网范文：“Conceptual structure and the brain” 概念是精神世界的粘胶，概念是理性的基本单位和语言定义，英语论文，寻找概念结构之间的相似之处,大脑和身体经验似乎基于明显的认知科学,但实际上是一个相对较新的发展。这篇哲学范文进行了略论。在早期认知主义的强烈作用下，英语论文网站，略论语言哲学的传统。在这个传统概念进行略论的基础上,正式的抽象模型,准则上与身体无关，假设没有参与感觉运动系统的构想。在这个角度看,概念被定义为抽象思维。
探讨的结果好像大脑和身体没有丝毫联系。概念和心理表征的结构探讨，包括心理学家,神经学家和语言学家。概念常常被定义为身体的知识存储,归纳推理等。下面的范文进行详述。
“Concepts are the glue that holds our mental world together” (Murphy 2017: 1). Concepts are the elementary units of reason and linguistic meaning and have long been at the centre of cognitive science research. Searching for parallels between conceptual structure, the brain, and bodily experience may seem an obvious direction for research in cognitive science, but is in fact a relatively recent development. Early cognitivism operated under a strong influence of the analytic tradition of philosophy of language. In this tradition concepts were analysed on the basis of formal abstract models, in principle unrelated to the body. The assumption was that there is no involvement of the sensorimotor system in conceptualisation. Within this perspective, concepts were defined as abstract, amodal, and arbitrary representations stored in the form of “language of thought” (Fodor 1975).
The mind was conceived of as a system whose processes can be described by means of a set of formal syntactic rules affecting these amodal abstract concepts (Fodor 1983). Conceptualisation was studied as if it bore no relation to the brain and body. Naturally, as shown in section 2.4, this is no longer believed to be the case. Concepts and the structure of mental representations are now studied by psychologists, neurologists and linguists alike. To what extent the assumptions, methods and paradigms of these disciplines overlap is another matter. Concepts are often defined as bodies of knowledge stored in long-term memory and used by default by our cognitive processes when we categorize, make inductions, understand languages, draw analogies, and so on (Machery 2017). However, although the notion of long-term memory sounds concrete and well-defined, in the context of the debate on lasting mental representations and dynamic systems it is no longer as clear cut 61 as initially imagined. What does it mean that concepts are stored in memory? Are they semantic in nature, or are they simulations recreating patterns of activation in the sensorimotor system? Are they static, or do they change with experience and context? These are just some of the many questions that researchers on conceptualisation have been trying to answer.
Neurolinguistics of semantic processing
Semantic processing, or access to knowledge about concepts is a central feature of human behaviour. It is not only important to language, but defines our ability to access stored knowledge and apply it to planning, decision making, and problem solving (Binder et al. 2017). The neural basis of semantic processing has been studied by analysing brain activation in patients who suffered from brain disorders, including Alzheimer's, dementia, aphasia and schizophrenia. Semantic processing has also been the subject of a plethora of neuroimaging studies conducted on healthy volunteers with the use of such methods as positron emission tomography (PET), functional magnetic resonance imaging (fMRI) and event related potential (ERP) research.
Neuroimaging studies on semantic processing distinguish between object (picture) and word recognition tasks. While word recognition is assumed to tap into conceptual knowledge, object recognition involves a more complex interaction between perception, abstraction and representation (Binder et al. 2017). This does not mean that the resources activated during object and word recognition tasks do not overlap, but there is evidence that these two processes are not identical (Reinholz and Pollmann 2017).
It is unclear whether word comprehension necessarily means activating a detailed perceptual representation of the object to which it refers (Chee et al. 2017; Bright et al. 2017; Gates and Yoon 2017). Patients with profound visual object recognition disorders may retain word comprehension abilities, which also suggests that the knowledge systems underlying word and object recognition are not the same (Davidoff and Debleser 1994). A review and meta-analysis of over seven hundred semantic processing neuroimaging studies suggest that there is no one specific region involved in semantic processing, although there is a 62 tendency for left hemisphere lateralisation (Binder et al. 2017). In fact, patterns of activation differ for different types of concepts and tasks. Similarly to cognitive linguistics, the difference between the processing of abstract and concrete concepts is often operationalised in neuroimaging research.
Are some concepts amodal? In the beginning of this it was mentioned that the process by which humans were able to develop abstract concepts from concrete perception has been the subject of a prolonged debate. Gallese and Lakoff argued in favour of the embodied view of conceptual knowledge (2017: 456). Within this approach the sensorimotor system provides structure to both types of conceptual representations, and constrains their semantic content. Some neurological studies refer to the so-called amodal areas of the cortex as being associated with semantic processes in the brain, but this terminology is misleading. While indeed researchers in neurolinguistics distinguish between modal and amodal cortices, this distinction was based on the primary functions of these regions.
The input to the modal cortex comes from a dominant sensory or motor modality, whereas the amodal cortex likely plays a role in integrative processes which is why it is also called heteromodal or supramodal (Binder et al. 2017). More recent studies show that even cortical regions formerly considered ‘‘unimodal’’ receive inputs from multiple sensory modalities (Schroeder and Foxe 2017). Binder et al. propose to draw the distinction between the ‘‘modal’’ cortex where processing reflects a dominant sensory or motor modality, and the ‘‘amodal’’ cortex where input from multiple modalities is more nearly balanced and highly convergent (2017: 2774). Semantic processes in the brain are associated mainly with the amodal part of the cortex, which is also bigger in the human brain than in any known primate (Binder et al. 2017).
