What can cognitive linguistics tell us about language-image relations? A multidimensional approach to intersemiotic convergence in multimodal texts

In contrast to symbol-manipulation approaches, Cognitive Linguistics offers a modal rather than an amodal account of meaning in language. From this perspective, the meanings attached to linguistic expressions, in the form of conceptualisations, have various properties in common with visual forms of representation. This makes Cognitive Linguistics a potentially useful framework for identifying and analysing language-image relations in multimodal texts. In this paper, we investigate language-image relations with a specific focus on intersemiotic convergence. Analogous with research on gesture, we extend the notion of co-text images and argue that images and language usages which are proximal to one another in a multimodal text can be expected to exhibit the same or consistent construals of the target scene. We outline some of the dimensions of conceptualisation along which intersemiotic convergence may be enacted in texts, including event-structure, viewpoint, distribution of attention and metaphor. We take as illustrative data photographs and their captions in online news texts covering a range of topics including immigration, political protests, and inter-state conflict. Our analysis suggests the utility of Cognitive Linguistics in allowing new potential sites of intersemiotic convergence to be identified and in proffering an account of language-image relations that is based in language cognition.

Situated Cognition Approach in Translation Studies: Origins and Heuristic Potential

The article analyzes the explanatory possibilities of the situated cognition approach to translation study on the basis of the theoretical apparatus developed in the Viennese translation school of Professor H. Risku. The relevance of the study is determined by the appeal to the analysis of the specialized translation process in the authentic context of the functioning of the main subject in the modern translation market, i.e. a translation bureau. Incorporating the research techniques from the early stages of cognitive science development (theory of information processing based on symbol manipulation and neural networks analysis within the framework of connectionism), the approach under consideration is based on the modern concept of extended situated embodied cognition, which, as applied to translation studies, provides new arguments in favor of transition from interpretation of the interaction "human – text" to the study of multimedia communication of a subjects group in the translation networks' composition, which have arisen as a result of outsourcing processes in the translation industry. The features of cooperative interaction between a team of translators and experts, as well as artifacts in the process of translation text generation using information technologies, are demonstrated. The practical application of the situated cognition approach to the analysis of the participants' activities in translation cooperative networks (customer company, technical editor, translation bureau, external translation bureau, translator) is especially relevant in the context of glocalization in the emerging Russian translation market.

Digitalisation in Agriculture: Knowledge and Learning Requirements of German Dairy Farmers

Purpose: This study aims at investigating how digitalisation (in the sense of industry 4.0) has changed the work of farmers and how they experience the changes from more traditional work to digitalised agriculture. It also investigates what knowledge farmers require on digitalised farms and how they acquire it. Dairy farming was used as domain of investigation since it, unlike other industries, has strongly been affected by digitalisation throughout the last years.Method: Exploratory interviews with 10 livestock farmers working on digitalised dairy farms were analysed using qualitative content analysis. A deductive and inductive coding strategy was used. Findings: Farming work has changed from more manual tasks towards symbol manipulation and data processing. Farmers must be able to use computers and other digital devices to retrieve and analyse sensor data that allow them to monitor and control the processes on their farm. For this new kind of work, farmers require elaborated mental models that link traditional farming knowledge with knowledge about digital systems, including a strong understanding of production processes underlying their farm. Learning is mostly based on instructions offered by manufacturers of the new technology as well as informal and non-formal learning modes. Even younger farmers report that digital technology was not sufficiently covered in their (vocational) degrees. In general, farmers emphasises the positive effects of digitalisation both on their working as well as private life. Conclusions: Farmers should be aware of the opportunities as well as the potential drawbacks of the digitalisation of work processes in agriculture. Providers of agricultural education (like vocational schools or training institutes) need to incorporate the knowledge and skills required to work in digitalised environments (e.g., data literacy) in their syllabi. Further studies are required to assess how digitalisation changes farming practices and what knowledge as well as skills linked to these developments are required in the future.

