Connectionist Modeling

A “Triangle Model” of Chinese Reading Jianfeng Yang ([email protected]) State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, China Jason D Zevin ([email protected]) Sackler Institute, Weill Cornell Medical College, USA ...

the Atlantic, as seen from the South and the top, in On a Marché sur la Lune, Hergé, with the young reporter Tintin, the great Professeur Tryphon Tournesol, and poor treacherous Wolf ouverture, the grand Mare Nostrum, then a Mare Clausum: Tordesilhas XVI Century Portuguese commercial routes (in blue) and "Spanish" ones (in white)

system can selectively attend to the sensorimotor information input that it receives from naturomorphic system The sensorimotor input from the naturomorphic system may be in the form of visuomotor 'imagery' experienced as virtual reality, and it may include 'inner speech' or the 'inner voice'

Analogical inference depends on systematic substitution of the components of compositional structures. Simple systematic substitution has been achieved in a number of connectionist systems that support binding (the ability to create connectionist representations of the combination of component representations). These systems have used various implementations of two generic composition operators: bind() and bundle(). This paper introduces a novel implementation of the bind() operator that is simple, can be efficiently implemented, and highlights the relationship between retrieval queries and analogical mapping. A frame of role/filler bindings can easily be represented using bind() and bundle(). However, typical binding systems are unable to adequately represent multiple frames and arbitrary nested compositional structures. A novel family of representational operators (called braid()) is introduced to address these problems. Other binding systems make the strong assumption that the roles and fillers are disjoint in order to avoid ambiguities inherent in their representational idioms. The braid() operator can be used to avoid this assumption. The new representational idiom suggests how the cognitive processes of bottom-up and top-down object recognition might be implemented. These processes depend on analogical mapping to integrate disjoint representations and drive perceptual search.

This paper presents a brief, informal outline followed by a formal statement of an elemental associative learning model first described by McLaren, Kaye, and Mackintosh (1989). The model assumes representation of stimuli by sets of elements (i.e., microfeatures) and a set of associative algorithms that incorporate the following: real-time simulation of learning; an error-correcting learning rule; weight decay that distinguishes between transient and permanent associations; and modulation of associative learning that gives high salience to and, hence, promotes rapid learning with novel, unpredicted stimuli and reduces the salience for a stimulus as its error term declines. The model is applied in outline fashion to some of the basic phenomena of simple conditioning and, in greater detail, to the phenomena of latent inhibition and perceptual learning. A detailed account of generalization and discrimination will be provided in a later pape

Blurb (Book Description from the Publisher website):
Cognitive Design for Artificial Minds explains the crucial role that human cognition research plays in the design and realization of artificial intelligence systems, illustrating the steps necessary for the design of artificial models of cognition. It bridges the gap between the theoretical, experimental and technological issues addressed in the context of AI of cognitive inspiration and computational cognitive science.

Beginning with an overview of the historical, methodological and technical issues in the field of Cognitively-Inspired Artificial Intelligence, Lieto illustrates how the cognitive design approach has an important role to play in the development of intelligent AI technologies and plausible computational models of cognition. Introducing a unique perspective that draws upon Cybernetics and early AI principles, Lieto emphasizes the need for an equivalence between cognitive processes and implemented AI procedures, in order to realise biologically and cognitively inspired artificial minds. He also introduces the Minimal Cognitive Grid, a pragmatic method to rank the different degrees of biologically and cognitive accuracy of artificial systems in order project and predict their explanatory power with respect to the natural systems taken as source of inspiration.

Providing a comprehensive overview of cognitive design principles in constructing artificial minds, this text will be essential reading for students and researchers of artificial intelligence and cognitive science.

† Questi appunti sono ricavati dall'introduzione a Paul Smolensky, Il connessionismo tra simboli e neuroni, Marietti, Genova, 1992, traduzione italiana di" On the proper treatment of connectionism", Behavioral and Brain Sciences, 11, 1988, e dal capitolo" Connessionismo", in S. Gensini e A. Rainone (a cura di), Filosofie della Mente. Tradizione e attualità, Carocci, Roma, 2008.

Gives the original MKM model which is updated in McLaren and Mackintosh (2000, 2002). It combines a learning algorithm which has strong similarities to Wagner (1978) but has been re-worked to be real-time and incorporate trace decay. The representational assumptions are based on Estes (1950) stimulus sampling theory, but again are re-worked so that the whole model can be cast within the PDP framework.

