Cognition is predominantly seen as information processing in multidisciplinary landscape of cognition studies, despite having had a formidable opposition from embodied and embedded perspectives in the last few decades. This paper analyses cognitive processes involved in different task domains of Kashmiri carpet weaving practice from the theoretical frameworks of cognitivism and situated-distributed cognition. After introducing the practice and its task domains (Section −1), paradigmatic cognitive activities involved in them are discussed and how these are explained by the two theoretical paradigms (...) and why the latter framework explains these processes better (Section-2). During this discussion (Section – 3), a way via culture impacts the practice and resultant cognitive operations turns out to be workspace of the actors. (shrink)

Because visual narratives demand complex inference abilities, they can potentially be used as a tool for developing inferential skills in other domains, like reading. The Inferential Language Comprehension (iLC) Framework proposes an approach to using visual narratives in educational settings to sponsor inference skills by building on cognitive, developmental, and language research.

Understanding how people comprehend visual narratives (including picture stories, comics, and film) requires the combination of traditionally separate theories that span the initial sensory and perceptual processing of complex visual scenes, the perception of events over time, and comprehension of narratives. Existing piecemeal approaches fail to capture the interplay between these levels of processing. Here, we propose the Scene Perception & Event Comprehension Theory (SPECT), as applied to visual narratives, which distinguishes between front-end and back-end cognitive processes. Front-end processes occur (...) during single eye fixations and are comprised of attentional selection and information extraction. Back-end processes occur across multiple fixations and support the construction of event models, which reflect understanding of what is happening now in a narrative (stored in working memory) and over the course of the entire narrative (stored in long-term episodic memory). We describe relationships between front- and back-end processes, and medium-specific differences that likely produce variation in front-end and back-end processes across media (e.g., picture stories vs. film). We describe several novel research questions derived from SPECT that we have explored. By addressing these questions, we provide greater insight into how attention, information extraction, and event model processes are dynamically coordinated to perceive and understand complex naturalistic visual events in narratives and the real world. (shrink)

A response to a recent critique by Cem Bozşahin of the theory of syntactic semantics as it applies to Helen Keller, and some applications of the theory to the philosophy of computer science.

ABSTRACTThe purpose of the study was to investigate differences in writers’ abilities to perceive global text organisation in self-, peer- and teacher/researcher-authored essays. Eight student part...

There is today much interest in research of neuronal substrata in metaphor processing. It has been suggested that the right hemisphere yields a key role in the comprehension of figurative language and, particularly, in metaphors. Figurative language is included in pragmatics, a branch of linguistics that researches the use of language, in opposition to the study of the system of language. There lingers, though, an open debate in respect to the identification of the specific aspects concerning semantics, as opposed to (...) those dominated by pragmatics. Can studies from neuronal correlates clarify questions that relate to semantics/pragmatics representation? I shall analyze neuroscientific developments about implicit language to attempt to understand, in section 2, scientific techniques available and more suitable to the phenomenology of the act of understanding an implicit, figurative or implicated message in a certain language game. To do so, I shall start by reviewing the studies in philosophy of language, and accommodate the development of the research in pragmatics underlying metaphor, particularly, in Philosophical Investigations by Wittgenstein. Finally, I discuss the possibility of interpretative capabilities being socioculturally grounded. I expect this methodological analysis to contribute to the enlightenment of the problem of phenomenology of intersubjective pragmatics, and to its future experimental paradigms. (shrink)

