A Special Homotopy Continuation Method For A Class of Polynomial Systems

A special homotopy continuation method, as a combination of the polyhedral homotopy and the linear product homotopy, is proposed for computing all the isolated solutions to a special class of polynomial systems. The root number bound of this method is between the total degree bound and the mixed volume bound and can be easily computed. The new algorithm has been implemented as a program called LPH using C++. Our experiments show its efficiency compared to the polyhedral or other homotopies on such systems. As an application, the algorithm can be used to find witness points on each connected component of a real variety

Validated Intraclass Correlation Statistics to Test Item Performance Models

A new method, with an application program in Matlab code, is proposed for testing item performance models on empirical databases. This method uses data intraclass correlation statistics as expected correlations to which one compares simple functions of correlations between model predictions and observed item performance. The method rests on a data population model whose validity for the considered data is suitably tested, and has been verified for three behavioural measure databases. Contrarily to usual model selection criteria, this method provides an effective way of testing under-fitting and over-fitting, answering the usually neglected question "does this model suitably account for these data?

The British Lexicon Project: Lexical decision data for 28,730 monosyllabic and disyllabic English words

We present a new database of lexical decision times for English words and nonwords, for which two groups of British participants each responded to 14,365 monosyllabic and disyllabic words and the same number of nonwords for a total duration of 16 h (divided over multiple sessions). This database, called the British Lexicon Project (BLP), fills an important gap between the Dutch Lexicon Project (DLP; Keuleers, Diependaele, & Brysbaert, Frontiers in Language Sciences. Psychology, 1, 174, 2010) and the English Lexicon Project (ELP; Balota et al., 2007), because it applies the repeated measures design of the DLP to the English language. The high correlation between the BLP and ELP data indicates that a high percentage of variance in lexical decision data sets is systematic variance, rather than noise, and that the results of megastudies are rather robust with respect to the selection and presentation of the stimuli. Because of its design, the BLP makes the same analyses possible as the DLP, offering researchers with a new interesting data set of word-processing times for mixed effects analyses and mathematical modeling. The BLP data are available at http://crr.ugent.be/blp and as Electronic Supplementary Materials

Stress Priming in Reading and the Selective Modulation of Lexical and Sub-Lexical Pathways

Four experiments employed a priming methodology to investigate different mechanisms of stress assignment and how they are modulated by lexical and sub-lexical mechanisms in reading aloud in Italian. Lexical stress is unpredictable in Italian, and requires lexical look-up. The most frequent stress pattern (Dominant) is on the penultimate syllable [laVOro (work)], while stress on the antepenultimate syllable [MAcchina (car)] is relatively less frequent (non-Dominant). Word and pseudoword naming responses primed by words with non-dominant stress – which require whole-word knowledge to be read correctly – were compared to those primed by nonwords. Percentage of errors to words and percentage of dominant stress responses to nonwords were measured. In Experiments 1 and 2 stress errors increased for non-dominant stress words primed by nonwords, as compared to when they were primed by words. The results could be attributed to greater activation of sub-lexical codes, and an associated tendency to assign the dominant stress pattern by default in the nonword prime condition. Alternatively, they may have been the consequence of prosodic priming, inducing more errors on trials in which the stress pattern of primes and targets was not congruent. The two interpretations were investigated in Experiments 3 and 4. The results overall suggested a limited role of the default metrical pattern in word pronunciation, and showed clear effect of prosodic priming, but only when the sub-lexical mechanism prevailed

Representation of the verb's argument-structure in the human brain

<p>Abstract</p> <p>Background</p> <p>A verb's argument structure defines the number and relationships of participants needed for a complete event. One-argument (intransitive) verbs require only a subject to make a complete sentence, while two- and three-argument verbs (transitives and ditransitives) normally take direct and indirect objects. Cortical responses to verbs embedded into sentences (correct or with syntactic violations) indicate the processing of the verb's argument structure in the human brain. The two experiments of the present study examined whether and how this processing is reflected in distinct spatio-temporal cortical response patterns to isolated verbs and/or verbs presented in minimal context.</p> <p>Results</p> <p>The magnetoencephalogram was recorded while 22 native German-speaking adults saw 130 German verbs, presented one at a time for 150 ms each in experiment 1. Verb-evoked electromagnetic responses at 250 – 300 ms after stimulus onset, analyzed in source space, were higher in the left middle temporal gyrus for verbs that take only one argument, relative to two- and three-argument verbs. In experiment 2, the same verbs (presented in different order) were preceded by a proper name specifying the subject of the verb. This produced additional activation between 350 and 450 ms in or near the left inferior frontal gyrus, activity being larger and peaking earlier for one-argument verbs that required no further arguments to form a complete sentence.</p> <p>Conclusion</p> <p>Localization of sources of activity suggests that the activation in temporal and frontal regions varies with the degree by which representations of an event as a part of the verbs' semantics are completed during parsing.</p

