Neural mechanisms of visual object priming: evidence for perceptual and semantic distinctions in fusiform cortex

Previous functional imaging studies have shown that facilitated processing of a visual object on repeated, relative to initial, presentation (i.e., repetition priming) is associated with reductions in neural activity in multiple regions, including fusiforin/lateral occipital cortex. Moreover, activity reductions have been found, at diminished levels, when a different exemplar of an object is presented on repetition. In one previous study, the magnitude of diminished priming across exemplars was greater in the right relative to the left fusiform, suggesting greater exemplar specificity in the right. Another previous study, however, observed fusiform lateralization modulated by object viewpoint, but not object exemplar. The present fMRI study sought to determine whether the result of differential fusiform responses for perceptually different exemplars could be replicated. Furthermore, the role of the left fusiform cortex in object recognition was investigated via the inclusion of a lexical/semantic manipulation. Right fusiform cortex showed a significantly greater effect of exemplar change than left fusiform, replicating the previous result of exemplar-specific fusiform lateralization. Right fusiform and lateral occipital cortex were not differentially engaged by the lexical/semantic manipulation, suggesting that their role in visual object recognition is predominantly in the. C visual discrimination of specific objects. Activation in left fusiform cortex, but not left lateral occipital cortex, was modulated by both exemplar change and lexical/semantic manipulation, with further analysis suggesting a posterior-to-anterior progression between regions involved in processing visuoperceptual and lexical/semantic information about objects. The results are consistent with the view that the right fusiform plays a greater role in processing specific visual form information about objects, whereas the left fusiform is also involved in lexical/semantic processing. (C) 2003 Elsevier Science (USA). All rights reserved.

On-line processing of wh-questions in children with G-SLI and typically developing children

Background: The computational grammatical complexity ( CGC) hypothesis claims that children with G(rammatical)-specific language impairment ( SLI) have a domain-specific deficit in the computational system affecting syntactic dependencies involving 'movement'. One type of such syntactic dependencies is filler-gap dependencies. In contrast, the Generalized Slowing Hypothesis claims that SLI children have a domain-general deficit affecting processing speed and capacity. Aims: To test contrasting accounts of SLI we investigate processing of syntactic (filler-gap) dependencies in wh-questions. Methods & Procedures: Fourteen 10; 2 - 17; 2 G-SLI children, 14 age- matched and 17 vocabulary-matched controls were studied using the cross- modal picturepriming paradigm. Outcomes & Results: G-SLI children's processing speed was significantly slower than the age controls, but not younger vocabulary controls. The G- SLI children and vocabulary controls did not differ on memory span. However, the typically developing and G-SLI children showed a qualitatively different processing pattern. The age and vocabulary controls showed priming at the gap, indicating that they process wh-questions through syntactic filler-gap dependencies. In contrast, G-SLI children showed priming only at the verb. Conclusions: The findings indicate that G-SLI children fail to establish reliably a syntactic filler- gap dependency and instead interpret wh-questions via lexical thematic information. These data challenge the Generalized Slowing Hypothesis account, but support the CGC hypothesis, according to which G-SLI children have a particular deficit in the computational system affecting syntactic dependencies involving 'movement'. As effective remediation often depends on aetiological insight, the discovery of the nature of the syntactic deficit, along side a possible compensatory use of semantics to facilitate sentence processing, can be used to direct therapy. However, the therapeutic strategy to be used, and whether such similar strengths and weaknesses within the language system are found in other SLI subgroups are empirical issues that warrant further research.

Gaps in second language sentence processing

Four groups of second language (L2) learners of English from different language backgrounds (Chinese, Japanese, German, and Greek) and a group of native speaker controls participated in an online reading time experiment with sentences involving long-distance whdependencies. Although the native speakers showed evidence of making use of intermediate syntactic gaps during processing, the L2 learners appeared to associate the fronted wh-phrase directly with its lexical subcategorizer, regardless of whether the subjacency constraint was operative in their native language. This finding is argued to support the hypothesis that nonnative comprehenders underuse syntactic information in L2 processing.

The representation of delayed intentions: a prospective subject-performed task?