Contemporary semantic theories can be classified along two dimensions: (i) the way and time-course in which contextual factors influence sentence truth-conditions; and (ii) whether and to what extent comprehension involves sensory, motor and emotional processes. In order to explore this theoretical space, our ERP study investigates the time-course of the interaction between the lexically specified telic component of a noun (the function of the object to which the noun refers to, e.g., a funnel is generally used to pour liquids into (...) containers) and an ad-hoc affordance contextually induced by the situation described in the discourse. We found that, if preceded by a neutral discourse context, a verb incongruent with the noun’s telic component as in “She uses the funnel to hang her coat” elicited an enhanced N400 compared to a congruent verb as in “She uses the funnel to pour water into a container”. However, if the situation introduced in the preceding discourse induced a new function for the object as an ad-hoc affordance (e.g., the funnel is glued to the wall and the agent wants to hang the coat), we observed a crossing-over regarding the direction of the N400 effect: comparing the ad-hoc affordance-inducing context with the neutral context, the N400 for the incongruent verb was significantly reduced, whereas the N400 for the congruent verb was significantly enhanced. We explain these results as a consequence of the incorporation of the contextually triggered ad-hoc affordance into the meaning of the noun. Combining these results with an analysis of semantic similarity values between test sentences and contexts, we argue that one possibility is that the incorporation of an ad-hoc affordance may be explained on the basis of the mental simulation of concurrent motor information. (shrink)

A long tradition of research including classical rhetoric, esthetics and poetics theory, formalism and structuralism, as well as current perspectives in (neuro)cognitive poetics has investigated structural and functional aspects of literature reception. Despite a wealth of literature published in specialized journals like Poetics, however, still little is known about how the brain processes and creates literary and poetic texts. Still, such stimulus material might be suited better than other genres for demonstrating the complexities with which our brain constructs the world (...) in and around us, because it unifies thought and language, music and imagery in a clear, manageable way, most often with play, pleasure, and emotion (Schrott and Jacobs, 2011 ). In this paper, I discuss methods and models for investigating the neuronal and cognitive-affective bases of literary reading together with pertinent results from studies on poetics, text processing, emotion, or neuroaesthetics, and outline current challenges and future perspectives. (shrink)

Causal reasoning is one of our most central cognitive competencies, enabling us to adapt to our world. Causal knowledge allows us to predict future events, or diagnose the causes of observed facts. We plan actions and solve problems using knowledge about cause-effect relations. Without our ability to discover and empirically test causal theories, we would not have made progress in various empirical sciences. In the past decades, the important role of causal knowledge has been discovered in many areas of cognitive (...) psychology. Despite the ubiquity of causal reasoning, textbooks of cognitive psychology have neglected this growing field. The goal of The Oxford Handbook of Causal Reasoning is to fill this gap. The handbook brings together the leading researchers in the field of causal reasoning and offers state-of-the-art presentations of theories and research. It provides introductions of competing theories of causal reasoning, and discusses its role in various cognitive functions and domains. The final section presents research from neighboring fields. 1. Causal Reasoning: An Introduction Michael R. Waldmann Part I: Theories of Causal Cognition 2. Associative Accounts of Causal Cognition Mike E. Le Pelley, Oren Griffiths, and Tom Beesley 3. Rules of Causal Judgment: Mapping Statistical Information Onto Causal Beliefs José C. Perales, Andrés Catena, Antonio Cándido, and Antonio Maldonado 4. The Inferential Reasoning Theory of Causal Learning: Toward a Multi- Process Propositional Account Yannick Boddez, Jan De Houwer, and Tom Beckers 5. Causal Invariance as an Essential Constraint for Creating a Causal Representation of the World: Generalizing the Invariance of Causal Power Patricia W. Cheng and Hongjing Lu 6. The Acquisition and Use of Causal Structure Knowledge Benjamin Margolin Rottman 7. Formalizing Prior Knowledge in Causal Induction Thomas L. Griffiths 8. Causal Mechanisms Samuel G. B. Johnson and Woo-kyoung Ahn 9. Force Dynamics and Causation Phillip Wolff and Robert Thorstad 10. Mental Models and Causation P. N. Johnson- Laird and Sangeet S. Khemlani 11. Pseudocontingencies Klaus Fiedler and Florian Kutzner 12. Singular Causation David Danks 13. Cognitive Neuroscience of Causal Reasoning Joachim T. Operskalski and Aron K. Barbey Part II: Basic Cognitive Functions 14. Visual Impressions of Causality Peter White 15. Goal-Directed Actions Bernhard Hommel 16. Planning and Control Magda Osman 17. Reinforcement Learning and Causal Models Samuel J. Gershman 18. Causation and the Probability of Causal Conditionals David E. Over 19. Causal Models and Conditional Reasoning Mike Oaksford and Nick Chater 20. Concepts as Causal Models: Categorization Bob Rehder 21. Concepts as Causal Models: Induction Bob Rehder 22. Causal Explanation Tania Lombrozo and Nadya Vasilyeva 23. Diagnostic Reasoning Björn Meder and Ralf Mayrhofer 24. Inferring Causal Relations by Analogy Keith J. Holyoak and Hee-Seung Lee 25. Causal Argument Ulrike Hahn, Roland Bluhm, and Frank Zenker 26. Causality in Decision- Making York Hagmayer and Philip M. Fernbach Part III: Domains of Causal Reasoning 27. Intuitive Theories Tobias Gerstenberg and Joshua B. Tenenbaum 28. Space, Time, and Causality Marc J. Buehner 29. Causation in Legal and Moral Reasoning David A. Lagnado and Tobias Gerstenberg 30. The Role of Causal Knowledge in Reasoning About Mental Disorders Woo-kyoung Ahn, Nancy S. Kim, and Matthew S. Lebowitz 31. Causality and Causal Reasoning in Natural Language Torgrim Solstad and Oliver Bott 32. Social Attribution and Explanation Denis Hilton Part IV: Development, Phylogeny, and Culture 33. The Development of Causal Reasoning Paul Muentener and Elizabeth Bonawitz 34. Causal Reasoning in Non-Human Animals Christian Schloegl and Julia Fischer 35. Causal Cognition and Culture Andrea Bender, Sieghard Beller, and Douglas L. Medin. (shrink)

