuss recent findings that have identified similar constraints in young infants.
Abstract: Infants’ developing causal expectations for the outcome of a simple tool-use event from ages 8 to 12 months were investigated. Causal expectations were studied by comparing infants’ developing tool-use actions (i.e. as tool-use agents) with their developing perceptual reactions (i.e. as tool-use observers) to possible and impossible tool-use events. In Experiment 1, tool-use actions were studied by presenting infants, ages 8 and 12 months, with tool-use object-retrieval problems. In Experiment 2, a second age-matched sample of infants watched a comparable series of possible and impossible tool-use events in which a tool was used to retrieve a goal-object. Two core related findings were made. First, infants’ causal action and causal perception develop in parallel. In both action and perception, supporting tool-use develops before surrounding tool-use. Second, infants’ tool-use action develops before their causal perception of comparable tool-use events. The findings support the constructivist hypothesis that infants’ causal actions may develop before and inform their causal perceptions.Langer, J., Schlesinger, M., Spinozzi, G., & Natale, F. (1998). Developing classification in action: I. Human infants. Human Evolution, 13, 107-124.
Spinozzi, G., Natale, F., Langer, J., & Schlesinger, M. (1998). Developing classification in action: II. Young chimpanzees (Pan troglodytes). Human Evolution, 13, 125-139.
Proceedings Papers
Mahdi, A., Schlesinger, M., Amso, D., & Qin, J. (2015). Infants' gaze pattern analysis using contrast entropy minimization. To appear in Proceedings of the Fifth Joint IEEE Conference on Development and Learning and on Epigenetic Robotics. New York: IEEE.
Schlesinger, M., Johnson, S.P., & Amso, D. (2015). Do infants’ gaze sequences predict their looking time? Testing the sequential-learnability model. To appear in Proceedings of the Fifth Joint IEEE Conference on Development and Learning and on Epigenetic Robotics. New York: IEEE.
Abstract: Our recent work, using both computer-animated and natural images, demonstrates that infants’ eye movements are sequentially structured. In particular, we employ a simple recurrent network (SRN) to estimate the learnability of infants’ gaze sequences. The current study directly compares this learnability metric with infants’ global looking time on a trial-by-trial basis, during both an habituation display and two posthabituation test displays. The results confirmed our prediction that the relative learnability of infants’ gaze sequences during the latter part of the habituation phase predicts global looking time. We also found that learnability during the later habituation trials predicts looking time to one of the two posthabituation test displays. These findings provide support for our hypothesis that infants’ visual exploration is guided implicitly by the goal to generate gaze patterns that are sequentially predictable.
Abstract: In the present study, we assess the iCub simulator as a platform for investigating perceptual development in human infants. In particular, we evaluate the simulator as a quasi-realistic virtual environment for conducting object-perception experiments. As a case study, we describe our simulation of the perceptual-completion task, which measures infants’ perception of a moving, partially-occluded object. We present here two simulation studies. The first study replicates our previous findings, which demonstrate that increasing spatial competition in our eye-movement model results in increased attention toward the occluded object. We then extend these findings in a second simulation study by demonstrating that reducing the width of the occluder also increases attention toward the occluded object, though unexpectedly, only for low levels of spatial competition. We conclude by highlighting the value of the iCub simulator as a research tool for psychologists, and also by noting how our eye-movement model can be further improved and elaborated.
Abstract: Our previous work provides support for the idea that the development of visual perception in early infancy depends on progressive improvements in oculomotor skill. In particular, we have proposed and tested an eye-movement model that successfully reproduces infants’ gaze patterns on two measures of visual attention. However, this result is due to explicit hand-tuning of a key parameter in the model. In the current simulation study, we investigate whether manipulating this parameter (i.e., the duration of spatial competition) enhances visual prediction learning. As expected, we find that prediction learning becomes more accurate as spatial competition in the eye-movement model is increased. This finding suggests that visual prediction learning can provide a meaningful error-feedback signal, which can be used to modulate spatial competition in the eye-movement model.Schlesinger, M. (2008). Heterochrony: It’s (all) about time! In M. Schlesinger, L. Berthouze, & C. Balkenius (Eds.), Proceedings of the Eighth International Workshop on Epigenetic Robotics: Modeling Cognitive Development in Robotic Systems (pp. 111-117). Sweden: Lund University Cognitive Studies.
Schlesinger, M., Amso, D., & Johnson, S.P. (2007a). Simulating infants' gaze patterns during the development of perceptual completion. In L. Berthouze, C.G. Prince, M. Littman, H. Kozima, & C. Balkenius (Eds.), Proceedings of the Seventh International Workshop on Epigenetic Robotics: Modeling Cognitive Development in Robotic Systems (pp. 157-164). Sweden: Lund University Cognitive Studies.
Schlesinger, M. (2006b). Neural constraints on the development of perceptual completion: A computational account. In the Proceedings of the Fifth International Conference on Development and Learning. Bloomington, IN: Department of Psychological and Brain Sciences.
Schlesinger, M., & Limongi, R. (2005). Towards a what-and-where model of infants’ object representations. In D. Blank & L. Meeden (Eds.), Proceedings of the AAAI 2005 Spring Symposium on Developmental Robotics.