To-be-enacted material is more accessible in tests of recognition and lexical decision than material not intended for action (T. Goschke J. Kuhl, 1993; R. L. Marsh, J. L. Hicks, & M. L. Bink, 1998). This finding has been attributed to the superior status of intention-related information. The current article explores an alternative (action-superiority) account that draws parallels between the intended enactment effect (IEE) and the subject-performed task effect. Using 2 paradigms, the authors observed faster recognition latencies for both enacted and to-be-enacted material. It is crucial to note that there was no evidence of an IEE for items that had already been executed during encoding. The IEE was also eliminated when motor processing was prevented after verbal encoding. These findings suggest an overlap between overt and intended enactment and indicate that motor information may be activated for verbal material in preparation for subsequent execution.

Verb retrieval in fluent aphasia: a clinical study

Background: Problems with lexical retrieval are common across all types of aphasia but certain word classes are thought to be more vulnerable in some aphasia types. Traditionally, verb retrieval problems have been considered characteristic of non-fluent aphasias but there is growing evidence that verb retrieval problems are also found in fluent aphasia. As verbs are retrieved from the mental lexicon with syntactic as well as phonological and semantic information, it is speculated that an improvement in verb retrieval should enhance communicative abilities in this population as in others. We report on an investigation into the effectiveness of verb treatment for three individuals with fluent aphasia. Methods & Procedures: Multiple pre-treatment baselines were established over 3 months in order to monitor language change before treatment. The three participants then received twice-weekly verb treatment over approximately 4 months. All pre-treatment assessments were administered immediately after treatment and 3 months post-treatment. Outcome & Results: Scores fluctuated in the pre-treatment period. Following treatment, there was a significant improvement in verb retrieval for two of the three participants on the treated items. The increase in scores for the third participant was statistically nonsignificant but post-treatment scores moved from below the normal range to within the normal range. All participants were significantly quicker in the verb retrieval task following treatment. There was an increase in well-formed sentences in the sentence construction test and in some samples of connected speech. Conclusions: Repeated systematic treatment can produce a significant improvement in verb retrieval of practised items and generalise to unpractised items for some participants. An increase in well-formed sentences is seen for some speakers. The theoretical and clinical implications of the results are discussed.

Towards dynamical system models of language-related brain potentials

Event-related brain potentials (ERP) are important neural correlates of cognitive processes. In the domain of language processing, the N400 and P600 reflect lexical-semantic integration and syntactic processing problems, respectively. We suggest an interpretation of these markers in terms of dynamical system theory and present two nonlinear dynamical models for syntactic computations where different processing strategies correspond to functionally different regions in the system's phase space.

Perspex machine VII: The universal perspex machine

The perspex machine arose from the unification of projective geometry with the Turing machine. It uses a total arithmetic, called transreal arithmetic, that contains real arithmetic and allows division by zero. Transreal arithmetic is redefined here. The new arithmetic has both a positive and a negative infinity which lie at the extremes of the number line, and a number nullity that lies off the number line. We prove that nullity, 0/0, is a number. Hence a number may have one of four signs: negative, zero, positive, or nullity. It is, therefore, impossible to encode the sign of a number in one bit, as floating-, point arithmetic attempts to do, resulting in the difficulty of having both positive and negative zeros and NaNs. Transrational arithmetic is consistent with Cantor arithmetic. In an extension to real arithmetic, the product of zero, an infinity, or nullity with its reciprocal is nullity, not unity. This avoids the usual contradictions that follow from allowing division by zero. Transreal arithmetic has a fixed algebraic structure and does not admit options as IEEE, floating-point arithmetic does. Most significantly, nullity has a simple semantics that is related to zero. Zero means "no value" and nullity means "no information." We argue that nullity is as useful to a manufactured computer as zero is to a human computer. The perspex machine is intended to offer one solution to the mind-body problem by showing how the computable aspects of mind and. perhaps, the whole of mind relates to the geometrical aspects of body and, perhaps, the whole of body. We review some of Turing's writings and show that he held the view that his machine has spatial properties. In particular, that it has the property of being a 7D lattice of compact spaces. Thus, we read Turing as believing that his machine relates computation to geometrical bodies. We simplify the perspex machine by substituting an augmented Euclidean geometry for projective geometry. This leads to a general-linear perspex-machine which is very much easier to pro-ram than the original perspex-machine. We then show how to map the whole of perspex space into a unit cube. This allows us to construct a fractal of perspex machines with the cardinality of a real-numbered line or space. This fractal is the universal perspex machine. It can solve, in unit time, the halting problem for itself and for all perspex machines instantiated in real-numbered space, including all Turing machines. We cite an experiment that has been proposed to test the physical reality of the perspex machine's model of time, but we make no claim that the physical universe works this way or that it has the cardinality of the perspex machine. We leave it that the perspex machine provides an upper bound on the computational properties of physical things, including manufactured computers and biological organisms, that have a cardinality no greater than the real-number line.

