In this study bibliometric mapping method was employed to visualise the current research trends and the impact of the two most influential models of working memory, namely: A. D. Baddeley and G. J. Hitch’s multicomponent working memory model and N. Cowan’s embedded-processes model of working memory. Using VOSviewer software two maps were generated based on the index-term words extracted from the research papers citing Baddeley and Cowan, respectively. The maps represent networks of co-occurrences of index terms and (...) can be interpreted as an indication of the main research fields related to the examined models of WM. The results of the analysis revealed that the spheres of influence of the two main conceptualisations of WM are rather different than similar. Although the first two clusters, i.e. “brain mapping” and “higher-level cognition and development” are present in both maps, their relative importance varies. The remaining clusters are unique to each map. Baddeley’s theory seems to have a greater influence on “neuropsychology”, while Cowan’s theory - on basic research on “biological systems”, including the nervous system in humans and animals. The second difference between these theories concerns their relations to functions and dysfunctions associated with particular sensory modalities: in Baddelay’s theory with the “auditory modality” cluster, and in Cowan’s - with the “visual modality” one. (shrink)

It remains a dogma in cognitive neuroscience to separate human attention and memory into distinct modules and processes. Here we propose that brain rhythms reflect the embedded nature of these processes in the human brain, as evident from their shared neural signatures: gamma oscillations (30–90 Hz) reflect sensory information processing and activated neural representations (memory items). The theta rhythm (3–8 Hz) is a pacemaker of explicit control processes (central executive), structuring neural information processing, bit by bit, as reflected in (...) the theta-gamma code. By representing memory items in a sequential and time-compressed manner the theta-gamma code is hypothesized to solve key problems of neural computation: (1) attentional sampling (integrating and segregating information processing), (2) mnemonic updating (implementing Hebbian learning), and (3) predictive coding (advancing information processing ahead of the real time to guide behavior). In this framework, reduced alpha oscillations (8–14 Hz) reflect activated semantic networks, involved in both explicit and implicit mnemonic processes. Linking recent theoretical accounts and empirical insights on neural rhythms to the embedded-process model advances our understanding of the integrated nature of attention and memory – as the bedrock of human cognition. (shrink)

A process-oriented model of belief is presented which permits the representation of nested propositional attitudes within first-order logic. The model (NIM, for nested intensional model) is axiomatized, sense-based (via intensions), and sanctions inferences involving nested epistemic attitudes, with different agents and different times. Because NIM is grounded upon senses, it provides a framework in which agents may reason about the beliefs of another agent while remaining neutral with respect to the syntactic forms used to express the (...) latter agent's beliefs. Moreover, NIM provides agents with a conceptual map, interrelating the concepts of knowledge, belief, truth, and a number of cognate concepts, such as infers, retracts, and questions. The broad scope of NIM arises in part from the fact that its axioms are represented in a novel extension of first-order logic, -FOL (presented herein). -FOL simultaneously permits the representation of truth ascriptions, implicit self-reference, and arbitrarily embedded sentences within a first-order setting. Through the combined use of principles derived from Frege, Montague, and Kripke, together with context-sensitive semantic conventions, -FOL captures the logic of truth inferences, while avoiding the inconsistencies exhibited by Tarski. Applications of -FOL and NIM to interagent reasoning are described and the soundness and completeness of -FOL are established herein. (shrink)

We present a detailed process theory of the moment‐by‐moment working‐memory retrievals and associated control structure that subserve sentence comprehension. The theory is derived from the application of independently motivated principles of memory and cognitive skill to the specialized task of sentence parsing. The resulting theory construes sentence processing as a series of skilled associative memory retrievals modulated by similarity‐based interference and fluctuating activation. The cognitive principles are formalized in computational form in the Adaptive Control of Thought–Rational (ACT–R) architecture, and (...) our process model is realized in ACT–R. We present the results of 6 sets of simulations: 5 simulation sets provide quantitative accounts of the effects of length and structural interference on both unambiguous and garden‐path structures. A final simulation set provides a graded taxonomy of double center embeddings ranging from relatively easy to extremely difficult. The explanation of center‐embedding difficulty is a novel one that derives from the model' complete reliance on discriminating retrieval cues in the absence of an explicit representation of serial order information. All fits were obtained with only 1 free scaling parameter fixed across the simulations; all other parameters were ACT–R defaults. The modeling results support the hypothesis that fluctuating activation and similarity‐based interference are the key factors shaping working memory in sentence processing. We contrast the theory and empirical predictions with several related accounts of sentence‐processing complexity. (shrink)

Memoires by eminently creative people often describe architectural spaces and qualities they believe instrumental for their creativity. However, places designed to encourage creativity have had mixed results, with some found to decrease creative productivity for users. This may be due, in part, to lack of suitable empirical theory or model to guide design strategies. Relationships between creative cognition and features of the physical environment remain largely uninvestigated in the scientific literature, despite general agreement among researchers that human cognition is (...) physically and socially situated. This paper investigates what role architectural settings may play in creative processes by examining documented first person and biographical accounts of creativity with respect to three central theories of situated cognition. First, the embodied thesis argues that cognition encompasses both the mind and the body. Second, the embedded thesis maintains that people exploit features of the physical and social environment to increase their cognitive capabilities. Third, the enaction thesis describes cognition as dependent upon a person’s interactions with the world. Common themes inform three propositions, illustrated in a new theoretical framework describing relationships between people and their architectural settings with respect to different cognitive processes of creativity. The framework is intended as a starting point toward an ecological model of creativity, which may be used to guide future creative process research and architectural design strategies to support user creative productivity. (shrink)