How the Brain might use Division

One of the most fundamental questions in Biology or Artificial Intelligence is how the human brainperforms mathematical functions. How does a neural architecture that may organise itself mostly throughstatistics, know what to do? One possibility is to extract the problem to something more abstract. This becomesclear when thinking about how the brain handles large numbers, for example to the power of something, whensimply summing to an answer is not feasible. In this paper, the author suggests that the maths question can beanswered more easily if the problem is changed into one of symbol manipulation and not just number counting.If symbols can be compared and manipulated, maybe without understanding completely what they are, then themathematical operations become relative and some of them might even be rote learned. The proposed systemmay also be suggested as an alternative to the traditional computer binary system. Any of the actual maths stillbreaks down into binary operations, while a more symbolic level above that can manipulate the numbers andreduce the problem size, thus making the binary operations simpler. An interesting result of looking at this is thepossibility of a new fractal equation resulting from division, that can be used as a measure of good fit and wouldhelp the brain decide how to solve something through self-replacement and a comparison with this good fit.

Morphological, Natural, Analog, and Other Unconventional Forms of Computing for Cognition and Intelligence

According to the currently dominant view, cognitive science is a study of mind and intelligence focused on computational models of knowledge in humans. It is described in terms of symbol manipulation over formal language. This approach is connected with a variety of unsolvable problems, as pointed out by Thagard. In this paper, I argue that the main reason for the inadequacy of the traditional view of cognition is that it detaches the body of a human as the cognizing agent from the higher-level abstract knowledge generation. It neglects the dynamical aspects of cognitive processes, emotions, consciousness, and social aspects of cognition. It is also uninterested in other cognizing agents such as other living beings or intelligent machines. Contrary to the traditional computationalism in cognitive science, the morphological computation approach offers a framework that connects low-level with high-level approaches to cognition, capable of meeting challenges listed by Thagard. To establish this connection, morphological computation generalizes the idea of computation from symbol manipulation to natural/physical computation and the idea of cognition from the exclusively human capacity to the capacity of all goal-directed adaptive self-reflective systems, living organisms as well as robots. Cognition is modeled as a layered process, where at the lowest level, systems acquire data from the environment, which in combination with the already stored data in the morphology of an agent, presents the basis for further structuring and self-organization of data into information and knowledge.

Morphological, Natural, Analog, and Other Unconventional Forms of Computing for Cognition and Intelligence

According to the currently dominant view, cognitive science is a study of mind and intelligence focused on computational models of knowledge in humans. It is described in terms of symbol manipulation over formal language. This approach is connected with a variety of unsolvable problems, as pointed out by Thagard. In this paper, I argue that the main reason for the inadequacy of the traditional view of cognition is that it detaches the body of a human as the cognizing agent from the higher-level abstract knowledge generation. It neglects the dynamical aspects of cognitive processes, emotions, consciousness, and social aspects of cognition. It is also uninterested in other cognizing agents such as other living beings or intelligent machines. Contrary to the traditional computationalism in cognitive science, the morphological computation approach offers a framework that connects low-level with high-level approaches to cognition, capable of meeting challenges listed by Thagard. To establish this connection, morphological computation generalizes the idea of computation from symbol manipulation to natural/physical computation and the idea of cognition from the exclusively human capacity to the capacity of all goal-directed adaptive self-reflective systems, living organisms as well as robots. Cognition is modeled as a layered process, where at the lowest level, systems acquire data from the environment, which in combination with the already stored data in the morphology of an agent, presents the basis for further structuring and self-organization of data into information and knowledge.

Post, Emil Leon (1897–1954)

Emil Post was a pioneer in the theory of computation, which investigates the solution of problems by algorithmic methods. An algorithmic method is a finite set of precisely defined elementary directions for solving a problem in a finite number of steps. More specifically, Post was interested in the existence of algorithmic decision procedures that eventually give a yes or no answer to a problem. For instance, in his dissertation, Post introduced the truth-table method for deciding whether or not a formula of propositional logic is a tautology. Post developed a notion of ‘canonical systems’ which was intended to encompass any algorithmic procedure for symbol manipulation. Using this notion, Post partially anticipated, in unpublished work, the results of Gödel, Church and Turing in the 1930s. This showed that many problems in logic and mathematics are algorithmically unsolvable. Post’s ideas influenced later research in logic, computer theory, formal language theory and other areas.