To see the other types of publications on this topic, follow the link: Internal environment representations.
Journal articles on the topic 'Internal environment representations'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Internal environment representations.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Schwartz, Geoffrey. "Refining representations for L2 phonology." Second Language Research 36, no. 4 (June 9, 2019): 691–707. http://dx.doi.org/10.1177/0267658319852383.
Full textAbstract:
This article discusses the implications of phonological representation for the study of L2 speech acquisition. It is argued, on the basis of empirical findings from diverse phenomena in L2 phonology, that refined representations in which ‘segments’ have internal prosodic structure offer a more insightful view of cross-linguistic phonetic interaction than traditional phonological models. These refinements may be implemented in the Onset Prominence representational environment, in which diverse structural parses affect sub-segmental phonetic properties, transitions between segments, and the formation of prosodic boundaries.
2
French, Robert M., and Elizabeth Thomas. "Why localist connectionist models are inadequate for categorization." Behavioral and Brain Sciences 23, no. 4 (August 2000): 477. http://dx.doi.org/10.1017/s0140525x00323354.
Full textAbstract:
Two categorization arguments pose particular problems for localist connectionist models. The internal representations of localist networks do not reflect the variability within categories in the environment, whereas networks with distributed internal representations do reflect this essential feature of categories. We provide a real biological example of perceptual categorization in the monkey that seems to require population coding (i.e., distributed internal representations).
3
Byrne, Patrick, and Suzanna Becker. "A Principle for Learning Egocentric-Allocentric Transformation." Neural Computation 20, no. 3 (March 2008): 709–37. http://dx.doi.org/10.1162/neco.2007.10-06-361.
Full textAbstract:
Numerous single-unit recording studies have found mammalian hippocampal neurons that fire selectively for the animal's location in space, independent of its orientation. The population of such neurons, commonly known as place cells, is thought to maintain an allocentric, or orientation-independent, internal representation of the animal's location in space, as well as mediating long-term storage of spatial memories. The fact that spatial information from the environment must reach the brain via sensory receptors in an inherently egocentric, or viewpoint-dependent, fashion leads to the question of how the brain learns to transform egocentric sensory representations into allocentric ones for long-term memory storage. Additionally, if these long-term memory representations of space are to be useful in guiding motor behavior, then the reverse transformation, from allocentric to egocentric coordinates, must also be learned. We propose that orientation-invariant representations can be learned by neural circuits that follow two learning principles: minimization of reconstruction error and maximization of representational temporal inertia. Two different neural network models are presented that adhere to these learning principles, the first by direct optimization through gradient descent and the second using a more biologically realistic circuit based on the restricted Boltzmann machine (Hinton, 2002; Smolensky, 1986). Both models lead to orientation-invariant representations, with the latter demonstrating place-cell-like responses when trained on a linear track environment.
4
Niebur, Ernst. "Sensorimotor contingencies do not replace internal representations, and mastery is not necessary for perception." Behavioral and Brain Sciences 24, no. 5 (October 2001): 994–95. http://dx.doi.org/10.1017/s0140525x01460110.
Full textAbstract:
Sensorimotor contingencies are certainly of great importance for perception but they are no substitute for the internal representation of perceived information. I argue that internal, non-iconic representations of perceptions must, and do, exist and that sensorimotor contingencies are an integral part of them. Further, I argue that mastery of the sensory apparatus or environment is not a prerequisite for perception and that perception is possible in the absence of any control over the perceptual process.
5
Marstaller, Lars, Arend Hintze, and Christoph Adami. "The Evolution of Representation in Simple Cognitive Networks." Neural Computation 25, no. 8 (August 2013): 2079–107. http://dx.doi.org/10.1162/neco_a_00475.
Full textAbstract:
Representations are internal models of the environment that can provide guidance to a behaving agent, even in the absence of sensory information. It is not clear how representations are developed and whether they are necessary or even essential for intelligent behavior. We argue here that the ability to represent relevant features of the environment is the expected consequence of an adaptive process, give a formal definition of representation based on information theory, and quantify it with a measure R. To measure how R changes over time, we evolve two types of networks—an artificial neural network and a network of hidden Markov gates—to solve a categorization task using a genetic algorithm. We find that the capacity to represent increases during evolutionary adaptation and that agents form representations of their environment during their lifetime. This ability allows the agents to act on sensorial inputs in the context of their acquired representations and enables complex and context-dependent behavior. We examine which concepts (features of the environment) our networks are representing, how the representations are logically encoded in the networks, and how they form as an agent behaves to solve a task. We conclude that R should be able to quantify the representations within any cognitive system and should be predictive of an agent's long-term adaptive success.
6
Zbrishchak, Svetlana G. "CONCEPTUAL MANAGEMENT: COGNITIVE PROCESSES, COGNITIVE STRUCTURES, COGNITIVE STYLE." SOFT MEASUREMENTS AND COMPUTING 7/2, no. 68 (2023): 31–39. http://dx.doi.org/10.36871/2618-9976.2023.07-2.004.
Full textAbstract:
The conceptualization of managerial activity is due to the complexity of the modern environment. The basic concepts of cognitive activity are considered: mental representations, cognitive processes, cognitive structures, cognitive style. It is shown that conceptual management is based on the concepts of "shared understanding" and "shared sensemaking", which are formed within the framework Managerial and Organizational Cognition as a model of collective perception and representation of the internal and external environment of the organization.
7
Avraham, Guy, Firas Mawase, Amir Karniel, Lior Shmuelof, Opher Donchin, Ferdinando A. Mussa-Ivaldi, and Ilana Nisky. "Representing delayed force feedback as a combination of current and delayed states." Journal of Neurophysiology 118, no. 4 (October 1, 2017): 2110–31. http://dx.doi.org/10.1152/jn.00347.2017.
Full textAbstract:
To adapt to deterministic force perturbations that depend on the current state of the hand, internal representations are formed to capture the relationships between forces experienced and motion. However, information from multiple modalities travels at different rates, resulting in intermodal delays that require compensation for these internal representations to develop. To understand how these delays are represented by the brain, we presented participants with delayed velocity-dependent force fields, i.e., forces that depend on hand velocity either 70 or 100 ms beforehand. We probed the internal representation of these delayed forces by examining the forces the participants applied to cope with the perturbations. The findings showed that for both delayed forces, the best model of internal representation consisted of a delayed velocity and current position and velocity. We show that participants relied initially on the current state, but with adaptation, the contribution of the delayed representation to adaptation increased. After adaptation, when the participants were asked to make movements with a higher velocity for which they had not previously experienced with the delayed force field, they applied forces that were consistent with current position and velocity as well as delayed velocity representations. This suggests that the sensorimotor system represents delayed force feedback using current and delayed state information and that it uses this representation when generalizing to faster movements. NEW & NOTEWORTHY The brain compensates for forces in the body and the environment to control movements, but it is unclear how it does so given the inherent delays in information transmission and processing. We examined how participants cope with delayed forces that depend on their arm velocity 70 or 100 ms beforehand. After adaptation, participants applied opposing forces that revealed a partially correct representation of the perturbation using the current and the delayed information.
8
van Ede, Freek, Alexander G. Board, and Anna C. Nobre. "Goal-directed and stimulus-driven selection of internal representations." Proceedings of the National Academy of Sciences 117, no. 39 (September 14, 2020): 24590–98. http://dx.doi.org/10.1073/pnas.2013432117.
Full textAbstract:
Adaptive behavior relies on the selection of relevant sensory information from both the external environment and internal memory representations. In understanding external selection, a classic distinction is made between voluntary (goal-directed) and involuntary (stimulus-driven) guidance of attention. We have developed a task—the anti-retrocue task—to separate and examine voluntary and involuntary guidance of attention to internal representations in visual working memory. We show that both voluntary and involuntary factors influence memory performance but do so in distinct ways. Moreover, by tracking gaze biases linked to attentional focusing in memory, we provide direct evidence for an involuntary “retro-capture” effect whereby external stimuli involuntarily trigger the selection of feature-matching internal representations. We show that stimulus-driven and goal-directed influences compete for selection in memory, and that the balance of this competition—as reflected in oculomotor signatures of internal attention—predicts the quality of ensuing memory-guided behavior. Thus, goal-directed and stimulus-driven factors together determine the fate not only of perception, but also of internal representations in working memory.