A computational model of inference during story comprehension is presented, in which story situations are represented distributively as points in a high‐dimensional “situation‐state space.” This state space organizes itself on the basis of a constructed microworld description. From the same description, causal/temporal world knowledge is extracted. The distributed representation of story situations is more flexible than Golden and Rumelhart's [Discourse Proc 16 (1993) 203] localist representation.A story taking place in the microworld corresponds to a trajectory through situation‐state space. During the (...) inference process, world knowledge is applied to the story trajectory. This results in an adjusted trajectory, reflecting the inference of propositions that are likely to be the case. Although inferences do not result from a search for coherence, they do cause story coherence to increase. The results of simulations correspond to empirical data concerning inference, reading time, and depth of processing.An extension of the model for simulating story retention shows how coherence is preserved during retention without controlling the retention process. Simulation results correspond to empirical data concerning story recall and intrusion. (shrink)

This paper is a commentary on the target article by Lokendra Shastri & Venkat Ajjanagadde [S&A]: “From simple associations to systematic reasoning: A connectionist representation of rules, variables and dynamic bindings using temporal synchrony” in same issue of the journal, pp.417–451. -/- It puts S&A's temporal-synchrony binding method in a broader context, comments on notions of pointing and other ways of associating information - in both computers and connectionist systems - and mentions types of reasoning that are a challenge to (...) S&A's system (amongst many other connectionist systems, recognizing that S&A's is much more capable as regards reasoning than most other contemporary systems). (shrink)

Although for many years a sharp distinction has been made in language research between rules and words—with primary interest on rules—this distinction is now blurred in many theories. If anything, the focus of attention has shifted in recent years in favor of words. Results from many different areas of language research suggest that the lexicon is representationally rich, that it is the source of much productive behavior, and that lexically specific information plays a critical and early role in the interpretation (...) of grammatical structure. But how much information can or should be placed in the lexicon? This is the question I address here. I review a set of studies whose results indicate that event knowledge plays a significant role in early stages of sentence processing and structural analysis. This poses a conundrum for traditional views of the lexicon. Either the lexicon must be expanded to include factors that do not plausibly seem to belong there; or else virtually all information about word meaning is removed, leaving the lexicon impoverished. I suggest a third alternative, which provides a way to account for lexical knowledge without a mental lexicon. (shrink)

