We investigate how Reinforcement Learning methods can be used to solve the production selection and production ordering problem in ACT‐R. We focus on four algorithms from the Q learning family, tabular Q and three versions of Deep Q Networks, as well as the ACT‐R utility learning algorithm, which provides a baseline for the Q algorithms. We compare the performance of these five algorithms in a range of lexical decision tasks framed as sequential decision problems.

The psycholinguistic literature has identified two syntactic adaptation effects in language production: rapidly decaying short-term priming and long-lasting adaptation. To explain both effects, we present an ACT-R model of syntactic priming based on a wide-coverage, lexicalized syntactic theory that explains priming as facilitation of lexical access. In this model, two well-established ACT-R mechanisms, base-level learning and spreading activation, account for long-term adaptation and short-term priming, respectively. Our model simulates incremental language production and in a series of modeling (...) studies, we show that it accounts for (a) the inverse frequency interaction; (b) the absence of a decay in long-term priming; and (c) the cumulativity of long-term adaptation. The model also explains the lexical boost effect and the fact that it only applies to short-term priming. We also present corpus data that verify a prediction of the model, that is, that the lexical boost affects all lexical material, rather than just heads. (shrink)

This paper presents a computational model of the way humans inductively identify and aggregate concepts from the low-level stimuli they are exposed to. Based on the idea that humans tend to select the simplest structures, it implements a dynamic hierarchical chunking mechanism in which the decision whether to create a new chunk is based on an information-theoretic criterion, the Minimum Description Length (MDL) principle. We present theoretical justifications for this approach together with results of an experiment in which (...) participants, exposed to meaningless symbols, have been implicitly encouraged to create high-level concepts by grouping them. Results show that the designed model, called hereafter MDLChunker, makes precise quantitative predictions both on the kind of chunks created by the participants and also on the moment at which these creations occur. They suggest that the simplicity principle used to design MDLChunker is particularly efficient to model chunking mechanisms. The main interest of this model over existing ones is that it does not require any adjustable parameter. (shrink)

Recent studies of emotion mindreading reveal that for three emotions, fear, disgust, and anger, deficits in face-based recognition are paired with deficits in the production of the same emotion. What type of mindreading process would explain this pattern of paired deficits? The simulation approach and the theorizing approach are examined to determine their compatibility with the existing evidence. We conclude that the simulation approach offers the best explanation of the data. What computational steps might be used, however, in (...) simulation-style emotion detection? Four alternative models are explored: a generate-and-test model, a reverse simulation model, a variant of the reverse simulation model that employs an “as if” loop, and an unmediated resonance model. (shrink)

This article takes off from Johan van Benthem’s ruminations on the interface between logic and cognitive science in his position paper “Logic and reasoning: Do the facts matter?”. When trying to answer Van Benthem’s question whether logic can be fruitfully combined with psychological experiments, this article focuses on a specific domain of reasoning, namely higher-order social cognition, including attributions such as “Bob knows that Alice knows that he wrote a novel under pseudonym”. For intelligent interaction, it is important that (...) the participants recursively model the mental states of other agents. Otherwise, an international negotiation may fail, even when it has potential for a win-win solution, and in a time-critical rescue mission, a software agent may depend on a teammate’s action that never materializes. First a survey is presented of past and current research on higher-order social cognition, from the various viewpoints of logic, artificial intelligence, and psychology. Do people actually reason about each other’s knowledge in the way proscribed by epistemic logic? And if not, how can logic and cognitive science productively work together to construct more realistic models of human reasoning about other minds? The paper ends with a delineation of possible avenues for future research, aiming to provide a better understanding of higher-order social reasoning. The methodology is based on a combination of experimental research, logic, computational cognitive models, and agent-based evolutionary models. (shrink)

This article takes off from Johan van Benthem’s ruminations on the interface between logic and cognitive science in his position paper “Logic and reasoning: Do the facts matter?”. When trying to answer Van Benthem’s question whether logic can be fruitfully combined with psychological experiments, this article focuses on a specific domain of reasoning, namely higher-order social cognition, including attributions such as “Bob knows that Alice knows that he wrote a novel under pseudonym”. For intelligent interaction, it is important that (...) the participants recursively model the mental states of other agents. Otherwise, an international negotiation may fail, even when it has potential for a win-win solution, and in a time-critical rescue mission, a software agent may depend on a teammate’s action that never materializes. First a survey is presented of past and current research on higher-order social cognition, from the various viewpoints of logic, artificial intelligence, and psychology. Do people actually reason about each other’s knowledge in the way proscribed by epistemic logic? And if not, how can logic and cognitive science productively work together to construct more realistic models of human reasoning about other minds? The paper ends with a delineation of possible avenues for future research, aiming to provide a better understanding of higher-order social reasoning. The methodology is based on a combination of experimental research, logic, computational cognitive models, and agent-based evolutionary models. Keywords Epistemic logic - Cognitive science - Intelligent interaction - Cognitive modeling. (shrink)

