Representation and learning in motor action – Bridges between experimental research and cognitive robotics

To gain a better understanding of the functionality of representation and categorization in action and interaction, it is fundamental that researchers understand how movements are represented in long-term memory. It is our position that human motor control requires that our actions be planned and represented in terms of intended perceptual effects and future task demands, and that the individual has a well-structured mental representation of the task so that the movement can be carried out successfully. Basic Action Concepts (BACs) are identified as major building blocks of cognitive representation in long-term memory, which are cognitive tools used to master the functional demands of movement tasks. In this paper, we consider relevant issues in research methodology and present an experimental method that can be used to assess action-relevant representational structures. This method permits us to observe the strong relationship between cognitive representation and performance in manual action. For example, the specific differences in the mental representations of participants are strongly related to skill level, as well as biomechanical and task constraints. We then discuss results from our learning experiments, where we have examined the development and changes in cognitive representation over time. From these experiments we have found that cognitive reference structures include task-specific spatial information, which provides the basis for action control in skilled voluntary movement. We have implemented these results on various robotic platforms. We argue that the insights gained from various experimental approaches in the field of cognitive psychology and motor control enable researchers to explore the possibilities and limitations of artificial control architectures in robot systems. Finally, we argue that this is not a unidirectional process. Researchers from the field of cognitive psychology and motor control can profit from the advances in technological systems, which enhance the understanding of human motor control in skilled voluntary action.     

Mental representation and two kinds of eliminativism

The battle over the proper place of mental representation in cognitive science is often portrayed as a clash between realism and eliminativism. But this simple dichotomy belies the variety of different ontological positions available. This article investigates the various stances that one can adopt toward the ontology of mental representation, and in so doing, shows that eliminativism is in fact best understood as two distinct positions: a posteriori eliminativism and a priori eliminativism. Furthermore, I show that a priori eliminativism faces two crippling challenges. I argue that once we put a priori eliminativism aside, determining the ultimate ontological status of representation can be postponed while we assess its utility across different domains of cognitive science—something all remaining positions can agree on.     

Two Kinds of Mental Realism

I argue that there is a distinction to be drawn between two kinds of mental realism, and I draw some lessons for the realism-antirealism debate. Although it is already at hand, the distinction has not yet been drawn clearly. The difference to be shown consists in what realism is about: it may be either about the interpretation of folk psychology, or the ontology of mental entities. I specify the commitment to the fact-stating character of the discourse as the central component of realism about folk psychology, and from this I separate realism about mental entities as an ontological commitment towards them. I point out that the two views are mutually independent, which provides the possibility of considering folk psychology as not being in cognitive competition with scientific psychology. At the end I make a tentative suggestion as to how to interpret the former in order to avoid this conflict.     

Seeing is believing: the effect of brain images on judgments of scientific reasoning

Brain images are believed to have a particularly persuasive influence on the public perception of research on cognition. Three experiments are reported showing that presenting brain images with articles summarizing cognitive neuroscience research resulted in higher ratings of scientific reasoning for arguments made in those articles, as compared to articles accompanied by bar graphs, a topographical map of brain activation, or no image. These data lend support to the notion that part of the fascination, and the credibility, of brain imaging research lies in the persuasive power of the actual brain images themselves. We argue that brain images are influential because they provide a physical basis for abstract cognitive processes, appealing to people's affinity for reductionistic explanations of cognitive phenomena.     

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.     

Decoding the Large-Scale Structure of Brain Function by Classifying Mental States Across Individuals

ABSTRACT— Brain-imaging research has largely focused on localizing patterns of activity related to specific mental processes, but recent work has shown that mental states can be identified from neuroimaging data using statistical classifiers. We investigated whether this approach could be extended to predict the mental state of an individual using a statistical classifier trained on other individuals, and whether the information gained in doing so could provide new insights into how mental processes are organized in the brain. Using a variety of classifier techniques, we achieved cross-validated classification accuracy of 80% across individuals (chance = 13%). Using a neural network classifier, we recovered a low-dimensional representation common to all the cognitive-perceptual tasks in our data set, and we used an ontology of cognitive processes to determine the cognitive concepts most related to each dimension. These results revealed a small organized set of large-scale networks that map cognitive processes across a highly diverse set of mental tasks, suggesting a novel way to characterize the neural basis of cognition.     

Computational model of enactive visuospatial mental imagery using saccadic perceptual actions

From the onset of cognitive revolution, the concept of mental imagery has been given different, many times opposing, theoretical accounts. Mental imagery appears to be a ubiquitous, yet wholly individual, easy to explain experience on the one hand, being hard to deal with scientifically on the other hand. The focus of this research is on an enactive approach to visuospatial mental imagery, inspired by Sima’s perceptual instantiation theory. We designed a hybrid computational model, composed of a forward model, an inverse model, both implemented as neural networks, and a memory/controller module, that grounds simple mental concepts, such as a triangle and a square, in perceptual actions, and is able to reimagine these objects by performing the necessary perceptual actions in a simulated humanoid robot. We tested the model on three tasks – salience-based object recognition, imagination-based object recognition and object imagination – and achieved very good results showing, as a proof of concept, that perceptual actions are a viable candidate for grounding the visuospatial mental concepts as well as the credible substrate of visuospatial mental imagery.     

