245 research outputs found
Combinatorial structures and processing in Neural Blackboard Architectures
We discuss and illustrate Neural Blackboard Architectures (NBAs) as the basis for variable binding and combinatorial processing the brain. We focus on the NBA for sentence structure. NBAs are based on the notion that conceptual representations are in situ, hence cannot be copied or transported. Novel combinatorial struc- tures can be formed with these representations by embedding them in NBAs. We discuss and illustrate the main characteristics of this form of combinatorial pro- cessing. We also illustrate the NBA for sentence structures by simulating neural activity as found in recently reported intracranial brain observations. Furthermore, we will show how the NBA can account for ambiguity resolution and garden path effects in sentence processing
Precis of neuroconstructivism: how the brain constructs cognition
Neuroconstructivism: How the Brain Constructs Cognition proposes a unifying framework for the study of cognitive development that brings together (1) constructivism (which views development as the progressive elaboration of increasingly complex structures), (2) cognitive neuroscience (which aims to understand the neural mechanisms underlying behavior), and (3) computational modeling (which proposes formal and explicit specifications of information processing). The guiding principle of our approach is context dependence, within and (in contrast to Marr [1982]) between levels of organization. We propose that three mechanisms guide the emergence of representations: competition, cooperation, and chronotopy; which themselves allow for two central processes: proactivity and progressive specialization. We suggest that the main outcome of development is partial representations, distributed across distinct functional circuits. This framework is derived by examining development at the level of single neurons, brain systems, and whole organisms. We use the terms encellment, embrainment, and embodiment to describe the higher-level contextual influences that act at each of these levels of organization. To illustrate these mechanisms in operation we provide case studies in early visual perception, infant habituation, phonological development, and object representations in infancy. Three further case studies are concerned with interactions between levels of explanation: social development, atypical development and within that, developmental dyslexia. We conclude that cognitive development arises from a dynamic, contextual change in embodied neural structures leading to partial representations across multiple brain regions and timescales, in response to proactively specified physical and social environment
TOWARDS THE GROUNDING OF ABSTRACT CATEGORIES IN COGNITIVE ROBOTS
The grounding of language in humanoid robots is a fundamental problem, especially
in social scenarios which involve the interaction of robots with human beings. Indeed,
natural language represents the most natural interface for humans to interact
and exchange information about concrete entities like KNIFE, HAMMER and abstract
concepts such as MAKE, USE. This research domain is very important not
only for the advances that it can produce in the design of human-robot communication
systems, but also for the implication that it can have on cognitive science.
Abstract words are used in daily conversations among people to describe events and
situations that occur in the environment. Many scholars have suggested that the
distinction between concrete and abstract words is a continuum according to which
all entities can be varied in their level of abstractness.
The work presented herein aimed to ground abstract concepts, similarly to concrete
ones, in perception and action systems. This permitted to investigate how different
behavioural and cognitive capabilities can be integrated in a humanoid robot in
order to bootstrap the development of higher-order skills such as the acquisition of
abstract words. To this end, three neuro-robotics models were implemented.
The first neuro-robotics experiment consisted in training a humanoid robot to perform
a set of motor primitives (e.g. PUSH, PULL, etc.) that hierarchically combined
led to the acquisition of higher-order words (e.g. ACCEPT, REJECT). The
implementation of this model, based on a feed-forward artificial neural networks,
permitted the assessment of the training methodology adopted for the grounding of
language in humanoid robots.
In the second experiment, the architecture used for carrying out the first study
was reimplemented employing recurrent artificial neural networks that enabled the
temporal specification of the action primitives to be executed by the robot. This
permitted to increase the combinations of actions that can be taught to the robot
for the generation of more complex movements.
For the third experiment, a model based on recurrent neural networks that integrated
multi-modal inputs (i.e. language, vision and proprioception) was implemented for
the grounding of abstract action words (e.g. USE, MAKE). Abstract representations
of actions ("one-hot" encoding) used in the other two experiments, were replaced
with the joints values recorded from the iCub robot sensors.