9
AIBA, AKIRA, KAZUMASA YOKOTA, and HIROSHI TSUDA. "HETEROGENEOUS DISTRIBUTED COOPERATIVE PROBLEM SOLVING SYSTEM HELIOS AND ITS COOPERATION MECHANISMS." International Journal of Cooperative Information Systems 04, no. 04 (December 1995): 369–85. http://dx.doi.org/10.1142/s0218843095000160.
Full textAbstract:
For advanced and complicated knowledge processing, we need to integrate various kinds of problem-solvers such as constraint solvers, databases, and application programs. A heterogeneous distributed cooperative problem solving system HELIOS achieves this integration by introducing capsule and environment modules. To integrate heterogeneous problem-solvers that may be implemented in different languages and may have different knowledge representations, those heterogeneity should be absorbed. Capsules and environments are introduced into HELIOS for this purpose. A capsule surrounds each problem-solver and translates the contents of communication to and from the internal representation and a common representation. We call an encapsulated problem-solver an agent. An environment is a module which provides a field giving common representation, and agents communicate and cooperate with each other in each environment. Since an encapsulated environment with its agents can be considered as an agent, agent-environment structures can be nested in HELIOS. For negotiation between agents, negotiation protocol can be defined in each environment. A negotiation strategy that suits the given negotiation protocol can be defined in each capsule of an agent. In this framework, we define a transaction-based negotiation protocol. To check the validity of HELIOS design and its implementation model on computers connected by network, we implemented an experimental version of HELIOS on UNIX workstations.
10
Cobb, Paul, Erna Yackel, and Terry Wood. "A Constructivist Alternative to the Representational View of Mind in Mathematics Education." Journal for Research in Mathematics Education 23, no. 1 (January 1992): 2–33. http://dx.doi.org/10.5951/jresematheduc.23.1.0002.
Full textAbstract:
The representational view of mind in mathematics education is evidenced by theories that characterize learning as a process in which students modify their internal mental representations to construct mathematical relationships or structures that mirror those embodied in external instructional representations. It is argued that, psychologically, this view falls prey to the learning paradox, that, anthropologically, it fails to consider the social and cultural nature of mathematical activity and that, pedagogically, it leads to recommendations that are at odds with the espoused goal of encouraging learning with understanding. These difficulties are seen to arise from the dualism created between mathematics in students' heads and mathematics in their environment. An alternative view is then outlined and illustrated that attempts to transcend this dualism by treating mathematics as both an individual, constructive activity and as a communal, social practice. It is suggested that such an approach might make it possible to explain how students construct mathematical meanings and practices that, historically, took several thousand years to evolve without attributing to students the ability to peek around their internal representations and glimpse a mathematically prestructured environment. In addition, it is argued that this approach might offer a way to go beyond the traditional tripartite scheme of the teacher, the student, and mathematics that has traditionally guided reform efforts in mathematics education.
11
Gilemkhanova, E. N. "Social Representations about Safety among Students and Teachers." Psychological-Educational Studies 13, no. 3 (2021): 68–82. http://dx.doi.org/10.17759/psyedu.2021130305.
Full textAbstract:
The relevance of the study is due to the role that social ideas play in the regulation of behavior and the formation of a system of relations among subjects of the educational environment in conditions of special attention to the safety of the educational environment of the school, provoked by destructive precedents among young people (school shooting, near-football, bullying, anarchism, neo-Nazism, etc.). J. Abric’s “Central system, peripheral system” concept was the theoretical framework of this study of ZhK. Aprica. The research hypothesis was based on the idea that violations of the psychological safety of the educational environment are caused by the problem of correlating nuclear and peripheral social representations about safety among different participants in educational settings. We use the prototypical analysis of P. Verges to study social representations of safety. The study sample was 137 students of the 7th grade (72 male, 65 female), 416 students of the 8th grade (201 male, 215 female), 490 students of the 9th grade (201 male, 289 female), 154 students of the 10th grade (84 male, 70 female), 117 students of the 11th grade (50 male, 67 female) and 20 teachers (1 male, 19 female). The results of the study demonstrate that 1) according to social representations about safety, students and teachers have diametrically opposed views on the role of the teacher in ensuring a safe educational environment; 2) students, who highly assess the safety of the educational environment, have active external and internal protection social representations about safety; students who rate the safety of the educational environment, have social representations in the context of passive protection; 3) the analysis of three age categories shows a tendency of the social representations about safety from the norm (students in grades 7-8) through external protection (high school students) to the internal individual resources (high school students and teachers). The new research data obtained on the peculiarities of representations about the safety of students and teachers can become the basis for understanding the growing tension in the field of the safety of the educational environment and the frequent incidents of its violation.
12
O'Regan, J. Kevin, and Alva Noë. "A sensorimotor account of vision and visual consciousness." Behavioral and Brain Sciences 24, no. 5 (October 2001): 939–73. http://dx.doi.org/10.1017/s0140525x01000115.
Full textAbstract:
Many current neurophysiological, psychophysical, and psychological approaches to vision rest on the idea that when we see, the brain produces an internal representation of the world. The activation of this internal representation is assumed to give rise to the experience of seeing. The problem with this kind of approach is that it leaves unexplained how the existence of such a detailed internal representation might produce visual consciousness. An alternative proposal is made here. We propose that seeing is a way of acting. It is a particular way of exploring the environment. Activity in internal representations does not generate the experience of seeing. The outside world serves as its own, external, representation. The experience of seeing occurs when the organism masters what we call the governing laws of sensorimotor contingency. The advantage of this approach is that it provides a natural and principled way of accounting for visual consciousness, and for the differences in the perceived quality of sensory experience in the different sensory modalities. Several lines of empirical evidence are brought forward in support of the theory, in particular: evidence from experiments in sensorimotor adaptation, visual “filling in,” visual stability despite eye movements, change blindness, sensory substitution, and color perception.
13
Cocchini, Gianna, Toni Galligan, Laura Mora, and Gustav Kuhn. "The magic hand: Plasticity of mental hand representation." Quarterly Journal of Experimental Psychology 71, no. 11 (January 1, 2018): 2314–24. http://dx.doi.org/10.1177/1747021817741606.
Full textAbstract:
Internal spatial body configurations are crucial to successfully interact with the environment and to experience our body as a three-dimensional volumetric entity. These representations are highly malleable and are modulated by a multitude of afferent and motor information. Despite some studies reporting the impact of sensory and motor modulation on body representations, the long-term relationship between sensory information and mental representation of own body parts is still unclear. We investigated hand representation in a group of expert sleight-of-hand magicians and in a group of age-matched adults naïve to magic (controls). Participants were asked to localise landmarks of their fingers when their hand position was congruent with the mental representation (Experiment 1) and when proprioceptive information was “misleading” (Experiment 2). Magicians outperformed controls in both experiments, suggesting that extensive training in sleight of hand has a profound effect in refining hand representation. Moreover, the impact of training seems to have a high body-part specificity, with a maximum impact for those body sections used more prominently during the training. Interestingly, it seems that sleight-of-hand training can lead to a specific improvement of hand mental representation, which relies less on proprioceptive information.
14
Thomas, Manoj A., Richard T. Redmond, and Victoria Y. Yoon. "Using Ontological Reasoning for an Adaptive E-Commerce Experience." International Journal of Intelligent Information Technologies 5, no. 4 (October 2009): 41–52. http://dx.doi.org/10.4018/jiit.2009080703.
Full textAbstract:
As e-commerce applications proliferates the Web, the authors are often overwhelmed by the task of sifting through the copious volumes of information. Since the nature of foraging for information in such digital spaces can be characterized as the interaction between internal task representation and the external problem domain, the authors look at how expert systems can be used to reduce complexity of the task. They describe a conceptual framework to analyze user interactions based on mental representations. They also detail an expert system implementation using the ontology language OWL to express the semantics of the representations and the rule language SWRL to define the rule base for contextual reasoning. The chapter illustrates how an expert system can be used to guide users in an e-commerce setting by orchestrating a cognitive fit between the task environment and the task solution.
15
Rafati, Jacob, and David C. Noelle. "Learning Representations in Model-Free Hierarchical Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 10009–10. http://dx.doi.org/10.1609/aaai.v33i01.330110009.
