Monotone Quantifiers Emerge via Iterated Learning

Natural languages exhibit many semantic universals, that is, properties of meaning shared across all languages. In this paper, we develop an explanation of one very prominent semantic universal, the monotonicity universal. While the existing work has shown that quantifiers satisfying the monotonicity universal are easier to learn, we provide a more complete explanation by considering the emergence of quantifiers from the perspective of cultural evolution. In particular, we show that quantifiers satisfy the monotonicity universal evolve reliably in an iterated learning paradigm with neural networks as agents.     

Semantic clustering as a neuropsychological predictor for amnestic-MCI

ABSTRACT

Recent research has demonstrated that patients with Alzheimer's disease (AD) show deficits in semantic processing when compared to cognitively healthy individuals. This difference is thought to be attributed to losses in higher cortical systems that are predominantly associated with executive functioning. The first aim of the study will be to determine if differences in semantic clustering can accurately differentiate patients with amnestic mild cognitive impairment (aMCI) from cognitively normal (CN) individuals. The second aim will be to determine the extent to which semantic processing might be associated with executive functions. Data from 202 (134 CN, 68 aMCI) participants were analyzed to quantify differences in semantic clustering ratios on the HVLT-R. Study participants ages ranged from 51 to 87 with education ranging from 6 to 20 years. ANCOVA revealed statistically significant differences on semantic clustering ratios (p < .001). Moderate correlations between semantic clustering Category Fluency Test (r = .45) were also found. Statistically significant group differences were also present on Trails-B and WAIS-R Digit Symbol performance (p < .001). Overall, these data indicate that deficits in semantic clustering are present in aMCI patients.     

CoTO: A novel approach for fuzzy aggregation of semantic similarity measures

Semantic similarity measurement aims to determine the likeness between two text expressions that use different lexicographies for representing the same real object or idea. There are a lot of semantic similarity measures for addressing this problem. However, the best results have been achieved when aggregating a number of simple similarity measures. This means that after the various similarity values have been calculated, the overall similarity for a pair of text expressions is computed using an aggregation function of these individual semantic similarity values. This aggregation is often computed by means of statistical functions. In this work, we present CoTO (Consensus or Trade-Off) a solution based on fuzzy logic that is able to outperform these traditional approaches.     

Timing matters! The neural signature of intuitive judgments differs according to the way information is presented

One can conceive of intuition as the preliminary perception of coherence. Since this requires holistic perception, it is hypothesized that underlying processing strategies are dependent on the possibility to obtain all relevant information at once. The present study used magnetoencephalography (MEG) to investigate neural mechanisms underlying intuitive coherence perception when semantic concepts are presented all together (simultaneously) or one after the other (sequentially). With simultaneous presentation, absolute activation increases in the left OFC when participants recognize coherence. With sequential presentation activation increases in the right OFC when participants conclude that there is no common associate between the words presented. Behavioral performance was similar in the two experiments. These results demonstrate that the way information is revealed over time changes the processing of intuitive coherence perception. We propose that such changes must be taken into account to disentangle the neural and behavioral mechanisms underlying different accounts of intuition and related phenomena.     

Modelling the effects of semantic ambiguity in word recognition

Most words in English are ambiguous between different interpretations; words can mean different things in different contexts. We investigate the implications of different types of semantic ambiguity for connectionist models of word recognition. We present a model in which there is competition to activate distributed semantic representations. The model performs well on the task of retrieving the different meanings of ambiguous words, and is able to simulate data reported by Rodd, Gaskell, and Marslen-Wilson [J. Mem. Lang. 46 (2002) 245] on how semantic ambiguity affects lexical decision performance. In particular, the network shows a disadvantage for words with multiple unrelated meanings (e.g., bark) that coexists with a benefit for words with multiple related word senses (e.g., twist). The ambiguity disadvantage arises because of interference between the different meanings, while the sense benefit arises because of differences in the structure of the attractor basins formed during learning. Words with few senses develop deep, narrow attractor basins, while words with many senses develop shallow, broad basins. We conclude that the mental representations of word meanings can be modelled as stable states within a high-dimensional semantic space, and that variations in the meanings of words shape the landscape of this space.     

Language knowledge and event knowledge in language use

This paper examines how semantic knowledge is used in language comprehension and in making judgments about events in the world. We contrast knowledge gleaned from prior language experience (“language knowledge”) and knowledge coming from prior experience with the world (“world knowledge”). In two corpus analyses, we show that previous research linking verb aspect and event representations have confounded language and world knowledge. Then, using carefully chosen stimuli that remove this confound, we performed four experiments that manipulated the degree to which language knowledge or world knowledge should be salient and relevant to performing a task, finding in each case that participants use the type of knowledge most appropriate to the task. These results provide evidence for a highly context-sensitive and interactionist perspective on how semantic knowledge is represented and used during language processing.     

The missing link between faces and names: Evidence from Alzheimer's disease patients

Retrieval of proper names is a cause of concern and complaint among elderly adults and it is an early symptom of patients suffering from neurodegenerative diseases such as Alzheimer's disease (AD). While it is well established that AD patients have deficits of proper name retrieval, the nature of such impairment is not yet fully understood. Specifically, it is unknown whether this deficit is due to a degradation of the links between faces and proper names, or due to deficits in intentionally accessing and retrieving proper names from faces. Here, we aim to investigate the integrity of the links between famous faces and proper names in AD while minimizing the impact of the explicit retrieval. We compare the performances of AD patients and elderly controls in a face-name priming task. We assess the integrity of the link between faces and names at two different levels: identity level - the name and face belong to the same person; and semantic level - the name and face belong to the same category (e.g., politicians). Our results reveal that AD patients compared with controls show intact semantic priming but reduced priming for person identity. This suggests that the deficits in intentionally retrieving proper names in AD are the result of a partial disruption of the network at the identity level, i.e., the links between known faces and proper names.