Negotiation between two individuals is a common task that typically involves two goals: maximize individual outcomes and obtain an agreement. However, research on the simplest negotiation tasks demonstrates that although naive subjects can be induced to improve their performance, they are often no more likely to achieve fully optimal solutions. The present study tested the prediction that a decrease in a particular type of argumentative behavior, substantiation, would result in an increase in optimal agreements. As substantiation behaviors depend primarily on (...) supplied content of the negotiation task, it was also predicted that substantiation behavior would be reduced by curtailing the content. A 2 × 2 experimental design was employed, where both negotiation tactics (list of tactics present versus absent) and negotiation task content (high versus low) were varied to determine the processes leading beyond solution improvement to solution optimality. Sixty‐one dyads engaged in a two‐party, four‐issue negotiation task. All negotiations were videotaped and analyzed. Although the list of negotiation tactics resulted in improved performance, only the content manipulation resulted in a significant increase in dyads achieving optimal solutions. Analyses of the coded protocols indicated that the key difference in achieving optimality was a reduction in persistent substantiation‐related operators (substantiation, along with single‐issue preferences and procedures) and an increase in a complex macro‐operator, multi‐issue offers that reduced the problem space, facilitating the search for optimality. (shrink)

This paper provides a computational characterization of coherence that applies to a wide range of philosophical problems and psychological phenomena. Maximizing coherence is a matter of maximizing satisfaction of a set of positive and negative constraints. After comparing five algorithms for maximizing coherence, we show how our characterization of coherence overcomes traditional philosophical objections about circularity and truth.

Deliberate contextual vocabulary acquisition (CVA) is a reader’s ability to figure out a (not the) meaning for an unknown word from its “context”, without external sources of help such as dictionaries or people. The appropriate context for such CVA is the “belief-revised integration” of the reader’s prior knowledge with the reader’s “internalization” of the text. We discuss unwarranted assumptions behind some classic objections to CVA, and present and defend a computational theory of CVA that we have adapted to a new (...) classroom curriculum designed to help students use CVA to improve their reading comprehension. (shrink)

Cognitive architectures are unified theories of cognition that take the form of computational formalisms. They support computational models that collectively account for large numbers of empirical regularities using small numbers of computational mechanisms. Empirical coverage and parsimony are the most prominent criteria by which architectures are designed and evaluated, but they are not the only ones. This paper considers three additional criteria that have been comparatively undertheorized. (a) Successful architectures possess subjective and intersubjective meaning, making cognition comprehensible to individual cognitive (...) scientists and organizing groups of like-minded cognitive scientists into genuine communities. (b) Successful architectures provide idioms that structure the design and interpretation of computational models. (c) Successful architectures are strange: They make provocative, often disturbing, and ultimately compelling claims about human information processing that demand evaluation. (shrink)

Conceptual knowledge is acquired through recurrent experiences, by extracting statistical regularities at different levels of granularity. At a fine level, patterns of feature co-occurrence are categorized into objects. At a coarser level, patterns of concept co-occurrence are categorized into contexts. We present and test CONCAT, a connectionist model that simultaneously learns to categorize objects and contexts. The model contains two hierarchically organized CALM modules (Murre, Phaf, & Wolters, 1992). The first module, the Object Module, forms object representations based on co-occurrences (...) between features. These representations are used as input for the second module, the Context Module, which categorizes contexts based on object co-occurrences. Feedback connections from the Context Module to the Object Module send activation from the active context to those objects that frequently occur within this context. We demonstrate that context feedback contributes to the successful categorization of objects, especially when bottom-up feature information is degraded or ambiguous. (shrink)