Full textAbstract:
Common approaches to Reinforcement Learning (RL) are seriously challenged by large-scale applications involving huge state spaces and sparse delayed reward feedback. Hierarchical Reinforcement Learning (HRL) methods attempt to address this scalability issue by learning action selection policies at multiple levels of temporal abstraction. Abstraction can be had by identifying a relatively small set of states that are likely to be useful as subgoals, in concert with the learning of corresponding skill policies to achieve those subgoals. Many approaches to subgoal discovery in HRL depend on the analysis of a model of the environment, but the need to learn such a model introduces its own problems of scale. Once subgoals are identified, skills may be learned through intrinsic motivation, introducing an internal reward signal marking subgoal attainment. We present a novel model-free method for subgoal discovery using incremental unsupervised learning over a small memory of the most recent experiences of the agent. When combined with an intrinsic motivation learning mechanism, this method learns subgoals and skills together, based on experiences in the environment. Thus, we offer an original approach to HRL that does not require the acquisition of a model of the environment, suitable for large-scale applications. We demonstrate the efficiency of our method on a variant of the rooms environment.
16
Kubicek, Bernice, Ananya Sen Gupta, and Ivars Kirsteins. "Feature Extraction and Classification of Simulated Monostatic Acoustic Echoes from Spherical Targets of Various Materials Using Convolutional Neural Networks." Journal of Marine Science and Engineering 11, no. 3 (March 7, 2023): 571. http://dx.doi.org/10.3390/jmse11030571.
Full textAbstract:
Active sonar target classification remains an ongoing area of research due to the unique challenges associated with the problem (unknown target parameters, dynamic oceanic environment, different scattering mechanisms, etc.). Many feature extraction and classification techniques have been proposed, but there remains a need to relate and explain the classifier results in the physical domain. This work examines convolutional neural networks trained on simulated data with a known ground truth projected onto two time-frequency representations (spectrograms and scalograms). The classifiers were trained to discriminate the target material type, geometry, and internal fluid filling, while the hyperparameters were tuned to the classification task using Bayesian optimization. The trained networks were examined using an explainable artificial intelligence technique, gradient-weighted class activation mapping, to uncover the informative features used in discrimination. This analysis resulted in visual representations that allowed the CNN choices to be related to the physical domain. It was found that the scalogram representation provided a negligible classification accuracy increase compared with the spectrograms. Networks trained to discriminate between target geometries resulted in the highest accuracy, and the networks trained to discriminate the internal fluid of the target resulted in the lowest accuracy.
17
Wurm, Franz, Benjamin Ernst, and Marco Steinhauser. "The influence of internal models on feedback-related brain activity." Cognitive, Affective, & Behavioral Neuroscience 20, no. 5 (August 18, 2020): 1070–89. http://dx.doi.org/10.3758/s13415-020-00820-6.
Full textAbstract:
Abstract Decision making relies on the interplay between two distinct learning mechanisms, namely habitual model-free learning and goal-directed model-based learning. Recent literature suggests that this interplay is significantly shaped by the environmental structure as represented by an internal model. We employed a modified two-stage but one-decision Markov decision task to investigate how two internal models differing in the predictability of stage transitions influence the neural correlates of feedback processing. Our results demonstrate that fronto-central theta and the feedback-related negativity (FRN), two correlates of reward prediction errors in the medial frontal cortex, are independent of the internal representations of the environmental structure. In contrast, centro-parietal delta and the P3, two correlates possibly reflecting feedback evaluation in working memory, were highly susceptible to the underlying internal model. Model-based analyses of single-trial activity showed a comparable pattern, indicating that while the computation of unsigned reward prediction errors is represented by theta and the FRN irrespective of the internal models, the P3 adapts to the internal representation of an environment. Our findings further substantiate the assumption that the feedback-locked components under investigation reflect distinct mechanisms of feedback processing and that different internal models selectively influence these mechanisms.
18
Markova, G. M., and S. I. Bartsev. "Heuristic modeling of reflection in reflexive games." Philosophical Problems of IT & Cyberspace (PhilIT&C), no. 2 (December 18, 2023): 61–79. http://dx.doi.org/10.17726/philit.2023.2.5.
Full textAbstract:
The functioning of a subject in a changing environment is most effective from the point of view of survival if the subject can form, maintain and use internal representations of the external world for decision-making. These representations are also called reflection in a broad sense. Using it, one can win in reflexive games since an internal representation of the enemy allows predicting their future moves. The goal is to assess the reflexive potential of heuristic model objects – artificial neural networks – in the reflexive games “Even-Odd” (or “Matching pennies”) and “Rock-Paper-Scissors”. We used homogeneous fully connected neural networks of small sizes (from 8 to 45 neurons). Games were played between neural networks with different configurations and parameters (size, step size for modifying weight coefficients). A set of reflexivity criteria is presented, corresponding to different levels of consideration: neuronal, behavioral, formal. The transitivity of formal success in the game is shown. The most successful configurations, however, may not meet other criteria of reflexivity. We hypothesize that the best compliance with the criteria and, as a consequence, universal success in reflection tasks is achievable for heterogeneous configurations with a structure in which the formation of hierarchical systems of attractors is possible.
19
Avarguès-Weber, Aurore, Valerie Finke, Márton Nagy, Tūnde Szabó, Daniele d’Amaro, Adrian G. Dyer, and József Fiser. "Different mechanisms underlie implicit visual statistical learning in honey bees and humans." Proceedings of the National Academy of Sciences 117, no. 41 (September 28, 2020): 25923–34. http://dx.doi.org/10.1073/pnas.1919387117.
Full textAbstract:
The ability of developing complex internal representations of the environment is considered a crucial antecedent to the emergence of humans’ higher cognitive functions. Yet it is an open question whether there is any fundamental difference in how humans and other good visual learner species naturally encode aspects of novel visual scenes. Using the same modified visual statistical learning paradigm and multielement stimuli, we investigated how human adults and honey bees (Apis mellifera) encode spontaneously, without dedicated training, various statistical properties of novel visual scenes. We found that, similarly to humans, honey bees automatically develop a complex internal representation of their visual environment that evolves with accumulation of new evidence even without a targeted reinforcement. In particular, with more experience, they shift from being sensitive to statistics of only elemental features of the scenes to relying on co-occurrence frequencies of elements while losing their sensitivity to elemental frequencies, but they never encode automatically the predictivity of elements. In contrast, humans involuntarily develop an internal representation that includes single-element and co-occurrence statistics, as well as information about the predictivity between elements. Importantly, capturing human visual learning results requires a probabilistic chunk-learning model, whereas a simple fragment-based memory-trace model that counts occurrence summary statistics is sufficient to replicate honey bees’ learning behavior. Thus, humans’ sophisticated encoding of sensory stimuli that provides intrinsic sensitivity to predictive information might be one of the fundamental prerequisites of developing higher cognitive abilities.
20
Schab, Esteban, Carla Casanova, and Fabiana Piccoli. "Graph Representations for Reinforcement Learning." Journal of Computer Science and Technology 24, no. 1 (April 22, 2024): e03. http://dx.doi.org/10.24215/16666038.24.e03.
Full textAbstract:
Graph analysis is becoming increasingly important due to the expressive power of graph models and the efficient algorithms available for processing them. Reinforcement Learning is one domain that could benefit from advancements in graph analysis, given that a learning agent may be integrated into an environment that can be represented as a graph. Nevertheless, the structural irregularity of graphs and the lack of prior labels make it difficult to integrate such a model into modern Reinforcement Learning frameworks that rely on artificial neural networks. Graph embedding enables the learning of low-dimensional vector representations that are more suited for machine learning algorithms, while retaining essential graph features. This paper presents a framework for evaluating graph embedding algorithms and their ability to preserve the structure and relevant features of graphs by means of an internal validation metric, without resorting to subsequent tasks that require labels for training. Based on this framework, three defined algorithms that meet the necessary requirements for solving a specific problem of Reinforcement Learning in graphs are selected, analyzed, and compared. These algorithms are Graph2Vec, GL2Vec, and Wavelet Characteristics, with the latter two demonstrating superior performance.
21
Slater, Mel, and Martin Usoh. "Representations Systems, Perceptual Position, and Presence in Immersive Virtual Environments." Presence: Teleoperators and Virtual Environments 2, no. 3 (January 1993): 221–33. http://dx.doi.org/10.1162/pres.1993.2.3.221.