An orthogonal forward regression technique for sparse kernel density estimation

Using the classical Parzen window (PW) estimate as the desired response, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density (SKD) estimates. The proposed algorithm incrementally minimises a leave-one-out test score to select a sparse kernel model, and a local regularisation method is incorporated into the density construction process to further enforce sparsity. The kernel weights of the selected sparse model are finally updated using the multiplicative nonnegative quadratic programming algorithm, which ensures the nonnegative and unity constraints for the kernel weights and has the desired ability to reduce the model size further. Except for the kernel width, the proposed method has no other parameters that need tuning, and the user is not required to specify any additional criterion to terminate the density construction procedure. Several examples demonstrate the ability of this simple regression-based approach to effectively construct a SKID estimate with comparable accuracy to that of the full-sample optimised PW density estimate. (c) 2007 Elsevier B.V. All rights reserved.

Probability density estimation with tunable kernels using orthogonal forward regression

A generalized or tunable-kernel model is proposed for probability density function estimation based on an orthogonal forward regression procedure. Each stage of the density estimation process determines a tunable kernel, namely, its center vector and diagonal covariance matrix, by minimizing a leave-one-out test criterion. The kernel mixing weights of the constructed sparse density estimate are finally updated using the multiplicative nonnegative quadratic programming algorithm to ensure the nonnegative and unity constraints, and this weight-updating process additionally has the desired ability to further reduce the model size. The proposed tunable-kernel model has advantages, in terms of model generalization capability and model sparsity, over the standard fixed-kernel model that restricts kernel centers to the training data points and employs a single common kernel variance for every kernel. On the other hand, it does not optimize all the model parameters together and thus avoids the problems of high-dimensional ill-conditioned nonlinear optimization associated with the conventional finite mixture model. Several examples are included to demonstrate the ability of the proposed novel tunable-kernel model to effectively construct a very compact density estimate accurately.

The relation between content and structure in language production: an analysis of speech errors in semantic dementia

In order to explore the impact of a degraded semantic system on the structure of language production, we analysed transcripts from autobiographical memory interviews to identify naturally-occurring speech errors by eight patients with semantic dementia (SD) and eight age-matched normal speakers. Relative to controls, patients were significantly more likely to (a) substitute and omit open class words, (b) substitute (but not omit) closed class words, (c) substitute incorrect complex morphological forms and (d) produce semantically and/or syntactically anomalous sentences. Phonological errors were scarce in both groups. The study confirms previous evidence of SD patients’ problems with open class content words which are replaced by higher frequency, less specific terms. It presents the first evidence that SD patients have problems with closed class items and make syntactic as well as semantic speech errors, although these grammatical abnormalities are mostly subtle rather than gross. The results can be explained by the semantic deficit which disrupts the representation of a pre-verbal message, lexical retrieval and the early stages of grammatical encoding.

To 'be', and not to 'have': auxiliary selection in unaccusative verbs in Italian

This paper investigates the characteristics of unaccusative verbs in Italian with respect to the consistency with which these verbs select the auxiliaries ‘be’ (essere) and ‘have’ (avere) in compound tense forms. The study builds on the gradient approach to split intransitivity (Sorace 2000) by exploring the behaviour of 29 intransitive Italian verbs with respect to their core-peripheral features: auxiliary selection acceptability ratings and associated variance measures. Although there is clear support for the gradient approach in relation to the general order of semantic categories along the unaccusativity gradient, the results reveal that the ordering of subclasses within the Change group conflict with that currently proposed in the literature. In addition, the findings demonstrate the aspectual and lexical semantic characteristics of internally-caused change-of-state verbs in Italian require further investigation before their auxiliary selection behaviour can be properly understood. Furthermore, contrary to the gradient account, Existence verbs, the most stative and therefore the most peripheral subclass in the unaccusativity hierarchy, exhibit behaviour more characteristic of core unaccusative verbs. This study examines a wider range of semantic subclasses of unaccusative verbs than has hitherto been reported and identifies the core-peripheral boundary for Italian.1

