Connectionism as metaphor: Towards an integrated, unified conception of self-system and individual difference

Comments on an article by Kashima et al. (see record 2007-10111-001). In their target article Kashima and colleagues try to show how a connectionist model conceptualization of the self is best suited to capture the self's temporal and socio-culturally contextualized nature. They propose a new model and to support this model, the authors conduct computer simulations of psychological phenomena whose importance for the self has long been clear, even if not formally modeled, such as imitation, and learning of sequence and narrative. As explicated when we advocated connectionist models as a metaphor for self in Mischel and Morf (2003), we fully endorse the utility of such a metaphor, as these models have some of the processing characteristics necessary for capturing key aspects and functions of a dynamic cognitive-affective self-system. As elaborated in that chapter, we see as their principal strength that connectionist models can take account of multiple simultaneous processes without invoking a single central control. All outputs reflect a distributed pattern of activation across a large number of simple processing units, the nature of which depends on (and changes with) the connection weights between the links and the satisfaction of mutual constraints across these links (Rummelhart & McClelland, 1986). This allows a simple account for why certain input features will at times predominate, while others take over on other occasions. (PsycINFO Database Record (c) 2008 APA, all rights reserved)

Towards an Emergent Semantic Web

In his in uential article about the evolution of the Web, Berners-Lee [1] envisions a Semantic Web in which humans and computers alike are capable of understanding and processing information. This vision is yet to materialize. The main obstacle for the Semantic Web vision is that in today's Web meaning is rooted most often not in formal semantics, but in natural language and, in the sense of semiology, emerges not before interpretation and processing. Yet, an automated form of interpretation and processing can be tackled by precisiating raw natural language. To do that, Web agents extract fuzzy grassroots ontologies through induction from existing Web content. Inductive fuzzy grassroots ontologies thus constitute organically evolved knowledge bases that resemble automated gradual thesauri, which allow precisiating natural language [2]. The Web agents' underlying dynamic, self-organizing, and best-effort induction, enable a sub-syntactical bottom up learning of semiotic associations. Thus, knowledge is induced from the users' natural use of language in mutual Web interactions, and stored in a gradual, thesauri-like lexical-world knowledge database as a top-level ontology, eventually allowing a form of computing with words [3]. Since when computing with words the objects of computation are words, phrases and propositions drawn from natural languages, it proves to be a practical notion to yield emergent semantics for the Semantic Web. In the end, an improved understanding by computers on the one hand should upgrade human- computer interaction on the Web, and, on the other hand allow an initial version of human- intelligence amplification through the Web.

Norms on the gender perception of role nouns in Czech, English, French, German, Italian, Norwegian, and Slovak

We collected norms on the gender stereotypicality of an extensive list of role nouns in Czech, English, French, German, Italian, Norwegian, and Slovak, to be used as a basis for the selection of stimulus materials in future studies. We present a Web-based tool (available at https://www.unifr.ch/lcg/) that we developed to collect these norms and that we expect to be useful for other researchers, as well. In essence, we provide (a) gender stereotypicality norms across a number of languages and (b) a tool to facilitate cross-language as well as cross-cultural comparisons when researchers are interested in the investigation of the impact of stereotypicality on the processing of role nouns.

Unravelling the distinct strains of Tharu ancestry

The northern region of the Indian subcontinent is a vast landscape interlaced by diverse ecologies, e.g. the Gangetic plain and the Himalayas. A great number of ethnic groups are found there, displayed as a multitude of languages and cultures. The Tharu represent one of the largest and linguistically most diversified such groups, scattered across the Tarai region of Nepal and bordering Indian states. Their origins are uncertain. Hypotheses have been advanced about an Austroasiatic affinity, Tibeto-Burman origins, as well as aboriginal roots in the Tarai. Several Tharu groups speak a variety of Indo-Aryan languages, but have traditionally been described by ethnographers as representing an East Asian phenotype. Their ancestry and intra-population diversity had previously been tested only for haploid (mtDNA and Y-chromosome) markers in a small portion of the population. This study presents the first systematic genetic survey of the Tharu from both Nepal and the Indian states of Uttaranchal and Uttar Pradesh, using genome-wide SNPs and haploid (mtDNA and Y-chromosome) markers. The results suggest that the 'ethnic' construct of Tharu is likely to have lain in the Tarai region, with a reconstructible radiation to Uttaranchal and Uttar Pradesh. Despite extensive admixture with other local communities, Tharu sub-populations preserve strong genetic signatures that indicate a common ancestry.

Probabilistic contingency learning with limbic or prefrontal damage

AB A fundamental capacity of the human brain is to learn relations (contingencies) between environmental stimuli and the consequences of their occurrence. Some contingencies are probabilistic; that is, they predict an event in some situations but not in all. Animal studies suggest that damage to limbic structures or the prefrontal cortex may disturb probabilistic learning. The authors studied the learning of probabilistic contingencies in amnesic patients with limbic lesions, patients with prefrontal cortex damage, and healthy controls. Across 120 trials, participants learned contingent relations between spatial sequences and a button press. Amnesic patients had learning comparable to that of control subjects but failed to indicate what they had learned. Across the last 60 trials, amnesic patients and control subjects learned to avoid a noncontingent choice better than frontal patients. These results indicate that probabilistic learning does not depend on the brain structures supporting declarative memory.

Multimodal sequence learning

While sequence learning research models complex phenomena, previous studies have mostly focused on unimodal sequences. The goal of the current experiment is to put implicit sequence learning into a multimodal context: to test whether it can operate across different modalities. We used the Task Sequence Learning paradigm to test whether sequence learning varies across modalities, and whether participants are able to learn multimodal sequences. Our results show that implicit sequence learning is very similar regardless of the source modality. However, the presence of correlated task and response sequences was required for learning to take place. The experiment provides new evidence for implicit sequence learning of abstract conceptual representations. In general, the results suggest that correlated sequences are necessary for implicit sequence learning to occur. Moreover, they show that elements from different modalities can be automatically integrated into one unitary multimodal sequence.