Full textAbstract:
This paper discusses factors that may contribute to the participant's sense of presence in immersive virtual environments. We distinguish between external factors, that is those wholly determined by the hardware and software technology employed to generate the environment, and subjective factors, that is how sensory inputs to the human participant are processed internally. The therapeutic technique known as neurolinguistic programming (NLP) is used as a basis for measuring such internal factors. NLP uses the idea of representation systems (visual, auditory, and kinesthetic) and perceptual position (egocentric or exocentric) to code subjective experience. The paper also considers one external factor, that is how the virtual environment represents a participant—either as a complete body, or just an arrow cursor that responds to hand movements. A case-control pilot experiment is described, where the controls have self-representation as an arrow cursor, and the experimental group subjects as a simple virtual body. Measurements of subjects' preferred representation systems and perceptual positions are obtained based on counts of types of predicates and references used in essays written after the experiment. These, together with the control variable (possession/absence of a virtual body), are used as explanatory variables in a regression analysis, with reported sense of presence as the dependent variable. Although tentative and exploratory in nature, the data analysis does suggest a relationship between reported sense of presence, preferred representation system, perceptual position, and an interaction effect between these and the virtual body factor.
22
Hills, Thomas T., and Stephen Butterfill. "From foraging to autonoetic consciousness: The primal self as a consequence of embodied prospective foraging." Current Zoology 61, no. 2 (April 1, 2015): 368–81. http://dx.doi.org/10.1093/czoolo/61.2.368.
Full textAbstract:
Abstract The capacity to adapt to resource distributions by modulating the frequency of exploratory and exploitative behaviors is common across metazoans and is arguably a principal selective force in the evolution of cognition. Here we (1) review recent work investigating behavioral and biological commonalities between external foraging in space and internal foraging over environments specified by cognitive representations, and (2) explore the implications of these commonalities for understanding the origins of the self. Behavioural commonalities include the capacity for what is known as area-restricted search in the ecological literature: this is search focussed around locations where resources have been found in the past, but moving away from locations where few resources are found, and capable of producing movement patterns mimicking Lévy flights. Area-restricted search shares a neural basis across metazoans, and these biological commonalities in vertebrates suggest an evolutionary homology between external and internal foraging. Internal foraging, and in particular a form we call embodied prospective foraging, makes available additional capacities for prediction based on search through a cognitive representation of the external environment, and allows predictions about outcomes of possible future actions. We demonstrate that cognitive systems that use embodied prospective foraging require a primitive sense of self, needed to distinguish actual from simulated action. This relationship has implications for understanding the evolution of autonoetic consciousness and self-awareness.
23
Leite, Cláudio M. F., Carlos E. Campos, Crislaine R. Couto, and Herbert Ugrinowitsch. "An internal model approach for motor behavior." Brazilian Journal of Motor Behavior 15, no. 5 (December 1, 2021): 356–71. http://dx.doi.org/10.20338/bjmb.v15i5.273.
Full textAbstract:
Interacting with the environment requires a remarkable ability to control, learn, and adapt motor skills to ever-changing conditions. The intriguing complexity involved in the process of controlling, learning, and adapting motor skills has led to the development of many theoretical approaches to explain and investigate motor behavior. This paper will present a theoretical approach built upon the top-down mode of motor control that shows substantial internal coherence and has a large and growing body of empirical evidence: The Internal Models. The Internal Models are representations of the external world within the CNS, which learn to predict this external world, simulate behaviors based on sensory inputs, and transform these predictions into motor actions. We present the Internal Models’ background based on two main structures, Inverse and Forward models, explain how they work, and present some applicability.
24
Piccardi, L., M. Risetti, and R. Nori. "Familiarity and Environmental Representations of a City: A Self-Report Study." Psychological Reports 109, no. 1 (August 2011): 309–26. http://dx.doi.org/10.2466/01.13.17.pr0.109.4.309-326.
Full textAbstract:
“Sense of direction” is usually assessed by self-report. Several internal factors contribute to proficiency in navigation: spatial cognitive style, respondent's sex, and familiarity with the environment; however, questionnaires assessing sense of direction do not include all these factors. In a recent study, Nori and Piccardi reported that environmental familiarity was crucial for topographical orientation. Regardless of a person's spatial cognitive style (i.e., landmark, route, or survey), the greater their familiarity with the environment, the better their performance. In this study, a questionnaire was used, the Familiarity and Spatial Cognitive Style Scale, to measure 208 women's sense of direction and knowledge of their city of residence. Analysis showed that Spatial Cognitive Style predicted sense of direction but not town knowledge. By contrast, familiarity played a crucial role in both areas, confirming the importance of having a tool to assess this factor.
25
Chai, Joyce Y., Rui Fang, Changsong Liu, and Lanbo She. "Collaborative Language Grounding Toward Situated Human-Robot Dialogue." AI Magazine 37, no. 4 (January 17, 2017): 32–45. http://dx.doi.org/10.1609/aimag.v37i4.2684.
Full textAbstract:
To enable situated human-robot dialogue, techniques to support grounded language communication are essential. One particular challenge is to ground human language to robot internal representation of the physical world. Although copresent in a shared environment, humans and robots have mismatched capabilities in reasoning, perception, and action. Their representations of the shared environment and joint tasks are significantly misaligned. Humans and robots will need to make extra effort to bridge the gap and strive for a common ground of the shared world. Only then, is the robot able to engage in language communication and joint tasks. Thus computational models for language grounding will need to take collaboration into consideration. A robot not only needs to incorporate collaborative effort from human partners to better connect human language to its own representation, but also needs to make extra collaborative effort to communicate its representation in language that humans can understand. To address these issues, the Language and Interaction Research group (LAIR) at Michigan State University has investigated multiple aspects of collaborative language grounding. This article gives a brief introduction to this research effort and discusses several collaborative approaches to grounding language to perception and action.
26
Kalaska, John, Allan Smith, and Yves Lamarre. "Spatial Representations and Sensorimotor Transformations." Canadian Journal of Physiology and Pharmacology 66, no. 4 (April 1, 1988): 429. http://dx.doi.org/10.1139/y88-072.
Full textAbstract:
Each year, the Centre de recherche en sciences neurologiques of the Université de Montréal organizes a symposium on a topic in the neurosciences. For the IXth International Symposium, the theme chosen was "Spatial Representations and Sensorimotor Transformations."Many of the diverse functions performed by the central nervous system have an important spatial component in common. For instance, there are neural mechanisms for the analysis and perception of the three-dimensional structure of visual space, such as the location, form, and movement of objects in the visual environment. There exist processes to determine the spatial location of auditory stimuli. One can also regard the body as an "internal" space for which mechanisms have evolved for the kinesthetic perception of the position and movement of body parts relative to one another, and for the position and orientation of the body within its immediate external environment. Motor control also requires spatial information, since many movements of the eyes, head, and limbs follow specific paths or are aimed at the specific spatial location of an object as signalled by sensory processes.One can argue, therefore, that a major aspect of the functioning of the brain involves the generation of many different spatial representations, and the exchange of information among them. Each of these neural representations provides a spatial coordinate framework whose coordinate axes are based on certain types of information. For instance, movement of the limb toward an object can be described equally well in several different coordinate systems, such as those based on its spatial path, its dynamics (the direction and level of forces, torques, and external loads), or the muscle activity by which it is achieved. A better understanding of the coordinates in which the CNS codes these various types of information will provide a better appreciation of the neural mechanisms generating the spatial representations. It will also provide a clearer understanding of the transformations that must occur to relay information between sensory and motor representations, which permit an animal to interact successfully with its environment.The participants at this Symposium were invited to examine some of these issues as they pertain to the somatic and visual systems.
27
WENG, JUYANG, TIANYU LUWANG, HONG LU, and XIANGYANG XUE. "A MULTILAYER IN-PLACE LEARNING NETWORK FOR DEVELOPMENT OF GENERAL INVARIANCES." International Journal of Humanoid Robotics 04, no. 02 (June 2007): 281–320. http://dx.doi.org/10.1142/s0219843607001072.
Full textAbstract:
Currently, there is a lack of general-purpose, in-place learning engines that incrementally learn multiple tasks, to develop "soft" multi-task-shared invariances in the intermediate internal representation while a developmental robot interacts with its environment. In-place learning is a biologically inspired concept, rooted in the genomic equivalence principle, meaning that each neuron is responsible for its own development while interacting with its environment. With in-place learning, there is no need for a separate learning network. Computationally, biologically inspired, in-place learning provides unusually efficient learning algorithms whose simplicity, low computational complexity, and generality are set apart from typical conventional learning algorithms. We present in this paper the multiple-layer in-place learning network (MILN) for this ambitious goal. As a key requirement for autonomous mental development, the network enables both unsupervised and supervised learning to occur concurrently, depending on whether motor supervision signals are available or not at the motor end (the last layer) during the agent's interactions with the environment. We present principles based on which MILN automatically develops invariant neurons in different layers and why such invariant neuronal clusters are important for learning later tasks in open-ended development. From sequentially sensed sensory streams, the proposed MILN incrementally develops a hierarchy of internal representations. The global invariance achieved through multi-layer invariances, with increasing invariance from early layers to the later layers. Experimental results with statistical performance measures are presented to show the effects of the principles.