The marketization of interest rate is an inevitable requirement for China’s financial reform and joining the WTO to connect with the international financial market. It is also an important link to improve the marketization degree of China’s financial system. The marketization of interest rate in China is gradually advancing according to its preset mode. In the process of interest rate marketization, an unavoidable problem is that while the interest rate marketization gives the commercial banks the autonomy of capital pricing, (...) the fluctuation of interest rate is more and more frequent. However, due to the fluctuation of interest rate, the loan as the main assets of commercial banks will be prepayed by borrowers, and the time deposit as the main liabilities of commercial banks will be withdrawn by depositors in advance; that is, embedded options are implied in asset liability items, which makes it difficult for commercial banks to accurately calculate the actual interest margin of deposits and loans and manage the interest rate risk. Therefore, it is of great significance to identify and price such embedded option value. On the basis of identifying and decomposing the embedded options in deposit and loan of commercial banks, according to the change characteristics of deposit and loan interest rate of Chinese commercial banks, this paper chooses jump-diffusion interest rate model to describe the change of benchmark interest rate of deposit and loan in China and demonstrates the advantages of this model compared with other models. Based on Monte Carlo simulation technology, the embedded options of five-year fixed deposit and ten-year prepayable loan in China are priced. On this basis, it points out that the real interest margin of commercial bank’s deposit and loan should be the nominal interest margin minus the value of deposit and loan’s embedded options. In the process of interest rate risk management, we should pay attention to the existence of embedded options and carry out effective management. (shrink)

Multinational companies (MNCs) can have positive and negative impacts on the human rights situation of a country. More and more MNCs have made a commitment to respect human rights. So far, little research has been done on how MNCs can embed their commitment and which factors determine its success. This paper therefore aims to describe and learn from the process of embedding human rights in six subsidiaries of the multinational oil company Royal Dutch Shell (in short, Shell), operating in (...) different parts of the world. It develops an approach based on the model of Tatiana Kostova. Using a specific tool called the Human Rights Compliance Assessment, the paper concludes that, despite differences in local context, the process requirements of embedding global standardscan be the same. (shrink)

In this paper, I argue for an embodied, embedded approach to predictive processing and thus align the framework with situated cognition. The recent popularity of theories conceiving of the brain as a predictive organ has given rise to two broad camps in the literature that I callfree energy enactivismandcognitivist predictive processing. The two approaches vary in scope and methodology. The scope ofcognitivist predictive processingis narrow and restricts cognition to brain processes and structures; it does not consider the body-beyond-brain and (...) the environment as constituents of cognitive processes.Free energy enactivism, on the other hand, includes all self-organizing systems that minimize free energy (including non-living systems) and thus does not offer any unique explanations for more complex cognitive phenomena that are unique to human cognition. Furthermore, because of its strong commitment to the mind-life continuity thesis, it does not provide an explanation of what distinguishes more sophisticated cognitive systems from simple systems. The account that I develop in this paper rejects both of these radical extremes. Instead, I propose a compromise that highlights the necessary components of predictive processing by making use of a mechanistic methodology of explanation. The starting point of the argument in this paper is that despite the interchangeable use of the terms, prediction error minimization and the free energy principle are not identical. But this distinction does not need to disrupt thestatus quoof the literature if we consider an alternative approach: Embodied, Embedded Predictive Processing (EEPP). EEPP accommodates the free energy principle, as argued for by free energy enactivism, but it also allows for mental representations in its explanation of cognition. Furthermore, EEPP explains how prediction error minimization is realized but, unlike cognitivist PP, it allocates a constitutive role to the body in cognition. Despite highlighting concerns regarding cognitivist PP, I do not wish to discredit the role of the neural domain or representations as free energy enactivism does. Neural structures and processes undeniably contribute to the minimization of prediction error but the role of the body is equally important. On my account, prediction error minimization and free energy minimization are deeply dependent on the body of an agent, such that the body-beyond-brain plays aconstitutiverole in cognitive processing. I suggest that the body plays three constitutive roles in prediction error minimization: The bodyregulatescognitive activity, ensuring that cognition and action are intricately linked. The body acts asdistributorin the sense that it carries some of the cognitive load by fulfilling the function of minimizing prediction error. Finally, the body serves toconstrainthe information that is processed by an agent. In fulfilling these three roles, the agent and environment enter into a bidirectional relation through influencing and modeling the structure of the other. This connects EEPP to the free energy principle because the whole embodied agent minimizes free energy in virtue of being a model of its econiche. This grants the body a constitutive role as part of the collection of mechanisms that minimize prediction error and free energy. The body can only fulfill its role when embedded in an environment, of which it is a model. In this sense, EEPP offers the most promising alternative to cognitivist predictive processing and free energy enactivism. (shrink)

An English double-embedded relative clause from which the middle verb is omitted can often be processed more easily than its grammatical counterpart, a phenomenon known as the grammaticality illusion. This effect has been found to be reversed in German, suggesting that the illusion is language specific rather than a consequence of universal working memory constraints. We present results from three self-paced reading experiments which show that Dutch native speakers also do not show the grammaticality illusion in Dutch, whereas both (...) German and Dutch native speakers do show the illusion when reading English sentences. These findings provide evidence against working memory constraints as an explanation for the observed effect in English. We propose an alternative account based on the statistical patterns of the languages involved. In support of this alternative, a single recurrent neural network model that is trained on both Dutch and English sentences is shown to predict the cross-linguistic difference in the grammaticality effect. (shrink)

Public companies in the US stock market must annually report their activities and financial performances to the SEC by filing the so-called 10-K form. Recent studies have demonstrated that changes in the textual content of the corporate annual filing can convey strong signals of companies’ future returns. In this study, we combine natural language processing techniques and network science to introduce a novel 10-K-based network, named Lazy Network, that leverages year-on-year changes in companies’ 10-Ks detected using a neural network embedding (...) model. The Lazy Network aims to capture textual changes derived from financial or economic changes on the equity market. Leveraging the Lazy Network, we present a novel investment strategy that attempts to select the least disrupted and stable companies by capturing the peripheries of the Lazy Network. We show that this strategy earns statistically significant risk-adjusted excess returns. Specifically, the proposed portfolios yield up to 95 basis points in monthly five-factor alphas, outperforming similar strategies in the literature. (shrink)