I consider how we might begin to redress a cognitive model for thought experimental and other imagery-based scientific reasoning from an embodied cognition viewpoint. The paper gravitates on clarifying tour issues: (i) the danger of understanding the genuine novelty of thought-experimental reasoning and other imagery-based reasoning as a product of ‘quasi-perceiving’ new phenomenology with the ‘mind’s eye’ (as asserted by quasi-pictorialist theories of imagery); (ii) the erroneous choice of units of analysis that assume equivalence of external reports (...) of visual imagery with those internal structures that govern imagery-based reasoning, which are, as I will argue, largely linked to motor processes; (iii) the establishment of thought experimentation as imagery-based reasoning by providing evidence for the psychological necessity of imagistic simulation in thought experiments; (iv) a cognitive model for how learning via thought experimentation and other imagery-based reasoning takes place. The study was underpinned by constructivist assumptions. Case methodology was adopted, the case being a pair of final year A-level physics students. Data was collected through non-participant observation over two sessions of collaborative problem-solving. The tasks drew upon Newtonian mechanics. (shrink)

Autonomist accounts of cognitive science suggest that cognitive model building and theory construction (can or should) proceed independently of findings in neuroscience. Common functionalist justifications of autonomy rely on there being relatively few constraints between neural structure and cognitive function (e.g., Weiskopf, 2011). In contrast, an integrative mechanistic perspective stresses the mutual constraining of structure and function (e.g., Piccinini & Craver, 2011; Povich, 2015). In this paper, I show how model-based cognitive neuroscience (MBCN) epitomizes the (...) integrative mechanistic perspective and concentrates the most revolutionary elements of the cognitive neuroscience revolution (Boone & Piccinini, 2016). I also show how the prominent subset account of functional realization supports the integrative mechanistic perspective I take on MBCN and use it to clarify the intralevel and interlevel components of integration. (shrink)

We present a model of similarity‐based retrieval that attempts to capture three seemingly contradictory psychological phenomena: (a) structural commonalities are weighed more heavily than surface commonalities in similarity judgments for items in working memory; (b) in retrieval, superficial similarity is more important than structural similarity; and yet (c) purely structural (analogical) remindings e sometimes experienced. Our model, MAC/FAC, explains these phenomena in terms of a two‐stage process. The first stage uses a computationally cheap, non‐structural matcher to filter candidate long‐term (...) memory items. It uses content vectors, a redundant encoding of structured representations whose dot product estimates how well the corresponding structural representations will match. The second stage uses SME (structure‐mapping engine) to compute structural matches on the handful of items found by the first stage. We show the utility of the MAC/FAC model through a series of computational experiments: (a) We demonstrate that MAC/FAC can model patterns of access found in psychological data; (b) we argue via sensitivity analyses that these simulation results rely on the theory; and (c) we compare the performance of MAC/FAC with ARCS, an alternate model of similarity‐based retrieval, and demonstrate that MAC/FAC explains the data better than ARCS. Finally, we discuss limitations and possible extensions of the model, relationships with other recent retrieval models, and place MAC/FAC in the context of other recent work on the nature of similarity. (shrink)

Syntactic priming in language production is the increased likelihood of using a recently encountered syntactic structure. In this paper, we examine two theories of why speakers can be primed: error‐driven learning accounts (Bock, Dell, Chang, & Onishi, 2007; Chang, Dell, & Bock, 2006) and activation‐based accounts (Pickering & Branigan, 1999; Reitter, Keller, & Moore, 2011). Both theories predict that speakers should be primed by the syntactic choices of others, but only activation‐based accounts predict that speakers should be (...) able to prime themselves. Here we test whether speakers can be primed by their own productions in three behavioral experiments and find evidence of structural persistence following both comprehension and speakers’ own productions. We also find that comprehension‐based priming effects are larger for rarer syntactic structures than for more common ones, which is most consistent with error‐driven accounts. Because neither error‐driven accounts nor activation‐based accounts fully explain the data, we propose a hybrid model. (shrink)

The literature on the evolution of religion has been divided by a fundamental debate between adaptation theories, which explain religious traits as products of selection for religion, and byproduct theories, which explain religious traits as products of selection for other, non-religious functions. Recently, however, a new position has emerged in this debate, as an influential new theory based on cultural selection claims to integrate adaptation theories with byproduct theories, yielding a single, unified account. I argue that the proponents of (...) this view do not say enough about how integration is actually supposed to work, from a logical point of view. Basic questions arise from the assumptions required for unifying these apparently conflicting approaches, which the authors of the account do not address. In response to these questions, I provide a model of the religious phenotype, the Goldberg Exaptation Model, which shows that adaptation and byproduct theories are consistent, and explains how they are positively related, over and above mere consistency. On this view, the religious phenotype is best understood on analogy with a Rube Goldberg device: it is assembled by selection for religion, but using parts designed by selection for other, non-religious functions. (shrink)