It is often thought that consciousness has a qualitative dimension that cannot be tracked by science. Recently, however, some philosophers have argued that this worry stems not from an elusive feature of the mind, but from the special nature of the concepts used to describe conscious states. Marc Champagne draws on the neglected branch of philosophy of signs or semiotics to develop a new take on this strategy. 

The term “semiotics” was introduced by John Locke in the modern period – its etymology is ancient Greek, and its theoretical underpinnings are medieval. Charles Sanders Peirce made major advances in semiotics, so he can act as a pipeline for these forgotten ideas. Most philosophers know Peirce as the founder of American pragmatism, but few know that he also coined the term “qualia,” which is meant to capture the intrinsic feel of an experience. Since pragmatic verification and qualia are now seen as conflicting commitments, Champagne endeavors to understand how Peirce could (or thought he could) have it both ways. The key, he suggests, is to understand how humans can insert distinctions between features that are always bound.

Recent attempts to take qualities seriously have resulted in versions of panpsychism, but Champagne outlines a more plausible way to achieve this. So, while semiotics has until now been the least known branch of philosophy ending in –ics, his book shows how a better understanding of that branch can move one of the liveliest debates in philosophy forward.

This paper is intended to elucidate some implications of usage-based linguistic theory for statistical and computational models of language acquisition, focusing on morphology and morphophonology. I discuss the need for grammar (a.k.a. abstraction), the contents of individual grammars (a potentially infinite number of constructions, paradigmatic mappings and predictive relationships between phonological units), the computational characteristics of constructions (complex non-crossover interactions among partially redundant features), resolution of competition among constructions (probability matching), and the need for multimodel inference in modeling internal grammars underlying the linguistic performance of a community.

Connectionist models of sentence processing must learn to behave systematically by generalizing from a small training set. To what extent recurrent neural networks manage this generalization task is investigated. In contrast to Van der Velde et al. (2004), it is found that simple recurrent networks do show so-called weak combinatorial systematicity, although their performance remains limited. It is argued that these limitations arise from overfitting in large networks. Generalization can be improved by increasing the size of the recurrent layer without training its connections, thereby combining a large short-term memory with a small long-term memory capacity. Performance can be improved further by increasing the number of word types in the training set.

Valerjev, P. (2006). Povijest i perspektiva razvoja umjetne inteligencije u istraživanju uma. In M. S. Žebec, G. Sabol, M. Šakić, & M. Kotrla Topić (Eds.), Mozak i um - Trajni izazov čovjeku (pp. 105-122). Zagreb: Institut društvenih znanosti Ivo Pilar.

Svrha ovog teksta jest da pruži sažet uvod u granu računalne i kognitivne znanosti koju nazivamo umjetna inteligencija. U početku se već ukazuje na problematičnost definicije te raznolikost ciljeva i smjernica istraživanja koja su pokrivena nazivom umjetna inteligencija. Nadalje, ukratko je opisana njena povijest, trenutno stanje slučaja kao i ono što od nje možemo očekivati u bliskoj budućnosti. U cijelom tekstu naglasak je postavljen na onaj aspekt umjetne inteligencije koji je sastavni dio kognitivne znanosti – znanosti o inteligentnim sustavima. Takva primjena umjetne inteligencije naziva se kognitivno modeliranje i ima za zadatak razvoj takvih sustava koji simuliraju određeni aspekt kognicije na način na koji on funkcionira u prirodnom kognitivnom sustavu – najčešće ljudskom. Opisana su dva temeljna pristupa u kognitivnom modeliranju – simbolizam i konekcionizam i prikazani su adekvatni primjeri takvih arhitektura. Konačno, umjetna inteligencija je problematizirana s filozofskih gledišta navođenjem glavnih kritika i nedostataka upućenih mogućnosti stvaranja "mislećih strojeva" te iz socijalnog, pravnog i etičkog gledišta pri razmatranju mogućnosti što bi pojava "pravih" umjetnih inteligentnih bića mogla napraviti društvu budućnosti.