The vigorous development of online education has produced massive amounts of education data. How to mine and analyze education big data has become an urgent problem in the field of education and big data knowledge engineering. As for the dynamic learning data, knowledge tracing aims to track learners’ knowledge status over time by analyzing the learners’ exercise data, so as to predict their performance in the next time step. Deep learning knowledge tracking performs well, but they mainly model the (...) knowledge components while ignoring the personalized information of questions and learners, and provide limited interpretability in the interaction between learners’ knowledge status and questions. A context-aware attentive knowledge query network model is proposed in this paper, which combines flexible neural network models with interpretable model components inspired by psychometric theory. We use the Rasch model to regularize the embedding of questions and learners’ interaction tuples, and obtain personalized representations from them. In addition, the long-term short-term memory network and monotonic attention mechanism are used to mine the contextual information of learner interaction sequences and question sequences. It can not only retain the ability to model sequences, but also use the monotonic attention mechanism with exponential decay term to extract the hidden forgetting behavior and other characteristics of learners in the learning process. Finally, the vector dot product is used to simulate the interaction between the learners’ knowledge state and questions to improve the interpretability. A series of experimental results on 4 real-world online learning datasets show that CAKQN has the best performance, and its AUC value is improved by an average of 2.945% compared with the existing optimal model. Furthermore, the CAKQN proposed in this paper can not only track learners’ knowledge status like other models but also model learners’ forgetting behavior. In the future, our research will have high application value in the realization of personalized learning strategies, teaching interventions, and resource recommendations for intelligent online education platforms. (shrink)

The servitization of construction enterprises based on value chain integration means that construction enterprises use prefabricated buildings and combine advantageous resources to integrate preconstruction feasibility analysis, investment and financing services, design, etc., and postconstruction decoration, operation and maintenance, and waste disposal. This article takes the equipment-manufacturing industry as the research object, and based on the analysis of the service-based value chain integration process, it puts forward research hypotheses, constructs research models, and conducts data simulation research to explore how the (...) equipment-manufacturing industry can realize the logic of industrial value through service reintegration and optimization reveal the changing laws and key influencing factors of the equipment-manufacturing industry’s value-added capabilities during this process. The results show that the industrial connection density, service element embedding methods, and knowledge absorption capacity have a significant impact on the value-added ability of the equipment-manufacturing industry during the integration process. The increase in industrial connection density promotes the enhancement of value-added capabilities, and it is significant at the initial stage and then weakened. Both the input-side and output-side service element embedding can affect the value-added ability, but the effects of the two are different. The improvement of knowledge absorptive capacity can promote the occurrence of service innovation, thereby enhancing the value-added capacity of the equipment-manufacturing industry. (shrink)

Among theories of human language comprehension, cue‐based memory retrieval has proven to be a useful framework for understanding when and how processing difficulty arises in the resolution of long‐distance dependencies. Most previous work in this area has assumed that very general retrieval cues like [+subject] or [+singular] do the work of identifying (and sometimes misidentifying) a retrieval target in order to establish a dependency between words. However, recent work suggests that general, handpicked retrieval cues like these may not be enough (...) to explain illusions of plausibility (Cunnings & Sturt, 2018), which can arise in sentences like The letter next to the porcelain plate shattered. Capturing such retrieval interference effects requires lexically specific features and retrieval cues, but handpicking the features is hard to do in a principled way and greatly increases modeler degrees of freedom. To remedy this, we use well‐established word embedding methods for creating distributed lexical feature representations that encode information relevant for retrieval using distributed retrieval cue vectors. We show that the similarity between the feature and cue vectors (a measure of plausibility) predicts total reading times in Cunnings and Sturt’s eye‐tracking data. The features can easily be plugged into existing parsing models (including cue‐based retrieval and self‐organized parsing), putting very different models on more equal footing and facilitating future quantitative comparisons. (shrink)

Standard models of visual perception hold that vision is an inferential or interpretative process. Such models are said to be superior to competing, non-inferential views in explanatory power. In particular, they are said to be capable of explaining a number of otherwise mysterious, visual phenomena such as multi-stable perception. Multi-stable perception paradigmatically occurs in the presence of ambiguous figures, single images that can give rise to two or more distinct percepts. Different interpretations are said to produce the different percepts. (...) In this paper, I argue that a non-inferential account of visual perception is just as capable of explaining multi-stable perception. I propose an embedded understanding of vision, and show how the embedded account can, after properly qualifying them, use the explanatory resources of the inferential view to explain just what such a view explains. (shrink)

In this paper, I develop a template-based analysis to include several elements of _processes_ through which templates are transferred between fields of inquiry. The analysis builds on Justin Price’s identification of the importance of a “landing zone” in the recipient domain, from which “conceptual pressure” may be created. I will argue that conceptual pressure is a characteristic feature of the process of template transfer; that this means that there are costs to the process of transfer as well as (...) benefits; and that it would be reasonable if modelers try to mitigate these costs. I will discuss two such mitigation strategies: ‘conceptual embedding’ and ‘customization’. I illustrate the claims, focusing on the mitigation strategies, with a case study: that of pioneering applications of reaction–diffusion equations in mathematical ecology. (shrink)

Standard approaches to cognition emphasise structures (representations and rules) much more than processes, in part because this appears to be necessary to capture the normative features of cognition. However the resultant models are inflexible and face the problem of computational intractability. I argue that the ability of real world cognition to cope with complexity results from deep and subtle coupling between cognitive and non-cognitive processes. In order to capture this, theories of cognition must shift from a structural rule-defined conception of (...) cognition to a thoroughgoing embedded process approach. (shrink)

This article answers several calls—coming as well from corporate governance practitioners as from corporate governance researchers—concerning the possibility of complying simultaneously with requirements of innovation and ethics. Revealing the long-term orientation as the variable which permits us to link the principal goal of organization, being “survival,” with innovation and ethic, the article devises a framework for incorporating ethics into a company’s processes and strategies for innovation. With the principal goal of organizations being “survival” in the long-term, it is assumed that (...) innovation is necessary in order to realize a going concern. Firms that do not innovate and adapt to rapidly changing business environments are less likely to be sustainable. Thus, it is in a business’ best interests to adopt an innovation process for long-term success. We posit that there are two simultaneous sources of innovation and change that are unavoidable and embedded in the corporate landscape. First, we argue for genetically embedded, Darwinian explanations for adaptations that enable an entity’s survival. This view is combined with more conventional, social science explanations for change. Our new, comprehensive model of the governance of innovation processes hinges on the one hand on an organization’s long-term orientation, which we argue, is not possible without a consideration of an ethical dimension. On the other hand, the model employs, for the first time, the concept of duality, in order to make the positive coexistence of innovation and ethic analytically visible. Guided by this concept the roots of the ethic within innovation are traced from both natural science forces for change, and cultural pressures operating on members of an organization. We present our Integrated Causal Model of Innovation and propose theoretical relationships that will generate numerous avenues for future research in the field, and help managers to reorient their governance strategies. (shrink)