The Cognitive Science of Nationalistic Behavior, presented in this paper, integrates the political sciences of nationalities as invented communities with an evolutionary cognitive analysis of social forms as products of the human mind. The framework is modeled after the Cognitive Science of Religion, where decades of cross-disciplinary work has generated standards, predictions, and data about the role of individual cognitive tendencies in shaping societies. We study the nationalistic calendar as a cultural attractor and draw on cue- (...) class='Hi'>based behavioral motivation and differential autobiographical memory systems to explain its appeal to the human mind. Calendrical elements are analyzed in the context of essentialist thought patterns and action representation systems. We conclude with the implications of calendrical thinking on the control of elites who aim to forge and reinforce national identities. This paper lays the groundwork for applying a similar approach to the study of other nationalistic elements. (shrink)

This paper presents two cognitive models that simulate the production of referring expressions in the iMAP task—a task-oriented dialog. One general model is based on Dale and Reiter’s (1995)incremental algorithm, and the other is a simple template model that has a higher correlation with the data but is specifically geared toward the properties of the iMAP task. The property of the iMAP task environment that is modeled here is that the color feature is unreliable for identifying (...) referents while other features are reliable. The low computational cost of the incremental algorithm for generating referring expressions makes it an interesting starting point for a cognitive model. However, its explanatory power is limited, because it generates uniquely distinguishing referring expressions and because it considers features for inclusion in the referring expression in a fixed order. The first model extends the original incremental algorithm by an ability to adapt to feedback of whether a referring expression was used successfully, but it seems to overpredict the frequency with which distinguishing expressions are made and underpredict the frequency of overspecified referring expressions. The second model produces features for referring expressions purely based on its current estimate of a feature’s utility. Both models predict the observed human behavior of decreasing use of color terms and increasing use of useful feature terms. (shrink)

The fields of machine learning (ML) and cognitive science have developed complementary approaches to computationally modeling human behavior. ML's primary concern is maximizing prediction accuracy; cognitive science's primary concern is explaining the underlying mechanisms. Cross-talk between these disciplines is limited, likely because the tasks and goals usually differ. The domain of e-learning and knowledge acquisition constitutes a fruitful intersection for the two fields’ methodologies to be integrated because accurately tracking learning and forgetting over time and predicting future performance (...) based on learning histories are central to developing effective, personalized learning tools. Here, we show how a state-of-the-art ML model can be enhanced by incorporating insights from a cognitive model of human memory. This was done by exploiting the predictive performance equation's (PPE) narrow but highly specialized domain knowledge with regard to the temporal dynamics of learning and forgetting. Specifically, the PPE was used to engineer timing-related input features for a gradient-boosted decision trees (GBDT) model. The resulting PPE-enhanced GBDT outperformed the default GBDT, especially under conditions in which limited data were available for training. Results suggest that integrating cognitive and ML models could be particularly productive if the available data are too high-dimensional to be explained by a cognitive model but not sufficiently large to effectively train a modern ML algorithm. Here, the cognitive model's insights pertaining to only one aspect of the data were enough to jump-start the ML model's ability to make predictions—a finding that holds promise for future explorations. (shrink)

The fields of machine learning (ML) and cognitive science have developed complementary approaches to computationally modeling human behavior. ML's primary concern is maximizing prediction accuracy; cognitive science's primary concern is explaining the underlying mechanisms. Cross-talk between these disciplines is limited, likely because the tasks and goals usually differ. The domain of e-learning and knowledge acquisition constitutes a fruitful intersection for the two fields’ methodologies to be integrated because accurately tracking learning and forgetting over time and predicting future performance (...) based on learning histories are central to developing effective, personalized learning tools. Here, we show how a state-of-the-art ML model can be enhanced by incorporating insights from a cognitive model of human memory. This was done by exploiting the predictive performance equation's (PPE) narrow but highly specialized domain knowledge with regard to the temporal dynamics of learning and forgetting. Specifically, the PPE was used to engineer timing-related input features for a gradient-boosted decision trees (GBDT) model. The resulting PPE-enhanced GBDT outperformed the default GBDT, especially under conditions in which limited data were available for training. Results suggest that integrating cognitive and ML models could be particularly productive if the available data are too high-dimensional to be explained by a cognitive model but not sufficiently large to effectively train a modern ML algorithm. Here, the cognitive model's insights pertaining to only one aspect of the data were enough to jump-start the ML model's ability to make predictions—a finding that holds promise for future explorations. (shrink)

Based on an endorsement of the hypothesis of extended cognition , this paper proposes a criticism of the representationalist assumptions that still pertain to these contemporary models of cognition. I first rehearse some basic problems akin to any representationalist model of cognition, before proposing some more specific arguments directed against the necessity, the plausibility, and the coherence of the marriage between extended cognition and contemporary representationalism . Extended and distributed models of cognition have the resources to get (...) rid of representationalism, and they should better do it. Their adherence to representationalism might be an by-product of the extended character of the scientific study of cognition. (shrink)

