The role of robotic modelling in cognitive science

From the perspective of cognitive robotics, this paper presents a modern interpretation of Newell’s (1973) reasoning and suggestions for why and how cognitive psychologists should develop models of cognitive phenomena. We argue that the shortcomings of current cognitive modelling approaches are due in significant part to a lack of exactly the kind of integration required for the development of embodied autonomous robotics. Moreover we suggest that considerations of embodiment, situatedness, and autonomy, intrinsic to cognitive robotics, provide an appropriate basis for the integration and theoretic cumulation that Newell argued was necessary for psychology to mature. From this perspective we analyse the role of embodiment and modes of situatedness in terms of integration, cognition, emotion, and autonomy. Four complementary perspectives on embodied and situated cognitive science are considered in terms of their potential to contribute to cognitive robotics, cognitive science, and psychological theorizing: minimal cognition and organization, enactive perception and sensorimotor contingency, homeostasis and emotion, and social embedding. In combination these perspectives provide a framework for cognitive robotics, not only wholly compatible with the original aims of cognitive modelling, but as a more appropriate methodology than those currently in common use within psychology.     

认知神经心理学简介

认知神经心理学是认知心理学的一个分支。它的目的是探讨当人们执行认知活动的时候,心理信息加工过程是怎样的,所采用的手段是研究这些认知功能受损的病人。它与认知神经科学的不同在于：认知神经心理学关注的是心理（mind）,而认知神经科学关注的是大脑（特别是关注与认知有关的大脑机制）。研究认知神经心理学的方法也可以用于研究发展性认知障碍,如阅读障碍,或者特殊的语言损伤,这就是发展性认知神经心理学。这些方法还可以用于高级认知发面的研究,如信念形成和心理理论。这些高级认知方面的障碍是精神病学的范畴,因此这类研究错觉、幻想或虚构等的认知神经心理学叫做认知神经精神病学。认知神经心理学的典型特征有：1）研究症状,而不是并发症;2）采用个案研究,而不是群体研究;3）主要数据来源是症状间的双分离;4）致力于模块化认知模型的建立。     

Grasping language – A short story on embodiment

The new concept of embodied cognition theories has been enthusiastically studied by the cognitive sciences, by as well as such disparate disciplines as philosophy, anthropology, neuroscience, and robotics. Embodiment theory provides the framework for ongoing discussions on the linkage between “low” cognitive processes as perception and “high” cognition as language processing and comprehension, respectively. This review gives an overview along the lines of argumentation in the ongoing debate on the embodiment of language and employs an ALE meta-analysis to illustrate and weigh previous findings. The collected evidence on the somatotopic activation of motor areas, abstract and concrete word processing, as well as from reported patient and timing studies emphasizes the important role of sensorimotor areas in language processing and supports the hypothesis that the motor system is activated during language comprehension.     

Value systems for developmental cognitive robotics: A survey

This paper surveys value systems for developmental cognitive robotics. A value system permits a biological brain to increase the likelihood of neural responses to selected external phenomena. Many machine learning algorithms capture the essence of this learning process. However, computational value systems aim not only to support learning, but also autonomous attention focus to direct learning. This combination of unsupervised attention focus and learning aims to address the grand challenge of autonomous mental development for machines. This survey examines existing value systems for developmental cognitive robotics in this context. We examine the definitions of value used—including recent pioneering work in intrinsic motivation as value—as well as initialisation strategies for innate values, update strategies for acquired value and the data structures used for storing value. We examine the extent to which existing value systems support attention focus, learning and prediction in an unsupervised setting. The types of robots and applications in which these value systems are used are also examined, as well as the ways that these applications are evaluated. Finally, we study the strengths and limitations of current value systems for developmental cognitive robots and conclude with a set of research challenges for this field.     

Non-representationalist cognitive science and realism

Embodied and extended cognition is a relatively new paradigm within cognitive science that challenges the basic tenet of classical cognitive science, viz. cognition consists in building and manipulating internal representations. Some of the pioneers of embodied cognitive science have claimed that this new way of conceptualizing cognition puts pressure on epistemological and ontological realism. In this paper I will argue that such anti-realist conclusions do not follow from the basic assumptions of radical embodied cognitive science. Furthermore I will show that one can develop a form of realism that reflects rather than just accommodates the core principles of non-representationalist embodied cognitive science.     

A computational cognitive framework of spatial memory in brains and robots

Computational cognitive models of spatial memory often neglect difficulties posed by the real world, such as sensory noise, uncertainty, and high spatial complexity. On the other hand, robotics is unconcerned with understanding biological cognition. Here, we describe a computational framework for robotic architectures aiming to function in realistic environments, as well as to be cognitively plausible.We motivate and describe several mechanisms towards achieving this despite the sensory noise and spatial complexity inherent in the physical world. We tackle error accumulation during path integration by means of Bayesian localization, and loop closing with sequential gradient descent. Finally, we outline a method for structuring spatial representations using metric learning and clustering. Crucially, unlike the algorithms of traditional robotics, we show that these mechanisms can be implemented in neuronal or cognitive models.We briefly outline a concrete implementation of the proposed framework as part of the LIDA cognitive architecture, and argue that this kind of probabilistic framework is well-suited for use in cognitive robotic architectures aiming to combine spatial functionality and psychological plausibility.     