The Science of Language, published in the sixth decade of Noam Chomsky’s linguistic career, defends views that are visibly out of touch with recent research in formal linguistics,
developmental child psychology, computational modeling of language acquisition, and language evolution. I argue that the poor quality of this volume is representative of the serious shortcomings of Chomsky’s recent scholarship, especially of his criticism of and contribution to debates about language evolution. Chomsky creates the impression that he is quoting titbits of a massive body of scientific work he has conducted or is intimately familiar with. Yet his speculations reveal a lack of even basic understanding of biology, and an unwillingness to engage seriously with the relevant literature. At the same time, he ridicules the work of virtually all other theorists, without spelling out the views he disagrees with. A critical analysis of the ‘Galilean method’ demonstrates that Chomsky uses appeal to authority to insulate his own proposals against falsification by empirical counter-evidence. This form of discourse bears no serious relation to the way science proceeds.

A P ter verkrijging van de graad van doctor aan de Universiteit van Amsterdam op gezag van de Rector Magni cus prof. dr. D. C. van den Boom ten overstaan van een door het college voor promoties ingestelde commissie, in het openbaar te verdedigen in de Agnietenkapel der Universiteit op woensdag juli , te . uur door Hartmut Fitz geboren te Urach, Duitsland

By pointing to deep philosophical confusions endemic to cognitive science, Wittgenstein might seem an enemy of computational approaches. We agree (with Mills 1993) that while Wittgenstein would reject the classicist’s symbols and rules approach, his observations align well with connectionist or neural network approaches. While many connectionisms that dominated the later twentieth century could fall prey to criticisms of biological, pedagogical, and linguistic implausibility, current connectionist approaches can resolve those problems in a Wittgenstein-friendly manner. We (a) present the basics of a Vector Symbolic Architecture formalism, inspired by Smolensky (1990), and indicate how high-dimensional vectors can operate in a context-sensitive and object-independent manner in biologically plausible time scales, reflecting Wittgenstein’s notions of language-games and family resemblance; we (b) show how “soft” symbols for such a formalism can be formed with plausible learning cycles u...

Contemporary accounts of the possession conditions for basic logical concepts rely on either the subject being primitively compelled by transitions that are isomorphic to the introduction and elimination rules for the logical constant or the rules are an innate property of the subject’s language of thought. Drawing from Heidegger’s work in the philosophy of logic and Boghossian’s work on inference, I argue that any attempt to explain logical inference as a form of rule following will lead to arguments for the impossibility of logic or an infinite regress. An under-explored alternative to defining all inferences as instances of following a rule is to take some inferences as basic – inferences that do not follow rules and provide a starting point for our inferential practices. I propose that neural networks can provide a useful model for understanding basic inferences as a primitive ability since they show how one can infer in accordance with a logical principle without necessarily following a rule. Determining whether a subject possesses a basic logical concept is arrived at then by interpreting their inferential behaviour.

Traditional artificial intelligence studies generally approach the problem of representing knowledge following the so-called knowledge representation hypothesis, as formulated by Brian Smith. More recently the development of the connectionist paradigm has questioned the symbolic approach to the study of the mind bringing about a more articulated view of the problem. This article singles out five possible approaches to the problem of knowledge representation in cognitive science: compositional symbolic approaches, local non-compositional approaches, distributed non compositional approaches, cognitive subsymbolic approaches and "neural" subsymbolic approaches. In particular, in the subsymbolic cognitive approach the elements that make up the representation system are not symbols with an ascribed meaning nor do they correspond to anatomic entities at a neurological level; rather they are to be considered as "theoretical constructs" of a theory of the cognitive level which permit the deduction (in the sense of "computation") of cognitive behaviours which cannot be otherwise modelled. We consider the development of models of this kind to be essential to a computational approach to the problem of reference without hypothesizing "magical qualities" of the mind (in the sense of assuming a necessary connection between mental symbols and their referents), while remaining within a functionalist vision, in the wider sense, which does not make reference to the specific physical properties of the neural hardware.

A kétnyelvűségre irányuló pszicholingvisztikai kutatások egyik központi problémája a kétnyelvűség kialakulásának kérdése. A korai kétnyelvűség kapcsán alapvetően két elképzelés áll egymással szemben. Az egységes nyelvi rendszer hipotézis képviselői úgy vélik, hogy a születésétől fogva két nyelvnek kitett gyerek egy hibrid rendszert kezd el használni, amely a fejlődés során fokozatosan szétválik. Az elkülönült nyelvi rendszerek hipotézis szerint viszont a születéstől két nyelvnek kitett gyerek korán különbséget tud tenni a két nyelvi rendszer között, s a két nyelv elsajátítása során a nyelvek egymástól függetlenül fejlődnek. Előadásomban azt igyekszem bemutatni, hogy miként értelmezhető a kétnyelvű nyelvelsajátítás problematikája a közösségi tapasztalatokat is szem előtt tartó konnekcionista közelítésmóddal.

