Parallel Distributed Processing Research Articles

Simulating Cooperative Interactions to Investigate the Neural Correlates of Joint Attention

Event Abstract Back to Event Simulating Cooperative Interactions to Investigate the Neural Correlates of Joint Attention Nathan Caruana1*, Alexandra Woolgar1 and Jon Brock1 1 ARC Centre of Excellence in Cognition and its Disorders , Cognitive Science, Australia Joint attention is a fundamental aspect of social cognition, enabling effective intersubjective experiences. However, due to lacking ecological measures, we currently know little about its neural representation, or how this diverges in developmental disorders such as autism. Joint attention is conceptualised as involving two distinct functions. Responding to joint attention (RJA) involves converging with someone else's focus of attention. Initiating joint attention (IJA), involves guiding others to converge with one's own attentional focus. The Parallel Distributed Processing (PDP) model accounts for the overlapping function, and developmental divergence of RJA and IJA. Consistent with this model, we anticipated that sources of activation associated with RJA and IJA would reflect partially independent regions, existing in a distally distributed, posterior and anterior network. Inspired by the second person perspective (2PP) approach to social cognition research, we developed a co-operative, virtual reality task for the acquisition of fMRI and MEG data. Using eye-tracking techniques, subjects interacted with an onscreen avatar, which responded contingently to their gaze. Subjects completed the task under the impression that they were interacting with a confederate, outside the scanner, who was controlling the avatar's eye movements. Our analyses have focused on comparing neural activation during the execution of RJA and IJA behaviour, as well as during conditions where participants complete the same task 'alone'. Our fMRI univariate whole brain analyses revealed temporoparietal and anterior networks associated with gaze detection, attention orienting and mentalising processes. An MEG case study, using the same task and a novel saccade-related beamforming analysis was also conducted to localise activation associated with preparing saccades for the execution of RJA and IJA. These data and critical issues in designing interactive paradigms will be discussed. Keywords: fMRI, MEG, eye tracking, joint attention, virtual reality Conference: XII International Conference on Cognitive Neuroscience (ICON-XII), Brisbane, Queensland, Australia, 27 Jul - 31 Jul, 2014. Presentation Type: Poster Topic: Emotional and Social Processes Citation: Caruana N, Woolgar A and Brock J (2015). Simulating Cooperative Interactions to Investigate the Neural Correlates of Joint Attention. Conference Abstract: XII International Conference on Cognitive Neuroscience (ICON-XII). doi: 10.3389/conf.fnhum.2015.217.00046 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 19 Feb 2015; Published Online: 24 Apr 2015. * Correspondence: Mr. Nathan Caruana, ARC Centre of Excellence in Cognition and its Disorders, Cognitive Science, North Ryde, Australia, nathan.caruana@mq.edu.au Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Nathan Caruana Alexandra Woolgar Jon Brock Google Nathan Caruana Alexandra Woolgar Jon Brock Google Scholar Nathan Caruana Alexandra Woolgar Jon Brock PubMed Nathan Caruana Alexandra Woolgar Jon Brock Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

科学衛星で観測された波形データ処理を用いたNICTサイエンスクラウド上での並列分散処理の評価

昨今，種々の目的で打ち上げられる科学衛星や地球観測衛星の観測データは蓄積され続け，膨大な量となっている．自然科学ビッグデータをプロセス間通信等の複雑なプログラミングや大規模な環境設定なしに解析するためのデータ処理手法と環境の需要が高まっている．本論文では，サイエンスクラウド上で容易に並列処理の実装が行えるタスクスケジューラを用いたヘテロタイプの並列分散処理の性能評価について議論する．評価を行うにあたって，月周回衛星かぐやに搭載されたWFC-Lが観測した波形データセットと既存のデータ解析プログラムを用いた．今回提案するタスクスケジューリング技術および並列分散処理技術がヘテロタイプの時系列データ処理に適していることを実証する．

Connecting functional brain imaging and Parallel Distributed Processing

Functional neuroimaging and Parallel Distributed Processing (PDP) theory, both introduced to cognitive science in the 1980s, led to influential research programmes that have proceeded in parallel with little mutual influence. The PDP approach advanced specific claims about the nature of neural representations that, perhaps surprisingly, have gone largely untested in functional brain imaging. One reason may be the widespread use of univariate statistical methods for analysing brain imaging data, which typically rely on assumptions that render them unable to detect distributed representations of the kind that PDP predicts. More recent multivariate methods for image analysis may be better suited to detecting such representations. In the current article, we consider why univariate methods have been insufficient to test PDP's representational claims, articulate some of the properties that neural representations ought to have if the PDP view is valid and then survey the recent neuroimaging literature for evidence that neural representations do or do not have these properties. The survey establishes that the PDP view of distributed representations has considerable evidential support. This analysis underscores the importance of understanding how the assumptions underlying methods for analysing functional imaging data constrain the kinds of questions that can be addressed. We then consider the implications for our developing understanding of the neural bases of cognition and for the design of future brain imaging studies.