28
Makarenko, A. S. "Mentality issues in the transformation processes of the postmodernity society." Ukrainian Society 76, no. 1 (April 8, 2021): 12–22. http://dx.doi.org/10.15407/socium2021.01.012.
Full textAbstract:
The paper deals with transforming social systems and their reflection in the concepts and models of the corresponding processes. A new approach to modelling and research of large social systems was proposed. The author emphasis's and describes qualitatively some aspects of the approach that are important for considering the transformation of society. The main feature of such systems is the following properties. Firstly, these systems operate in a small number of reasonably stable states. (In our approach, this is interpreted as so-called associative memory). Secondly, the evolution of such systems (actually, history for long periods) has an analogue in the learning process in models, when connections are established between the system elements. Furthermore, thirdly, the proposed approach was able to consider individuals’ internal properties (mentality) – participants in large social systems. New models of socio-economic systems are described, and an interpretation of their behaviour as trajectories on certain surfaces in the space of variables is given. The ways of transforming systems are explained: revolutionary, evolutionary and with a unique trajectory of transition. The classes are considered, into which the internal (mental) variables of individuals are divided. The internal representations of the individual from some slice of reality (in psychology, for example, this is called “internal plans”) are represented as a network of objects, concepts and the like, depending on the “object” and purpose of modelling. There are three networks (patterns, drawings) of descriptions: the actual state of affairs, presented in the form of a network; the state of things in the imagination of a particular individual and the desired (ideal) state of affairs in the imagination of a particular individual. The dynamics of changes in the parameters that characterize the individual depends on the state of the environment “externally” and on the “internal” mental variables and the above three ideas about the external environment. Adequate consideration of the external environment by representing and internal representations of surrounding individuals is possible. Such an approach is proposed when considering archetypes in the transformation of the social system. The problem of sustainable development of education and science systems is considered.
29
Hameed, Ansa, and Haroon N. Alsager. "A Semiotic Study of Contemporary Middle Eastern Internal Dilemmas in Arab News Cartoons." World Journal of English Language 14, no. 1 (December 15, 2023): 472. http://dx.doi.org/10.5430/wjel.v14n1p472.
Full textAbstract:
Many parts of the Middle Eastern region have a history of persistent and long-term crises. The media, and particularly the news media, endeavors to highlight these issues in various forms. One established format among them is caricatures, or cartoonish representations, which retain a visually captivating quality for the intended audience. Undeniably, cartoons depict the bitter realities in candid yet convincing forms. In this regard, the present study aims to analyze the Arab News cartoons that depict the internal predicaments faced by the selected Middle Eastern countries. The primary objective of this study is to examine the intricate relationship between semiotics and socio-political intricacies in the selected regions. This study employs Barthes’ semiotic lens theory to investigate the methods employed by the cartoonist in conveying messages, creating narratives, and interacting with the socio-political environment. The results reveal that the caricatured representations effectually depict several underlying causes and conflicts that fuel the internal chaotic situation inside the region, using signs, symbols, and pictorial rhetoric. These findings help in understanding the essence of the challenges faced by the chosen Middle Eastern nations quite meritoriously. At the same time, the results endorse cartoons as an authentic medium for discussing such harsh realities.
30
Yang, Hanlin, William Zhu, and Xianchao Zhu. "Generalization Enhancement of Visual Reinforcement Learning through Internal States." Sensors 24, no. 14 (July 12, 2024): 4513. http://dx.doi.org/10.3390/s24144513.
Full textAbstract:
Visual reinforcement learning is important in various practical applications, such as video games, robotic manipulation, and autonomous navigation. However, a major challenge in visual reinforcement learning is the generalization to unseen environments, that is, how agents manage environments with previously unseen backgrounds. This issue is triggered mainly by the high unpredictability inherent in high-dimensional observation space. To deal with this problem, techniques including domain randomization and data augmentation have been explored; nevertheless, these methods still cannot attain a satisfactory result. This paper proposes a new method named Internal States Simulation Auxiliary (ISSA), which uses internal states to improve generalization in visual reinforcement learning tasks. Our method contains two agents, a teacher agent and a student agent: the teacher agent has the ability to directly access the environment’s internal states and is used to facilitate the student agent’s training; the student agent receives initial guidance from the teacher agent and subsequently continues to learn independently. From another perspective, our method can be divided into two phases, the transfer learning phase and traditional visual reinforcement learning phase. In the first phase, the teacher agent interacts with environments and imparts knowledge to the vision-based student agent. With the guidance of the teacher agent, the student agent is able to discover more effective visual representations that address the high unpredictability of high-dimensional observation space. In the next phase, the student agent autonomously learns from the visual information in the environment, and ultimately, it becomes a vision-based reinforcement learning agent with enhanced generalization. The effectiveness of our method is evaluated using the DMControl Generalization Benchmark and the DrawerWorld with texture distortions. Preliminary results indicate that our method significantly improves generalization ability and performance in complex continuous control tasks.
31
Vallesi, Antonino, Daniela Mapelli, and Paolo Cherubini. "Neural correlates of inference-driven attention in perceptual and symbolic tasks: An event-related potential study." Quarterly Journal of Experimental Psychology 62, no. 9 (September 2009): 1805–31. http://dx.doi.org/10.1080/17470210802596783.
Full textAbstract:
The inferential system anticipates the external environment by building up internal representations of its regularities. To that purpose, two sources of information are especially important and attract attentional resources: expected and unexpected events, which are useful for checking the accuracy of internal representations. In the present study, we investigated the behavioural properties and the neural mechanisms underlying the strategic allocation of attention triggered by those events. To that end, event-related potentials (ERPs) were recorded during the performance of two tasks requiring detection of predictable and unpredictable response events embedded in a visuospatial or numeric sequence. The behavioural results in the two tasks mirror each other, suggesting the recruitment of similar attentional allocation processes between the two domains. The ERPs showed partially similar effects. In both tasks, a P3a-like component signalled the capture of attention by events clashing with previous expectations, whilst a P3b-like component marked the focusing of attention on predicted events and its redistribution among all possible response events occurring after the detection of an unexpected event.
32
Wang, Dongshu, and Yihai Duan. "Natural Language Acquisition: State Inferring and Thinking." International Journal on Artificial Intelligence Tools 25, no. 04 (August 2016): 1650022. http://dx.doi.org/10.1142/s0218213016500226.
Full textAbstract:
Natural language understanding plays an important role in our daily life. It is very significant to study how to make the computer understand the human language and produce the corresponding action or response. Most of the prior language acquisition models adopt handcrafted internal representation, and they are not sufficiently brain-based and not sufficiently comprehensive to account for all branches in psychology and cognitive science. An emergent developmental network (DN) is used to learn, infer and think a knowledge base represented as a finite automaton, from sensory and motor experience grounded in this operational environments. This work is different in the sense that we emphasize on the mechanism that enable a system to develop its emergent representations from its operational experience. By emergent, we mean a pattern of responses of multiple elements that corresponds to an event outside the closed skull but each element (e.g. pixel, muscle, neuron) of the pattern typically does not have a meaning. In this work, internal unsupervised neurons of the DN are used to represent short contexts, and the competitions among internal neurons enable them to represent different short contexts. By internal, we mean that all the neurons inside a brain are not directly supervised by the external environment — outside the brain skull. In this work, we analyze how internal neurons represent temporal contexts and how the feature neurons of the DN represent earlier contexts. Accuracy of Z state inferring and $X$ thinking of a relative complex training sequence (denoted as DN-2 in this work) can reach 100% and 75%, respectively. Comparative experiment results between this emergent method and the symbolic method, their corresponding Z state inferring and $X$ thinking accuracy are 100% and 82.1%, 85.7% and 75%, respectively (taking DN-6 in this work as the example), demonstrate the efficiency of the DN on natural language inferring and thinking. Complexity of the finite automaton is low and so is the temporal contexts, but the same principle is potentially applicable to more complex cases.
33
Chetverikov, Andrey, Gianluca Campana, and Árni Kristjánsson. "Representing Color Ensembles." Psychological Science 28, no. 10 (September 2017): 1510–17. http://dx.doi.org/10.1177/0956797617713787.