Schlesinger, M., & Casey, P. (2003b). Visual expectations in infants: Evaluating the gaze-direction model. In C.G. Prince, L. Berthouze, H. Kozima, D. Bullock, G. Stojanov, & C. Balkenius (Eds.), Proceedings of the Third International Workshop on Epigenetic Robotics: Modeling Cognitive Development in Robotic Systems (pp. 115-122). Sweden: Lund University Cognitive Studies.
Schlesinger, M., & Young, M.E. (2003). Examining the role of prediction in infants' physical knowledge. In R. Alterman and D. Kirsh (Eds.), Proceedings of the Twenty-Fifth Annual Meeting of the Cognitive Science Society (pp. 1047-1052). Boston: Cognitive Science Society.
Schlesinger, M. (2002). A lesson from robotics: Modeling infants as autonomous agents. In C.G. Prince, Y. Demiris, Y. Marom, H. Kozima, & C. Balkenius (Eds.), Proceedings of the Second International Workshop on Epigenetic Robotics: Modeling Cognitive Development in Robotic Systems (pp. 133-140). Sweden: Lund University Cognitive Studies.
Berthier, N.E., Barto, A.G., and Schlesinger, M. (2000). Learning and dynamics. Proceedings of the NSF DARPA Conference on Learning and Development.
Abstract: There is a growing debate among developmental theorists concerning the perception of causality in young infants. Some theorists advocate a top-down view, e.g., that infants reason about causal events on the basis of intuitive physical principles. Others argue instead for a bottom-up view of infant causal knowledge, in which causal perception emerges from a simple set of associative learning rules. In order to test the limits of the bottom-up view, we propose an optimal control model (OCM) of infant causal perception. OCM is trained to find an optimal pattern of eye movements for maintaining sight of a target object. We first present a series of simulations which illustrate OCM’s ability to anticipate the outcome of novel, occluded causal events, and then compare OCM’s performance with that of 9-month-old infants. The implications for developmental theory and research are discussed.
Books/Edited Proceedings/Chapters
Schlesinger, M., & Cangelosi, A. (2015). Developmental robotics: From babies to robots. Cambridge, MA: MIT Press.
Schlesinger, M. (2013). Investigating the origins of intrinsic motivation in human infants. In G. Baldassarre & M. Mirolli (Eds.), Intrinsically motivated learning in natural and artificial systems (pp. 367-392). Berlin: Springer-Verlag.
Schlesinger, M., Movellan, J., Morrison, C., Nagai, Y., Fasel, I., & Morse, A. (Eds.) (2012). Proceedings of the Second Joint IEEE Conference on Development and Learning and on Epigenetic Robotics. New York: IEEE.
Cangelosi, A., Triesch, J., Fasel, I., Rohlfing, K., Nori, F., Oudeyer, P.-Y., Schlesinger, M., and Nagai, Y. (Eds.) (2011). Proceedings of the First Joint IEEE Conference on Development and Learning and on Epigenetic Robotics. New York: IEEE.
Schlesinger, M. (2009a). The robot as a new frontier for connectionism and dynamic systems theory. Invited chapter in J.P. Spencer, M.S.C Thomas, & J.L. McClelland (Eds.), Toward a unified theory of development: Connectionism and dynamic systems theory re-considered (pp. 182-199). New York: Oxford University Press.
Schlesinger, M. (2009b). Connectionism. Invited chapter in E.M. Anderman & L.H. Anderman (Eds.), Psychology of classroom learning: An encyclopedia, (pp. 260-262). New York: Cengage.
Schlesinger, M., Berthouze, L., & Balkenius, C. (Eds.). (2008). Proceedings of the Eighth International Workshop on Epigenetic Robotics: Modeling Cognitive Development in Robotic Systems. Sweden: Lund University Cognitive Studies.
Schlesinger, M., & Parisi, D. (2007). Connectionism in an Artificial Life perspective: Simulating motor, cognitive, and language development. In D. Mareschal, S. Sirois, G. Westermann, & M.H. Johnson (Eds.), Neuroconstructivism: Vol. 2. Prospectives and prospects (pp. 129-158). Oxford, UK: Oxford University Press.
Langer, J., Rivera, S., Schlesinger, M., & Wakeley, A. (2003). Cognitive development in the first two years. In J. Valsiner and K. Connolly (Eds.), Handbook of developmental psychology (pp. 141-171). London: Sage Publications.
Schlesinger, M., & Parisi, D. (2001c). Coordinating multiple sensory modalities while learning to reach. In B. French and J. Sougne (Eds.), Connectionist models of learning, development, and evolution, pp. 113-122. Sage: London.
Commentaries/Reviews
TopSchlesinger, M. (2015). The interface theory of perception leaves me hungry for more: Commentary on Hoffman, Singh, and Prakash, “The interface theory of perception.” Psychonomic Bulletin and Review, 22, 1548-1550. doi: 10.3758/s13423-014-0776-1
Schlesinger, M. & Amso, D. (2011). Oculomotor skill supports the development of object representations. Behavioral and Brain Sciences, 34, 147-148.
Schlesinger, M. (2006c). Review of the book Computational Developmental Psychology. Philosophical Psychology, 19, 557-561.Schlesinger, M. (2001a). Reexamining visual cognition in human infants: On the necessity of representation. Behavioral and Brain Sciences, 24, 1003-1004.
Schlesinger, M. (2001b). Building a better baby: Embodied models of infant cognition. Trends in Cognitive Sciences, 5, 139.