A frontotemporoparietal network common to initiating and responding to joint attention bids

Joint attention is a fundamental cognitive ability that supports daily interpersonal relationships and communication. The Parallel Distributed Processing model (PDPM) postulates that responding to (RJA) and initiating (IJA) joint attention are predominantly supported by posterior-parietal and frontal regions respectively. It also argues that these neural networks integrate during development, supporting the parallel processes of self- and other-attention representation during interactions. However, direct evidence for the PDPM is limited due to a lack of ecologically valid experimental paradigms that can capture both RJA and IJA. Building on existing interactive approaches, we developed a virtual reality paradigm where participants engaged in an online interaction to complete a cooperative task. By including tightly controlled baseline conditions to remove activity associated with non-social task demands, we were able to directly contrast the neural correlates of RJA and IJA to determine whether these processes are supported by common brain regions. Both RJA and IJA activated broad frontotemporoparietal networks. Critically, a conjunction analysis identified that a subset of these regions were common to both RJA and IJA. This right-lateralised network included the dorsal portion of the middle frontal gyrus (MFG), inferior frontal gyrus (IFG), middle temporal gyrus (MTG), precentral gyrus, posterior superior temporal sulcus (pSTS), temporoparietal junction (TPJ) and precuneus. Additional activation was observed in this network for IJA relative to RJA at MFG, IFG, TPJ and precuneus. This is the first imaging study to directly investigate the neural correlates common to RJA and IJA engagement, and thus support the assumption that a broad integrated network underlies the parallel aspects of both initiating and responding to joint attention.

Theories and control models and motor learning: Clinical applications in neurorehabilitation

IntroductionIn recent decades there has been a special interest in theories that could explain the regulation of motor control, and their applications. These theories are often based on models of brain function, philosophically reflecting different criteria on how movement is controlled by the brain, each being emphasised in different neural components of the movement. The concept of motor learning, regarded as the set of internal processes associated with practice and experience that produce relatively permanent changes in the ability to produce motor activities through a specific skill, is also relevant in the context of neuroscience. Thus, both motor control and learning are seen as key fields of study for health professionals in the field of neurorehabilitation. DevelopmentThe major theories of motor control are described, which include, motor programming theory, systems theory, the theory of dynamic action, and the theory of parallel distributed processing, as well as the factors that influence motor learning and its applications in neurorehabilitation. ConclusionsAt present there is no consensus on which theory or model defines the regulations to explain motor control. Theories of motor learning should be the basis for motor rehabilitation. The new research should apply the knowledge generated in the fields of control and motor learning in neurorehabilitation.

About the unidirectionality of interference: Insight from the musical Stroop effect

The asymmetry of interference in a Stroop task usually refers to the well-documented result that incongruent colour words slow colour naming (Stroop effect) but incongruent colours do not slow colour word reading (no reverse Stroop effect). A few other studies have suggested that, more generally, a reverse Stroop effect can be occasionally observed but at the expense of the Stroop effect itself, as if interference was inherently unidirectional, from the stronger to the weaker of the two competing processes. We describe here a situation conducive to a pervasive mutual interference effect. Musicians were exposed to congruent and incongruent note name/note position patterns, and they were asked either to read the word while ignoring the location of the note within the staff, or to name the note while ignoring the note name written inside the note picture. Most of the participants exhibited interference in the two tasks. Overall, this result pattern runs against the still prevalent model of the Stroop phenomenon [Cohen, J. D., Dunbar, K., & McClelland, J. L. (1990). On the control of automatic processes: A parallel distributed processing account of the Stroop effect. Psychological Review, 97(3), 332-361]. However, further analyses lend support to one of the key tenets of the model, namely that the pattern of interference depends on the relative strength of the two competing pathways. The reasons for the impressive differences between the results collected in the present study and in the standard colour-word (or picture-word) paradigms are also examined. We suggest that these differences reveal the importance of stimulus-response contingency in the formation of automatisms.

Parallel Distributed Processing at 25: further explorations in the microstructure of cognition.

