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This chapter retraces the genealogical development of deduction in the Latin and Arabic medieval traditions and in the early modern period, and finally the emergence of mathematical logic in the nineteenth century. It is shown that dialogical conceptions of logic remained pervasive in the Latin medieval tradition, but that they coexisted with other, non-dialogical conceptualizations, in part because of the influence of Arabic logic. In the modern period, however, mentalistic conceptions of logic and deduction became increasingly prominent. The chapter thus explains why we (i.e. twenty-first-century philosophers) have by and large forgotten the dialogical roots of deduction.
This chapter returns to the three main features of deduction defined in Chapter 1 from a cognitive, empirically informed perspective: necessary truth-preservation, perspicuity, and belief-bracketing. It discusses experimental findings that lend support to the dialogical conceptualization of these three features presented in Chapter 4. It also discusses the notion of internalization as formulated by Lev Vygotsky, which allows for an explanation of how deductive practices can also take place in purely mono-agent situations: as an intrapersonal enactment of interpersonal dialogues. The upshot is that framing deductive practices dialogically provides cognitive scaffolding that facilitates the ontogenetic development of deductive reasoning in an individual.
In this chapter, it is argued that what is needed to make progress on the issues described in Chapter 1 is a ‘roots’ approach, i.e. going back to the roots of deduction. The distinction between phylogenetic, ontogenetic, and historical roots is introduced, and it is argued that all three perspectives must be taken into account. The chapter further briefly presents the four main senses in which deduction has dialogical roots treated in this book: philosophical roots, historical roots, cognitive roots, and with respect to mathematical practices.
Negation is a complex linguistic phenomenon present in all human languages. It can be seen as an operator that transforms an expression into another expression whose meaning is in some way opposed to the original expression. In this article, we survey previous work on negation with an emphasis on computational approaches. We start defining negation and two important concepts: scope and focus of negation. Then, we survey work in natural language processing that considers negation primarily as a means to improve the results in some task. We also provide information about corpora containing negation annotations in English and other languages, which usually include a combination of annotations of negation cues, scopes, foci, and negated events. We continue the survey with a description of automated approaches to process negation, ranging from early rule-based systems to systems built with traditional machine learning and neural networks. Finally, we conclude with some reflections on current progress and future directions.
This chapter presents an overview of experimental work on deductive reasoning, which has shown that human reasoners do not seem to reason spontaneously according to the deduction canons. However, there are also experimental results suggesting that, when tackling deductive tasks in groups, performance comes much closer to the canons. These findings offer a partial vindication of the dialogical conception of deduction insofar as they show that, when given the opportunity to engage in dialogues with others, humans become better deductive reasoners.
Generating designs via machine learning has been an on-going challenge in computer-aided design. Recently, deep learning methods have been applied to randomly generate images in fashion, furniture and product design. However, such deep generative methods usually require a large number of training images and human aspects are not taken into account in the design process. In this work, we seek a way to involve human cognitive factors through brain activity indicated by electroencephalographic measurements (EEG) in the generative process. We propose a neuroscience-inspired design with a machine learning method where EEG is used to capture preferred design features. Such signals are used as a condition in generative adversarial networks (GAN). First, we employ a recurrent neural network Long Short-Term Memory as an encoder to extract EEG features from raw EEG signals; this data are recorded from subjects viewing several categories of images from ImageNet. Second, we train a GAN model conditioned on the encoded EEG features to generate design images. Third, we use the model to generate design images from a subject’s EEG measured brain activity. To verify our proposed generative design method, we present a case study, in which the subjects imagine the products they prefer, and the corresponding EEG signals are recorded and reconstructed by our model for evaluation. The results indicate that a generated product image with preference EEG signals gains more preference than those generated without EEG signals. Overall, we propose a neuroscience-inspired artificial intelligence design method for generating a design taking into account human preference. The method could help improve communication between designers and clients where clients might not be able to express design requests clearly.
This chapter presents a dialogical rationale based on the Prover–Skeptic model for the three main features of deduction identified in Chapter 1: necessary truth-preservation, perspicuity, and belief-bracketing. Moreover, it addresses four important ongoing debates in the philosophy of logic: the normativity of logic, logical pluralism, logical paradoxes, and logical consequence. It is shown that the Prover–Skeptic model provides a promising vantage point to address the questions raised in these debates.