A Quantitative Empirical Analysis of the Abstract/Concrete Distinction

This study presents original evidence that abstract and concrete concepts are organized and represented differently in the mind, based on analyses of thousands of concepts in publicly available data sets and computational resources. First, we show that abstract and concrete concepts have differing patterns of association with other concepts. Second, we test recent hypotheses that abstract concepts are organized according to association, whereas concrete concepts are organized according to (semantic) similarity. Third, we present evidence suggesting that concrete representations are more strongly feature‐based than abstract concepts. We argue that degree of feature‐based structure may fundamentally determine concreteness, and we discuss implications for cognitive and computational models of meaning.     

从表征和认知过程上看表象与知觉、记忆的关系

表象、知觉和记忆是一个整合的认知系统。由于知觉和记忆提供了表象生成的材料,因而三者共享相似的表征,并激活广泛而相似的脑区。然而在认知加工过程上三者存在一定的差异。与知觉相比,表象的编码方式更抽象、更依赖过去经验的参与且处理细节能力更弱;与记忆相比,表象更容易受无关信息的干扰。未来对三者关系的研究应关注不同来源和不同类型的表象与知觉、记忆之间的关系,以及工作记忆在三者关系中所起的作用。     

Concepts as Semantic Pointers: A Framework and Computational Model

The reconciliation of theories of concepts based on prototypes, exemplars, and theory‐like structures is a longstanding problem in cognitive science. In response to this problem, researchers have recently tended to adopt either hybrid theories that combine various kinds of representational structure, or eliminative theories that replace concepts with a more finely grained taxonomy of mental representations. In this paper, we describe an alternative approach involving a single class of mental representations called “semantic pointers.” Semantic pointers are symbol‐like representations that result from the compression and recursive binding of perceptual, lexical, and motor representations, effectively integrating traditional connectionist and symbolic approaches. We present a computational model using semantic pointers that replicates experimental data from categorization studies involving each prior paradigm. We argue that a framework involving semantic pointers can provide a unified account of conceptual phenomena, and we compare our framework to existing alternatives in accounting for the scope, content, recursive combination, and neural implementation of concepts.     

The plurality of concepts

Traditionally, theories of concepts in psychology assume that concepts are a single, uniform kind of mental representation. But no single kind of representation can explain all of the empirical data for which concepts are responsible. I argue that the assumption that concepts are uniformly the same kind of mental structure is responsible for these theories’ shortcomings, and outline a pluralist theory of concepts that rejects this assumption. On pluralism, concepts should be thought of as being constituted by multiple representational kinds, with the particular kind of concept used on an occasion being determined by the context. I argue that endorsing pluralism does not lead to eliminativism about concepts as an object of scientific interest.     

Cognitive and emotional processes during dreaming: a neuroimaging view

Dream is a state of consciousness characterized by internally-generated sensory, cognitive and emotional experiences occurring during sleep. Dream reports tend to be particularly abundant, with complex, emotional, and perceptually vivid experiences after awakenings from rapid eye movement (REM) sleep. This is why our current knowledge of the cerebral correlates of dreaming, mainly derives from studies of REM sleep. Neuroimaging results show that REM sleep is characterized by a specific pattern of regional brain activity. We demonstrate that this heterogeneous distribution of brain activity during sleep explains many typical features in dreams. Reciprocally, specific dream characteristics suggest the activation of selective brain regions during sleep. Such an integration of neuroimaging data of human sleep, mental imagery, and the content of dreams is critical for current models of dreaming; it also provides neurobiological support for an implication of sleep and dreaming in some important functions such as emotional regulation.     

想象未来的认知加工成分及其脑网络

合理预期和想象未来事件或场景, 有利于个体做出适应性反应, 促进生存与发展。近年来, 研究者对想象未来的认知加工成分和神经机制进行了深入探讨, 提出了想象未来的相关概念和理论。我们首先概述了想象未来实验常用的思维采样范式和词语-线索范式, 接着介绍了想象未来的自我参照加工、心理场景构建等认知加工成分。此外, 特别介绍了想象未来的默认网络模型以及与之相关的其他大尺度脑网络, 并进一步提出了想象未来的脑网络研究框架。未来的研究可从想象未来的动态脑网络、复杂脑网络以及与疾病的关系等角度入手, 深入探究想象未来的神经机制。     