Experimental results showed that motor primitives have different activation patterns
according to the action's sequence in which they are embedded. Furthermore, the
performed simulations suggested that the acquisition of concepts related to abstract
action words requires the reactivation of similar internal representations activated
during the acquisition of the basic concepts, directly grounded in perceptual and
sensorimotor knowledge, contained in the hierarchical structure of the words used
to ground the abstract action words.This study was financed by the EU project RobotDoC (235065) from the Seventh
Framework Programme (FP7), Marie Curie Actions Initial Training Network
TopoloÅ”ko strateÅ”ko konstruiranje znaÄenja: frazni glagoli s up i down u jeziku slijepih
The aim of this paper is to investigate the role of the particles upand downin the strategic
meaning construal of particle verbs (PVs) in blind and sighted users of English as
L2. The paper is situated within the cognitive linguistic framework. Based on the results
of a speakerājudgment study with 20 blind and 20 sighted users of English, we show that
PVs with downare more informative to all the participants, and that blind users rely
on the particles (particularly the particle up) more than sighted users. We claim that the
difference in informativeness is related to the experiential status of upand down. Downis
more informative because it is at human scale, which limits its metaphorization potential.
Upis more openāended, making it more schematic and allowing greater departure from
its original topology. Blind users rely on the particles more because they are more inclined
to analyzing linguistic cues, since they often serve as additional experiential input.
Moreover, the blind rely more on egocentric topology, which produces similar results for
down, and different for up.Cilj je ovoga rada istražiti ulogu up \u27gore\u27 i down\u27dolje\u27 u strateŔkome konstruiranju fraznih
glagola kod slijepih i videÄih govornika engleskoga kao drugoga jezika. Rad se oslanja na
teorijske postavke kognitivne lingvistike u kojoj su jezik i naŔe svakodnevno iskustvo neodvojivo
povezani. U istraživanju je sudjelovalo 20 slijepih i 20 videÄih govornika engleskoga kao drugoga
jezika kojima je materinski jezik hrvatski. Ispitanici su rjeŔavali upitnik u kojemu su trebali
odrediti na koji naÄin sastavnice zajedno pridonose znaÄenju fraznoga glagola (primjerice, jedan
ispitanik kaže da glagol go down \u27biti poslan u zatvor\u27 ima smisla jer se down \u27dolje\u27 odnosi na
dno druŔtva). Rezultati pokazuju da je down\u27dolje\u27 informativniji svim ispitanicima. Nadalje,
slijepi ispitanici znaÄenje objaÅ”njavaju viÅ”e se oslanjajuÄi na sastavnice up i down, osobito na
up \u27gore\u27, a manje na glagol kao sastavnicu konstrukcije. Dva su temeljna zakljuÄka rada: prvo,
smatramo da je down\u27dolje\u27 opÄenito informativniji jer je bliži tzv. Ā»ljudskoj mjeriĀ« (Turner
2014), Å”to ograniÄava koliko ga je moguÄe metaforizirati, dok je up\u27gore\u27 shematiÄniji pa ima
i veÄi metaforiÄki potencijal, te drugo, da slijepi ispitanici u procesu konstruiranja znaÄenja daju
prioritet prostornim sastavnicama (u ovome sluÄaju up i down) jer su skloniji analiziranju jezika.
Navedena sklonost analizi proizlazi velikim dijelom iz Äinjenice da im jezik služi kao dodatan
naÄin stvaranja iskustvenih veza i znaÄajan izvor informacija o svijetu. Ipak, razlika izmeÄu
videÄih i slijepih ispitanika nije znaÄajna za down, Å”to tumaÄimo kao rezultat veÄe uloge iskustva vlastite
smjeŔtenosti u prostoru
Mental content : consequences of the embodied mind paradigm
The central difference between objectivist cognitivist semantics and embodied cognition consists in the fact that the latter is, in contrast to the former, mindful of binding meaning to context-sensitive mental systems. According to Lakoff/Johnson's experientialism, conceptual structures arise from preconceptual kinesthetic image-schematic and basic-level structures. Gallese and Lakoff introduced the notion of exploiting sensorimotor structures for higherlevel cognition. Three different types of X-schemas realise three types of environmentally embedded simulation: Areas that control movements in peri-personal space; canonical neurons of the ventral premotor cortex that fire when a graspable object is represented; the firing of mirror neurons while perceiving certain movements of conspecifics. ..