Logical models of argument formalize commonsense reasoning while taking process and computation seriously. This survey discusses the main ideas which characterize di erent logical models of argument. It presents the formal features of a few main approaches to the modeling of argumentation. We trace the evolution of argumentationfrom the mid-80's, when argumentsystems emerged as an alternative to nonmonotonic formalisms based on classical logic, to the present, as argument is embedded in di erent complex systems for real-world applications, and (...) allows more formal work to be done in different areas, such as AI & Law, case-based reasoning and negotiation among intelligent agents. (shrink)

This article answers several calls—coming as well from corporate governance practitioners as from corporate governance researchers—concerning the possibility of complying simultaneously with requirements of innovation and ethics. Revealing the long-term orientation as the variable which permits us to link the principal goal of organization, being “survival,” with innovation and ethic, the article devises a framework for incorporating ethics into a company’s processes and strategies for innovation. With the principal goal of organizations being “survival” in the long-term, it is assumed that (...) innovation is necessary in order to realize a going concern. Firms that do not innovate and adapt to rapidly changing business environments are less likely to be sustainable. Thus, it is in a business’ best interests to adopt an innovation process for long-term success. We posit that there are two simultaneous sources of innovation and change that are unavoidable and embedded in the corporate landscape. First, we argue for genetically embedded, Darwinian explanations for adaptations that enable an entity’s survival. This view is combined with more conventional, social science explanations for change. Our new, comprehensive model of the governance of innovation processes hinges on the one hand on an organization’s long-term orientation, which we argue, is not possible without a consideration of an ethical dimension. On the other hand, the model employs, for the first time, the concept of duality, in order to make the positive coexistence of innovation and ethic analytically visible. Guided by this concept the roots of the ethic within innovation are traced from both natural science forces for change, and cultural pressures operating on members of an organization. We present our Integrated Causal Model of Innovation and propose theoretical relationships that will generate numerous avenues for future research in the field, and help managers to reorient their governance strategies. (shrink)

We recorded participants' eye movements while they read sentences containing verb‐phrase coordination. Results showed evidence of immediate processing disruption when a reflexive pronoun embedded in the conjoined verb phrase mismatched the sentence subject. We argue that this result is incompatible with models of human parsing that employ only bottom‐up parsing procedures, even when flexible constituency is employed. Models need to incorporate a mechanism similar to the adjoining operation in Tree‐Adjoining Grammar, in which one structure is inserted into another.

Binding theory (e.g., Chomsky, 1981) has played a central role in both syntactic theory and models of language processing. Its constraints are designed to predict that the referential domains of pronouns and reflexives are nonoverlapping, that is, are complementary; these constraints are also thought to play a role in online reference resolution. The predictions of binding theory and its role in sentence processing were tested in four experiments that monitored participants' eye movements as they followed spoken instructions to have a (...) doll touch a picture belonging to another doll. The instructions used pronouns and reflexives embedded in picture noun phrases (PNPs) containing possessor phrases (e.g., Pick up Ken. Have Ken touch Harry's picture of himself). Although the interpretations assigned to pronouns were generally consistent with binding theory, reflexives were frequently assigned interpretations that violated binding theory. In addition, the timing and pattern of eye movements were inconsistent with models of language processing that assume that binding theory acts as an early filter to restrict the referential domain. The interpretations assigned to reflexives in PNPs with possessors suggest that they are binding‐theory‐exempt logophors, a conclusion that unifies the treatment of reflexives in PNPs. (shrink)

The definition of “model of a system” in terms of a homomorphism of the states of the system is evaluated and an alternative definition in terms of sequence generators is proposed. Sequence generators are finite graphs whose points represent complete states of a system. Sequence generators include finite automata and other information processing systems as special cases. It is shown how to define models in terms of a projection operator which applies to any sequence generator which has an output (...) projection and yields a new sequence generator. A model produced by the projection operator is embedded in the system it models. The notion of embedding is discussed informally and some questions raised about the relations of deterministic, indeterministic, and probabilistic models and systems. (shrink)

Standard approaches to cognition emphasise structures (representations and rules) much more than processes, in part because this appears to be necessary to capture the normative features of cognition. However the resultant models are inflexible and face the problem of computational intractability. I argue that the ability of real world cognition to cope with complexity results from deep and subtle coupling between cognitive and non-cognitive processes. In order to capture this, theories of cognition must shift from a structural rule-defined conception of (...) cognition to a thoroughgoing embedded process approach. (shrink)

Meaning depends on context. This applies in obvious cases like deictics or sarcasm as well as more subtle situations like framing or persuasion. One key aspect of this is the identity of the participants in an interaction. Our interpretation of an utterance shifts based on a variety of factors, including personal history, background knowledge, and our relationship to the source. While obviously an incomplete model of individual differences, demographic factors provide a useful starting point and allow us to capture (...) some of this variance. However, the relevance of specific demographic factors varies between situations—where age might be the key factor in one context, ideology might dominate in another. To address this challenge, we introduce a method for combining demographics and context into situated demographic embeddings—mapping representations into a continuous geometric space appropriate for the given domain, showing the resulting representations to be functional and interpretable. We further demonstrate how to make use of related external data so as to apply this approach in low‐resource situations. Finally, we show how these representations can be incorporated into improve modeling of real‐world natural language understanding tasks, improving model performance and helping with issues of data sparsity. (shrink)

Naturally occurring speech contains only a limited amount of complex recursive structure, and this is reflected in the empirically documented difficulties that people experience when processing such structures. We present a connectionist model of human performance in processing recursive language structures. The model is trained on simple artificial languages. We find that the qualitative performance profile of the model matches human behavior, both on the relative difficulty of center‐embedding and cross‐dependency, and between the processing of these complex (...) recursive structures and right‐branching recursive constructions. We analyze how these differences in performance are reflected in the internal representations of the model by performing discriminant analyses on these representations both before and after training. Furthermore, we show how a network trained to process recursive structures can also generate such structures in a probabilistic fashion. This work suggests a novel explanation of people's limited recursive performance, without assuming the existence of a mentally represented competence grammar allowing unbounded recursion. (shrink)

