James W. Minett

Self‐organization and selection in the emergence of vocabulary

Human language may have started from a consistent set of mappings between meanings and signals. These mappings, referred to as the early vocabulary, are considered to be the results of conventions established among the agents of a population. In this study, we report simulation models for investigating how such conventions can be reached. We propose that convention is essentially the product of self-organization of the population through interactions among the agents and that cultural selection is another mechanism that speeds up the establishment of convention. Whereas earlier studies emphasize either one or the other of these two mechanisms, our focus is to integrate them into one hybrid model. The combination of these two complementary mechanisms, i.e., self-organization and cultural selection, provides a plausible explanation for cultural evolution, which progresses with high transmission rate. Furthermore, we observe that as the vocabulary tends to convergence there is a uniform tendency to exhibit a sharp phase transition.

Inferring Phylogenetic Trees Using Answer Set Programming

We describe the reconstruction of a phylogeny for a set of taxa, with a character-based cladistics approach, in a declarative knowledge representation formalism, and show how to use computational methods of answer set programming to generate conjectures about the evolution of the given taxa. We have applied this computational method in two domains: historical analysis of languages and historical analysis of parasite-host systems. In particular, using this method, we have computed some plausible phylogenies for Chinese dialects, for Indo-European language groups, and for Alcataenia species. Some of these plausible phylogenies are different from the ones computed by other software. Using this method, we can easily describe domain-specific information (e.g., temporal and geographical constraints), and thus prevent the reconstruction of some phylogenies that are not plausible.

The use of fuzzy spaces in signal detection

The fuzzy constant false alarm rate (CFAR) detector, which is based on the M-out-of-N binary detector, is characterized and compared with the optimal Neyman–Pearson detector. It replaces the crisp M-out-of-N binary threshold with a soft, continuous threshold, implemented as a membership function. This function is chosen so that the output is equal to the false alarm rate of the binary detector, and therefore maps the observation set to a false alarm space corresponding to the false alarm rate, PFA. An analogous membership function is also developed mapping observations to a detection space which corresponds to the detection rate, PD. These two spaces allow different detectors to be compared directly with respect to the two important detection performance indices, PFA and PD. Comparison of the false alarm space and detection space indicates that the fuzzy CFAR detector and Neyman–Pearson detector detect signals in a different manner and have different detection properties. Nevertheless, performance results illustrate that the fuzzy CFAR detector achieves detection performance comparable to the optimal Neyman–Pearson detector.

Character-Based cladistics and answer set programming

We describe the reconstruction of a phylogeny for a set of taxa, with a character-based cladistics approach, in a declarative knowledge representation formalism, and show how to use computational methods of answer set programming to generate conjectures about the evolution of the given taxa. We have applied this computational method in two domains: to historical analysis of languages, and to historical analysis of parasite-host systems. In particular, using this method, we have computed some plausible phylogenies for Chinese dialects, for Indo-European language groups, and for Alcataenia species. Some of these plausible phylogenies are different from the ones computed by other software. Using this method, we can easily describe domain specific information (e.g. temporal and geographical constraints), and thus prevent the reconstruction of some phylogenies that are not plausible.

The impact of tone systems on the categorical perception of lexical tones: An event-related potentials study

This study investigates the categorical perception (CP) of pitch contours (level and rising) by native listeners of two tone languages, Mandarin and Cantonese, for both speech and nonspeech. Language background was found to modulate participants’ behavioural and electrophysiological responses to stimuli presented in an active oddball paradigm, comprising a standard and two equally spaced deviants (within- and across-category). The stimuli were divided into two sets according to the results of a two-alternative forced-choice identification test: a rising set, using a standard that listeners identified as high rising tone, and a level set, using a standard that listeners identified as high level tone. For the rising set, both groups of listeners exhibited CP in terms of their behavioural response. However, only Cantonese listeners exhibited a significant CP effect in terms of P300 amplitude. For the level set, the behavioural data revealed a shift in category boundary due, in part, to the range–frequency effect. According to the d′ scores, the CP effect elicited from Mandarin listeners was greater for nonspeech stimuli than for speech, suggesting the presence of apsychophysical boundary. There was no such behavioural contrast for Cantonese listeners. However, Cantonese listeners exhibited a significant CP effect in P300 amplitude that was influenced by the range–frequency effect, as well as a possible secondary phonological boundary. P300 amplitude is believed to index the ease of discrimination of speech stimuli by phonological information. We conclude that Cantonese listeners engaged phonological processing in order to discriminate speech stimuli more efficiently than Mandarin listeners. These findings may be due to the different tonal inventories of Mandarin and Cantonese, with Cantonese listeners required to make finer distinctions in perception of pitch height and slope than Mandarin listeners in order to discriminate the denser tone system of Cantonese.