This paper introduces a special issue of Cognitive Science initiated on the 25th anniversary of the publication of Parallel Distributed Processing (PDP), a two-volume work that introduced the use of neural network models as vehicles for understanding cognition. The collection surveys the core commitments of the PDP framework, the key issues the framework has addressed, and the debates the framework has spawned, and presents viewpoints on the current status of these issues. The articles focus on both historical roots and contemporary developments in learning, optimality theory, perception, memory, language, conceptual knowledge, cognitive control, and consciousness. Here we consider the approach more generally, reviewing the original motivations, the resulting framework, and the central tenets of the underlying theory. We then evaluate the impact of PDP both on the field at large and within specific subdomains of cognitive science and consider the current role of PDP models within the broader landscape of contemporary theoretical frameworks in cognitive science. Looking to the future, we consider the implications for cognitive science of the recent success of machine learning systems called "deep networks"-systems that build on key ideas presented in the PDP volumes.

학습 시스템을 위한 빅데이터 처리 환경 구축

In order to create an environment for Apache Hadoop for parallel distributed processing system of Bigdata, by connecting a plurality of computers, or to configure the node, using the configuration of the virtual nodes on a single computer it is necessary to build a cloud fading environment. However, be constructed in practice for education in these systems, there are many constraints in terms of cost and complex system configuration. Therefore, it is possible to be used as training for educational institutions and beginners in the field of Bigdata processing, development of learning systems and inexpensive practical is urgent. Based on the Raspberry Pi board, training and analysis of Big data processin g, such as Hadoop and NoSQL is now the design and implementation of a learning system of parallel distributed processing of possible Bigdata in this study. It is expected that Bigdata parallel distributed processing system that has been implemented, and be a useful system for beginners who want to start a Bigdata and education.

An Architecture for Big Data Analytics

Big Data is the new experience curve in the new economy driven by data with high volume, velocity, variety, and veracity. They come from various sources that include the Internet, mobile devices, social media, geospatial devices, sensors, and other machine-generated data. Unlocking the value of Big Data allows business to better sense and respond to the environment, and is becoming a key to creating competitive advantages in a complex and rapidly changing market. Government is also taking notice of the Big Data phenomenon and has created initiatives to exploit Big Data in many areas such as science and engineering, healthcare and national security. Traditional data processing and analysis of structured data using RDBMS and data warehousing no longer satisfy the challenges of Big Data. Technology trends for Big Data embrace open source software, commodity servers, and massively parallel-distributed processing platforms. Analytics is at the core of exploiting values from Big Data to create consumable insights for business and government. This paper presents architecture for Big Data Analytics and explores Big Data technologies that include NoSQL databases, Hadoop Distributed File System and MapReduce.

Syllabic Length Effect in Visual Word Recognition by SLA Learners

Studies on visual word recognition have resulted in different and sometimes contradictory proposals as Multi-Trace Memory Model (MTM), Dual-Route Cascaded Model (DRC), and Parallel Distribution Processing Model (PDP). The role of the number of syllables in word recognition was examined by the use of five groups of English words and non-words. The reaction time of the participants to these words was measured using reaction time measuring software. The results indicated that there was syllabic effect on recognition of both high and low frequency words. The pattern was incremental in terms of syllable number. This pattern prevailed in high and low frequency words and non-words except in one syllable words. In general, the results are in line with the PDP model which claims that a single processing mechanism is used in both words and non-words recognition. In other words, the findings suggest that lexical items are mainly processed via a lexical route. A pedagogical implication of the findings would be that reading in English as a foreign language involves analytical processing of the syllable of the words.

Feature extraction technique using ERB like wavelet sub-band periodic and aperiodic decomposition for TIMIT phoneme recognition

In the recent years, wavelet transform has been found to be an effective tool for the time---frequency analysis for non-stationary and quasi-stationary signals such as speech signals. In the recent past, wavelet transform has been used as feature extraction in speech recognition applications. Here we propose a wavelet based feature extraction technique that signifies both the periodic and aperiodic information along with sub-band instantaneous frequency of speech signal for robust speech recognition in noisy environment. This technique is based on parallel distributed processing technique inspired by the human speech perception process. This frontend feature processing technique employs equivalent rectangular bandwidth (ERB) filter like wavelet speech feature extraction method called Wavelet ERB Sub-band based Periodicity and Aperiodicity Decomposition (WERB-SPADE), and examines its validity for TIMIT phone recognition task in noisy environments. The speech sound is filtered by 24 band ERB like wavelet filter banks, and then the equal loudness pre-emphasized output of each band is processed through comb filter. Each comb filter is designed individually for each frequency sub-band to decompose the signal into periodic and aperiodic features. Thus it takes the advantage of the robustness shown by periodic features without losing certain important information like formant transition incorporated in aperiodic features. Speech recognition experiments with a standard HMM recognizer under both clean-training and multi-training condition training is conducted. Proposed technique shows more robustness compared to other features especially in noisy condition.