Among the myriad of technical approaches and abstract guidelines proposed to the topic of AI bias, there has been an urgent call to translate the principle of fairness into the operational AI reality with the involvement of social sciences specialists to analyse the context of specific types of bias, since there is not a generalizable solution. This article offers an interdisciplinary contribution to the topic of AI and societal bias, in particular against the poor, providing a conceptual framework of the (...) issue and a tailor-made model from which meaningful data are obtained using Natural Language Processing word vectors in pretrained Google Word2Vec, Twitter and Wikipedia GloVe word embeddings. The results of the study offer the first set of data that evidences the existence of bias against the poor and suggest that Google Word2vec shows a higher degree of bias when the terms are related to beliefs, whereas bias is higher in Twitter GloVe when the terms express behaviour. This article contributes to the body of work on bias, both from and AI and a social sciences perspective, by providing evidence of a transversal aggravating factor for historical types of discrimination. The evidence of bias against the poor also has important consequences in terms of human development, since it often leads to discrimination, which constitutes an obstacle for the effectiveness of poverty reduction policies. (shrink)

If a trademark becomes a generic term, it may be cancelled under trademark law, a process known as genericide. Typically, in genericide cases, consumer surveys are brought into evidence to establish a mark’s semantic status as generic or distinctive. Some drawbacks of surveys are cost, delay, small sample size, lack of reproducibility, and observer bias. Today, however, much discourse involving marks is online. As a potential complement to consumer surveys, therefore, we explore an artificial intelligence approach based chiefly on (...) word embeddings: mathematical models of meaning based on distributional semantics that can be trained on texts selected for jurisdictional and temporal relevance. After identifying two main factors in mark genericness, we first offer a simple screening metric based on the ngram frequency of uncapitalized variants of a mark. We then add two word embedding metrics: one addressing contextual similarity of uncapitalized variants, and one comparing the neighborhood density of marks and known generic terms in a category. For clarity and validation, we illustrate our metrics with examples of genericized, somewhat generic, and distinctive marks such as, respectively, DUMPSTER, DOBRO, and ROLEX. (shrink)

Teleological theories are often dismissed in the study of animal behaviour, because of both the anthropomorphic element, and the paradox of retro-causation. Instead, emergent properties of animal systems, such as those which drive behaviour and decision making, are generally deemed to be non-purposeful. Nonetheless, organisms’ interactions with the environment, including sensory processing, have long been subject to biological study, and the resulting models include Jakob von Uexküll’s functional circle (part of his ‘Umwelt Theory’). The functional circle is modelled on an (...) assumption of three- dimensional space containing matter and energy, and one-dimensional, linear time. Moreover, the function of such models relies upon feedback within biological systems, and generally assume that the functional feedback loops close. I argue that this is impossible, because a feedback loop cannot go back intime to close itself, and so whilst it may approximate closure in space, it does not close in the dimension of time. To address this problem, I propose a conceptual model where time is treated as having a three- dimensional structure, and is measured in terms of past, future, and subjective present, termed the ‘period,’ ‘present’ and ‘phase,’ respectively. Space and matter, meanwhile, occur as a two- dimensional intersect, in which three-dimensional emergent properties which occur ion time are embedded. The model relies on functional helices rather than circles, and the loops of each helix (unlike circular loops) never achieve closure. I explain how this therefore results in a biological system, based on both feedback and anticipatory probabilities, which is both autopoietic and teleological. I also provide examples of how the concept can be applied, using foraging behaviour as an example. I further propose the theoretical origin of such a system is ‘timing’ and ‘rhythm,’ both of which I argue have their origins in single cellular organisms’ chemotaxis and the associated gradient descent search. (shrink)

Emotion theories in present philosophical discussion propose different models of relationship between feeling and appraisal. The multicomponent model considers appraisal as separate component and distinguishes it from feeling and physiological body changes thus presupposing what may be called 'disembodied' and 'disembedded' appraisal as representational. The recently emerged concept of enactment, in contrast, argues that appraisal is closely linked to feeling and physiological body changes presupposing what can be called 'embodied' and 'embedded' appraisal as relational. The aim of the (...) paper is to investigate which concept of appraisal, the 'disembedded' or the 'embedded' one, is better compatible with current neuroimaging data on emotion processing and thus neurophilosophically more tenable. The 'disembodied' and 'disembedded' concept implies distinct and independent brain regions underlying feeling and appraisal whereas 'embodied' and 'embedded' appraisal implies overlapping and dependent brain regions. Recent neuroimaging studies demonstrate that medial and lateral prefrontal cortical regions are involved in both feeling and appraisal and that there seems to be reciprocal modulation between these regions. Though preliminary, these data suggest that feeling and appraisal are associated with different patterns of neural activity across overlapping and interdependent brain regions. I therefore conclude that current neuroscientific evidence is rather in favor of the 'embodied' and 'embedded' concept of appraisal as relational than the one of 'disembodied' and 'disembedded' appraisal as representational that is presupposed in current multicomponent theories of emotions. (shrink)

Feature selection is a widely studied technique whose goal is to reduce the dimensionality of the problem by removing irrelevant features. It has multiple benefits, such as improved efficacy, efficiency and interpretability of almost any type of machine learning model. Feature selection techniques may be divided into three main categories, depending on the process used to remove the features known as Filter, Wrapper and Embedded. Embedded methods are usually the preferred feature selection method that efficiently obtains (...) a selection of the most relevant features of the model. However, not all models support an embedded feature selection that forces the use of a different method, reducing the efficiency and reliability of the selection. Neural networks are an example of a model that does not support embedded feature selection. As neural networks have shown to provide remarkable results in multiple scenarios such as classification and regression, sometimes in an ensemble with a model that includes an embedded feature selection, we attempt to embed a feature selection process with a general-purpose methodology. In this work, we propose a novel general-purpose layer for neural networks that removes the influence of irrelevant features. The Feature-Aware Drop Layer is included at the top of the neural network and trained during the backpropagation process without any additional parameters. Our methodology is tested with 17 datasets for classification and regression tasks, including data from different fields such as Health, Economic and Environment, among others. The results show remarkable improvements compared to three different feature selection approaches, with reliable, efficient and effective results. (shrink)