Full textAbstract:
Colors are rarely uniform, yet little is known about how people represent color distributions. We introduce a new method for studying color ensembles based on intertrial learning in visual search. Participants looked for an oddly colored diamond among diamonds with colors taken from either uniform or Gaussian color distributions. On test trials, the targets had various distances in feature space from the mean of the preceding distractor color distribution. Targets on test trials therefore served as probes into probabilistic representations of distractor colors. Test-trial response times revealed a striking similarity between the physical distribution of colors and their internal representations. The results demonstrate that the visual system represents color ensembles in a more detailed way than previously thought, coding not only mean and variance but, most surprisingly, the actual shape (uniform or Gaussian) of the distribution of colors in the environment.
34
Atkinson, Dwight. "Language learning in mindbodyworld: A sociocognitive approach to second language acquisition." Language Teaching 47, no. 4 (April 3, 2013): 467–83. http://dx.doi.org/10.1017/s0261444813000153.
Full textAbstract:
Based on recent research in cognitive science, interaction, and second language acquisition (SLA), I describe a sociocognitive approach to SLA. This approach adopts anon-cognitivistview of cognition: Instead of an isolated computational process in which input is extracted from the environment and used to build elaborate internal knowledge representations, cognition is seen asadaptive intelligence,enabling our close and sensitivealignmentto our ecosocial environment in order to survive in it. Mind, body, and world are thus functionally integrated from a sociocognitive perspective instead of radically separated.Learning plays a major part in this scenario: If environments are ever-changing, then adaptation to them is continuous. Learning is part of our natural ability to so adapt, while retaining traces of that adaptation in the integrated mind-body-world system. Viewed in this way, SLA is adaptation to/engagement with L2 environments.Interactionalso plays a central role in sociocognitive SLA: We learn L2s through interacting with/in L2 environments. Founded on innate, universal skills which evolutionarilyprecededlanguage and make it possible, interaction supports SLA at every turn. Having presented this argument, I illustrate it by analyzing a video clip of an EFL tutoring session, indicating various ‘sociocognitive tools’ for interactive alignment which undergird L2 development.
35
Schmidhuber, Jürgen. "Learning Factorial Codes by Predictability Minimization." Neural Computation 4, no. 6 (November 1992): 863–79. http://dx.doi.org/10.1162/neco.1992.4.6.863.
Full textAbstract:
I propose a novel general principle for unsupervised learning of distributed nonredundant internal representations of input patterns. The principle is based on two opposing forces. For each representational unit there is an adaptive predictor, which tries to predict the unit from the remaining units. In turn, each unit tries to react to the environment such that it minimizes its predictability. This encourages each unit to filter "abstract concepts" out of the environmental input such that these concepts are statistically independent of those on which the other units focus. I discuss various simple yet potentially powerful implementations of the principle that aim at finding binary factorial codes (Barlow et al. 1989), i.e., codes where the probability of the occurrence of a particular input is simply the product of the probabilities of the corresponding code symbols. Such codes are potentially relevant for (1) segmentation tasks, (2) speeding up supervised learning, and (3) novelty detection. Methods for finding factorial codes automatically implement Occam's razor for finding codes using a minimal number of units. Unlike previous methods the novel principle has a potential for removing not only linear but also nonlinear output redundancy. Illustrative experiments show that algorithms based on the principle of predictability minimization are practically feasible. The final part of this paper describes an entirely local algorithm that has a potential for learning unique representations of extended input sequences.
36
Langlois, Thomas A., Nori Jacoby, Jordan W. Suchow, and Thomas L. Griffiths. "Serial reproduction reveals the geometry of visuospatial representations." Proceedings of the National Academy of Sciences 118, no. 13 (March 26, 2021): e2012938118. http://dx.doi.org/10.1073/pnas.2012938118.
Full textAbstract:
An essential function of the human visual system is to locate objects in space and navigate the environment. Due to limited resources, the visual system achieves this by combining imperfect sensory information with a belief state about locations in a scene, resulting in systematic distortions and biases. These biases can be captured by a Bayesian model in which internal beliefs are expressed in a prior probability distribution over locations in a scene. We introduce a paradigm that enables us to measure these priors by iterating a simple memory task where the response of one participant becomes the stimulus for the next. This approach reveals an unprecedented richness and level of detail in these priors, suggesting a different way to think about biases in spatial memory. A prior distribution on locations in a visual scene can reflect the selective allocation of coding resources to different visual regions during encoding (“efficient encoding”). This selective allocation predicts that locations in the scene will be encoded with variable precision, in contrast to previous work that has assumed fixed encoding precision regardless of location. We demonstrate that perceptual biases covary with variations in discrimination accuracy, a finding that is aligned with simulations of our efficient encoding model but not the traditional fixed encoding view. This work demonstrates the promise of using nonparametric data-driven approaches that combine crowdsourcing with the careful curation of information transmission within social networks to reveal the hidden structure of shared visual representations.
37
Baber, Chris. "Thinking Through Tools: What Can Tool-Use Tell Us About Distributed Cognition?" Studies in Logic, Grammar and Rhetoric 41, no. 1 (June 1, 2015): 25–40. http://dx.doi.org/10.1515/slgr-2015-0018.
Full textAbstract:
Abstract In this paper, I question the notion that tool-use must be driven by an internal representation which specifies the “motor program” enacted in the behaviour of the tool-user. Rather, it makes more sense to define tool-use in terms of characteristics of the dynamics of this behaviour. As the behaviour needs to be adjusted to suit changes in context, so there is unlikely to be a one-to-one, linear mapping between an action and its effect. Thus, tool-use can best be described using concepts from Nonlinear Dynamics. Such an approach can be used to create a sort of cybernetic model of tool-use. However, there is a danger that such a model can either lead us back to internal representations (in that the comparator used to evaluate feedback during behaviour could be assumed to be pre-defined) or could fail to capture cognitive aspects of behaviour. In particular, the question of how the craftworker’s intent can be enacted in the use of tools to produce a specific object seems to be lost in the cybernetic account. My solution is two-fold. First, the “model” is created on-the-fly and adapted through moment-by-moment interactions in the system of tool-user–tool–material–environment. This means that, rather than assuming a pre-defined internal representation that drives behaviour, I propose that cognition involves the selection of salient parameters that characterize the behaviour and the continued monitoring and management of behaviour in terms of these parameters. Second, intent is only loosely defined a priori but crystallizes through the continued interactions between craftworker and object through a process in which the affordances of the object become apparent to, and responded to by, the craftworker.
38
Borodulina, Natalia Yurievna, and Marina Nikolaevna Makeeva. "Types of metaphors in economic discourse (based on the representation of enterprise activities in Russian, English, German, and French)." Philology. Issues of Theory and Practice 17, no. 4 (April 23, 2024): 1244–51. http://dx.doi.org/10.30853/phil20240180.
Full textAbstract:
This paper is written within the framework of a cognitive study of the economic discourse metaphor used to objectify various types of activities of an enterprise and its environment. The research aims to identify the semantic and pragmatic features characterizing the linguistic and axiological implementation of metaphorical representation of the internal structure of a modern economic company and its relationships with the external business world. The work analyzes the types of metaphors, highlights the features of lexical units that are actualized during the transfer of meanings, and demonstrates evaluative connotations. The scientific novelty of the research lies in systematizing metaphors of such an important segment of economic discourse as entrepreneurial activity and providing their typology based on Russian, English, German, and French, revealing the common and specific features in the conceptualization of relationships and characteristics of a modern company. As a result, the dominant metaphorical models in the representations of an economic enterprise, their axiological and cultural characteristics are shown, the types of metaphors in the “Economics” subject area corresponding to the functional aspect of metaphors are listed.
39
Reaume, Christopher J., Marla B. Sokolowski, and Frederic Mery. "A natural genetic polymorphism affects retroactive interference in Drosophila melanogaster." Proceedings of the Royal Society B: Biological Sciences 278, no. 1702 (July 28, 2010): 91–98. http://dx.doi.org/10.1098/rspb.2010.1337.
Full textAbstract:
As environments change, animals update their internal representations of the external world. New information about the environment is learned and retained whereas outdated information is disregarded or forgotten. Retroactive interference (RI) occurs when the retrieval of previously learned information is less available owing to the acquisition of recently acquired information. Even though RI is thought to be a major cause of forgetting, its functional significance is still under debate. We find that natural allelic variants of the Drosophila melanogaster foraging gene known to affect rover and sitter behaviour differ in RI. More specifically, rovers who were previously shown to experience greater environmental heterogeneity while foraging display RI whereas sitters do not. Rover responses are biased towards more recent learning events. These results provide an ecological context to investigate the function of forgetting via RI and a suitable genetic model organism to address the evolutionary relevance of cognitive tasks.