An Activation Force-based Affinity Measure for Analyzing Complex Networks

Affinity measure is a key factor that determines the quality of the analysis of a complex network. Here, we introduce a type of statistics, activation forces, to weight the links of a complex network and thereby develop a desired affinity measure. We show that the approach is superior in facilitating the analysis through experiments on a large-scale word network and a protein-protein interaction (PPI) network consisting of ∼5,000 human proteins. The experiment on the word network verifies that the measured word affinities are highly consistent with human knowledge. Further, the experiment on the PPI network verifies the measure and presents a general method for the identification of functionally similar proteins based on PPIs. Most strikingly, we find an affinity network that compactly connects the cancer-associated proteins to each other, which may reveal novel information for cancer study; this includes likely protein interactions and key proteins in cancer-related signal transduction pathways

Phrase Frequency Effects in Language Production

A classic debate in the psychology of language concerns the question of the grain-size of the linguistic information that is stored in memory. One view is that only morphologically simple forms are stored (e.g., ‘car’, ‘red’), and that more complex forms of language such as multi-word phrases (e.g., ‘red car’) are generated on-line from the simple forms. In two experiments we tested this view. In Experiment 1, participants produced noun+adjective and noun+noun phrases that were elicited by experimental displays consisting of colored line drawings and two superimposed line drawings. In Experiment 2, participants produced noun+adjective and determiner+noun+adjective utterances elicited by colored line drawings. In both experiments, naming latencies decreased with increasing frequency of the multi-word phrase, and were unaffected by the frequency of the object name in the utterance. These results suggest that the language system is sensitive to the distribution of linguistic information at grain-sizes beyond individual words

Does Sleep Improve Your Grammar? : Preferential Consolidation of Arbitrary Components of New Linguistic Knowledge

We examined the role of sleep-related memory consolidation processes in learning new form-meaning mappings. Specifically, we examined a Complementary Learning Systems account, which implies that sleep-related consolidation should be more beneficial for new hippocampally dependent arbitrary mappings (e.g. new vocabulary items) relative to new systematic mappings (e.g. grammatical regularities), which can be better encoded neocortically. The hypothesis was tested using a novel language with an artificial grammatical gender system. Stem-referent mappings implemented arbitrary aspects of the new language, and determiner/suffix+natural gender mappings implemented systematic aspects (e.g. tib scoiffesh + ballerina, tib mofeem + bride; ked jorool + cowboy, ked heefaff + priest). Importantly, the determiner-gender and the suffix-gender mappings varied in complexity and salience, thus providing a range of opportunities to detect beneficial effects of sleep for this type of mapping. Participants were trained on the new language using a word-picture matching task, and were tested after a 2-hour delay which included sleep or wakefulness. Participants in the sleep group outperformed participants in the wake group on tests assessing memory for the arbitrary aspects of the new mappings (individual vocabulary items), whereas we saw no evidence of a sleep benefit in any of the tests assessing memory for the systematic aspects of the new mappings: Participants in both groups extracted the salient determiner-natural gender mapping, but not the more complex suffix-natural gender mapping. The data support the predictions of the complementary systems account and highlight the importance of the arbitrariness/systematicity dimension in the consolidation process for declarative memories

Interference between Sentence Processing and Probabilistic Implicit Sequence Learning

During sentence processing we decode the sequential combination of words, phrases or sentences according to previously learned rules. The computational mechanisms and neural correlates of these rules are still much debated. Other key issue is whether sentence processing solely relies on language-specific mechanisms or is it also governed by domain-general principles.In the present study, we investigated the relationship between sentence processing and implicit sequence learning in a dual-task paradigm in which the primary task was a non-linguistic task (Alternating Serial Reaction Time Task for measuring probabilistic implicit sequence learning), while the secondary task were a sentence comprehension task relying on syntactic processing. We used two control conditions: a non-linguistic one (math condition) and a linguistic task (word processing task). Here we show that the sentence processing interfered with the probabilistic implicit sequence learning task, while the other two tasks did not produce a similar effect.Our findings suggest that operations during sentence processing utilize resources underlying non-domain-specific probabilistic procedural learning. Furthermore, it provides a bridge between two competitive frameworks of language processing. It appears that procedural and statistical models of language are not mutually exclusive, particularly for sentence processing. These results show that the implicit procedural system is engaged in sentence processing, but on a mechanism level, language might still be based on statistical computations