{"title":"A reinforcement learning model of social referencing","authors":"H. Jasso, Jochen Triesch, G. Deák","doi":"10.1109/DEVLRN.2008.4640844","DOIUrl":"https://doi.org/10.1109/DEVLRN.2008.4640844","url":null,"abstract":"We present a novel computational model of social referencing. The model replicates a classic social referencing experiment where an infant is presented with a novel object and has the choice of consulting an adultpsilas informative facial expression before reacting to the object. The infant model learns the value of consulting the adultpsilas facial expression using the temporal difference learning algorithm. The model is used to make hypotheses about the reason for a lack of social referencing found in autistic individuals, based on an aversion to faces. Comparisons are made between this reinforcement learning model and a previous model based on mood contagion.","PeriodicalId":366099,"journal":{"name":"2008 7th IEEE International Conference on Development and Learning","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121219121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Frederick Shic, K. Chawarska, Jessica Bradshaw, B. Scassellati
{"title":"Autism, eye-tracking, entropy","authors":"Frederick Shic, K. Chawarska, Jessica Bradshaw, B. Scassellati","doi":"10.1109/DEVLRN.2008.4640808","DOIUrl":"https://doi.org/10.1109/DEVLRN.2008.4640808","url":null,"abstract":"Using eye-tracking, we examine the scanning patterns of 2 year old and 4 year old toddlers with and without autism spectrum disorder as they view static images of faces. We use several measures, such as the entropy of scanning patterns, in order to characterize the differences in attention towards faces by these children. We find a differential pattern of both fine attention (towards specific regions of the face) as well as gross attention (looking at the faces at all) which seem to suggest different developmental trajectories for the two groups of children. We discuss the implications of these trends and the development of simple, effective, and robust measures and methodology for evaluating scanning patterns.","PeriodicalId":366099,"journal":{"name":"2008 7th IEEE International Conference on Development and Learning","volume":"2020 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128172831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A robotic model of the development of gaze following","authors":"Hyundo Kim, H. Jasso, G. Deák, J. Triesch","doi":"10.1109/DEVLRN.2008.4640836","DOIUrl":"https://doi.org/10.1109/DEVLRN.2008.4640836","url":null,"abstract":"For humanoid robots, the skill of gaze following is a foundational component in social interaction and imitation learning.We present a robotic system capable of learning the gaze following behavior in a real-world environment. First, the system learns to detect salient objects and to distinguish a caregiverpsilas head poses in a semi-autonomous manner. Then we present multiple scenes containing different combinations of objects and head poses to the robot head. The system learns to associate the detected head pose with correct spatial location of where potentially ldquorewardingrdquo objects would be using a biologically plausible reinforcement learning mechanism.","PeriodicalId":366099,"journal":{"name":"2008 7th IEEE International Conference on Development and Learning","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133910743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Parental action modification highlighting the goal versus the means","authors":"Y. Nagai, K. Rohlfing","doi":"10.1109/DEVLRN.2008.4640796","DOIUrl":"https://doi.org/10.1109/DEVLRN.2008.4640796","url":null,"abstract":"Parents significantly alter their infant-directed actions compared to adult-directed ones, which is assumed to assist the infantspsila processing of the actions. This paper discusses differences in parental action modification depending on whether the goal or the means is more crucial. When demonstrating a task to an infant, parents try to emphasize the important aspects of the task by suppressing or adding their movement. Our hypothesis is that in a goal-crucial task, the initial and final states of the task should be highlighted by parental actions, whereas in a means-crucial task the movement is underlined. Our analysis using a saliency-based attention model partially verified it: When focusing on the goal, parents tended to emphasize the initial and final states of the objects used in the task by taking a long pause before/after they started/fulfilled the task. When focusing on the means, parents shook the object to highlight it, which consequently made its state invisible. We discuss our findings regarding the uniqueness and commonality of the parental action modification. We also describe our contribution to the development of robots capable of imitating human actions.","PeriodicalId":366099,"journal":{"name":"2008 7th IEEE International Conference on Development and Learning","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130939443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"TAMER: Training an Agent Manually via Evaluative Reinforcement","authors":"W. B. Knox, P. Stone","doi":"10.1109/DEVLRN.2008.4640845","DOIUrl":"https://doi.org/10.1109/DEVLRN.2008.4640845","url":null,"abstract":"Though computers have surpassed humans at many tasks, especially computationally intensive ones, there are many tasks for which human expertise remains necessary and/or useful. For such tasks, it is desirable for a human to be able to transmit knowledge to a learning agent as quickly and effortlessly as possible, and, ideally, without any knowledge of the details of the agentpsilas learning process. This paper proposes a general framework called Training an Agent Manually via Evaluative Reinforcement (TAMER) that allows a human to train a learning agent to perform a common class of complex tasks simply by giving scalar reward signals in response to the agentpsilas observed actions. Specifically, in sequential decision making tasks, an agent models the humanpsilas reward function and chooses actions that it predicts will receive the most reward. Our novel algorithm is fully implemented and tested on the game Tetris. Leveraging the human trainerspsila feedback, the agent learns to clear an average of more than 50 lines by its third game, an order of magnitude faster than the best autonomous learning agents.","PeriodicalId":366099,"journal":{"name":"2008 7th IEEE International Conference on Development and