Over the past twenty-five years, or so, considerable advances have been made in understanding how learning occurs in the brain, though much of this research is still to make its way into education. One contribution it should be making is to furnish the philosophical critique of past and current theory with supporting empirical evidence. For example, motivation theory and its cognate expectancy-value theory continue to be taught in teacher education, even though their rational cognitivist foundations are philosophically shaky, and their (...) shared representational notion of the mind is incompatible with what is now known about the learning brain. Furthermore, the central claim of motivation theory that children need to be motivated in order to learn is educationally problematic, not least because it incorporates a deficit model of the child as learner. This paper provides a philosophical and educational critique of motivation and expectancy-value theories and begins to sketch out an alternative dynamic systems approach, which argues that value is deeply embedded in the learning process, not only in the value students may or may not attach to their learning, but also neurobiologically. In opposition to motivation and its notion of the learner this paper argues that every student is equipped with a brain that is value-orientated towards learning and will learn, when nested in suitably affordant learning environments. In place of expectancy-value, we argue for value-embedded learning, which provides a non-reductive and biologically plausible account of how value and emotional salience are dynamically interwoven in the learning process. (shrink)

A distributed neural network model called SPEC for processing sentences with recursive relative clauses is described. The model is based on separating the tasks of segmenting the input word sequence into clauses, forming the case‐role representations, and keeping track of the recursive embeddings into different modules. The system needs to be trained only with the basic sentence constructs, and it generalizes not only to new instances of familiar relative clause structures but to novel structures as well. SPEC exhibits (...) plausible memory degradation as the depth of the center embeddings increases, its memory is primed by earlier constituents, and its performance is aided by semantic constraints between the constituents. The ability to process structure is largely due to a central executive network that monitors and controls the execution of the entire system. This way, in contrast to earlier subsymbolic systems, parsing is modeled as a controlled high‐level process rather than one based on automatic reflex responses. (shrink)

Designing models of complex phenomena is a difficult task in engineering that can be tackled by composing a number of partial models to produce a global model of the phenomena. We propose to embed the partial models in software agents and to implement their composition as a cooperative negotiation between the agents. The resulting multiagent system provides a global model of a phenomenon. We applied this approach in modelling two complex physiological processes: the heart rate regulation and the (...) glucose-insulin metabolism. Beyond the effectiveness demonstrated in these two applications, the idea of using models associated to software agents to give reason of complex phenomena is in accordance with current tendencies in epistemology, where it is evident an increasing use of computational models for scientific explanation and analysis. Therefore, our approach has not only a practical, but also a theoretical significance: agents embedding models are a technology suitable both to representing and to investigating reality. (shrink)

Wittgenstein’s ideas are a common ground for developers of Natural Language Processing (NLP) systems and linguists working on Language Acquisition and Mastery (LAM) models (Mills 1993; Lowney, Levy, Meroney and Gayler 2020; Skelac and Jandrić 2020). In recent years, we have witnessed a fast development of NLP systems capable of performing tasks as never before. NLP and LAM have been implemented based on deep learning neural networks, which learn concepts representation from rough data, but are nonetheless very effective in tasks (...) such as question answering, textual entailment, and translation (Devlin et al. 2019; Kitaev, Cao and Klein 2019; Wang et al. 2019). In this paper, I will debate some Wittgensteinian concepts that impact the architectures of many NLP deep-learning systems. I will focus, in particular, on the attempt to build a specific kind of architecture to model a private language. The discussion, I think, helps extract philosophical assumptions leading the research and development of AI systems capable of language modelling. In this paper, I will address some of the main features of NLP systems2 used for word embedding and one proposal to manipulate through a neural network a form of private language (Lawney et al. 2020). In §2, I will reconstruct the complex path of the private language argument (PLA). In §3, I will discuss connectivist language models and introduce notions about NPL systems' architecture. An overview of this kind of model is helpful to introduce the work of Lowney, Levy, Meroney and Gayler (2020). They submit that their model can respond to the issues raised by Wittgenstein in the notorious Private Language Argument (PLA). This argument unexpectedly turned out to be relevant not only for the philosophy of language but also for NLP and LAM modellers. I will describe the language game concept in NLP, how it is embedded, and its role in inductive systems development. This central concept in Wittgenstein’s work is relevant to describe the role of context in understanding the meanings of words. In §4, I present the Wittgensteinian main concepts in play in the connectivist paradigm. I argue that the connectionist theoretical framework can better catch the dependency of word meaning on context. There seems to be a correlation between Wittgenstein's invitation to look, an invitation to dismiss the aim of theorizing about languages, and the absence of theory-ladenness in deep learning technologies involved in NLP. In §5, I criticize Lowney and colleagues’ claim, whose model does not successfully capture Wittgenstein’s Beetle in the Box case. Moreover, I argue that even if we can distinguish a strong and a weak definition of a private language, Wittgenstein’s argument also holds for deep-learning models, and his worries are still a good guide for NLP developers. (shrink)

Automatic synonym extraction plays an important role in many natural language processing systems, such as those involving information retrieval and question answering. Recently, research has focused on extracting semantic relations from word embeddings since they capture relatedness and similarity between words. However, using word embeddings alone poses problems for synonym extraction because it cannot determine whether the relation between words is synonymy or some other semantic relation. In this paper, we present a novel solution for this problem by proposing the (...) SynoExtractor pipeline, which can be used to filter similar word embeddings to retain synonyms based on specified linguistic rules. Our experiments were conducted using KSUCCA and Gigaword embeddings and trained with CBOW and SG models. We evaluated automatically extracted synonyms by comparing them with Alma’any Arabic synonym thesauri. We also arranged for a manual evaluation by two Arabic linguists. The results of experiments we conducted show that using the SynoExtractor pipeline enhances the precision of synonym extraction compared to using the cosine similarity measure alone. SynoExtractor obtained a 0.605 mean average precision for the King Saud University Corpus of Classical Arabic with 21% improvement over the baseline and a 0.748 MAP for the Gigaword corpus with 25% improvement. SynoExtractor outperformed the Sketch Engine thesaurus for synonym extraction by 32% in terms of MAP. Our work shows promising results for synonym extraction suggesting that our method can also be used with other languages. (shrink)