40
Giallonardo, Ester, Francesco Poggi, Davide Rossi, and Eugenio Zimeo. "Semantics-Driven Programming of Self-Adaptive Reactive Systems." International Journal of Software Engineering and Knowledge Engineering 30, no. 06 (June 2020): 805–34. http://dx.doi.org/10.1142/s0218194020400082.
Full textAbstract:
In recent years, new classes of highly dynamic, complex systems are gaining momentum. These classes include, but are not limited to IoT, smart cities, cyber-physical systems and sensor networks. These systems are characterized by the need to express behaviors driven by external and/or internal changes, i.e. they are reactive and context-aware. A desirable design feature of these systems is the ability of adapting their behavior to environment changes. In this paper, we propose an approach to support adaptive, reactive systems based on semantic runtime representations of their context, enabling the selection of equivalent behaviors, i.e. behaviors that have the same effect on the environment. The context representation and the related knowledge are managed by an engine designed according to a reference architecture and programmable through a declarative definition of sensors and actuators. The knowledge base of sensors and actuators (hosted by an RDF triplestore) is bound to the real world by grounding semantic elements to physical devices via REST APIs. The proposed architecture along with the defined ontology tries to address the main problems of dynamically re-configurable systems by exploiting a declarative, queryable approach to enable runtime reconfiguration with the help of (a) semantics to support discovery in heterogeneous environment, (b) composition logic to define alternative behaviors for variation points, (c) bi-causal connection life-cycle to avoid dangling links with the external environment. The proposal is validated in a case study aimed at designing an edge node for smart buildings dedicated to cultural heritage preservation.
41
Vakarina, Elena A. "Psychosemantic Research of the Psychological Well-Being of Artists." Moscow University Psychology Bulletin 47, no. 2 (2024): 116–33. http://dx.doi.org/10.11621/lpj-24-17.
Full textAbstract:
Background. Psychological well-being ensures the positive functioning of the artist's personality, which includes internal (subjective) and external (objective) factors. The dynamism of the art market, instability of demand and orders, displacement of individuality by mass art lead to emotional tension, increased anxiety and, as a consequence, to a decrease in the level of well-being and to the growth of psychological insecurity of artists. Psychosemantic space reflects social representations that reveal the most significant and prioritised sources of maintaining an optimal level of psychological well-being for artists. Objectives. The aim of the study was to identify and describe meaning groups of sources of psychological well-being of artists. Study Participants. The research participants were 57 people aged 19 to 60. Data processing was carried out by content analysis. The criterion for selecting the sample was the profile education obtained in a specialized secondary or higher education institution. Methods. A survey with an open type of questions about well-being among artists was conducted. Data processing was carried out by content analysis and by the method of condensation of meaning. Results. The most frequently occurring semantic groups of factors of psychological well-being are identified. External factors of psychological well-being of artists include environment, relationships, quality of life and educational environment. The internal factors of artists' psychological well-being include: health and self-perception. Conclusions. The selected semantic groups are reflections of emotional, communicative and behavioral components of external and internal sources of psychological well-being of artist. They determine the psychosemantic space of the studied phenomenon. Practical application of the results. The findings give rise to the study of social representations of artists’ psychological well-being. These results can be used by artists to optimize their current state; by psychologists and teachers to develop psycho-corrective and psycho-prophylactic programmes aimed to increase the level of well-being through the actualization of artists' personal resources.
42
Schleicher, Marianne. "Engaging All the Senses." Postscripts: The Journal of Sacred Texts, Cultural Histories, and Contemporary Contexts 8, no. 1-2 (August 19, 2017): 39–56. http://dx.doi.org/10.1558/post.32694.
Full textAbstract:
Based on an analysis of the process of making and inaugurating a Torah scroll, this article describe what is likely to trigger sensory responses in the participants in each phase of the process and the function of activating the five senses of touch, hearing, vision, smell, and taste. By distinguishing between hermeneutical and artefactual uses of sacred texts and drawing on sensory integration theory, it argues that multi-sensory stimulation in handling the Torah scroll brings people close and enables nonconscious internal negotiation between individual memories, cultural representations, and the immediate environment. In this way, sense-stimulation facilitates the transitivity crucial for individual subject formation as part of a greater collective.
43
Droit-Volet, Sylvie, and Sandrine Gil. "The time–emotion paradox." Philosophical Transactions of the Royal Society B: Biological Sciences 364, no. 1525 (July 12, 2009): 1943–53. http://dx.doi.org/10.1098/rstb.2009.0013.
Full textAbstract:
The present manuscript discusses the time–emotion paradox in time psychology: although humans are able to accurately estimate time as if they possess a specific mechanism that allows them to measure time (i.e. an internal clock), their representations of time are easily distorted by the context. Indeed, our sense of time depends on intrinsic context, such as the emotional state, and on extrinsic context, such as the rhythm of others' activity. Existing studies on the relationships between emotion and time suggest that these contextual variations in subjective time do not result from the incorrect functioning of the internal clock but rather from the excellent ability of the internal clock to adapt to events in one's environment. Finally, the fact that we live and move in time and that everything, every act, takes more or less time has often been neglected. Thus, there is no unique, homogeneous time but instead multiple experiences of time. Our subjective temporal distortions directly reflect the way our brain and body adapt to these multiple time scales.
44
Wijmans, Erik, Manolis Savva, Irfan Essa, Stefan Lee, Ari S. Morcos, and Dhruv Batra. "Emergence of Maps in the Memories of Blind Navigation Agents." AI Matters 9, no. 2 (June 2023): 8–14. http://dx.doi.org/10.1145/3609468.3609471.
Full textAbstract:
Decades of research into intelligent animal navigation posits that organisms build and maintain internal spatial representations (or maps) 1 of their environment, that enables the organism to determine and follow task-appropriate paths (Epstein, Patai, Julian, & Spiers, 2017; O'keefe & Nadel, 1978; Tollman, 1948). Hamsters, wolves, chimpanzees, and bats leverage prior exploration to determine and follow shortcuts they may never have taken before (Chapuis & Scardigli, 1993; Harten, Katz, Goldshtein, Handel, & Yovel, 2020; Menzel, 1973; Peters, 1976; Toledo et al., 2020). Even blind mole rats and animals rendered situationally-blind in dark environments demonstrate shortcut behaviors (Avni, Tzvaigrach, & Eilam, 2008; Kimchi, Etienne, & Terkel, 2004; Maaswinkel & Whishaw, 1999). Ants forage for food along meandering paths but take near-optimal return trips (Müller & Wehner, 1988), though there is some controversy about whether insects like ants and bees are capable of forming maps (Cheung et al., 2014; Cruse & Wehner, 2011).
45
Rosinol, Antoni, Andrew Violette, Marcus Abate, Nathan Hughes, Yun Chang, Jingnan Shi, Arjun Gupta, and Luca Carlone. "Kimera: From SLAM to spatial perception with 3D dynamic scene graphs." International Journal of Robotics Research 40, no. 12-14 (December 2021): 1510–46. http://dx.doi.org/10.1177/02783649211056674.
Full textAbstract:
Humans are able to form a complex mental model of the environment they move in. This mental model captures geometric and semantic aspects of the scene, describes the environment at multiple levels of abstractions (e.g., objects, rooms, buildings), includes static and dynamic entities and their relations (e.g., a person is in a room at a given time). In contrast, current robots’ internal representations still provide a partial and fragmented understanding of the environment, either in the form of a sparse or dense set of geometric primitives (e.g., points, lines, planes, and voxels), or as a collection of objects. This article attempts to reduce the gap between robot and human perception by introducing a novel representation, a 3D dynamic scene graph (DSG), that seamlessly captures metric and semantic aspects of a dynamic environment. A DSG is a layered graph where nodes represent spatial concepts at different levels of abstraction, and edges represent spatiotemporal relations among nodes. Our second contribution is Kimera, the first fully automatic method to build a DSG from visual–inertial data. Kimera includes accurate algorithms for visual–inertial simultaneous localization and mapping (SLAM), metric–semantic 3D reconstruction, object localization, human pose and shape estimation, and scene parsing. Our third contribution is a comprehensive evaluation of Kimera in real-life datasets and photo-realistic simulations, including a newly released dataset, uHumans2, which simulates a collection of crowded indoor and outdoor scenes. Our evaluation shows that Kimera achieves competitive performance in visual–inertial SLAM, estimates an accurate 3D metric–semantic mesh model in real-time, and builds a DSG of a complex indoor environment with tens of objects and humans in minutes. Our final contribution is to showcase how to use a DSG for real-time hierarchical semantic path-planning. The core modules in Kimera have been released open source.