A computational model of perception and action for cognitive robotics

Robots are increasingly expected to perform tasks in complex environments. To this end, engineers provide them with processing architectures that are based on models of human information processing. In contrast to traditional models, where information processing is typically set up in stages (i.e., from perception to cognition to action), it is increasingly acknowledged by psychologists and robot engineers that perception and action are parts of an interactive and integrated process. In this paper, we present HiTEC, a novel computational (cognitive) model that allows for direct interaction between perception and action as well as for cognitive control, demonstrated by task-related attentional influences. Simulation results show that key behavioral studies can be readily replicated. Three processing aspects of HiTEC are stressed for their importance for cognitive robotics: (1) ideomotor learning of action control, (2) the influence of task context and attention on perception, action planning, and learning, and (3) the interaction between perception and action planning. Implications for the design of cognitive robotics are discussed.     

Cognition is a matter of trust: Distrust tunes cognitive processes

The current review proposes that exposure to a specific untrustworthy source of information engages a mode of thought—a distrust mindset—that is also evoked by incidental distrust contexts and by personality characteristics. The review summarises empirical research demonstrating that—in contrast to trust, which leads to the familiar congruent type of cognitive processes—distrust triggers a spontaneous activation of alternatives and incongruent associations for a given concept. These alternatives dilute the activation level of the given concept, indicating that our mind can spontaneously stop the congruent-processing flow. Consequently, distrust blocks congruent effects such as confirmatory biases, accessibility effects, stereotyping, and routine reasoning. Thus, the review suggests that the basic flow of our cognition is (dis)trust dependent. The review concludes with a discussion of the effect of the distrust mindset as a demonstration of (1) situated cognition and (2) a spontaneous negation process.     

The cognitive neuroscience of signed language

The present article is an assessment of the current state of knowledge in the field of cognitive neuroscience of signed language. Reviewed lesion data show that the left hemisphere is dominant for perception and production of signed language in aphasics, in a fashion similar to spoken language aphasia. Several neuropsychological dissociations support this claim: Non-linguistic visuospatial functions can be dissociated from spatial functions and general motor deficits can be dissociated from execution of signs. Reviewed imaging data corroborate the lesion data in that the importance of the left hemisphere is re-confirmed. The data also establish the role of the right hemisphere in signed language processing. Alternative hypotheses regarding what aspects of signed language processing are handled by the right hemisphere are currently tested. The second section of the paper starts by addressing the role that early acquisition of signed and spoken language play for the neurofunctional activation patterns in the brain. Compensatory cognitive and communicative enhancements have also been documented as a function of early sign language use, suggesting an interesting interaction between language and cognition. Recent behavioural data on sign processing in working memory--a cognitive system important for language perception and production suggest e.g. phonological loop effects analogous to those obtained for speech processing. Neuroimaging studies will have to address this potential communality.     

Representation and cognitive science

‘Representation’ is a concept which occurs both in cognitive science and philosophy. It has common features in both settings in that it concerns the explanation of behaviour in terms of the way the subject categorizes and systematizes responses to its environment. The prevailing model sees representations as causally structured entities correlated on the one hand with elements in a natural language and on the other with clearly identifiable items in the world. This leads to an analysis of representation and cognition in terms of formal symbols and their relations. But human perception and cognition use multiple informational constraints and deal with unsystematic and messy input in a way best explained by Parallel Distributed Processing models. This undermines the claim that a formal representational theory of mind is ‘the only game in town’. In particular it suggests a radically different model of brain function and its relation to epistemology from that found in current representational theories.     

Cognitive neuroimaging: cognitive science out of the armchair

Cognitive scientists were not quick to embrace the functional neuroimaging technologies that emerged during the late 20th century. In this new century, cognitive scientists continue to question, not unreasonably, the relevance of functional neuroimaging investigations that fail to address questions of interest to cognitive science. However, some ultra-cognitive scientists assert that these experiments can never be of relevance to the study of cognition. Their reasoning reflects an adherence to a functionalist philosophy that arbitrarily and purposefully distinguishes mental information-processing systems from brain or brain-like operations. This article addresses whether data from properly conducted functional neuroimaging studies can inform and subsequently constrain the assumptions of theoretical cognitive models. The article commences with a focus upon the functionalist philosophy espoused by the ultra-cognitive scientists, contrasting it with the materialist philosophy that motivates both cognitive neuroimaging investigations and connectionist modelling of cognitive systems. Connectionism and cognitive neuroimaging share many features, including an emphasis on unified cognitive and neural models of systems that combine localist and distributed representations. The utility of designing cognitive neuroimaging studies to test (primarily) connectionist models of cognitive phenomena is illustrated using data from functional magnetic resonance imaging (fMRI) investigations of language production and episodic memory.     