A Chinese Text Input Brain-Computer Interface Based on the P300 Speller

A visual speller is a brain–computer interface that empowers users with limited motor functionality to input text into a computer by measuring their electroencephalographic responses to visual stimuli. Most prior research on visual spellers has focused on input of alphabetic text. Adapting a speller for other types of segmental or syllabic script is straightforward because such scripts comprise sufficiently few characters that they may all be displayed to the user simultaneously. Logographic scripts, such as Chinese hanzi, however, impose a challenge: How should the thousands of Chinese characters be displayed to the user? Here, we present a visual speller, based on Farwell and Donchins P300 Speller, for Chinese character input. The speller uses a novel shape-based method called the First–Last, or FLAST, method to encode more than 7,000 Chinese characters. Characters are input by selecting two components, from a set of 56 distinct components, that match the shape of the target character, followed by selection of the character itself. At the input speed of one character per 107 s, 24 able-bodied participants achieved mean online accuracy of 82.8% per component selection and 63.5% per character input. At the faster input speed of one character per 77 s, mean online accuracy was 59.4% per component selection and 33.3% per character input.

Cultural background influences the liminal perception of Chinese characters: An ERP study

The event-related brain potentials elicited by rapid visual presentation of Chinese characters and non-characters were studied for two groups of adult native Chinese speakers: one group of Putonghua speakers, who could read Simplified Chinese characters, and one group of Hong Kong Cantonese speakers, who could read Traditional Chinese characters. For Putonghua participants, but not Hong Kong Cantonese participants, liminally perceived characters were found to elicit significantly greater P300 amplitude than non-characters. Based on the context updating hypothesis, this result indicates that Putonghua participants discriminated stimuli according to their linguistic function (character versus non-character) more easily than Hong Kong Cantonese participants. Putonghua participants were also better able to discriminate characters based on their physical properties (high symmetry character versus low symmetry character). These findings are consistent with the hypothesis that Simplified character readers have greater visual discrimination skill than Traditional character readers. The results also provide the first evidence that cultural background shapes sensitivity in the liminal perception of Chinese characters, an important step toward a general theory of the cognitive processes involved in reading.

The networks of syllables and characters in Chinese

Abstract We develop networks using the syllables (both base syllables and tonal syllables) and characters of Chinese. The nodes (vertices) of the networks represent the syllables of the syllable network and the characters of the character network respectively. The links (edges) are established between any two syllables (or two characters) that form part of one or more words. We use two dictionaries to perform the analysis: a Putonghua 1 dictionary and a Cantonese dictionary. All networks here show low distances and high clustering coefficients compared with ER random networks. The degree distributions all follow a power-law; however, the exponents for the base syllable, tonal syllable and Chinese character networks differ considerably. These differences may account for the different cognitive processes used when constructing new Chinese words. The networks are compared to the syllabic networks of Portuguese in terms of the magnitude of the power-law exponent. The Chinese character network is found to be the most similar to the Portuguese syllabic network (γ≈ 1.4). 1Putonghua is a generally accepted term for Modern Standard Chinese (MSC).

Exploring social structure effect on language evolution based on a computational model

A compositionality-regularity coevolution model is adopted to explore the effect of social structure on language emergence and maintenance. Based on this model, we explore language evolution in three experiments, and discuss the role of a popular agent in language evolution, the relationship between mutual understanding and social hierarchy, and the effect of inter-community communications and that of simple linguistic features on convergence of communal languages in two communities. This work embodies several important interactions during social learning, and introduces a new approach that manipulates individuals’ probabilities to participate in social interactions to study the effect of social structure. We hope it will stimulate further theoretical and empirical explorations on language evolution in a social environment.

CFAR data fusion using fuzzy integration

This paper presents a new approach to constant false alarm rate (CFAR) data fusion using fuzzy integration. The paper describes how any CFAR scheme may be implemented as part of a fuzzy data fusion scheme by choosing an appropriate membership function to represent the CFAR threshold. Once the threshold membership function of the fuzzy integrator has been set up, the false alarm rate of the scheme is independent of fluctuations in interference mean power and depends only on the number of signals integrated by the data fusion unit and the required false alarm rate.