There is a vast literature within philosophy of artificial intelligence that focuses on the architecture of cognition, yet hardly touches on epistemological questions. There is also a growing body of work in philosophy of science about models and simulations that hardly mentions examples from cognitive science. Here these discussions are connected. Insights developed in the models and simulations literature about the importance of idealization provide a way of understanding the neural implausibility of connectionist networks. Insights from neurocognitive modeling shed light on how relevant similarities between models and targets are picked out, how inferences from models to targets are justified, and on the metaphysical status of models.

Steel connections:1. CONNECTIONS IN STEEL STRUCTURES
2.  Connections are structural elements used for joining different members of a structural steel frame work.  Steel Structure is an assemblage of different member such as “BEAMS,COLUMNS” which are connected to one other, usually at member ends fastners,so that it shows a single composite unit.3.  Bolts  Weld ,4.  Connecting Plates  Connecting Angles, 5.  On the Basis of Connecting Medium.  According to the type of internal forces.  According to the type of structural Elements  According to the type of members joining, 6.  Riveted Connections  Bolted Connections, 7.  Welded Connections  Bolted-Welded Connections, 8.  Used for very long time.  Made up of: Round Ductile steel bar called shank. A head at one end The length of the rivet should sufficient to form the second head.  Design - very similar to bearing type of bolted connection.
9.  Heating of the rivet  Inserting it to an oversize hole pressure to the head.  Squeezing the plain End by Pneumatic driver Round head.  On Cooling Reduces in Length–Clamping Force
10.  The introduction of high strength structural bolts.  The labour costs associated with large riveting crews  The cost involved in careful inspection and removal of poorly installed rivets.  The high level of noise associated with driving rivets., 11.  Fastened Together primarily by Bolts.  Bolts may be loaded in: Tension Shear Both Tension & Shear  Threads of bolts under shear force: Excluded - Increased strength Included - Decreased stregth.
12.  Bearing type bolts  High strength friction grip bolts (HSFG) , 13.  The most common type is bearing bolts in clearance holes, often referred to as 1. Black Bolts  Ordinary, unfinished, rough, or common bolts.  Least Expensive  Primarily - Light structures under static load such as small trusses, purlins etc, 14. 2. Turned Bolts  Similar to unfinished bolts.  Shanks - Hexagonal Rods  Primarily - Light structures under static load such as small trusses, purlins etc  Expensive – Limited use – Structures with no Slippage Connections, 15. 3. Ribbed Bolts • Round head similar to Rivets. • Raised ribs parallel to the shank. • Actual Diameter - slightly Larger than the hole • Tightly fit into the hole. • Popular - Economical in Material & Installation, 16.  Uses when bearing type bolts slips under shear  High strength bolts (8G or 10K grade) Pre-tensioned against the plates to be bolted together so that contact pressure developed between the plates being joined  Prevents relative slip when extra shear is applied  Higher Shear Resistance., 17. Advantages 1. The bolting operation is very silent 2. Bolting is a cold process hence there is no risk of fire 3. Bolting operation is more quicker than riveting. 4. Less man power is required in making the connections. Disadvantages 1. If subjected to vibratory loads, results in reduction in strength get loosened. 2. Unfinished bolts have lesser strength because of non uniform diameter
18.  whose components are joined together primarily by welds. Welding Notations were developed by American Welding Society (AWS)., 19. • Groove ( More reliable than others)  Fillet (Mostly used, Weaker than groove and others)  Plug (expensive – poor transmission of tensile forces)  Slot (expensive - poor transmission of tensile forces)  Plug and Slot welds – stitch different parts of members together.
20.  Horizontal  Vertical  Overhead  Flat
21.  Economical – Cost of materials and labors.  Efficiency is 100% as compared to rivets (75- 90%)  Fabrication of Complex Structures – Easy – like Circular Steel pipes.  Provides Rigid Joints – Modern Practice is of Rigid Joints.,22.  No provision for expansion or contraction therefore greater chances of cracking.  Uneven heating and cooling - member may distort - may result in additional stresses.  Inspection is difficult and more costlier than rivets
23.  Most connections are Shop Welded and Field Bolted types.  More Cost Effective  Better Strength & Ductility characteristics –Fully welded.
24.  Shear (semi rigid, simple) connections  Moment (rigid) connections
25.  Allows the beam end to rotate without a significant restraint.  Transfers shear out of beam  Most Common Types: Double clip Shear End Plate Fin Plate
26.  Designed to resist both Moment and Shear.  Often referred - rigid or fully restrained connections • Provide full continuity between the connected members • Designed to carry the full factored moments.  Principal Reason - buildings has to resist the effect of lateral forces such as wind and earthquake.
27. Bolted splice Moment Connection Field Bolted Moment Connection, 28.  Single plate angle Connections  Double web angle connections  Top and seated angle connections  Seated beam connections
29. Two Step Process  A plate is welded to secondary section (beam)  An Angle is welded to Primary Section (column or Beam)  single shear plate welded to secondary beam and bolted to Primary beam or column.
30.  Two angles welded or shop bolted to the web of a secondary beam.  After erection the angles are bolted or site welded to the primary member (beam or column).
31.  Generally used in case of moment connections.  Two angles are provided at top and bottom of the beam to resist moment.  Generally used for lesser moments where heavy loads are not acting
32.  Generally used in case of shear connections.  A seating angle - at bottom of secondary beam - shop welded to the primary member.  Seating angle resists vertical shear coming from the beam.
33.  Beam to beam connections  Column to column connections (column splices)  Beam to Column Connections  Column Base Plate Connections
34. Two Types  Primary Beam to Secondary Beam Connection  Beam Splice
35.  Connects column to column.  Column splice comes under this category.  Used to connect column sections of different sizes.  Splices - designed for both moment and shear unless intended to utilize the splices as internal hinges.
36.  Connects Beam to column.  Very Common  A wide range of different types are used Fin Plates End Plates Web or Flange Cleats Hunched Connections
37.  Beams are normally attached using two or more bolts through the web. End plate connections  single plate welded to the end of the beams  Bolted to the column flange or web - two or bolts pair. Fin plate connections  Single Plate welded to the Column.  Beams are normally attached using two or more bolts through the web.
38.  Steel plates placed at the bottom of Columns.  Function - to transmit column loads to the concrete pedestal.  The design of a column base plate: determining the size of the plate. determining the thickness of the plate
39.  A layer of grout should be placed between the base plate and its support for the purpose of leveling.  Anchor bolts should be provided to stabilize the column during erection or to prevent uplift.
40.  R Usually cost of fabrication and erection constitute as high as 50% of the total cost of steel structures, per tones of material used
41. BY NAVEEN ANKUR SWARNAKAR & AJEET SHARMA

