7,117 research outputs found

    The me in memory:the role of the self in autobiographical memory development

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    This paper tests the hypothesis that self development plays a role in the offset of childhood amnesia; assessing the importance of both the capacity to anchor a memory to the self-concept, and the strength of the self-concept as an anchor. We demonstrate for the first time that the volume of 3- to 6-year-old’s specific autobiographical memories is predicted by both the volume of their self-knowledge, and their capacity for self-source monitoring within self-referencing paradigms (N =186). Moreover, there is a bidirectional relationship between self and memory, such that autobiographical memory mediates the link between self-source monitoring and self-knowledge. These predictive relationships suggests that the self memory system is active in early childhood

    A Top-Down Approach for a Synthetic Autobiographical Memory System

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    Autobiographical memory (AM) refers to the organisation of one’s experience into a coherent narrative. The exact neural mechanisms responsible for the manifestation of AM in humans are unknown. On the other hand, the field of psychology has provided us with useful understanding about the functionality of a bio-inspired synthetic AM (SAM) system, in a higher level of description. This paper is concerned with a top-down approach to SAM, where known components and organisation guide the architecture but the unknown details of each module are abstracted. By using Bayesian latent variable models we obtain a transparent SAM system with which we can interact in a structured way. This allows us to reveal the properties of specific sub-modules and map them to functionality observed in biological systems. The top-down approach can cope well with the high performance requirements of a bio-inspired cognitive system. This is demonstrated in experiments using faces data

    DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self

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    This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot. The framework, based on a biologically-grounded theory of the brain and mind, integrates a reactive interaction engine, a number of state-of-the art perceptual and motor learning algorithms, as well as planning abilities and an autobiographical memory. The architecture as a whole drives the robot behavior to solve the symbol grounding problem, acquire language capabilities, execute goal-oriented behavior, and express a verbal narrative of its own experience in the world. We validate our approach in human-robot interaction experiments with the iCub humanoid robot, showing that the proposed cognitive architecture can be applied in real time within a realistic scenario and that it can be used with naive users

    Lifelong Augmentation of Multi-Modal Streaming Autobiographical Memories

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    Robot systems that interact with humans over extended periods of time will benefit from storing and recalling large amounts of accumulated sensorimotor and interaction data. We provide a principled framework for the cumulative organisation of streaming autobiographical data so that data can be continuously processed and augmented as the processing and reasoning abilities of the agent develop and further interactions with humans take place. As an example, we show how a kinematic structure learning algorithm reasons a-posteriori about the skeleton of a human hand. A partner can be asked to provide feedback about the augmented memories, which can in turn be supplied to the reasoning processes in order to adapt their parameters. We employ active, multi-modal remembering, so the robot as well as humans can gain insights of both the original and augmented memories. Our framework is capable of storing discrete and continuous data in real-time. The data can cover multiple modalities and several layers of abstraction (e.g. from raw sound signals over sentences to extracted meanings). We show a typical interaction with a human partner using an iCub humanoid robot. The framework is implemented in a platform-independent manner. In particular, we validate its multi platform capabilities using the iCub, Baxter and NAO robots. We also provide an interface to cloud based services, which allow automatic annotation of episodes. Our framework is geared towards the developmental robotics community, as it 1) provides a variety of interfaces for other modules, 2) unifies previous works on autobiographical memory, and 3) is licensed as open source software

    Developmental Bayesian Optimization of Black-Box with Visual Similarity-Based Transfer Learning

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    We present a developmental framework based on a long-term memory and reasoning mechanisms (Vision Similarity and Bayesian Optimisation). This architecture allows a robot to optimize autonomously hyper-parameters that need to be tuned from any action and/or vision module, treated as a black-box. The learning can take advantage of past experiences (stored in the episodic and procedural memories) in order to warm-start the exploration using a set of hyper-parameters previously optimized from objects similar to the new unknown one (stored in a semantic memory). As example, the system has been used to optimized 9 continuous hyper-parameters of a professional software (Kamido) both in simulation and with a real robot (industrial robotic arm Fanuc) with a total of 13 different objects. The robot is able to find a good object-specific optimization in 68 (simulation) or 40 (real) trials. In simulation, we demonstrate the benefit of the transfer learning based on visual similarity, as opposed to an amnesic learning (i.e. learning from scratch all the time). Moreover, with the real robot, we show that the method consistently outperforms the manual optimization from an expert with less than 2 hours of training time to achieve more than 88% of success

    Storytelling and story-acting: co-construction in action

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    In the light of sustained interest in the potential value of young children’s narrative play, this paper examines Vivian Gussin Paley’s (1990) approach to storytelling and story-acting, in this case with three to five year-olds. It scrutinizes how children’s narratives are co-constructed during adult-child and peer interactions through spoken and embodied modes, as their stories are scribed by an adult and later dramatised by their peers. Data are drawn from an evaluation of an eight-week training programme, based on Paley’s approach, designed for early years professionals and undertaken in different geographic and demographic locations in England. Naturalistic data collection techniques including video and field notes were used to record the storytelling and story-acting of 18 case study children. The resultant data were subject to close discursive and multimodal analysis of storytelling and story-acting interactions. Findings reveal discursive co-construction ‘in action’ and illustrate how the child story-tellers, story actors and practitioners co-construct narratives through complex combinations of gaze, body posture and speech in responsive and finely-tuned interactional patterns. The study contributes significantly to knowledge about how young children’s narratives are co-constructed through multiple modes in the classroom