The Road to General Intelligence
Humans have always dreamed of automating laborious physical and intellectual tasks, but the latter has proved more elusive than naively suspected. Seven decades of systematic study of Artificial Intelligence have witnessed cycles of hubris and despair. The successful realization of General Intelligence (evidenced by the kind of cross-domain flexibility enjoyed by humans) will spawn an industry worth billions and transform the range of viable automation tasks.The recent notable successes of Machine Learning has lead to conjecture that it might be the appropriate technology for delivering General Intelligence. In this book, we argue that the framework of machine learning is fundamentally at odds with any reasonable notion of intelligence and that essential insights from previous decades of AI research are being forgotten. We claim that a fundamental change in perspective is required, mirroring that which took place in the philosophy of science in the mid 20th century. We propose a framework for General Intelligence, together with a reference architecture that emphasizes the need for anytime bounded rationality and a situated denotational semantics. We given necessary emphasis to compositional reasoning, with the required compositionality being provided via principled symbolic-numeric inference mechanisms based on universal constructions from category theory. ā¢ Details the pragmatic requirements for real-world General Intelligence. ā¢ Describes how machine learning fails to meet these requirements. ā¢ Provides a philosophical basis for the proposed approach. ā¢ Provides mathematical detail for a reference architecture. ā¢ Describes a research program intended to address issues of concern in contemporary AI. The book includes an extensive bibliography, with ~400 entries covering the history of AI and many related areas of computer science and mathematics.The target audience is the entire gamut of Artificial Intelligence/Machine Learning researchers and industrial practitioners. There are a mixture of descriptive and rigorous sections, according to the nature of the topic. Undergraduate mathematics is in general sufficient. Familiarity with category theory is advantageous for a complete understanding of the more advanced sections, but these may be skipped by the reader who desires an overall picture of the essential concepts This is an open access book
Perceptual Generalization and Context in a Network MemoryInspired Long-Term Memory for Artificial Cognition
Abstract: In the framework of open-ended learning cognitive architectures for robots, this paper deals with thedesign of a Long-Term Memory (LTM) structure that can accommodate the progressive acquisition ofexperience-based decision capabilities, or what different authors call āautomationā of what is learnt, asa complementary system to more common prospective functions. The LTM proposed here provides fora relational storage of knowledge nuggets given the form of artificial neural networks (ANNs) that isrepresentative of the contexts in which they are relevant in a configural associative structure. It alsoaddresses the problem of continuous perceptual spaces and the task- and context-related generalizationor categorization of perceptions in an autonomous manner within the embodied sensorimotor apparatusof the robot. These issues are analyzed and a solution is proposed through the introduction of two newtypes of knowledge nuggets: P-nodes representing perceptual classes and C-nodes representing contexts.The approach is studied and its performance evaluated through its implementation and application to areal robotic experimentXunta de Galicia; ED431C 2017/12Xunta de Galicia; ED341D R2016/01
The propositional nature of human associative learning
The past 50 years have seen an accumulation of evidence suggesting that associative learning depends oil high-level cognitive processes that give rise to propositional knowledge. Yet, many learning theorists maintain a belief in a learning mechanism in which links between mental representations are formed automatically. We characterize and highlight the differences between the propositional and link approaches, and review the relevant empirical evidence. We conclude that learning is the consequence of propositional reasoning processes that cooperate with the unconscious processes involved in memory retrieval and perception. We argue that this new conceptual framework allows many of the important recent advances in associative learning research to be retained, but recast in a model that provides a firmer foundation for both immediate application and future research
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