Despite the exceptional performance of large language models (LLMs) on a wide range of tasks involving natural language processing and reasoning, there has been sharp disagreement as to whether their abilities extend to more creative human abilities. A core example is the interpretation of novel metaphors. Here we assessed the ability of GPT-4, a state-of-the-art large language model, to provide natural-language interpretations of a recent AI benchmark (Fig-QA dataset), novel literary metaphors drawn from Serbian poetry and translated into English, (...) and entire novel English poems. GPT-4 outperformed previous AI models on the Fig-QA dataset. For metaphors drawn from Serbian poetry, human judges – blind to the fact that an AI model was involved – rated metaphor interpretations generated by GPT-4 as superior to those provided by a group of college students. In interpreting reversed metaphors, GPT-4, as well as humans, exhibited signs of sensitivity to the Gricean cooperative principle. In addition, for several novel English poems GPT-4 produced interpretations that were rated as excellent or good by a human literary critic. These results indicate that LLMs such as GPT-4 have acquired an emergent ability to interpret literary metaphors, including those embedded in novel poems. (shrink)

The Ouroboros Model proposes a brain inspired cognitive architecture including detailed suggestions for the main processing steps in an overall conceptualization of cognition as embodied and embedded computing. All memories are structured into schemata, which are firmly grounded in the body of an actor. A cyclic and iterative data-acquisition and -processing loop forms the backbone of all cognitive activity. Ever more sophisticated schemata are built up incrementally from the wide combination of neural activity, concurrent at the point in (...) time when the memory is established; i.e., distinct representations are accrued. Later on, an entire schema can be reinstated from diverse subsets of its constituent features. In order to decode or compile ever more elaborate constructions, extant building blocks are concatenated. They are serially linked via common or “connection-attributes” of different representations and symbols, and they are employed for serial perception, processing and action, in particular, also for language production. At various levels, commonalities between different schemata lead to a similar preferred use of their respective tokens and subsequently shared categorizations. Rules, for the concatenation of words, akin to the ones governing chemical reactions, can be abstracted. As special kinds of symbols, words and word-classes along with the whole grammar of a language can thus be seen as a direct consequence of the processes outlined in the Ouroboros Model. Strong emphasis is put on the dimension of time over short intervals during active experience and performance, and, in particular, during interactions with other agents. (shrink)

Despite the exceptional performance of large language models (LLMs) on a wide range of tasks involving natural language processing and reasoning, there has been sharp disagreement as to whether their abilities extend to more creative human abilities. A core example is the interpretation of novel metaphors. Here we assessed the ability of GPT-4, a state-of-the-art large language model, to provide natural-language interpretations of a recent AI benchmark (Fig-QA dataset), novel literary metaphors drawn from Serbian poetry and translated into English, (...) and entire novel English poems. GPT-4 outperformed previous AI models on the Fig-QA dataset. For metaphors drawn from Serbian poetry, human judges – blind to the fact that an AI model was involved – rated metaphor interpretations generated by GPT-4 as superior to those provided by a group of college students. In interpreting reversed metaphors, GPT-4, as well as humans, exhibited signs of sensitivity to the Gricean cooperative principle. In addition, for several novel English poems GPT-4 produced interpretations that were rated as excellent or good by a human literary critic. These results indicate that LLMs such as GPT-4 have acquired an emergent ability to interpret literary metaphors, including those embedded in novel poems. (shrink)

Deep Learning has been widely used for tackling challenging natural language processing tasks over the recent years. Similarly, the application of Deep Neural Networks in legal analytics has increased significantly. In this survey, we study the early adaptation of Deep Learning in legal analytics focusing on three main fields; text classification, information extraction, and information retrieval. We focus on the semantic feature representations, a key instrument for the successful application of deep learning in natural language processing. Additionally, we share pre-trained (...) legal word embeddings using the word2vec model over large corpora, comprised legislations from UK, EU, Canada, Australia, USA, and Japan among others. (shrink)

What role do moment‐to‐moment comprehension processes play in visual attentional selection in picture stories? The current work uniquely tested the role of bridging inference generation processes on eye movements while participants viewed picture stories. Specific components of the Scene Perception and Event Comprehension Theory (SPECT) were tested. Bridging inference generation was induced by manipulating the presence of highly inferable actions embedded in picture stories. When inferable actions are missing, participants have increased viewing times for the immediately following critical image (...) (Magliano, Larson, Higgs, & Loschky, ). This study used eye‐tracking to test competing hypotheses about the increased viewing time: (a) Computational Load: inference generation processes increase overall computational load, producing longer fixation durations; (b) Visual Search: inference generation processes guide eye‐movements to pick up inference‐relevant information, producing more fixations. Participants had similar fixation durations, but they made more fixations while generating inferences, with that process starting from the fifth fixation. A follow‐up hypothesis predicted that when generating inferences, participants fixate scene regions important for generating the inference. A separate group of participants rated the inferential‐relevance of regions in the critical images, and results showed that these inferentially relevant regions predicted differences in other viewers’ eye movements. Thus, viewers’ event models in working memory affect visual attentional selection while viewing visual narratives. (shrink)

The neural basis of phonological working memory was investigated through an examination of the effects of irrelevant speech distractors and disruptive neural stimulation from transcranial magnetic stimulation. Embedded processes models argue that the same regions involved in speech perception are used to support phonological WM whereas buffer models assume that a region separate from speech perception regions is used to support WM. Thus, according to the embedded processes approach but not the buffer approach, irrelevant speech and TMS to (...) the speech perception region should disrupt the decoding of phonological WM representations. According to the buffer account, decoding of WM items should be possible in the buffer region despite distraction and should be disrupted with TMS to this region. Experiment 1 used fMRI and representational similarity analyses with a delayed recognition memory paradigm using nonword stimuli. Results showed that decoding of memory items in the speech perception regions was possible in the absence of distractors. However, the decoding evidence in the left STG was susceptible to interference from distractors presented during the delay period whereas decoding in the proposed buffer region persisted. Experiment 2 examined the causal roles of the speech processing region and the buffer region in phonological WM performance using TMS. TMS to the SMG during the early delay period caused a disruption in recognition performance for the memory nonwords, whereas stimulations at the STG and an occipital control region did not affect WM performance. Taken together, results from the two experiments are consistent with predictions of a buffer model of phonological WM, pointing to a critical role of the left SMG in maintaining phonological representations. (shrink)

