Results Of Cognitive Science Research Articles

A Model of Unified Perception and Cognition.

This article discusses an approach to add perception functionality to a general-purpose intelligent system, NARS. Differently from other AI approaches toward perception, our design is based on the following major opinions: (1) Perception primarily depends on the perceiver, and subjective experience is only partially and gradually transformed into objective (intersubjective) descriptions of the environment; (2) Perception is basically a process initiated by the perceiver itself to achieve its goals, and passive receiving of signals only plays a supplementary role; (3) Perception is fundamentally unified with cognition, and the difference between them is mostly quantitative, not qualitative. The directly relevant aspects of NARS are described to show the implications of these opinions in system design, and they are compared with the other approaches. Based on the research results of cognitive science, it is argued that the Narsian approach better fits the need of perception in Artificial General Intelligence (AGI).

Categorical Perception: A Groundwork for Deep Learning.

Classification is one of the major tasks that deep learning is successfully tackling. Categorization is also a fundamental cognitive ability. A well-known perceptual consequence of categorization in humans and other animals, categorical perception, is notably characterized by a within-category compression and a between-category separation: two items, close in input space, are perceived closer if they belong to the same category than if they belong to different categories. Elaborating on experimental and theoretical results in cognitive science, here we study categorical effects in artificial neural networks. We combine a theoretical analysis that makes use of mutual and Fisher information quantities and a series of numerical simulations on networks of increasing complexity. These formal and numerical analyses provide insights into the geometry of the neural representation in deep layers, with expansion of space near category boundaries and contraction far from category boundaries. We investigate categorical representation by using two complementary approaches: one mimics experiments in psychophysics and cognitive neuroscience by means of morphed continua between stimuli of different categories, while the other introduces a categoricality index that, for each layer in the network, quantifies the separability of the categories at the neural population level. We show on both shallow and deep neural networks that category learning automatically induces categorical perception. We further show that the deeper a layer, the stronger the categorical effects. As an outcome of our study, we propose a coherent view of the efficacy of different heuristic practices of the dropout regularization technique. More generally, our view, which finds echoes in the neuroscience literature, insists on the differential impact of noise in any given layer depending on the geometry of the neural representation that is being learned, that is, on how this geometry reflects the structure of the categories.

Plausibility designs, or a probabilistic and predictive take on scenario effectiveness

Plausibility is often defined in direct contrast with probability. A common stance is that plausibility is a good ally of specifically social scientific studies of the future. A recent version of this claim is that scenario effectiveness is the result of plausibility ascription, not of the scenario narratives themselves, however, but of the “co-conversation” between the builders and users of scenarios. In this approach, probability falls out as redundant. Interestingly, this has striking similarities with recent probabilistic developments in cognitive literary studies. This approach claims that our judgments of the “effectiveness” of a literary text are dependent upon both the workings of our probabilistic, predictive minds during the reading process, and the “ease” with which we do such predictions. In this approach, however, there is little room for plausibility. In this paper, I argue that these two perspectives can be combined into a synthetic approach, which views the plausibility of a scenario co-conversation as an outcome of the “ease” with which scenario users “read” the scenario texts based on the workings of their probabilistic minds. Not only is this in line with recent results in cognitive science. It also holds promise of relieving the current tension between plausibility and probability in futures studies.

Is Consciousness Computable?

This commentary reviews different scientific positions for and against consciousness being a computable property. The role that quantum mechanics may play in this question is also investigated. It is argued that the view which assigns consciousness a separate category is consistent with both quantum mechanics and certain results in cognitive science. It is further argued that computability of consciousness implies the solution to the halting problem which is computationally impossible.

An oscillatory neural network model that demonstrates the benefits of multisensory learning.

Since the world consists of objects that stimulate multiple senses, it is advantageous for a vertebrate to integrate all the sensory information available. However, the precise mechanisms governing the temporal dynamics of multisensory processing are not well understood. We develop a computational modeling approach to investigate these mechanisms. We present an oscillatory neural network model for multisensory learning based on sparse spatio-temporal encoding. Recently published results in cognitive science show that multisensory integration produces greater and more efficient learning. We apply our computational model to qualitatively replicate these results. We vary learning protocols and system dynamics, and measure the rate at which our model learns to distinguish superposed presentations of multisensory objects. We show that the use of multiple channels accelerates learning and recall by up to 80%. When a sensory channel becomes disabled, the performance degradation is less than that experienced during the presentation of non-congruent stimuli. This research furthers our understanding of fundamental brain processes, paving the way for multiple advances including the building of machines with more human-like capabilities.