How meaning similarity influences ambiguous word processing: the current state of the literature.

The majority of words in the English language do not correspond to a single meaning, but rather correspond to two or more unrelated meanings (i.e., are homonyms) or multiple related senses (i.e., are polysemes). It has been proposed that the different types of "semantically-ambiguous words" (i.e., words with more than one meaning) are processed and represented differently in the human mind. Several review papers and books have been written on the subject of semantic ambiguity (e.g., Adriaens, Small, Cottrell, & Tanenhaus, 1988; Burgess & Simpson, 1988; Degani & Tokowicz, 2010; Gorfein, 1989, 2001; Simpson, 1984). However, several more recent studies (e.g., Klein & Murphy, 2001; Klepousniotou, 2002; Klepousniotou & Baum, 2007; Rodd, Gaskell, & Marslen-Wilson, 2002) have investigated the role of the semantic similarity between the multiple meanings of ambiguous words on processing and representation, whereas this was not the emphasis of previous reviews of the literature. In this review, we focus on the current state of the semantic ambiguity literature that examines how different types of ambiguous words influence processing and representation. We analyze the consistent and inconsistent findings reported in the literature and how factors such as semantic similarity, meaning/sense frequency, task, timing, and modality affect ambiguous word processing. We discuss the findings with respect to recent parallel distributed processing (PDP) models of ambiguity processing (Armstrong & Plaut, 2008, 2011; Rodd, Gaskell, & Marslen-Wilson, 2004). Finally, we discuss how experience/instance-based models (e.g., Hintzman, 1986; Reichle & Perfetti, 2003) can inform a comprehensive understanding of semantic ambiguity resolution.

Discrete-time Zhang neural network of O(τ3) pattern for time-varying matrix pseudoinversion with application to manipulator motion generation

In addition to the high-speed parallel-distributed processing property, neural networks can be readily implemented by hardware and thus have been applied widely in various fields. In this paper, via a new numerical-differentiation formula, two Taylor-type discrete-time Zhang neural network (ZNN) models (termed T-ZNN-K and T-ZNN-U models) are first proposed, developed and investigated for online time-varying matrix pseudoinversion. For comparison as well as for illustration, Euler-type discrete-time ZNN models (termed E-ZNN-K and E-ZNN-U models) and Newton iteration are presented. In addition, according to the criterion of whether the time-derivative information of time-varying matrix is explicitly known or not, these discrete-time ZNN models are classified into two categories: (1) models with time-derivative information known (i.e., T-ZNN-K and E-ZNN-K models), and (2) models with time-derivative information unknown (i.e., T-ZNN-U and E-ZNN-U models). Moreover, theoretical analyses show that, the maximal steady-state residual errors (MSSREs) of T-ZNN-K and T-ZNN-U models have an O(τ3) pattern, the MSSREs of E-ZNN-K and E-ZNN-U models have an O(τ2) pattern, whereas the MSSRE of Newton iteration has an O(τ) pattern, with τ denoting the sampling gap. Finally, two illustrative numerical experiments and an application example to manipulator motion generation are provided and analyzed to substantiate the efficacy of the proposed Taylor-type discrete-time ZNN models for online time-varying matrix pseudoinversion.

Neural networks learn highly selective representations in order to overcome the superposition catastrophe.

A key insight from 50 years of neurophysiology is that some neurons in cortex respond to information in a highly selective manner. Why is this? We argue that selective representations support the coactivation of multiple "things" (e.g., words, objects, faces) in short-term memory, whereas nonselective codes are often unsuitable for this purpose. That is, the coactivation of nonselective codes often results in a blend pattern that is ambiguous; the so-called superposition catastrophe. We show that a recurrent parallel distributed processing network trained to code for multiple words at the same time over the same set of units learns localist letter and word codes, and the number of localist codes scales with the level of the superposition. Given that many cortical systems are required to coactivate multiple things in short-term memory, we suggest that the superposition constraint plays a role in explaining the existence of selective codes in cortex.

PSPs and ERPs: Applying the dynamics of post-synaptic potentials to individual units in simulation of temporally extended Event-Related Potential reading data

The Parallel Distributed Processing (PDP) framework is built on neural-style computation, and is thus well-suited for simulating the neural implementation of cognition. However, relatively little cognitive modeling work has concerned neural measures, instead focusing on behavior. Here, we extend a PDP model of reading-related components in the Event-Related Potential (ERP) to simulation of the N400 repetition effect. We accomplish this by incorporating the dynamics of cortical post-synaptic potentials—the source of the ERP signal—into the model. Simulations demonstrate that application of these dynamics is critical for model elicitation of repetition effects in the time and frequency domains. We conclude that by advancing a neurocomputational understanding of repetition effects, we are able to posit an interpretation of their source that is both explicitly specified and mechanistically different from the well-accepted cognitive one.