The Brain can be Thought of as a Tool

In this paper I argue that the recent tendency to elevate neuropsychology to the status of the one true scientific core of studies of people thinking, feeling, acting and perceiving is not best understood as a simple mereological fallacy, that is the fallacy of ascribing certain properties of wholes to their parts, in particular mental concepts to the material brain as a part of a person. In defending Svend Brinkmann's way of undermining the claims of neuroscience against the criticism offered by Gaeto and Cornejo of the cognitive task-brain as tool proposal, I argue that a person's brain is part of the body of that person, but that the body is not a part of a person. Hence the use of person-concepts to describe brain activity is not a mereological fallacy. Rather human bodies are sites for people. Material tools can be fashioned or found at such sites. It is a fallacy to present neuroscience as the core of psychology but it is the error of deriving an `ought' from an 'is' as identified by David Hume. Where to look for a positive guide to a philosophically respectable psychology, that is a study program that does justice to the phenomena of human thinking, feeling, acting and perceiving, without falling into logical and semantic traps. I show how to adapt Aristotle's schema for 'complete explanation' to this role.     

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。     

Twelve issues for cognitive science

I am struck by how little is known about so much of cognition. One goal of this paper is to argue for the need to consider a rich set of interlocking issues in the study of cognition. Mainstream work in cognition—including my own—ignores many critical aspects of animate cognitive systems. Perhaps one reason that existing theories say so little relevant to real world activities is the neglect of social and cultural factors, of emotion, and of the major points that distinguish an animate cognitive system from an artificial one: the need to survive, to regulate its own operation, to maintain itself, to exist in the environment, to change from a small, uneducated, immature system to an adult, developed, knowledgeable one. Human cognition is not the same as artificial cognition, if only because the human organism must also be concerned with the problems of life, of development, of survival. There must be a regulatory system that interacts with the cognitive component. And it may well be that it is the cognitive component that is subservient, evolved primarily for the benefit of the regulatory system, working through the emotions, through affect. I argue that several concepts must become fundamental parts of the study of cognition, including the roles of culture, of social interaction, of emotions, and of motivation. I argue that there are at least 12 issues that should comprise the study of cognition, and thereby, the field of Cognitive Science. We need to study a wide variety of behavior before we can hope to understand a single class. Cognitive scientists as a whole ought to make more use of evidence from the neurosciences, from brain damage and mental illness, from cognitive sociology and anthropology, and from clinical studies of the human. These must be accompanied, of course, with the study of language, of the psychological aspects of human processing structures, and of artificially intelligent mechanisms. The study of Cognitive Science requires a complex interaction among different issues of concern, an interaction that will not be properly understood until all parts are understood, with no part independent of the others, the whole requiring the parts, and the parts the whole.     

Classifying emotion: a developmental account

The aim of this paper is to propose a systematic classification of emotions which can also characterize their nature. The first challenge we address is the submission of clear criteria for a theory of emotions that determine which mental phenomena are emotions and which are not. We suggest that emotions as a subclass of mental states are determined by their functional roles. The second and main challenge is the presentation of a classification and theory of emotions that can account for all existing varieties. We argue that we must classify emotions according to four developmental stages: 1. pre-emotions as unfocussed expressive emotion states, 2. basic emotions, 3. primary cognitive emotions, and 4. secondary cognitive emotions. We suggest four types of basic emotions (fear, anger, joy and sadness) which are systematically differentiated into a diversity of more complex emotions during emotional development. The classification distinguishes between basic and non-basic emotions and our multi-factorial account considers cognitive, experiential, physiological and behavioral parameters as relevant for constituting an emotion. However, each emotion type is constituted by a typical pattern according to which some features may be more significant than others. Emotions differ strongly where these patterns of features are concerned, while their essential functional roles are the same. We argue that emotions form a unified ontological category that is coherent and can be well defined by their characteristic functional roles. Our account of emotions is supported by data from developmental psychology, neurobiology, evolutionary biology and sociology.     

Is running a state of mind? Sports training as a potential method for developing cognitive flexibility

The study aimed to test the significance of sports participation as a potential means of improving cognitive function, particularly cognitive flexibility. Based on the characteristics of orienteering, such as frequent changes of behavioural strategies in response to changes in the situation or the simultaneous performance of several mental activities, we assumed that practising this sport could foster the development of cognitive flexibility. Two groups of volunteers were compared: 50 middle and long-distance runners and 50 orienteering runners in terms of their performance on the following measures of cognitive flexibility: a divergent thinking task, a computer flexibility task, Cognitive Flexibility Scale, and Verbal Fluency Test as a measure of executive function. Orienteering runners outperformed others on all measures except the Cognitive Flexibility Scale. Furthermore, we found that training characteristics (regularity, frequency, participation in competitions) were associated with levels of cognitive flexibility, particularly among orienteering runners, where they explained between 38% and 39% of the overall flexibility variance. Our results suggest that cognitive flexibility can be developed through sports training requiring effective dealing in a changing, complex environment. We also discuss the implications of our results for cognitive training research.