In recent years, business ethics and economic scholars have been paying greater attention to the development of commons organizing. The latter refers to the processes by which communities of people work in common in the pursuit of the common good. In turn, this promotes commons organizational designs based on collective forms of common goods production, distribution, management and ownership. In this paper, we build on two main literature streams: the ethical approach based on the theory of the common good of (...) the firm in virtue ethics and the economic approach based on the theory of institutions for collective action developed by Ostrom’s research on common-pool resources to avert the tragedy of the commons. The latter expands to include the novel concepts of new commons, “commoning” and polycentric governance. Drawing on the analysis of what is new in these forms of organizing, we propose a comprehensive model, highlighting the integration of two sets of organizing principles—common good and collective action – and five problem-solving processes to explain the main dimensions of commons organizing. We contribute to business ethics literature by exploring the convergence between the ethical and economic approaches in the development of a commons organizing view. (shrink)

The contract review process can be a costly and time-consuming task for lawyers and clients alike, requiring significant effort to identify and evaluate the legal implications of individual clauses. To address this challenge, we propose the use of natural language processing techniques, specifically text classification based on deontic tags, to streamline the process. Our research question is whether natural language processing techniques, specifically dense vector embeddings, can help semi-automate the contract review process and reduce time and costs (...) for legal professionals reviewing deontic modalities in contracts. In this study, we create a domain-specific dataset and train both baseline and neural network models for contract sentence classification. This approach offers a more efficient and cost-effective solution for contract review, mimicking the work of a lawyer. Our approach achieves an accuracy of 0.90, showcasing its effectiveness in identifying and evaluating individual contract sentences. (shrink)

To carry out an ethnographic study on the work process in the sterilization unit of a hospital in Catalonia, we found the socially distributed cognition approaches of Hutchins and Kirsh useful. However, these approaches lack sufficient explanation on three important issues: the pragmatic criteria for identifying and delimiting a relevant unit of analysis and therefore the setting and contexts of the work process; the mechanisms and results of reciprocal influences between these levels of analysis; and the relation between (...) these levels. Therefore, we added several new elements to these approaches, in addition to Layder's model of contexts with some important modifications, with the aim of offering an interactive, cognitive and social proposal of analysis that clarifies these three issues, allowing a more exhaustive and broader empirical analysis that captures better the ‘social’ dimension embedded in the work processes. (shrink)

This paper reports part of a longitudinal research project, which sought to capture students’ conceptualization of caring practice as they progressed to different levels of study in a nursing diploma programme in Hong Kong. Model emulation was found to be an effective means of focusing students’ learning processes on the moral aspects of nursing practice. The theory of model emulation from a Chinese perspective and how it is applied to create a learning context to allow students to acquire (...) a moral sense of nursing are discussed. The participating students are invited to be sincere enquirers in the pursuit of the good embedded in practice through introspective self-examination and dialogue. They are asked to describe and share their experience of positive and negative examples of nursing in written accounts. Van Kaam’s phenomenological method was adopted to explicate the good and bad constituents of nursing from these examples, with the students assuming an active role in the explication process. The explication reveals that the students were able to articulate the good and bad practices in a variety of patient care situations. (shrink)

Predictive Processing (PP) framework construes perception and action (and perhaps other cognitive phenomena) as a matter of minimizing prediction error, i.e. the mismatch between the sensory input and sensory predictions generated by a hierarchically organized statistical model. There is a question of how PP fits into the debate between traditional, neurocentric and representation-heavy approaches in cognitive science and those approaches that see cognition as embodied, environmentally embedded, extended and (largely) representation-free. In the present paper, I aim to investigate (...) and clarify the cognitivist or ‘conservative’ reading of PP. I argue that the conservative commitments of PP can be divided into three distinct categories: (1) representationalism, (2) inferentialism, and (3) internalism. I show how these commitments and their relations should be understood and argue for an interpretation of each that is both non-trivial and largely ecumenical towards the 4E literature. Conservative PP is as progressive as conservatism gets. (shrink)

This paper re-frames business ethical codes as narrative processes by reflecting critically on key ontological assumptions underpinning the existing research, and introducing new and relevant concepts based on alternative assumptions. The first section draws on recent decision-making research to develop a theoretical account of BCEs as complex, socially embedded sensemaking processes. The second section addresses the content of codes, and differentiates between narrative and logico-scientific modes of reasoning. The third section focuses on the quality of code communication and identifies (...) several distinct types of narrative process. We provide research directions for how this new understanding of BCEs may be further developed, as well as implications for practitioners. In response to the call for new conceptual models, the paper provides organizational researchers with a more nuanced understanding of how BCEs enable or constrain ethical behavior in organizations. (shrink)

The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology – namely, Behaviorism and evolutionary psychology – that set aside mechanistic explanations or make use of optimality assumptions. Through (...) these comparisons, we identify a number of challenges that limit the rational program's potential contribution to psychological theory. Specifically, rational Bayesian models are significantly unconstrained, both because they are uninformed by a wide range of process-level data and because their assumptions about the environment are generally not grounded in empirical measurement. The psychological implications of most Bayesian models are also unclear. Bayesian inference itself is conceptually trivial, but strong assumptions are often embedded in the hypothesis sets and the approximation algorithms used to derive model predictions, without a clear delineation between psychological commitments and implementational details. Comparing multiple Bayesian models of the same task is rare, as is the realization that many Bayesian models recapitulate existing (mechanistic level) theories. Despite the expressive power of current Bayesian models, we argue they must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition. We lay out several means for such an integration, which take into account the representations on which Bayesian inference operates, as well as the algorithms and heuristics that carry it out. We argue this unification will better facilitate lasting contributions to psychological theory, avoiding the pitfalls that have plagued previous theoretical movements. (shrink)

The development of numerical models to produce realistic prospective scenarios for the evolution of biological diversity is essential. Only integrative impact assessment models are able to take into account the diverse and complex interactions embedded in social-ecological systems. The knowledge used is objective, the procedure of their integration is rigorous and the data massive. Nevertheless, the technical choices made at each stage of the development of models and scenarios are mostly circumstantial, depending on both the skills of modellers on (...) a project and the means available to them. In the end, the scenarios selected and the way they are simulated limit the futures explored, and the options offered to decision makers and stakeholders to act. The ethical implications of these circumstantial choices are generally not documented, explained or even perceived by modellers. Applied ethics propose a coherent set of principles to guide a critical reflection on the social and environmental consequences of integrative modelling and simulation of biodiversity scenarios. Such reflection should be incorporated into the actual modelling process, in a broad participatory framework, and foster effective moral involvement of modellers, policy-makers and stakeholders, in preference to the application of fixed ethical rules. (shrink)