46
Westrip, Simon P. "publCIF: software for editing, validating and formatting crystallographic information files." Journal of Applied Crystallography 43, no. 4 (June 24, 2010): 920–25. http://dx.doi.org/10.1107/s0021889810022120.
Full textAbstract:
publCIFis an application designed for creating, editing and validating crystallographic information files (CIFs) that are used in journal publication. It validates syntax and dictionary-defined data attributes through internal routines, and also provides a web interface to thecheckCIFservice of the International Union of Crystallography (IUCr), which provides a full crystallographic analysis of the structural data. The graphical interface allows users to edit the CIF either in its `raw' ASCII form (using a text editor with context-sensitive data validation and input facilities) or as a formatted representation of a structure report (using a word-processing environment), as well asviaa number of convenience tools (e.g.spreadsheet representations of looped data). Beyond file and data validation,publCIFprovides access to resources to facilitate preparation of a structure report (e.g.databases of author details, experimental data, standard referencesetc., either distributed with the program or collected during its use), along with tools for reference parsing, spell checking, structure visualization and image management.publCIFwas commissioned by the IUCr, both as free software for authors and as a tool for in-house journal production; the tool for authors is described here. Binary distributions for Linux, MacOS and Windows operating systems are available.
47
D'Esposito, Mark. "From cognitive to neural models of working memory." Philosophical Transactions of the Royal Society B: Biological Sciences 362, no. 1481 (March 30, 2007): 761–72. http://dx.doi.org/10.1098/rstb.2007.2086.
Full textAbstract:
Working memory refers to the temporary retention of information that was just experienced or just retrieved from long-term memory but no longer exists in the external environment. These internal representations are short-lived, but can be stored for longer periods of time through active maintenance or rehearsal strategies, and can be subjected to various operations that manipulate the information in such a way that makes it useful for goal-directed behaviour. Empirical studies of working memory using neuroscientific techniques, such as neuronal recordings in monkeys or functional neuroimaging in humans, have advanced our knowledge of the underlying neural mechanisms of working memory. This rich dataset can be reconciled with behavioural findings derived from investigating the cognitive mechanisms underlying working memory. In this paper, I review the progress that has been made towards this effort by illustrating how investigations of the neural mechanisms underlying working memory can be influenced by cognitive models and, in turn, how cognitive models can be shaped and modified by neuroscientific data. One conclusion that arises from this research is that working memory can be viewed as neither a unitary nor a dedicated system. A network of brain regions, including the prefrontal cortex (PFC), is critical for the active maintenance of internal representations that are necessary for goal-directed behaviour. Thus, working memory is not localized to a single brain region but probably is an emergent property of the functional interactions between the PFC and the rest of the brain.
48
Nonaka, Mio, Hajime Fujii, Ryang Kim, Takashi Kawashima, Hiroyuki Okuno, and Haruhiko Bito. "Untangling the two-way signalling route from synapses to the nucleus, and from the nucleus back to the synapses." Philosophical Transactions of the Royal Society B: Biological Sciences 369, no. 1633 (January 5, 2014): 20130150. http://dx.doi.org/10.1098/rstb.2013.0150.
Full textAbstract:
During learning and memory, it has been suggested that the coordinated electrical activity of hippocampal neurons translates information about the external environment into internal neuronal representations, which then are stored initially within the hippocampus and subsequently into other areas of the brain. A widely held hypothesis posits that synaptic plasticity is a key feature that critically modulates the triggering and the maintenance of such representations, some of which are thought to persist over time as traces or tags. However, the molecular and cell biological basis for these traces and tags has remained elusive. Here, we review recent findings that help clarify some of the molecular and cellular mechanisms critical for these events, by untangling a two-way signalling crosstalk route between the synapses and the neuronal soma. In particular, a detailed interrogation of the soma-to-synapse delivery of immediate early gene product Arc/Arg3.1, whose induction is triggered by heightened synaptic activity in many brain areas, teases apart an unsuspected ‘inverse’ synaptic tagging mechanism that likely contributes to maintaining the contrast of synaptic weight between strengthened and weak synapses within an active ensemble.
49
Pfeifer, Rolf, and Christian Scheier. "Representation in Natural and Artificial Agents: An Embodied Cognitive Science Perspective." Zeitschrift für Naturforschung C 53, no. 7-8 (August 1, 1998): 480–503. http://dx.doi.org/10.1515/znc-1998-7-804.
Full textAbstract:
Abstract The goal of the present paper is to provide an embodied cognitive science view on representation. Using the fundamental task of category learning, we will demonstrate that this perspective enables us to shed new light on many pertinent issues and opens up new prospects for investigation. The main focus of this paper is on the prerequisites to acquire representations of objects in the real world. We suggest that the main prerequisite is embodiment which allows an agent - human, animal or robot - to manipulate its sensory input such that invariances are generated. These invariances, in turn, are the basis of representation formation. In other words, the paper does not focus on representations per se, but rather discusses the various processes involved in order to make learning and representation acquisition possible. The argument structure is as follows. First we introduce two new perspectives on representation, namely frame-of-reference, and complete agent. Then we elaborate the complete agent perspective and focus in particular on embodiment and situatedness. We argue that embodiment has two main aspects, a dynamic and an information theoretic one. Focusing on the latter, there are a number of implications: Representation can only be understood if the embedding of the neural substrate in the physical agent is known, which includes morphology (shape), positioning and nature of sensors. Because an autonomous mobile agent in the real world is exposed to a continuously changing high-dimensional stream of sensory stimulation, if it is to learn category distinctions, it first needs a focus of attention mechanism, and then it must have a way to reduce the dimensionality of this high-dimensional sensory stream. Learning is very hard because the invariances are typically not found in the sensory data directly - the classical problem of object constancy: it is a so-called type 2 problem. Rather than trying to improve the learning algorithms - which is the standard approach - the embodied cognitive science view suggests a different approach which focuses on the nature of the data: the agent is not passively exposed to a given data distribution, but, by exploiting its body and through the interaction with the environment, it can actually generate the data. More specifically, it can generate correlated data that has the property that it can be easily learned. This learnability is due to redundancies resulting from the appropriate interactions with the environment. Through such interactions, the former type 2 problem is transformed into a type 1 problem, thus reducing the complexity of the learning task by orders of magnitude. By observing the frame-of-reference problem we will discuss to what extent these invariances are reflected - represented - in the “neural substrate”, i.e. the internal mechanisms of the agent. It is concluded, that representation is not a concept that can be studied in the abstract, but should be elaborated in the context of concrete agent-environment interactions. These ideas are all illustrated with examples of natural agents and artificial agents. In particular, we will present a suite of experiments on simulated and real-world artificial agents instantiating the main arguments
50
Mussa–Ivaldi, F. A., and E. Bizzi. "Motor learning through the combination of primitives." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 355, no. 1404 (December 29, 2000): 1755–69. http://dx.doi.org/10.1098/rstb.2000.0733.
Full textAbstract:
In this paper we discuss a new perspective on how the central nervous system (CNS) represents and solves some of the most fundamental computational problems of motor control. In particular, we consider the task of transforming a planned limb movement into an adequate set of motor commands. Tocarry out this task the CNS must solve a complex inverse dynamic problem. This problem involves the transformation from a desired motion to the forces that are needed to drive the limb. The inverse dynamic problem is a hard computational challenge because of the need to coordinate multiple limb segments and because of the continuous changes in the mechanical properties of the limbs and of the environment with which they come in contact. A number of studies of motor learning have provided support for the idea that the CNS creates, updates and exploits internal representations of limb dynamics in order to deal with the complexity of inverse dynamics. Here we discuss how such internal representations are likely to be built by combining the modular primitives in the spinal cord as well as other building blocks found in higher brain structures. Experimental studies on spinalized frogs and rats have led to the conclusion that the premotor circuits within the spinal cord are organized into a set of discrete modules. Each module, when activated, induces a specific force field and the simultaneous activation of multiple modules leads to the vectorial combination of the corresponding fields. We regard these force fields as computational primitives that are used by the CNS for generating a rich grammar of motor behaviours.