An Interdisciplinary Approach to Cognitive Modelling presents a new approach to cognition that challenges long-held views. It systematically develops a broad-based framework to model cognition, which is mathematically equivalent to the emerging 'quantum-like modelling' of the human mind. The book argues that a satisfactory physical and philosophical basis of such an approach is missing, a particular issue being the application of quantization to the mind for which there is no empirical evidence as yet. In response to this issue, (...) the book adopts a COM (classical optical modelling) approach, broad-based but mathematically equivalent to quantum-like modelling while avoiding its problematic features. It presents a philosophically informed and empirically motivated mathematical model of cognition, mainly concerning decision making processes. It also deals with applications to different areas of the social sciences. It will be of interest to scholars and research students interested in the mathematical modelling of cognition and decision making, and also interdisciplinary researchers interested in broader issues of cognition. (shrink)

Topics in Cognitive Science, Volume 14, Issue 2, Page 295-310, April 2022.

Based on an endorsement of the hypothesis of extended cognition, this paper proposes a criticism of the representationalist assumptions that still pertain to these contemporary models of cognition. I first rehearse some basic problems akin to any representationalist model of cognition, before proposing some more specific arguments directed against the necessity, the plausibility, and the coherence of the marriage between extended cognition and contemporary representationalism. Extended and distributed models of cognition have the resources to get rid of (...) representationalism, and they should better do it. Their adherence to representationalism might be an by-product of the extended character of the scientific study of cognition. (shrink)

Proof requires a dialogue between agents to clarify obscure inference steps, fill gaps, or reveal implicit assumptions in a purported proof. Hence, argumentation is an integral component of the discovery process for mathematical proofs. This work presents how argumentation theories can be applied to describe specific informal features in the development of proof-events. The concept of proof-event was coined by Goguen who described mathematical proof as a public social event that takes place in space and time. This new meta-methodological concept (...) is designed to cover not only “traditional” formal proofs but all kinds of proofs and inference steps, including incomplete or purported proofs. Our approach attempts to make proof-events more comprehensive to express the complete trajectory of a mathematical proof-event until the ultimate validation of the proving outcome. Thus, we advance an extended version of proof-event calculus which is built on argumentation theories designed to capture the internal and external structure of collaborative mathematical practice and highlight the relationship between proof, human reasoning, and cognitive processes. In addition, another area in which argumentation can make a significant contribution is dealing with the defeasible knowledge of the Web which is a product of its open and ubiquitous nature. This approach seems to be sufficient for the presentation of Web-based proving processes as manifested in the case of the Mini-Polymath 4 project. (shrink)

The Emergic Cognitive Model (ECM) is a unified computational model of visual filling-in based on the Emergic Network architecture. The Emergic Network was designed to help realize systems undergoing continuous change. In this thesis, eight different filling-in phenomena are demonstrated under a regime of continuous eye movement (and under static eye conditions as well). -/- ECM indirectly demonstrates the power of unification inherent with Emergic Networks when cognition is decomposed according to finer-grained functions supporting change. These can interact (...) to raise additional emergent behaviours via cognitive re-use, hence the Emergic prefix throughout. Nevertheless, the model is robust and parameter free. Differential re-use occurs in the nature of model interaction with a particular testing paradigm. -/- ECM has a novel decomposition due to the requirements of handling motion and of supporting unified modelling via finer functional grains. The breadth of phenomenal behaviour covered is largely to lend credence to our novel decomposition. -/- The Emergic Network architecture is a hybrid between classical connectionism and classical computationalism that facilitates the construction of unified cognitive models. It helps cutting up of functionalism into finer-grains distributed over space (by harnessing massive recurrence) and over time (by harnessing continuous change), yet simplifies by using standard computer code to focus on the interaction of information flows. Thus while the structure of the network looks neurocentric, the dynamics are best understood in flowcentric terms. Surprisingly, dynamic system analysis (as usually understood) is not involved. An Emergic Network is engineered much like straightforward software or hardware systems that deal with continuously varying inputs. Ultimately, this thesis addresses the problem of reduction and induction over complex systems, and the Emergic Network architecture is merely a tool to assist in this epistemic endeavour. -/- ECM is strictly a sensory model and apart from perception, yet it is informed by phenomenology. It addresses the attribution problem of how much of a phenomenon is best explained at a sensory level of analysis, rather than at a perceptual one. As the causal information flows are stable under eye movement, we hypothesize that they are the locus of consciousness, howsoever it is ultimately realized. (shrink)