Cross Laminated Timber (CLT) is a structural system for both modular housing and tall timber building construction that has been used in Europe and Oceania, and now emerges into the United States market. In floor slab applications, the out-of-plane loading typically controls the performance of the panels. The shear analogy is the most common used approach for modelling CLT panels under out-of-plane loading. However, due to limitations that exist in the shear analogy, this approach cannot be used to predict the capacity of the panels in complex panel geometries or complex loading scenarios. Prediction of failure loads and deformations are a challenge mainly due to the complex interaction between orthogonal layers of the CLT panel and the contribution of the rolling shear to failure response, which is known to control the performance of CLT panels in out-of-plane loading. To address this challenge, this paper presents a nonlinear multi-layered shell model linked with nonlinear interface springs. The model's ability to capture the response to failure due to out-of-plane loading is validated using a series of long-and short-span bending laboratory experiments performed on the CLT panels manufactured using Douglas-fir (DF) lumber.

Theories of how cognitive biases arise rely on heuristics that influence attention and reasoning. These heuristics serve as a post-hoc description of how attention and reasoning is weighted to produce the patterns of deviation in judgment. We are unaware of any process account of how these heuristics arise. In this article, we present several simulations of classic studies of the availability heuristic and describe how the availability heuristic arises through distributed symbolic representations and simple process interactions in an existing model of relational reasoning and concept development (Discovery of Relations by Analogy; Doumas, Hummel, & Sandhofer, 2008).

