A lesson from robotics: Modeling infants as autonomous agents

While 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 paper 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 eyemovement model, and discussing the implications of this work for infant cognition research

Visual Expectations in Infants: Evaluating the Gaze-Direction Model

Schlesinger (in press) 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" (1986; Baillargeon & DeVos, 1991). We find 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

Optimal control methods for simulating the perception of causality in young infants

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 impications for developmental theory and research are discusse

Image Free-Viewing as Intrinsically-Motivated Exploration: Estimating the Learnability of Center-of-Gaze Image Samples in Infants and Adults

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

Status of the tiger beetle Cicindela hirticollis Say (Coleoptera: Cicindelidae) in New York City and on Long Island, New York, USA

Coastal species are under considerable threat from recreational activities and climate change. The tiger beetle Cicindela hirticollis Say (Coleoptera: Cicindelidae) was recorded historically from 30 locations along the shores of New York City and Long Island, New York. We conducted surveys for extant populations of this species at 40 sites from 1989 to 2010. Adults of C. hirticollis were found at 13 beaches. Only four sites had 40 or more adults of C. hirticollis active at the time the beach was surveyed. No beetles were detected on the large coastal beaches that were formerly occupied by this species. Many coastal beaches of New York receive heavy human foot and vehicle traffic and are therefore unlikely to provide suitable habitat for C. hirticollis without a shift in beach management that recognizes the potential of some beaches as wilderness systems capable of supporting the full array of beach-dependent species

An external focus of attention enhances manual tracking performance

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 directly on vision

Mechanisms of action of hESC-secreted proteins that enhance human and mouse myogenesis.

Adult stem cells grow poorly in vitro compared to embryonic stem cells, and in vivo stem cell maintenance and proliferation by tissue niches progressively deteriorates with age. We previously reported that factors produced by human embryonic stem cells (hESCs) support a robust regenerative capacity for adult and old mouse muscle stem/progenitor cells. Here we extend these findings to human muscle progenitors and investigate underlying molecular mechanisms. Our results demonstrate that hESC-conditioned medium enhanced the proliferation of mouse and human muscle progenitors. Furthermore, hESC-produced factors activated MAPK and Notch signaling in human myogenic progenitors, and Delta/Notch-1 activation was dependent on MAPK/pERK. The Wnt, TGF-β and BMP/pSmad1,5,8 pathways were unresponsive to hESC-produced factors, but BMP signaling was dependent on intact MAPK/pERK. c-Myc, p57, and p18 were key effectors of the enhanced myogenesis promoted by the hECS factors. To define some of the active ingredients of the hESC-secretome which may have therapeutic potential, a comparative proteomic antibody array analysis was performed and identified several putative proteins, including FGF2, 6 and 19 which as ligands for MAPK signaling, were investigated in more detail. These studies emphasize that a youthful signaling of multiple signaling pathways is responsible for the pro-regenerative activity of the hESC factors

Prediction-learning in Infants as a Mechanism for Gaze Control during Object Exploration

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\u27 gaze patterns during image free-viewing (Schlesinger & Amso, 2013) provides support for this idea. In particular, this work demonstrates that infants\u27 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\u27 gaze patterns as they view a moving, partially-occluded object. We first use infants\u27 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

A Comparison Study of Saliency Models for Fixation Prediction on Infants and Adults

Various saliency models have been developed over the years. The performance of saliency models is typically evaluated based on databases of experimentally recorded adult eye fixations. Although studies on infant gaze patterns have attracted much attention recently, saliency based models have not been widely applied for prediction of infant gaze patterns. In this study, we conduct a comprehensive comparison study of eight state-ofthe- art saliency models on predictions of experimentally captured fixations from infants and adults. Seven evaluation metrics are used to evaluate and compare the performance of saliency models. The results demonstrate a consistent performance of saliency models predicting adult fixations over infant fixations in terms of overlap, center fitting, intersection, information loss of approximation, and spatial distance between the distributions of saliency map and fixation map. In saliency and baselines models performance ranking, the results show that GBVS and Itti models are among the top three contenders, infants and adults have bias toward the centers of images, and all models and the center baseline model outperformed the chance baseline model