A broad sketch for a model of speech production is outlined which describes developmental aspects of its cognitive-linguistic and sensorimotor components. A description of the emergence of phonological knowledge is a central point in our model sketch. It will be shown that the phonological form level emerges during speech acquisition and becomes an important representation at the interface between cognitive-linguistic and sensorimotor processes. Motor planning as well as motor programming are defined as separate processes in our model (...) sketch and it will be shown that both processes revert to the phonological information. Two computational simulation experiments based on quantitative implementations are undertaken to show proof of principle of key ideas of the model sketch: the emergence of phonological information over developmental stages, the adaptation process for generating new motor programs, and the importance of various forms of phonological representation in that process. Based on the ideas developed within our sketch of a production model and its quantitative spell-out within the simulation models, motor planning can be defined here as the process of identifying a succession of executable chunks from a currently activated phoneme sequence and of coding them as raw gesture scores. Motor programming can be defined as the process of building up the complete set of motor commands by specifying all gestures in detail. This full specification of gesture scores is achieved in our model by adapting motor information from phonologically similar syllables or by assembling motor programs from sub-syllabic units. (shrink)

We analyze the dynamics of repeated interaction of two players in the Prisoner's Dilemma under various levels of interdependency information and propose an instance-based learning cognitive model to explain how cooperation emerges over time. Six hypotheses are tested regarding how a player accounts for an opponent's outcomes: the selfish hypothesis suggests ignoring information about the opponent and utilizing only the player's own outcomes; the extreme fairness hypothesis weighs the player's own and the opponent's outcomes equally; the moderate fairness (...) hypothesis weighs the opponent's outcomes less than the player's own outcomes to various extents; the linear increasing hypothesis increasingly weighs the opponent's outcomes at a constant rate with repeated interactions; the hyperbolic discounting hypothesis increasingly and nonlinearly weighs the opponent's outcomes over time; and the dynamic expectations hypothesis dynamically adjusts the weight a player gives to the opponent's outcomes, according to the gap between the expected and the actual outcomes in each interaction. When players lack explicit feedback about their opponent's choices and outcomes, results are consistent with the selfish hypothesis; however, when this information is made explicit, the best predictions result from the dynamic expectations hypothesis. (shrink)

While cognitive changes in aging and neurodegenerative disease have been widely studied, language changes in these populations are less well understood. Inflecting novel words in a language with complex inflectional paradigms provides a good opportunity to observe how language processes change in normal and abnormal aging. Studies of language acquisition suggest that children inflect novel words based on their phonological similarity to real words they already know. It is unclear whether speakers continue to use the same strategy when (...) encountering novel words throughout the lifespan or whether adult speakers apply symbolic rules. We administered a simple speech elicitation task involving Finnish‐conforming pseudo‐words and real Finnish words to healthy older adults, individuals with mild cognitive impairment, and individuals with Alzheimer's disease (AD) to investigate inflectional choices in these groups and how linguistic variables and disease severity predict inflection patterns. Phonological resemblance of novel words to both a regular and an irregular inflectional type, as well as bigram frequency of the novel words, significantly influenced participants' inflectional choices for novel words among the healthy elderly group and people with AD. The results support theories of inflection by phonological analogy (single‐route models) and contradict theories advocating for formal symbolic rules (dual‐route models). (shrink)

Value co-creation goes through the stage of co-production, customer experience, service-dominant logic, and service ecosystem. The integration of science and technology has become a key factor to the process of VCC. The rise and application of artificial intelligence technology has added a new driving force to VCC and began to affect its original practical logic. Based on the consumer perspective, this study uses Q-methodology to measure consumer cognitive attitude toward the use of AI technology in VCC, aiming (...) to explore the key factors that affect VCC. The study found that content quality, information security, efficiency concern, and degree of manipulation have become the important concerns of consumers for VCC under AI integration. Moreover, their different statements have their specific focus and direction. The study demonstrates and analyzes the importance of the four factors and proposes the combination of human and non-human actors to shape the new model of VCC in the future, which is worth further deepening and exploring for academia and enterprises. (shrink)

Horton and Gerrig outlined a memory-based processing model of conversational common ground that provided a description of how speakers could both strategically and automatically gain access to information about others through domain-general memory processes acting over ordinary memory traces. In this article, we revisit this account, reviewing empirical findings that address aspects of this memory-based model. In doing so, we also take the opportunity to clarify what we believe this approach implies about the cognitive psychology of common (...) ground, and just as important, what it does not imply. We also highlight related areas of research demonstrating how general cognitive processes can constrain access to relevant knowledge in ways that shape both language production and comprehension. (shrink)

The article first addresses the importance of cognitive modeling, in terms of its value to cognitive science (as well as other social and behavioral sciences). In particular, it emphasizes the use of cognitive architectures in this undertaking. Based on this approach, the article addresses, in detail, the idea of a multi-level approach that ranges from social to neural levels. In physical sciences, a rigorous set of theories is a hierarchy of descriptions/explanations, in which causal relationships among (...) entities at a high level can be reduced to causal relationships among simpler entities at a more detailed level. We argue that a similar hierarchy makes possible an equally productive approach toward cognitive modeling. The levels of models that we conceive in relation to cognition include, at the highest level, sociological/anthropological models of collective human behavior, behavioral models of individual performance, cognitive models involving detailed mechanisms, representations, and processes, as well as biological/physiological models of neural circuits, brain regions, and other detailed biological processes. (shrink)

