Natalie M. Trumpp

Bio: Natalie M. Trumpp is an academic researcher from University of Ulm. The author has contributed to research in topics: Embodied cognition & Semantic memory. The author has an hindex of 10, co-authored 14 publications receiving 436 citations.

Handwriting or Typewriting? The Influence of Pen- or Keyboard-Based Writing Training on Reading and Writing Performance in Preschool Children.

Abstract: Digital writing devices associated with the use of computers, tablet PCs, or mobile phones are increasingly replacing writing by hand. It is, however, controversially discussed how writing modes influence reading and writing performance in children at the start of literacy. On the one hand, the easiness of typing on digital devices may accelerate reading and writing in young children, who have less developed sensory-motor skills. On the other hand, the meaningful coupling between action and perception during handwriting, which establishes sensory-motor memory traces, could facilitate written language acquisition. In order to decide between these theoretical alternatives, for the present study, we developed an intense training program for preschool children attending the German kindergarten with 16 training sessions. Using closely matched letter learning games, eight letters of the German alphabet were trained either by handwriting with a pen on a sheet of paper or by typing on a computer keyboard. Letter recognition, naming, and writing performance as well as word reading and writing performance were assessed. Results did not indicate a superiority of typing training over handwriting training in any of these tasks. In contrast, handwriting training was superior to typing training in word writing, and, as a tendency, in word reading. The results of our study, therefore, support theories of action-perception coupling assuming a facilitatory influence of sensory-motor representations established during handwriting on reading and writing.

The Semantic Content of Abstract Concepts: A Property Listing Study of 296 Abstract Words.

Abstract: The relation of abstract concepts to the modality-specific systems is discussed controversially. According to classical approaches, the semantic content of abstract concepts can only be coded by amodal or verbal-symbolic representations distinct from the sensory and motor systems, because abstract concepts lack a clear physical referent. Grounded cognition theories, in contrast, propose that abstract concepts do not depend only on the verbal system, but also on a variety of modal systems involving perception, action, emotion and internal states. In order to contribute to this debate, we investigated the semantic content of abstract concepts using a property generation task. Participants were asked to generate properties for 296 abstract concepts, which are relevant for constituting their meaning. These properties were categorized by a coding-scheme making a classification into modality-specific and verbal contents possible. Words were additionally rated with regard to concreteness/abstractness and familiarity. To identify possible subgroups of abstract concepts with distinct profiles of generated features, hierarchical cluster analyses were conducted. Participants generated a substantial proportion of introspective, affective, social, sensory and motor-related properties, in addition to verbal associations. Cluster analyses revealed different subcategories of abstract concepts, which can be characterized by the dominance of certain conceptual features. The present results are therefore compatible with grounded cognition theories, which emphasize the importance of linguistic, social, introspective and affective experiential information for the representation of abstract concepts. Our findings also indicate that abstract concepts are highly heterogeneous requiring the investigation of well-specified subcategories of abstract concepts, for instance as revealed by the present cluster analyses. The present study could thus guide future behavioral or imaging work further elucidating the representation of abstract concepts.

Toward a brain-based componential semantic representation

Abstract: Componential theories of lexical semantics assume that concepts can be represented by sets of features or attributes that are in some sense primitive or basic components of meaning. The binary features used in classical category and prototype theories are problematic in that these features are themselves complex concepts, leaving open the question of what constitutes a primitive feature. The present availability of brain imaging tools has enhanced interest in how concepts are represented in brains, and accumulating evidence supports the claim that these representations are at least partly "embodied" in the perception, action, and other modal neural systems through which concepts are experienced. In this study we explore the possibility of devising a componential model of semantic representation based entirely on such functional divisions in the human brain. We propose a basic set of approximately 65 experiential attributes based on neurobiological considerations, comprising sensory, motor, spatial, temporal, affective, social, and cognitive experiences. We provide normative data on the salience of each attribute for a large set of English nouns, verbs, and adjectives, and show how these attribute vectors distinguish a priori conceptual categories and capture semantic similarity. Robust quantitative differences between concrete object categories were observed across a large number of attribute dimensions. A within- versus between-category similarity metric showed much greater separation between categories than representations derived from distributional (latent semantic) analysis of text. Cluster analyses were used to explore the similarity structure in the data independent of a priori labels, revealing several novel category distinctions. We discuss how such a representation might deal with various longstanding problems in semantic theory, such as feature selection and weighting, representation of abstract concepts, effects of context on semantic retrieval, and conceptual combination. In contrast to componential models based on verbal features, the proposed representation systematically relates semantic content to large-scale brain networks and biologically plausible accounts of concept acquisition.

Concept Representation Reflects Multimodal Abstraction: A Framework for Embodied Semantics

Abstract: Recent research indicates that sensory and motor cortical areas play a significant role in the neural representation of concepts. However, little is known about the overall architecture of this representational system, including the role played by higher level areas that integrate different types of sensory and motor information. The present study addressed this issue by investigating the simultaneous contributions of multiple sensory-motor modalities to semantic word processing. With a multivariate fMRI design, we examined activation associated with 5 sensory-motor attributes--color, shape, visual motion, sound, and manipulation--for 900 words. Regions responsive to each attribute were identified using independent ratings of the attributes' relevance to the meaning of each word. The results indicate that these aspects of conceptual knowledge are encoded in multimodal and higher level unimodal areas involved in processing the corresponding types of information during perception and action, in agreement with embodied theories of semantics. They also reveal a hierarchical system of abstracted sensory-motor representations incorporating a major division between object interaction and object perception processes.

Experiential learning – a systematic review and revision of Kolb’s model

Abstract: Kolb’s experiential learning cycle is perhaps the most scholarly influential and cited model regarding experiential learning theory. However, a key issue in interpreting Kolb’s model concerns a lac...