Pattern similarity and cognitive transformations

An experiment on similarity judgment was conducted in order to investigate how man processes information when a pair of configurations are presented for Ss to compare. Linear cyclic configurations made of binary symbols were used. Thirty-two Ss rated similarity of configuration pairs characterized by inter-configurational structures of several types. Our transformation model predicted that two configurations which could be made identical by two or more basic transformations would be judged more similar than those which could be made identical by only one basic transformation. The latter configurations in turn would be judged more similar than those which could be made identical only by successive application of more than one different basic transformation. Two configurations which could not be transformed would be the least similar. These predictions were supported by our experimental results. Therefore, we conclude that the transformational structure between the given pair of configurations should be the very important and effective determinant for their similarity. We also suggest that similarity cognition is a dynamic cognitive process involving cognitive transformations of given configurations.     

How are religious concepts created? A form of cognition and its effects

Science that needs logical demonstration has failed to eliminate religious concepts. It is as if they have own validity that cannot be broken by scientific knowledge we trust the most at present. In this paper, I will attempt to establish a new cognitive theory to help explain the basis of belief in religious concepts. This form of cognition will be named simply unifying-induction or unifying-inductive cognition. As illustrations, I will consider some typical religious discourses involving concepts such as “all-in-one” or “one is everything.” It is these typically religious discourses that science has not been able to easily sweep away by its logical scientific proofs. In the end, although we perhaps cannot know if the religious beings such as gods really exist or not, we may understand these concepts are very the creation of human cognition. It also has important implications for other disciplines such as robotics, developmental psychology, cognitive archaeology, the history of science, the study of religion and so on.     

Attention and cognitive penetrability: The epistemic consequences of attention as a form of metacognitive regulation

A recent approach to the cognitive penetrability of perception, i.e. the possibility that perception is shaped top-down by high-level cognitive states such as beliefs and desires, proposes to understand the phenomenon on the basis of its consequences, among which there is a challenge for the epistemic role of perceptual experience in justifying beliefs (Stokes, 2015). In this paper, I argue that some attentional phenomena qualify as cases of cognitive penetrability under this consequentialist approach. I present a popular theory of attention, the biased-competition theory, on which basis I establish that attention is a form of metacognitive regulation. I argue that attention (as metacognitive regulation) involves the right kind of cognitive-perceptual relation and leads to the same epistemic consequences as other more traditional versions of cognitive penetrability.     

Doing cognitive neuroscience: a third way

The “top-down” and “bottom-up” approaches have been thought to exhaust the possibilities for doing cognitive neuroscience. We argue that neither approach is likely to succeed in providing a theory that enables us to understand how cognition is achieved in biological creatures like ourselves. We consider a promising third way of doing cognitive neuroscience, what might be called the “neural dynamic systems” approach, that construes cognitive neuroscience as an autonomous explanatory endeavor, aiming to characterize in its own terms the states and processes responsible for brain-based cognition. We sketch the basic motivation for the approach, describe a particular version of the approach, so-called ‘Dynamic Causal Modeling’ (DCM), and consider a concrete example of DCM. This third way, we argue, has the potential to avoid the problems that afflict the other two approaches.     

Directions in Connectionist Research: Tractable Computations Without Syntactically Structured Representations

Should connectionists abandon the quest for tractably computable cognitive transition functions while retaining syntactically structured mental representations? We argue, in opposition to Horgan, that it should not. We argue that the case against tractably computable functions, based upon the claimed isotropic and Quinean character of cognition, fails since cognition is not as isotropic and Quinean as Fodor and Horgan contend. Moreover, we illustrate how current research in both connectionism and cognitive neuroscience suggests that tractability can be preserved through division of overall computations into modular sub-processes, each of which is tractable. As to syntactically structured representations, we argue that they are unneeded for most cognitive tasks organisms confront, and that when they are needed, they may be provided by external representational media such as natural language. Moreover, we note that increasingly cognitive linguistics has become the ally of connectionism and that the research program of cognitive linguistics suggests that abilities to use natural languages may be developed without requiring syntactically structured mental representations to exist prior to natural language.     

统计学习与双语认知的关系

统计学习是提取环境输入潜在规则的一种认知机制,其与语言的关联已得到证实。双语认知是学界关注的热点之一。统计学习与双语认知的关系如何?文章先介绍统计学习认知机制及其与语言的关系,然后从“统计学习能力可否预测二语学习表现”、“统计学习训练能否促进二语学习”和“双语经验能否提高统计学习能力”三个维度述评相关文献,并指出未来可从输入特征、个体差异和神经科学角度进一步探讨统计学习与双语认知的关系。