Learning","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127521031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Trait or situation? ∼ Cultural differences in judgments of emotion ∼","authors":"M. Kuwabara, Ji Yeon Son, L.B. Smith","doi":"10.1109/DEVLRN.2008.4640823","DOIUrl":"https://doi.org/10.1109/DEVLRN.2008.4640823","url":null,"abstract":"Traditional research in cognition assumes that fundamental processes such as memory and attention are universal. However, a growing number of studies suggest cultural differences in the attention and evaluation of information (Masuda & Nisbet 2001; Maass, et al 2006; Markus & Kitayama 1991; Heddenn, et al 2008). One cultural comparison, between Westerners, such as Americans and Easterners such as Japanese suggest that whereas Westerners typically focus on a central single object in a scene Easterners often integrate their judgment of the focal object with surrounding contextual cues. The research reported here considers this cultural difference in the context of childrenpsilas developing understanding of emotions. The results demonstrate cultural differences in children as young as 3 and 4 years of age. In particular, Japanese children judge emotions based more on contextual information than facial expressions whereas the opposite is true for American children. The addition of language (labeling the emotions) increases the cultural differences.","PeriodicalId":366099,"journal":{"name":"2008 7th IEEE International Conference on Development and Learning","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124511283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"What does shaping mean for computational reinforcement learning?","authors":"Tom Erez, W. Smart","doi":"10.1109/DEVLRN.2008.4640832","DOIUrl":"https://doi.org/10.1109/DEVLRN.2008.4640832","url":null,"abstract":"This paper considers the role of shaping in applications of reinforcement learning, and proposes a formulation of shaping as a homotopy-continuation method. By considering reinforcement learning tasks as elements in an abstracted task space, we conceptualize shaping as a trajectory in task space, leading from simple tasks to harder ones. The solution of earlier, simpler tasks serves to initialize and facilitate the solution of later, harder tasks. We list the different ways reinforcement learning tasks may be modified, and review cases where continuation methods were employed (most of which were originally presented outside the context of shaping). We contrast our proposed view with previous work on computational shaping, and argue against the often-held view that equates shaping with a rich reward scheme. We conclude by discussing a proposed research agenda for the computational study of shaping in the context of reinforcement learning.","PeriodicalId":366099,"journal":{"name":"2008 7th IEEE International Conference on Development and Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126983418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Plunkett, J. Szemraj, D. Tilbury, T. Tardif, B. Felt, N. Kaciroti, R. Angulo-Barroso, T. Shafir, L. Wang
{"title":"Dynamic systems modeling of pre-schoolers’ response to an emotionally stressful event","authors":"M. Plunkett, J. Szemraj, D. Tilbury, T. Tardif, B. Felt, N. Kaciroti, R. Angulo-Barroso, T. Shafir, L. Wang","doi":"10.1109/DEVLRN.2008.4640828","DOIUrl":"https://doi.org/10.1109/DEVLRN.2008.4640828","url":null,"abstract":"This paper presents preliminary results for using dynamic systems models to describe physiological and behavioral responses (cortisol and activity) to emotionally stressful events. Linear discrete-time models are used to approximate the nonlinear model of the LHPA axis around an assumed nominal operating condition. Measurements are taken of cortisol (from saliva) and activity (with an accelerometer). These two measurements are considered as either inputs or outputs of the model. Modeling choices are discussed in detail. Results are presented that indicate activity is better interpreted as an input and cortisol as an output. In addition, the paper discusses briefly how the resulting dynamic systems models can be used for statistical analysis, as well as for integrating across multiple levels of stress responses.","PeriodicalId":366099,"journal":{"name":"2008 7th IEEE International Conference on Development and Learning","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115001139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Hierarchical voting experts: An unsupervised algorithm for hierarchical sequence segmentation","authors":"Matthew Miller, A. Stoytchev","doi":"10.1109/DEVLRN.2008.4640827","DOIUrl":"https://doi.org/10.1109/DEVLRN.2008.4640827","url":null,"abstract":"This paper extends the voting experts (VE) algorithm for unsupervised segmentation of sequences to create the hierarchical voting experts (HVE) algorithm for unsupervised segmentation of hierarchically structured sequences. The paper evaluates the strengths and weaknesses of the HVE algorithm to identify its proper domain of application. The paper also shows how higher order models of the sequence data can be used to improve lower level segmentation accuracy.","PeriodicalId":366099,"journal":{"name":"2008 7th IEEE International Conference on Development and Learning","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130155303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Motor initiated expectation through top-down connections as abstract context in a physical world","authors":"M. Luciw, J. Weng, Shuqing Zeng","doi":"10.1109/DEVLRN.2008.4640815","DOIUrl":"https://doi.org/10.1109/DEVLRN.2008.4640815","url":null,"abstract":"Recently, it has been shown that top-down connections improve recognition in supervised learning. In the work presented here, we show how top-down connections represent temporal context as expectation and how such expectation assists perception in a continuously changing physical world, with which an agent interacts during its developmental learning. In experiments in object recognition and vehicle recognition using two types of networks (which derive either global or local features), it is shown how expectation greatly improves performance, to nearly 100% after the transition periods. We also analyze why expectation will improve performance in such real world contexts.","PeriodicalId":366099,"journal":{"name":"2008 7th IEEE International Conference on Development and Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131073632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}