We readily acquire memories of events and situations in our daily lives. There is a broad consensus that the hippocampal system (HS) plays a critical role in the encoding and retrieval of such "episodic" memories. But how the HS subserves this mnemonic function is not fully understood. This article presents a computational model, SMRITI,that demonstrates how a transient pattern of activity representing an event can be transformed rapidly into a persistent and robust memory trace as a result of long-term potentiation within structures whose architecture and circuitry resemble those of the HS. Predictions and implications of the model are discussed. LONG ABSTRACT We readily remember events and situations in our daily lives and rapidly acquire memories of specific events by watching a telecast or reading a newspaper. There is a broad consensus that the hippocampal system (HS), consisting of the hippocampal formation and neighboring cortical areas, plays a critical role in the encoding and retrieval of such "episodic" memories. But how the HS subserves this mnemonic function is not fully understood. This article presents a computational model, SMRITI, that demonstrates how a cortically expressed transient pattern of activity representing an event can be transformed rapidly into a persistent and robust memory trace as a result of long-term potentiation within structures whose architecture and circuitry resemble those of the HS. Memory traces formed by the model respond to partial cues, and at the same time, reject similar but erroneous cues. During retrieval these memory traces, acting in concert with cortical circuits encoding semantic, causal, and procedural knowledge, can recreate activation-based representations of memorized events. The model explicates the representa...

Software understanding for documentation, maintenance or evolution is one of the longest-standing problems in Computer Science. The use of “high-level” programming paradigms and object-oriented languages helps, but fundamentally remains far from solving the problem. Most programming languages and systems have fallen prey to the assumption that they are supposed to capture idealized models of computation inspired by deceptively simple metaphors such as objects and mathematical functions. Aspect-oriented programming languages have made a significant breakthrough by noticing that, in many situations, humans think and describe in crosscutting terms. In this paper we suggest that the next breakthrough would require looking even closer to the way humans have been thinking and describing complex systems for thousand of years using natural languages. While natural languages themselves are not appropriate for programming, they contain a number of elements that make descriptions concise, effective and understandable. In particular, natural languages referentiality is a key factor in supporting powerful program organizations that can be easier understood by humans.

It has been argued that neural networks and other forms of analog computation may transcend the limits of Turing computation; proofs have been oered on both sides, subject to diering assumptions. In this report I argue that the important comparisons between the two models of computation are not so much mathematical as epistemological. The Turing machine model makes assumptions about information representation and processing that are badly matched to the realities of natural computation (information representation and processing in or inspired by natural systems). This points to the need for new models of computation addressing issues orthogonal to those that have occupied the traditional theory of computation. Keywords: computability, Turing machine, hypercomputation, natural computation, biocomputation, analog computer, analog computation, continuous computation 1.

Research in embodied cognitive science emphasizes that a close interaction of brain, body and environment is central to the emergence of cognitive processes. Much work on embodied artificial intelligence has therefore shifted focus from purely computational modeling to autonomous mobile robotics. Many researchers emphasize the importance of working with real robots rather than simulations which usually cannot fully capture the complexities of the physical world. However, from a cognitive science point of view, robot simulations nevertheless have an important, complementary role to play, due to the fact that in many cases they allow for more extensive, systematic experimentation as well as for experiments, e.g. with evolving robot morphologies, that can only be carried out in very limited form on real robots. Furthermore, it will be argued in this paper, robot simulations are very useful tools in experimentation with active adaptation of non-trivial environments, an aspect that is still largely ignored in much embodied artificial intelligence research. 1

Our aim in this article is to argue that an adequate account of semantic development in early first language acquisition requires a theory and methodology that synthesize the insights of cognitive and cultural linguistics with a Vygotskian socio-cultural approach to human development. This involves recasting and extending the notion of Embodiment, which is a central philosophical underpinning of cognitive linguistics. We discuss evidence from the cross-linguistic and cross-cultural study of spatial semantic development, and argue that current controversies regarding language-specific acquisition strategies and universal cognitive bases of semantic development may best be resolved by viewing the issue of "linguistic relativity" in a socio-cultural, as well as a grammatical, perspective.

Adaptation has become an important aspect of robot design. The work here describes the perception and motion control of MAKRO- an autonomous robot for sewer inspection- from the point of view of MAKRO’s adaptation to specific features of the sewer environment. Two features are crucial for MAKRO’s adaptation. First, narrow sewer pipes connected into a unified system via junctions, compose a graph-like structure with rather constraint surface geometry. Second, a sewer interior is absolutely dark. The visual sensing of MAKRO is not only well adapted to these specific conditions, in fact it benefits from them. Visual orientation by a hybrid vision system gives rise to a rather simple vision model, which is capable of supporting real time orientation in the sewer. This instantiates an important principle of embodied cognition, which states that adaptation of an agent to an environment allows the use of simple principles of “cheap vision” for navigation purposes. Moreover, a fast visual processing enables MAKRO to react rapidly to events in its surroundings. This in turn, changes our approach to movement control: MAKRO does not act in the “plan – move ” fashion; instead, it explores the environment, updates its heading and finds the right direction for the next move in real time. This leads to a second principle of the current work: if visual orientation of an agent operates in real time, all that is required for its successful navigation is to continuously update the right direction of motion. Navigation of MAKRO gives a powerful demonstration of how adaptation to an ecological niche and the exploitation of environmental constraints can lead to extraordinarily robust performance in a mobile robot. 1.

Abstract. Mobile maps show the current position of a map reader in their center. We will question this convention, which is in our opinion a relict from paper maps. We observe that, in contrast to paper maps, mobile maps have a narrower purpose and mobile devices have a different affordance and handling. This observation encourages thinking of a map design that allows an embodied experience of map reading. The latter is realized here by a shift of the current position of the map reader to the bottom of a mobile map. We expect that a map design closer to the bodily experience of a map reader reduces the cognitive workload of reading. In this paper we investigate the new design paradigm and its consequences. Furthermore, we present experiments of reaction times of map readers, to find evidence for the validity of our hypothesis.

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In this paper, we present a method for investigating concepts in use. Besides providing an inventory of mental representations as they can be inferred from the speakers' linguistic behaviour, also their conditions of usage are being analysed. The methodology proposed rests on the assumption that common sense reasoning is observable in the speakers' displays of their interpretations of the preceding utterances, which in the human-robot communication data investigated here are reformulations of the previous utterance. The linguistic choices speakers make and the order in which they appear allow us to infer the speakers' underlying hypotheses about language, the communication partner, and the world.

We are developing a large-scale model of language comprehension in ACT-R 6 for use in the creation of a synthetic teammate that can function as the Air Vehicle Operator (AVO) in a three-person simulation of an Unmanned Aerial Vehicle (UAV) team performing a reconnaissance mission. The use of ACT-R 6 to implement the core components of this system reflects the strong cognitive modeling orientation of this research. However, the focus is on creating cognitively plausible linguistic and associated nonlinguistic representations, rather than modeling the finegrained time course of language processing as is more typical of ACT-R models. In this regard, an empirical study aimed at discovering evidence of linguistic representations is discussed. Beside the focus on linguistic and non-linguistic representations, the large-scale nature of this effort distinguishes it from typical cognitive modeling research.