Connectionism coming of age: legacy and future challenges.

Connectionism coming of age: legacy and future challenges.

La ciencia cognitiva y el estudio de la mente

En la actualidad se observa un desarrollo vigoroso de la ciencia cognitiva, que es un campo multidisciplinario en el que confluyen varias disciplinas, como la psicología cognitiva, la inteligencia artificial, la lingüística, la filosofía, la neurociencia, la antropología cognitiva, entre otras, con la finalidad de estudiar la cognición. El estudio de la cognición y la mente se ha venido realizando desde el punto de vista de la filosofía estrictamente especulativa, desde la perspectiva de la filosofía de la mente y desde el punto de vista científico. Los aportes hipotéticos y/o teóricos de esos estudios de la mente son discutidos y, en la mayoría de los casos, sometidos a un análisis experimental para contrastar su validez científica. En ese sentido, la ciencia cognitiva ha ido estableciendo las propiedades esenciales de la mente y la cognición, tal como el carácter representacional de la mente, la relación causal entre la representación cognitiva y la conducta, el carácter simbólico de la información representada mentalmente, la mente como procesador de la información, la mente como un sistema de mecanismos, la modularidad de la mente, etc. Sin embargo, en la ciencia cognitiva actual es notoria la presencia de diferentes enfoques cognitivos que defienden el modelo simbólico clásico, por un lado, y por otro están aquellos que defienden el modelo conexionista o procesamiento distribuido en paralelo (PDP), y también aquellos que postulan modelos mentales no representacionalistas. En conclusión, se puede decir que hoy en día el estudio de la mente y la cognición es una empresa importante en el que están comprometidas varias disciplinas.

Not lost in translation: generalization of the primary systems hypothesis to Japanese-specific language processes.

The emergentist-connectionist approach assumes that language processing reflects interaction between primary neural systems (Primary Systems Hypothesis). This idea offers an overarching framework that generalizes to various kinds of (English) language and nonverbal cognitive activities. The current study advances this approach with respect to language in two new and important ways. The first is the provision of a neuroanatomically constrained implementation of the theory. The second is a test of its ability to generalize to a language other than English (in this case Japanese) and, in particular, to a feature of that language (pitch accent) for which there is no English equivalent. A corpus analysis revealed the presence and distribution of typical and atypical accent forms in Japanese vocabulary, forming a quasiregular domain. Consequently, according to the Primary Systems Hypothesis, there should be a greater semantic impact on the processing of words with an atypical pitch accent. In turn, when word meaning is intrinsically less rich (e.g., abstract words), speakers should be prone to regularization errors of pitch accent. We explored these semantic-phonological interactions, first, in a neuroanatomically constrained, parallel-distributed processing model of spoken language processing. This model captured the accent typicality effect observed in nonword repetition in Japanese adults and children and exhibited the predicted semantic impact on repetition of words with atypical accent patterns. Second, also as predicted, in word repetition and immediate serial recall of spoken words, human participants exhibited reduced pitch-accent accuracy and/or slower RT for low imageability words with atypical accent patterns, and they generated accent errors reflecting the more typical accent patterns found in Japanese.

Theoretical analysis of word production deficits in adult aphasia

The cognitive analysis of adult language disorders continues to draw heavily on linguistic theory, but increasingly it reflects the influence of connectionist, spreading activation models of cognition. In the area of spoken word production, 'localist' connectionist models represent a natural evolution from the psycholingistic theories of earlier decades. By contrast, the parallel distributed processing framework forces more radical rethinking of aphasic impairments. This paper exemplifies these multiple influences in contemporary cognitive aphasiology. Topics include (i) what aphasia reveals about semantic-phonological interaction in lexical access; (ii) controversies surrounding the interpretation of semantic errors and (iii) a computational account of the relationship between naming and word repetition in aphasia. Several of these topics have been addressed using case series methods, including computational simulation of the individual, quantitative error patterns of diverse groups of patients and analysis of brain lesions that correlate with error rates and patterns. Efforts to map the lesion correlates of nonword errors in naming and repetition highlight the involvement of sensorimotor areas in the brain and suggest the need to better integrate models of word production with models of speech and action.

The Emergence of Mind: Personal Knowledge and Connectionism

The Emergence of Mind: Personal Knowledge and Connectionism