Versione di ottobre 2009 † Questi appunti sono ricavati dall'introduzione a Paul Smolensky, Il connessionismo tra simboli e neuroni, Marietti, Genova, 1992, traduzione italiana di "On the proper treatment of connectionism", Behavioral and Brain Sciences, 11, 1988, e dal capitolo "Connessionismo", in S. Gensini e A. Rainone (a cura di), Filosofie della Mente. Tradizione e attualità, Carocci, Roma, 2008.

It has been shown that the ability of echo state networks (ESNs) to generalise in a sentence-processing task can be increased by adjusting their input connection weights to the training data. We present a qualitative analysis of the effect of such weight adjustment on an ESN that is trained to perform the next-word prediction task. Our analysis makes use of CrySSMEx, an algorithm for extracting finite state machines (FSMs) from the data about the inputs, internal states, and outputs of recurrent neural networks that process symbol sequences. We find that the ESN with adjusted input weights yields a concise and comprehensible FSM. In contrast, the standard ESN, which shows poor generalisation, results in a massive and complex FSM. The extracted FSMs show how the two networks differ behaviourally. Moreover, poor generalisation is shown to correspond to a highly fragmented quantisation of the network’s
state space. Such findings indicate that CrySSMEx can be a useful tool for analysing ESN sentence processing.

Porous memories fuse and interpenetrate. Fragments of song mingle in hot remembered afternoons, mysterious angers return at a flush with a chance forgotten postcard. Such memories were once the motions of old fluids, animal spirits which meandered and rummaged through the pores of the brain. They held experience and history in bodies which were themselves porous, uncertainly coupled across tissues and skin with their air, their ethics, their land. Now they are patterns of activation across vast neural networks, condensing and compressing innumerable possible trajectories into the particular vectors of flashing or torpid memories. Dynamic cognitive systems coevolving with the physiological, environmental, and social systems in which they are embedded (van Gelder and Port 1995: 27-30) need the wishful mixings of absence which interfering traces bring. These studies in the history of theories of memory are grounded in new interpretations of strange, neglected old French and English neurophilosophy. But only late twentieth-century worries about memory, science, and truth make sense of indulgent attention to 'seventeenth-century French connectionism' (Diamond 1969), and to bizarre historical beliefs about interactive relations between self, body, mind, and coursing nervous fluids. This kind of historical cognitive science aims to demonstrate that it is possible to attend to contexts and to brains at once. ... I undertake both the description and the defence of related theories of memory, from animal spirits to connectionism, which employ superpositional storage: memories are blended, not laid down independently once and for all, and are reconstructed rather than reproduced. In dissolving old and new lines of attack on such theories, I suggest that they exemplifY the sensitivity to culture and history which good psychological science can exhibit. Working between historical and contemporary material suggests that wider issues about the self and psychological control are also implicated in current debates. The models of memory distributed through these studies, in mosaic from Descartes to connectionism, hintata more reckless algebra, an understanding of how complex self-organising physical systems like us can be so psychologically plastic, attuned to the configurations of culture in which cognition and remembering are situated.

The growing field of machine morality has become increasingly concerned with how to develop artificial moral agents. However, there is little consensus on what constitutes an ideal moral agent let alone an artificial one. Leveraging a recent account of heroism in humans, the aim of this paper is to provide a prospective framework for conceptualizing, and in turn designing ideal artificial moral agents, namely those that would be considered heroic robots. First, an overview of what it means to be an artificial moral agent is provided. Then, an overview of a recent account of heroism that seeks to define the construct as the dynamic and interactive integration of character strengths (e.g., bravery and integrity) and situational constraints that afford the opportunity for moral behavior (i.e., moral affordances). With this as a foundation, a discussion is provided for what it might mean for a robot to be an ideal moral agent by proposing a dynamic and interactive connectionist model of robotic heroism. Given the limited accounts of robots engaging in moral behavior, a case for extending robotic moral capacities beyond just being a moral agent to the level of heroism is supported by drawing from exemplar situations where robots demonstrate heroism in popular film and fiction.

The authors, on the basis of brief arguments, have dismissed tensor networks
as a viable response to Jackendoff’s challenges. However, there are reasons to believe
that connectionist approaches descended from tensor networks are actually very well
suited to answering Jackendoff’s challenges. I rebut their arguments for dismissing tensor
networks and briefly compare the approaches.