Notes Toward a Theory of Spectating

Notes Toward a Theory of Spectating1 James Hamilton (bio) Over the past thirty years we have learned a lot about the contents of performances that spectators come to know or understand during performances. Reflection influenced by Marxism, feminism, race studies, queer studies, and postcolonial studies, to mention just a handful of theoretical perspectives, have contributed substantially to our understanding of what spectators can bring to and can also learn from theatrical performances. But what has not been studied in sufficient detail so far is how spectators come to grasp those contents. What I will now call “the new questions” for theatre theory are not so much what spectators know, when they know it, or even when they learn it. The new questions are how they learn it and why they get it right.2 The second clause in this formulation of the questions may strike some as tendentious; for we have rightly learned to be suspicious of some claims concerning “getting it right.” But, as I have argued elsewhere, there seems to be a basic level of understanding that many, perhaps most, spectators achieve that rests on facts about theatrical spectators that largely cut across cultural, ideological, gender, and generational lines in remarkable ways and for remarkable reasons: and this basic level of understanding is what underwrites the many disagreements spectators come to have with each other about the meaning and significance of what they see. Although I will address some issues about this formulation directly in the next section of the paper, I note here that this essay is primarily a contribution toward answering the questions just mentioned, those I have just called “the new questions.” How do spectators grasp the contents that many theoretical essays have convinced us they do grasp? How do they get it right? In 2007, I wrote a defense of the views that (a) there is a basic level of understanding that most spectators to any kind of theatrical performance often achieve, and (b) that the reason for this is that it is rational to expect most spectators to both converge on roughly the same features of a performance and project from these features roughly the same descriptions of what they have encountered.3 Some [End Page 105] of the reasoning that leads to (a) will be discussed in the first part of this paper. I have since come to see that, although it is rational to expect convergence on roughly the same features of a performance, this does not make it rational to expect that spectators will project even roughly the same descriptions of those performances. Yet many spectators do so. If it is a narrative performance, they are likely to describe a story, and pretty much the same story. If it is not narrative, they are likely to make plausible and frequently correct guesses as to its actual structure. So, it is important to figure out how they do that. And that is the focus of this paper. It must be stressed that this does not mean spectators will agree with each other concerning what the story is about, what it means, or what its significance is. Indeed, they frequently do not. And, because they often disagree on interpretations of what they describe, they also disagree regarding how to evaluate it.4 Departures from the dominant pattern of focusing on content grasped by an audience are present in the literature of theatre theory, especially in the last decade.5 One truly notable exception to that dominant pattern is Bruce McConachie’s Engaging Audiences that, as its subtitle suggests, offers “a cognitive approach to spectating.” McConachie deploys a range of results in cognitive science to suggest answers to a variety of pressing questions about the mechanisms of spectating.6 Other theatre theorists contributing to this trend include Evelyn Tribble, Amy Cook, and Rhonda Blair and John Lutterbie.7 As Blair and Lutterbie remark in their informative introduction to a special section of The Journal of Dramatic Theory and Criticism on Cognitive Studies, Theatre, and Performance, developments in the cognitive sciences “can support and enrich our appreciation of a whole range of existing performance and theatre theories.”8 And that, indeed, is the...

Motor skill depends on knowledge of facts

Those in 20th century philosophy, psychology, and neuroscience who have discussed the nature of skilled action have, for the most part, accepted the view that being skilled at an activity is independent of knowing facts about that activity, i.e., that skill is independent of knowledge of facts. In this paper we question this view of motor skill. We begin by situating the notion of skill in historical and philosophical context. We use the discussion to explain and motivate the view that motor skill depends upon knowledge of facts. This conclusion seemingly contradicts well-known results in cognitive science. It is natural, on the face of it, to take the case of H.M., the seminal case in cognitive neuroscience that led to the discovery of different memory systems, as providing powerful evidence for the independence of knowledge and skill acquisition. After all, H.M. seems to show that motor learning is retained even when previous knowledge about the activity has been lost. Improvements in skill generally require increased precision of selected actions, which we call motor acuity. Motor acuity may indeed not require propositional knowledge and has direct parallels with perceptual acuity. We argue, however, that reflection on the specifics of H.M.'s case, as well as other research on the nature of skill, indicates that learning to become skilled at a motor task, for example tennis, depends also on knowledge-based selection of the right actions. Thus skilled activity requires both acuity and knowledge, with both increasing with practice. The moral of our discussion ranges beyond debates about motor skill; we argue that it undermines any attempt to draw a distinction between practical and theoretical activities. While we will reject the independence of skill and knowledge, our discussion leaves open several different possible relations between knowledge and skill. Deciding between them is a task to be resolved by future research.

Deciphering the Face.

We argue that to make robust computer vision algorithms for face analysis and recognition, these should be based on configural and shape features. In this model, the most important task to be solved by computer vision researchers is that of accurate detection of facial features, rather than recognition. We base our arguments on recent results in cognitive science and neuroscience. In particular, we show that different facial expressions of emotion have diverse uses in human behavior/cognition and that a facial expression may be associated to multiple emotional categories. These two results are in contradiction with the continuous models in cognitive science, the limbic assumption in neuroscience and the multidimensional approaches typically employed in computer vision. Thus, we propose an alternative hybrid continuous-categorical approach to the perception of facial expressions and show that configural and shape features are most important for the recognition of emotional constructs by humans. We illustrate how these image cues can be successfully exploited by computer vision algorithms. Throughout the paper, we discuss the implications of these results in applications in face recognition and human-computer interaction.

Les sciences cognitives jettent un pont entre éducation et jeux sérieux

The notion of GBL (game-based learning) raises the issue of connecting educational questions with the design of educational tools that answer those very questions. Indicating GBL as a whole as a generic answer to the generic need of reforming education is not appropriate: different games and different contexts trigger different cognitive functions, and affect different forms of learning. Cognitive science can help bridge educational questions and answers shaped as educational video games. A specific case study will help illustrate how the models, methods, and results of cognitive science can contribute to the design of a video game for teaching and learning sciences (physics and chemistry, middle school); the author will propose an analysis of the literature in cognitive science that is relevant for the case study,