This experiment investigated the effect of format (line vs. bar), viewers’ familiarity with variables, and viewers’ graphicacy (graphical literacy) skills on the comprehension of multivariate (three variable) data presented in graphs. Fifty-five undergraduates provided written descriptions of data for a set of 14 line or bar graphs, half of which depicted variables familiar to the population and half of which depicted variables unfamiliar to the population. Participants then took a test of graphicacy skills. As predicted, the format influenced viewers’ interpretations (...) of data. Specifically, viewers were more likely to describe x–y interactions when viewing line graphs than when viewing bar graphs, and they were more likely to describe main effects and “z–y” (the variable in the legend) interactions when viewing bar graphs than when viewing line graphs. Familiarity of data presented and individuals’ graphicacy skills interacted with the influence of graph format. Specifically, viewers were most likely to generate inferences only when they had high graphicacy skills, the data were familiar and thus the information inferred was expected, and the format supported those inferences. Implications for multivariate data display are discussed. (shrink)

This target article presents a new computational theory of explanatory coherence that applies to the acceptance and rejection of scientific hypotheses as well as to reasoning in everyday life, The theory consists of seven principles that establish relations of local coherence between a hypothesis and other propositions. A hypothesis coheres with propositions that it explains, or that explain it, or that participate with it in explaining other propositions, or that offer analogous explanations. Propositions are incoherent with each other if they (...) are contradictory, Propositions that describe the results of observation have a degree of acceptability on their own. An explanatory hypothesis is acccpted if it coheres better overall than its competitors. The power of the seven principles is shown by their implementation in a connectionist program called ECHO, which treats hypothesis evaluation as a constraint satisfaction problem. Inputs about the explanatory relations are used to create a network of units representing propositions, while coherende and incoherence relations are encoded by excitatory and inbihitory links. ECHO provides an algorithm for smoothly integrating theory evaluation based on considerations of explanatory breadth, simplicity, and analogy. It has been applied to such important scientific cases as Lovoisier's argument for oxygen against the phlogiston theory and Darwin's argument for evolution against creationism, and also to cases of legal reasoning. The theory of explanatory coherence has implications for artificial intelligence, psychology, and philosophy. (shrink)

Glenberg's account falls short in several respects. Besides requiring clearer explication of basic concepts, his account fails to recognize the autonomous nature of perception. His account of what is remembered, and its description, is too static. His strictures against connectionist modeling might be overcome by combining the notions of psychological space and principled learning in an embodied and situated network.

We argue that existing learning algorithms are often poorly equipped to solve problems involving a certain type of important and widespread regularity that we call “type-2 regularity.” The solution in these cases is to trade achieved representation against computational search. We investigate several ways in which such a trade-off may be pursued including simple incremental learning, modular connectionism, and the developmental hypothesis of “representational redescription.”.

Human agents draw a variety of inferences effortlessly, spontaneously, and with remarkable efficiency – as though these inferences were a reflexive response of their cognitive apparatus. Furthermore, these inferences are drawn with reference to a large body of background knowledge. This remarkable human ability seems paradoxical given the complexity of reasoning reported by researchers in artificial intelligence. It also poses a challenge for cognitive science and computational neuroscience: How can a system of simple and slow neuronlike elements represent a large (...) body of systemic knowledge and perform a range of inferences with such speed? We describe a computational model that takes a step toward addressing the cognitive science challenge and resolving the artificial intelligence paradox. We show how a connectionist network can encode millions of facts and rules involving n-ary predicates and variables and perform a class of inferences in a few hundred milliseconds. Efficient reasoning requires the rapid representation and propagation of dynamic bindings. Our model (which we refer to as SHRUTI) achieves this by representing (1) dynamic bindings as the synchronous firing of appropriate nodes, (2) rules as interconnection patterns that direct the propagation of rhythmic activity, and (3) long-term facts as temporal pattern-matching subnetworks. The model is consistent with recent neurophysiological evidence that synchronous activity occurs in the brain and may play a representational role in neural information processing. The model also makes specific psychologically significant predictions about the nature of reflexive reasoning. It identifies constraints on the form of rules that may participate in such reasoning and relates the capacity of the working memory underlying reflexive reasoning to biological parameters such as the lowest frequency at which nodes can sustain synchronous oscillations and the coarseness of synchronization. (shrink)