Product differentiation has emerged as a central dynamic in contemporary agrofood systems. Departure from the mode of standardization emblematic of agrofood modernization raises questions about future technical trajectories and the ways in which learning will be sustained. This article examines two innovation trajectories: the rapid coupling of biotechnologies and information technologies to yield products differentiated by constituent components—a model based on a cognitive logic of decomposition/ recomposition—and the proliferation of product networks that mobilize distinctive, localized resources to create (...) complete identities—a model based on a cognitive logic of identity. The article analyzes the information structures—institutional mechanisms that support information exchange and learning—in each of these opposed development paradigms. We find that knowledge creation under each of the logics occurs through mechanisms not recognized within the respective paradigms. On this basis, we derive institutional hybridity as a fundamental resource in systems of innovation. (shrink)

In a range of recent and not so recent work, I have developed a novel semantics of attitude reports on which the notion of an attitudinal object or cognitive product takes center stage, that is, entities such as thoughts claims and decisions. The purpose of this note is to give a brief summary of this account against the background of the standard semantics of attitude reports and to show that the various sorts of criticism that Felappi recently advanced against (...) it are mistaken. (shrink)

The human conceptual system comprises simulated information of sensorimotor experience and linguistic distributional information of how words are used in language. Moreover, the linguistic shortcut hypothesis predicts that people will use computationally cheaper linguistic distributional information where it is sufficient to inform a task response. In a pre‐registered category production study, we asked participants to verbally name members of concrete and abstract categories and tested whether performance could be predicted by a novel measure of sensorimotor similarity (based on (...) an 11‐dimensional representation of sensorimotor strength) and linguistic proximity (based on word co‐occurrence derived from a large corpus). As predicted, both measures predicted the order and frequency of category production but, critically, linguistic proximity had an effect above and beyond sensorimotor similarity. A follow‐up study using typicality ratings as an additional predictor found that typicality was often the strongest predictor of category production variables, but it did not subsume sensorimotor and linguistic effects. Finally, we created a novel, fully grounded computational model of conceptual activation during category production, which best approximated typical human performance when conceptual activation was allowed to spread indirectly between concepts, and when candidate category members came from both sensorimotor and linguistic distributional representations. Critically, model performance was indistinguishable from typical human performance. Results support the linguistic shortcut hypothesis in semantic processing and provide strong evidence that both linguistic and grounded representations are inherent to the functioning of the conceptual system. All materials, data, and code are available at https://osf.io/vaq56/. (shrink)

This essay explores the nature and neurological basis of creativity in technical production. After presenting a model of expert technical cognition based in cognitive anthropology and cognitive psychology, the authors propose that craft production has three inherent sources of novelty — procedural drift, serendipitous error and fiddling. However, these are quite limited in their creative potential, which may help explain the virtual absence of innovation over the long millennia of the Palaeolithic. Innovation can be far (...) more rapid and effective via invention, which requires folk theories of causation and adequate working memory capacity, all fairly recent evolutionary developments. The neurological basis of expert technical cognition lies in well-known cortical and sub-cortical structures, but recent research has established a provocative role for the cerebellum in the formulation of novel arrangements. (shrink)

Social cognition can be based on two contrary axioms that answer the question of whether the society can provide for the universal survival of all of its members. Negative answer (relativist model of society) is more productive methodologically. The key notion here is the technosocial formula of society, the physical meaning of which is that the society as an aggregate of people needs bigger vital space than it can create. The growth of man in nature was the result not (...) only of the supplanting of other species but also of the intraspecific struggle which became the main factor of the development process. The criterion of social progress is reduced in the end to only one index – the ability of society’s survival and it doesn’t have any absolute value in space and time. (shrink)

Probabilistic methods are providing new explanatory approaches to fundamental cognitive science questions of how humans structure, process and acquire language. This review examines probabilistic models defined over traditional symbolic structures. Language comprehension and production involve probabilistic inference in such models; and acquisition involves choosing the best model, given innate constraints and linguistic and other input. Probabilistic models can account for the learning and processing of language, while maintaining the sophistication of symbolic models. A recent (...) burgeoning of theoretical developments and online corpus creation has enabled large models to be tested, revealing probabilistic constraints in processing, undermining acquisition arguments based on a perceived poverty of the stimulus, and suggesting fruitful links with probabilistic theories of categorization and ambiguity resolution in perception. (shrink)

The article aims to provide a general framework for assessing and categorizing the cognitive systems of human and non-human animals. Our approach stems from biosemiotic, ethological, and phenomenological investigations into the relations of organisms to one another and to their environment. Building on the analyses of Merleau-Ponty and Portmann, organismal bodies and surfaces are distinguished as the base for sign production and interpretation. Following the concept of modelling systems by Sebeok, we develop a concentric model of human and (...) non-human animal cognition that posits three intertwined spheres: corporeity, social communication, and culture. The model explicitly works with the pluralistic perspective that views the communication and cognition of humans as distinct, but not superior to those of non-human animals. Our position is substantiated by two case studies: the first one focuses on the acquisition and spread of nut-cracking technique among the chimpanzees in the Täi forest, the second one on the communication and cognition of deafblind persons. From an epistemological perspective, our paper is a contribution to contemporary attempts to link biosemiotics and ethology with phenomenological concepts of agency, living bodies, and lifeworld. (shrink)