It seems that abstract concepts can be amodal in the sense that their processing depends primarily on the integrating rather than unimodal areas of the neocortex. They are not amodal in the sense of being divorced from sensory and motor input. 63 Another relatively recent neurological discovery namely that imagining and doing evoke similar activation patterns seems to corroborate Lakoff's theory. This phenomenon is called motor resonance (Zwaan and Taylor 2017), referring to the observation that some words “resonate” in the sensorimotor systems as if they were actions. For example, "when people close their eyes and visualize a simple object such as the letter "a", the primary visual cortex lights up, just as it would if the subjects were actually looking at that letter” (Doidge 2017: 203–204).
The discovery of mirror neurons in primates, neural cells located in the motor cortex firing in response to seeing a performed action, increased the credibility of this theory even further. Although the existence of the mirror neuron system in humans is still considered a controversial topic, there is evidence of a relationship between language, gesture, and the mirror neuron system (Arbib 2017b; Rizzolatti and Craighero 2017). The mirror neuron theory of language development (MNT) is discussed in more detail further in this . At this point, the hypothesis that conceptual representations of physical objects are grounded in experience stands relatively uncontested. Nevertheless, “how people mentally represent these abstract domains has remained one of the mysteries of the mind” (Casasanto 2017: 453). Unsurprisingly, this has been a vexing issue for neurolinguistics as well.
Abstract and concrete concepts in the brain
The concreteness effect. Neurolinguistic studies show that words representing concrete concepts are remembered for longer (Paivio 1971; Fliessbach et al. 2017), recognised faster (West and Holcomb 2017), and more resilient to brain damage (Katz and Goodglass 1990) than words representing abstract concepts. This phenomenon is known as the concreteness effect, and has become the subject of extensive research in the last 15 years (West and Holcomb 2017; Binder et al. 2017; Casasanto et al. 2017; Fliessbach et al. 2017). It appears that concrete concepts have a significant processing advantage over abstract concepts. There are two main theories explaining the concreteness effect: the context-availability model and dual-coding theory (cf. Paivio 1991).
According to the context availability theory comprehension depends heavily on context that is either present 64 in the discourse or accessible through prior knowledge and associations that the speaker/listener possesses. This model argues that concrete concepts have access to more associations so that there is quantitively more available information which makes comprehension easier and faster. The dual-coding theory, on the other hand, assumes that all verbal stimuli initially activate representations in the mental lexicon. In addition, concrete words activate information in a nonverbal imagistic system to which they are connected. This part of the comprehension process is difficult, if not impossible for abstract concepts.
This model argues that there is a difference in the type of information connected with concrete words compared to abstract words. Although both models have received empirical support, the scale seems to shift in favour of the dual-coding theory (Paivio 1991). In an ERP study by Kounios and Holcomb (1994) participants were asked to judge the concreteness of a set of concrete and abstract words. The recorded interaction between word concreteness and distribution of scalp activation indicated that the cognitive resources tapped into during the processing of abstract and concrete words are not identical. This suggests that, rather than using more of the same resource, abstract and concrete concepts are processed differently which goes against the context availability theory.
What is more, West and Holcomb (2017) showed that abstract words are processed more slowly than concrete words in tasks that require semantic processing, but with the same speed in surface (orthographic) recognition tasks (for instance, “does the word “bird” contain the letter Y?”) which suggests that the differences in processing time should indeed be attributed to the semantic properties of the stimuli. Without doubt there are observable differences between abstract and concrete concepts. However, the exact nature of the difference between the two types is elusive. In other words, it seems that neuroimaging studies have encountered the same difficulty with defining concreteness as cognitive linguistic research. A common method for measuring the concreteness of a given concept, and one used also in the experimental part of the present thesis, is conducting a questionnaire among a set of subjects who will not be involved in subsequent studies based on the stimuli tested for concreteness8 .
Participants are asked to rate a set of concepts on a scale, and the data is used to compute the concreteness score of a given concept (Feng et al. 2017). Some studies draw information regarding concept concreteness from concept information databases like the MRC Psycholinguistic Database (Coltheart 1981). Such databases not only provide information about the perceived concreteness of a given concept based on multiple subject data, but also its frequency and familiarity. However, the latter method is not without its problems. First, there are still no objective criteria for defining whether a concept should be classified as abstract or concrete. Concrete words are associated with other traits such as being easy to imagine, whereas abstract words are not (Feng et al. 2017; see also: Paivio 1971), but this can hardly be considered objective criterion to distinguish between the two.
Furthermore, the concreteness (and imagibility) of a concept is calculated on the basis of subjective judgements of a group of people. The key question here is whether popular judgement can (and should) replace selection based on objective criteria. The problem may be illustrated simply. While many people say that dolphin is a fish, that does not necessarily mean it is true; many people claiming that mountain is a concrete concept does not constitute proof that it is. Naturally, seeing that concepts are cognitive phenomena rather than physical entities relying on introspection may seem an intuitive methodological choice.
Nevertheless, I would like to argue in this chapter that introducing a set of objective criteria as a basis for the abstract-concrete distinction is a prerequisite for a successful conceptual model, particularly if the model is to retain predictive power in different cultural contexts9 . The second problem with the questionnaire method of concreteness evaluation is that experiments usually utilise concepts that are located on the far ends of the (perceived) concreteness spectrum, making no predictions about concepts located in the middle and, more importantly, without introducing a scale for comparison. We know, for instance, that the words “umbrella” and “shoe” stand for concrete concepts, but is one of them statistically more concrete than the other? Does this hold for all tested subjects? If we compare concepts from the beginning with ones from the middle of the spectrum the difference in perceived concreteness may not be statistically important, which effectively means their rank on the concreteness scale is relative both to other concepts, and informants. When researchers freely use phrases like “highly abstract” (Lakens 2017), “more concrete” (Gibbs et al. 2017) and “effects of concreteness” (Binder et al. 2017) the apparent lack of definition regarding the property of concreteness seems like a gross methodological oversight.()
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