Jackendoff (2002) posed four challenges that linguistic combinatoriality and rules of language present to theories of brain function. The essence of these problems is the question of how to neurally instantiate the rapid construction and transformation of the compositional structures that are typically taken to be the domain of symbolic processing. He contended that typical connectionist approaches fail to meet these challenges and that the dialogue between linguistic theory and cognitive neuroscience will be relatively unproductive until the importance of these problems is widely recognised and the challenges answered by some technical innovation in connectionist modelling. This paper claims that a little-known family of connectionist models (Vector Symbolic Architectures) are able to meet Jackendoff's challenges.

Many theoretical models of reading assume that different writing systems require different processing assumptions. For example, it is often claimed that print-to-sound mappings in Chinese are not represented or processed sub-lexically. We present a connectionist model that learns the print to sound mappings of Chinese characters using the same functional architecture and learning rules that have been applied to English. The model predicts an interaction between item frequency and printto-sound consistency analogous to what has been found for English, as well as a language-specific regularity effect particular to Chinese. Behavioral naming experiments using the same test items as the model confirmed these predictions. Corpus properties and the analyses of internal representations that evolved over training revealed that the model was able to capitalize on information in "phonetic components" -sub-lexical structures of variable size that convey probabilistic information about pronunciation. The results suggest that adult reading performance across very different writing systems may be explained as the result of applying the same learning mechanisms to the particular input statistics of writing systems shaped by both culture and the exigencies of communicating spoken language in a visual medium.

The structural response of steel moment resisting frames (MRFs) is greatly dependent on the behavior of beamto-column joints, according to a properly detailed beam-bolts-plates-column structural chain, in light of capacity design principles. A modeling procedure for bolted top-and-seat angle components and connections for potential use in seismic MRFs is presented herein. Although these partially-restrained (PR) connection systems have been demonstrated to provide economic savings, they are not currently certified to be used for moment resistance in any major building specification jurisdiction. Examples of full-scale moment resisting connection systems, experimentally tested in past programs, have been numerically analyzed, focusing on top-and-seat angle components, which were observed to control the global response of the joint in terms of failure mechanisms, limiting the displacement ductility capacity and dissipation energy capabilities of the whole resisting system. Refined nonlinear solid FE models, accounting for the influence of friction, pretension of bolts, prying and relative slippage of components through highly nonlinear contact elements, have been developed to reproduce the cyclic-reversal test protocol. Simplified approaches, based on one-dimensional inelastic force-based fiber elements, combined with nonlinear links, to globally represent connection elements interaction, have been developed and validated by comparisons with experimental response. To propose an alternative and conservative method for quick rotational stiffness estimates of these PR bolted topand-seat angle connections, a series of detailed parametric solid FE analyses have been performed and the effectiveness of this analytical preliminary-design-stage tool quantified in comparison with some of the most commonly known analytical approaches.

Many theoretical models of reading assume that different writing systems require different processing assumptions. For example, it is often claimed that print-to-sound mappings in Chinese are not represented or processed sub-lexically. We present a connectionist model that learns the print-to-sound mappings of Chinese characters using the same functional architecture and learning rules that have been applied to English. The model predicts an interaction between item frequency and print-to-sound consistency analogous to what has been found for English, as well as a language-specific regularity effect particular to Chinese. Behavioral naming experiments using the same test items as the model confirmed these predictions. Corpus properties and the analyses of internal representations that evolved over training revealed that the model was able to capitalize on information in ''phonetic components" -sub-lexical structures of variable size that convey probabilistic information about pronunciation. The results suggest that adult reading performance across very different writing systems may be explained as the result of applying the same learning mechanisms to the particular input statistics of writing systems shaped by both culture and the exigencies of communicating spoken language in a visual medium.

In this paper we present two connectionist models of reading, using a parallel distributed processing framework that has been applied to English, to examine the extent to which such models can also account for developmental performance in Chinese. Simulation 1 was trained to map from orthography to phonology for a large corpus of stimuli, and simulates the frequency, regularity, consistency effects and interactions among them. Simulation 2 was trained to map among spelling, meaning, and sound for a smaller set of items, and captures basic effects of orthographic transparency and family size. Although the computational models used here are very similar to the English models, a very different developmental pattern emerged, such that mappings from orthography to semantics were learned more rapidly than mappings from orthography to phonology. The results show how qualitative differences in the development of reading skill across writing systems can arise from the functioning of the same general learning mechanisms.