Theories of grounded and embodied cognition offer a range of accounts of how reasoning and body‐based processes are related to each other. To advance theories of grounded and embodied cognition, we explore the cognitive relevance of particular body states to associated math concepts. We test competing models of action‐cognition transduction to investigate the cognitive relevance of directed actions to students’ mathematical reasoning in the area of geometry. The hypotheses we test include (1) that cognitively relevant directed (...) actions have a direct effect on performance (direct cognitive relevance hypothesis), (2) that cognitively relevant directed actions lead to more frequent production of gestures during explanations, which leads to improved performance (mediated cognitive relevance hypothesis), and (3) that performance effects of directed actions are influenced by the presence or absence of gesture production during mathematical explanations (moderated cognitive relevance hypothesis). We explore these hypotheses in an experiment where high school students (N = 85) evaluated the truth of geometry conjectures after performing cognitively relevant or cognitively irrelevant directed actions while playing a movement‐based video game. Contrary to the direct and mediated cognitive relevance hypotheses, we found no overall differences in performance or gesture production between relevant and irrelevant conditions. Consistent with the moderated cognitive relevance hypothesis, cognitive relevance influenced mathematical performance, as measured by the accuracy of students’ intuitions, insights, and the validity of their proofs, provided that students produced certain kinds of gestures during mathematical explanations (i.e., with explanatory gestures as the moderator). Implications for theories of grounded and embodied cognition and the design of embodied forms of educational interventions are discussed. (shrink)

We present a comprehension‐based computational model of UNIX user skill acquisition and performance in a training context (UNICOM). The work extends a comprehension‐based theory of planning to account for skill acquisition and learning. Individual models of 22 UNIX users were constructed and used to simulate user performance on successive command production problems in a training context. Comparisons of model and the human empirical data result in a high degree of agreement, validating the ability of UNICOM to (...) predict user response to training. (shrink)

Most everyday tasks involve multiple modalities, which raises the question of how the processing of these modalities is coordinated by the cognitive system. In this paper, we focus on the coordination of visual attention and linguistic processing during speaking. Previous research has shown that objects in a visual scene are fixated before they are mentioned, leading us to hypothesize that the scan pattern of a participant can be used to predict what he or she will say. We test this (...) hypothesis using a data set of cued scene descriptions of photo-realistic scenes. We demonstrate that similar scan patterns are correlated with similar sentences, within and between visual scenes; and that this correlation holds for three phases of the language production process (target identification, sentence planning, and speaking). We also present a simple algorithm that uses scan patterns to accurately predict associated sentences by utilizing similarity-based retrieval. (shrink)

In order to communicate effectively with a variety of conversation partners and in a variety of settings, bilingual children must develop language control, the ability to control which language is used for production. Past work has focused on linguistic skills as the limiting factor in children’s ability to control their language choice, while cognitive control has been the focus of adult models of language control. The current study examined the effects of both language ability and cognitive (...) control on language control in 4-6 year old Spanish/English bilingual children with a broad range of language skills, including those with low skills in both languages. To measure language control, children participated in an interactive scripted confederate dialogue paradigm in which they took turns describing picture scenes with video partners who presented themselves as monolingual speakers of English or monolingual speakers of Spanish. The paradigm had two conditions: a single-language context, in which children interacted with only one partner, and a dual-language context, in which children needed to switch between languages to address different partners. The Dimensional Change Card Sort (DCCS) indexed cognitive control. The findings revealed an overall effect of language ability, such that children with lower language skills were more likely to produce words in the language not understood by their conversation partner. There was also an effect of cognitive control on children’s ability to adjust to the dual-language context. Based on these findings, we suggest that a model of language control in children should consider both linguistic and cognitive factors. However, language ability appears to be the main limiting factor, with cognitive control playing a more restricted role in adapting to a dual-language context. (shrink)

This paper argues that neurolinguistics has the potential to yield insights that can feed back into corpus-based Cognitive Linguistics. It starts by discussing how far the cognitive realism of probabilistic statements derived from corpus data currently goes. Against this background, it argues that the cognitive realism of usage-based models could be further enhanced through deeper engagement with neurolinguistics, but also highlights a number of common misconceptions about what neurolinguistics can and cannot do for linguistic (...) theorizing. (shrink)