    Experiences of aiding autobiographical memory Using the SenseCam

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    Human memory is a dynamic system that makes accessible certain memories of events based on a hierarchy of information, arguably driven by personal significance. Not all events are remembered, but those that are tend to be more psychologically relevant. In contrast, lifelogging is the process of automatically recording aspects of one's life in digital form without loss of information. In this article we share our experiences in designing computer-based solutions to assist people review their visual lifelogs and address this contrast. The technical basis for our work is automatically segmenting visual lifelogs into events, allowing event similarity and event importance to be computed, ideas that are motivated by cognitive science considerations of how human memory works and can be assisted. Our work has been based on visual lifelogs gathered by dozens of people, some of them with collections spanning multiple years. In this review article we summarize a series of studies that have led to the development of a browser that is based on human memory systems and discuss the inherent tension in storing large amounts of data but making the most relevant material the most accessible

    Autobiographisches Gedächtnis, der Einfluss von frühkindlichem Stress und neuronalen Korrelaten

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    Vandekerckhove M. Autobiographical memory begins and ends with the self : autobiographical memory, consciousness, the influence of stress and neural correlates. Bielefeld (Germany): Bielefeld University; 2004.The general purpose of this thesis is to study some fundamental, yet still open questions about autobiographical memory and related consciousness, the influence of early stress on the brain and autobiographical memory and its neural correlates. The goal is to make some progress in the definitory confusion and conceptual understanding of the relationship of these concepts starting from a developmental point of view. In the attempt to throw some light on what can fall under the sum of autobiographical memory-related themes, no overview or review of the existing answers will be given but rather, after extended research of the theoretical and empirical literature and own empirical work, a new clarifying point of view of the different phenomena. Considering the close relationship between the experience of the world, consciousness and autobiographical memory, the first question that becomes addressed in the theoretical part is how a child develops autobiographical memory and what Tulving entitles as associated "autonoetic consciousness", a self-reflective mental state of awareness in time and space (Tulving, 1985; 2002). The second question is concerned with how early childhood can form the basal template in the buffering and facilitation of influences of stress on the brain and also on autobiographical memory, with psychogenic amnesia and altered autonoetic consciousness as a prototypical example. Autobiographical old memories are as a result of a high degree of anatomical interconnectivity and cognitive complexity most vulnerable to brain damage and other external and internal influences such as stress (Markowitsch, 1995; 1999; 2000; 2002; 2003; Tulving and Markowitsch, 1998). The second empirical part of the thesis is primarily focused on the fundamental research of autobiographic memory and its neural correlates. The different focuses within the chapters will bring up more questions for theoretical thinking and empirical research about the same complexity of which individuals are made of

    Experiences of aiding autobiographical memory using the sensecam

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    Human memory is a dynamic system that makes accessible certain memories of events based on a hierarchy of information, arguably driven by personal significance. Not all events are remembered, but those that are tend to be more psychologically relevant. In contrast, lifelogging is the process of automatically recording aspects of one's life in digital form without loss of information. In this article we share our experiences in designing computer-based solutions to assist people review their visual lifelogs and address this contrast. The technical basis for our work is automatically segmenting visual lifelogs into events, allowing event similarity and event importance to be computed, ideas that are motivated by cognitive science considerations of how human memory works and can be assisted. Our work has been based on visual lifelogs gathered by dozens of people, some of them with collections spanning multiple years. In this review article we summarize a series of studies that have led to the development of a browser that is based on human memory systems and discuss the inherent tension in storing large amounts of data but making the most relevant material the most accessible

    The Psychology of Intelligence Analysis: Drivers of Prediction Accuracy in World Politics

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    This article extends psychological methods and concepts into a domain that is as profoundly consequential as it is poorly understood: intelligence analysis. We report findings from a geopolitical forecasting tournament that assessed the accuracy of more than 150,000 forecasts of 743 participants on 199 events occurring over 2 years. Participants were above average in intelligence and political knowledge relative to the general population. Individual differences in performance emerged, and forecasting skills were surprisingly consistent over time. Key predictors were (a) dispositional variables of cognitive ability, political knowledge, and open-mindedness; (b) situational variables of training in probabilistic reasoning and participation in collaborative teams that shared information and discussed rationales (Mellers, Ungar, et al., 2014); and (c) behavioral variables of deliberation time and frequency of belief updating. We developed a profile of the best forecasters; they were better at inductive reasoning, pattern detection, cognitive flexibility, and open-mindedness. They had greater understanding of geopolitics, training in probabilistic reasoning, and opportunities to succeed in cognitively enriched team environments. Last but not least, they viewed forecasting as a skill that required deliberate practice, sustained effort, and constant monitoring of current affairs
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