Neurofuzzy network model construction using Bézier-Bernstein polynomial functions

Neurofuzzy modelling systems combine fuzzy logic with quantitative artificial neural networks via a concept of fuzzification by using a fuzzy membership function usually based on B-splines and algebraic operators for inference, etc. The paper introduces a neurofuzzy model construction algorithm using Bezier-Bernstein polynomial functions as basis functions. The new network maintains most of the properties of the B-spline expansion based neurofuzzy system, such as the non-negativity of the basis functions, and unity of support but with the additional advantages of structural parsimony and Delaunay input space partitioning, avoiding the inherent computational problems of lattice networks. This new modelling network is based on the idea that an input vector can be mapped into barycentric co-ordinates with respect to a set of predetermined knots as vertices of a polygon (a set of tiled Delaunay triangles) over the input space. The network is expressed as the Bezier-Bernstein polynomial function of barycentric co-ordinates of the input vector. An inverse de Casteljau procedure using backpropagation is developed to obtain the input vector's barycentric co-ordinates that form the basis functions. Extension of the Bezier-Bernstein neurofuzzy algorithm to n-dimensional inputs is discussed followed by numerical examples to demonstrate the effectiveness of this new data based modelling approach.

Generalized neurofuzzy network modeling algorithms using Bezier-Bernstein polynomial functions and additive decomposition

This paper introduces a new neurofuzzy model construction algorithm for nonlinear dynamic systems based upon basis functions that are Bezier-Bernstein polynomial functions. This paper is generalized in that it copes with n-dimensional inputs by utilising an additive decomposition construction to overcome the curse of dimensionality associated with high n. This new construction algorithm also introduces univariate Bezier-Bernstein polynomial functions for the completeness of the generalized procedure. Like the B-spline expansion based neurofuzzy systems, Bezier-Bernstein polynomial function based neurofuzzy networks hold desirable properties such as nonnegativity of the basis functions, unity of support, and interpretability of basis function as fuzzy membership functions, moreover with the additional advantages of structural parsimony and Delaunay input space partition, essentially overcoming the curse of dimensionality associated with conventional fuzzy and RBF networks. This new modeling network is based on additive decomposition approach together with two separate basis function formation approaches for both univariate and bivariate Bezier-Bernstein polynomial functions used in model construction. The overall network weights are then learnt using conventional least squares methods. Numerical examples are included to demonstrate the effectiveness of this new data based modeling approach.

Operationalising and measuring language dominance

The paper offers a new way to measure language ability in bilinguals, based on measures of lexical richness. The validity of proposed approach is tested in a variety of ways.

Age-related changes in the functional neuroanatomy of overt speech production

Alterations of existing neural networks during healthy aging, resulting in behavioral deficits and changes in brain activity, have been described for cognitive, motor, and sensory functions. To investigate age-related changes in the neural circuitry underlying overt non-lexical speech production, functional MRI was performed in 14 healthy younger (21–32 years) and 14 healthy older individuals (62–84 years). The experimental task involved the acoustically cued overt production of the vowel /a/ and the polysyllabic utterance /pataka/. In younger and older individuals, overt speech production was associated with the activation of a widespread articulo-phonological network, including the primary motor cortex, the supplementary motor area, the cingulate motor areas, and the posterior superior temporal cortex, similar in the /a/ and /pataka/ condition. An analysis of variance with the factors age and condition revealed a significant main effect of age. Irrespective of the experimental condition, significantly greater activation was found in the bilateral posterior superior temporal cortex, the posterior temporal plane, and the transverse temporal gyri in younger compared to older individuals. Significantly greater activation was found in the bilateral middle temporal gyri, medial frontal gyri, middle frontal gyri, and inferior frontal gyri in older vs. younger individuals. The analysis of variance did not reveal a significant main effect of condition and no significant interaction of age and condition. These results suggest a complex reorganization of neural networks dedicated to the production of speech during healthy aging.