This paper presents an attempt to bridge the gap between logical and cognitive treatments of strategic reasoning in games. There have been extensive formal debates about the merits of the principle of backward induction among game theorists and logicians. Experimental economists and psychologists have shown that human subjects, perhaps due to their bounded resources, do not always follow the backward induction strategy, leading to unexpected outcomes. Recently, based on an eye-tracking study, it has turned out that even human (...) subjects who produce the outwardly correct ‘backward induction answer’ use a different internal reasoning strategy to achieve it. The paper presents a formal language to represent different strategies on a finer-grained level than was possible before. The language and its semantics help to precisely distinguish different cognitive reasoning strategies, that can then be tested on the basis of computational cognitive models and experiments with human subjects. The syntactic framework of the formal system provides a generic way of constructing computational cognitive models of the participants of the Marble Drop game. (shrink)

In order to investigate cognition fundamental assumptions must be made about what, in general terms, it is. In cognitive science it is usually assumed that cognition is computational and representational. There have been well known disputes over these assumptions, with rival claims that cognition is dynamical, situated and embodied. In this paper I emphasize the relations between cognition and control. I present a model of cognition that makes the claim that it is a form of high-order control, and I (...) argue that viewing cognition as high-order control could be a useful framework assumption for cognitive science. Cognition has many aspects and different concepts can emphasize different aspects of it. Computational and representational assumptions have been very productive, and dynamical and embodied assumptions have also been productive, though to a much lesser extent so far. Control, however, has received insufficient attention in cognition science. The model I propose is based on a point that few will dispute, namely that control of behavior is the ultimate function of cognition. Bringing this to the foreground can be productive by highlighting the ways in which cognition is structured in relation to this control function. If this perspective is valuable it will be because the control function has a highly structuring effect on cognition. As a result a control-based perspective will predict many features of cognition and yield a coherent, integrated picture. (shrink)

This paper discusses the epistemological and methodological bases of a scientific theory of meaning and proposes a detailed version of a formal theory of argumentation based on Anscombre and Ducrot's conception. Argumentation is shown to be a concept which is not exclusively pragmatic, as it is usually believed, but has an important semantic body. The bridge between the semantic and pragmatic aspects of argumentation consists in a set of gradual inference rules, called topoi, on which the argumentative movement is (...) based. The content of each topos is determined at the pragmatic level, while the constraints on the forms of the topoi attached to a sentence are determined at the semantic level. Applications and possible applications toartificial intelligence and to cognitive sciences are discussed. In particular, the gradual models used to account for argumentation are shown to be extremely promising for Knowledge management, a discipline which includes knowledge acquisition, knowledge representation, transmission of knowledge (communication, interfaces, etc.), knowledge production (decision help, reasoning, etc.). A first formal model is presented and discussed: it is shown in details how it accounts for most of the argumentative features of sentences containing but, little and a little, and how it can be extended to describe sentences containing other argumentative connectives. However, this model is shown to be too simple and to violate the compositionality principle, which is shown, in the first section, to bean important methodological principle for any scientific theory. After a detailed analysis of the possible reasons for this violation, an improved model is proposed and its adequacy is discussed. (shrink)

Several accounts of representation in cognitive systems have recently been proposed. These look for a theory that will establish how a representation comes to have a certain content, and how these representations are used by cognitive systems. Covariation accounts are unsatisfactory, as they make intelligent reasoning and cognition impossible. Cummins' interpretation-based account cannot explain the distinction between cognitive and non-cognitive systems, nor how certain cognitive representations appear to have intrinsic meaning. Cognitive systems can (...) be defined as model-constructers, or systems that use information from interpreted models as arguments in the functions they execute. An account based on this definition solves many of the problems raised by the earlier proposals. (shrink)

Formal models of argumentation have been investigated in several areas, from multi-agent systems and artificial intelligence (AI) to decision making, philosophy and law. In artificial intelligence, logic-based models have been the standard for the representation of argumentative reasoning. More recently, the standard logic-based models have been shown equivalent to standard connectionist models. This has created a new line of research where (i) neural networks can be used as a parallel computational model for argumentation and (...) (ii) neural networks can be used to combine argumentation, quantitative reasoning and statistical learning. At the same time, non-standard logic models of argumentation started to emerge. In this paper, we propose a connectionist cognitive model of argumentation that accounts for both standard and non-standard forms of argumentation. The model is shown to be an adequate framework for dealing with standard and non-standard argumentation, including joint-attacks, argument support, ordered attacks, disjunctive attacks, meta-level attacks, self-defeating attacks, argument accrual and uncertainty. We show that the neural cognitive approach offers an adequate way of modelling all of these different aspects of argumentation. We have applied the framework to the modelling of a public prosecution charging decision as part of a real legal decision making case study containing many of the above aspects of argumentation. The results show that the model can be a useful tool in the analysis of legal decision making, including the analysis of what-if questions and the analysis of alternative conclusions. The approach opens up two new perspectives in the short-term: the use of neural networks for computing prevailing arguments efficiently through the propagation in parallel of neuronal activations, and the use of the same networks to evolve the structure of the argumentation network through learning (e.g. to learn the strength of arguments from data). (shrink)