Several psycholinguistic models represent words as vectors in a high-dimensional state space, such that distances between vectors encode the strengths of paradigmatic relations between the represented words. This chapter argues that such an organization develops because it facilitates fast sentence processing. A model is presented in which sentences, in the form of word-vector sequences, serve as input to a recurrent neural network that provides random dynamics. The word vectors are adjusted by a process of self-organization, aimed at reducing fluctuations in the dynamics. As it turns out, the resulting word vectors are organized paradigmatically.

Keywords: Word representation; Sentence processing; Self-organization; Recurrent neural network; Reservoir computing.

Providing explanations of language comprehension requires models that describe language processing and display strong systematicity. Although various extensions of connectionist models have been suggested in order to account for this phenomenon, we found that even a simple recurrent network that had been trained in a way that can be considered 'standard', could display strong systematicity. This finding was investigated in further detail by looking at the internal word representations of the best performing network.

Humans are remarkably sensitive to the statistical structure of language N. Ellis, 2008). However, different mechanisms have been proposed to account for such statistical learning. The present study compared adult learning of syntax and the ability of two models of statistical learning to simulate human performance: Simple Recurrent Networks , which learn by predictive computation, and PARSER (Perruchet & Vinter, 1998), which learns chunks as a byproduct of general principles of associative learning and memory. The computational models and human adults were trained on sentences from a semiartificial language with probabilistic syntax and their learning outcomes compared. Neither model was able to fully reproduce the human data, which may indicate less robust statistical learning effects in adults; however, PARSER was able to simulate more of the adult learning data than the Simple Recurrent Network, suggesting a possible role for chunk formation in early phases of adult learning of second language syntax.

The Symbolic Grounding Problem is viewed as a by-product of the classical cognitivist approach to studying the mind. In contrast, an epigenetic interpretation of connectionist approaches to studying the mind is shown to offer an account of symbolic skills as an emergent, developmental phenomenon. We describe a connectionist model ofconceptformation and vocabulary growth that auto-associates image representations and their associated labels. The image representations consist of clusters of random dot figures, generated by distorting prototypes. Any given label is associated with a cluster of random dot figures. The network model is tested on its abl7{fy to reproduce image representations given input labels alone (comprehension) and to identify labels given input images alone (production). The model implements several well-documented findings in the literature on early semantic deuelopment; the occurrence of over-and under-extension errors; a vocabulary spurt; a comprehension/production asymmetry; and a prototype effect. It is shown how these apparently disparate findings can be attributed to the operation of a single underlying mechanism rather than by invoking separate explanations for each phenomenon. The model represents a first step in the direction of providing a formal explanation of the emergence of symbolic behaviour in young children.

, and Sunderman and Kroll (2006) exposed participants to first-language/second-language (L1/L2) pairs of words and asked them to decide whether the second word was the correct translation of the first. In the critical condition, the L2 word was either the translation of the L1 word (man à hombre) or a form-relative of the translation (man à hambre). Less fluent speakers showed higher recognition latencies in the form-relative condition than did more fluent speakers. This report explores whether an appropriately trained Adaptive Resonance Theory (ART) neural network (Carpenter and Grossberg, 1987a) will suffer from form-relative interference, and the role of vigilance (a parameter of low-level attention sensitive to environmental complexity) in this effect. I argue that the learning environment of early bilinguals is more complex than that of adult L2 learners, and therefore adult learners may be less vigilant to word form. ART2 networks were trained with English and Spanish corpora under conditions emulating early and late second language acquisition at different vigilance levels and then serially exposed to the same types of word pairs used in the three studies mentioned above. Form-relative interference was observed, indicating that low-level attentional mechanisms may play a role in second language lexical learning and access.

Comprehending a sentence requires the construction of a mental representation of the situation the sentence describes. Many researchers assume that, apart from such a situational representation, there is a level of representation at which the propositional structure of the sentence is encoded. This paper presents a simple sentence comprehension model, consisting of a neural network that transforms sentences into representations of the events they describe. During training, the network develops internal representations of the sentences. An investigation of these representations reveals that they can encode propositional information without implementing propositional structure.