Feature-feature causal relations and statistical co-occurrences in object concepts

A Chinese Conceptual Semantic Feature Dataset (CCFD)

Memory and language are important high-level cognitive functions of humans, and the study of conceptual representation of the human brain is a key approach to reveal the principles of cognition. However, this research is often constrained by the availability of stimulus materials. The research on concept representation often needs to be based on a standardized and large-scale database of conceptual semantic features. Although Western scholars have established a variety of English conceptual semantic feature datasets, there is still a lack of a comprehensive Chinese version. In the present study, a Chinese Conceptual semantic Feature Dataset (CCFD) was established with 1,410 concepts including their semantic features and the similarity between concepts. The concepts were grouped into 28 subordinate categories and seven superior categories artificially. The results showed that concepts within the same category were closer to each other, while concepts between categories were farther apart. The CCFD proposed in this study can provide stimulation materials and data support for related research fields. All the data and supplementary materials can be found at https://osf.io/ug5dt/ .

The attribute priming effect in patients with Alzheimer's disease

Experiments with semantic priming (SP) paradigms have documented early hypopriming in patients with AD when concepts are used as primes and attribute concept features as targets, suggesting that concept attributes are vulnerable to damage very early in the disease course. The aims of this study were to confirm early priming reduction in the attribute condition in patients with AD and to determine which of several semantic indexes (such as the level of distinctiveness, correlation or feature dominance of concept features) best predicts the priming effect size in AD. We administered an SP attribute condition paradigm to 20 mildly demented patients with AD and to 10 NCs. We used concept-attribute pairs for which normative data of semantic indexes relative to both concept primes (i.e., number, type, mean level of dominance, distinctiveness and correlation of features constituting the concepts) and target features (i.e., level of feature dominance, correlation and distinctiveness) were available. Results showed that compared to NCs, the AD group obtained very reduced priming facilitation. Furthermore, the item regression analyses showed that the priming decrement in the AD group was predicted by the feature dominance of the target in the related pairs; that is, the lower the target feature dominance, the lower the priming effect elicited. These results confirmed hypopriming in the attribute condition from the very early phase of AD and support the view that attributes which are more salient for the identification of a given concept are also those most resistant to semantic memory degradation in AD pathology.

The role of variability in the property listing task

It is generally believed that concepts can be characterized by their properties (or features). When investigating concepts encoded in language, researchers often ask subjects to produce lists of properties that describe them (i.e., the Property Listing Task, PLT). These lists are accumulated to produce Conceptual Property Norms (CPNs). CPNs contain frequency distributions of properties for individual concepts. It is widely believed that these distributions represent the underlying semantic structure of those concepts. Here, instead of focusing on the underlying semantic structure, we aim at characterizing the PLT. An often disregarded aspect of the PLT is that individuals show intersubject variability (i.e., they produce only partially overlapping lists). In our study we use a mathematical analysis of this intersubject variability to guide our inquiry. To this end, we resort to a set of publicly available norms that contain information about the specific properties that were informed at the individual subject level. Our results suggest that when an individual is performing the PLT, he or she generates a list of properties that is a mixture of general and distinctive properties, such that there is a non-linear tendency to produce more general than distinctive properties. Furthermore, the low generality properties are precisely those that tend not to be repeated across lists, accounting in this manner for part of the intersubject variability. In consequence, any manipulation that may affect the mixture of general and distinctive properties in lists is bound to change intersubject variability. We discuss why these results are important for researchers using the PLT.

Critical brain regions for tool-related and imitative actions: a componential analysis

Numerous functional neuroimaging studies suggest that widespread bilateral parietal, temporal, and frontal regions are involved in tool-related and pantomimed gesture performance, but the role of these regions in specific aspects of gestural tasks remains unclear. In the largest prospective study of apraxia-related lesions to date, we performed voxel-based lesion-symptom mapping with data from 71 left hemisphere stroke participants to assess the critical neural substrates of three types of actions: gestures produced in response to viewed tools, imitation of tool-specific gestures demonstrated by the examiner, and imitation of meaningless gestures. Thus, two of the three gesture types were tool-related, and two of the three were imitative, enabling pairwise comparisons designed to highlight commonalities and differences. Gestures were scored separately for postural (hand/arm positioning) and kinematic (amplitude/timing) accuracy. Lesioned voxels in the left posterior temporal gyrus were significantly associated with lower scores on the posture component for both of the tool-related gesture tasks. Poor performance on the kinematic component of all three gesture tasks was significantly associated with lesions in left inferior parietal and frontal regions. These data enable us to propose a componential neuroanatomic model of action that delineates the specific components required for different gestural action tasks. Thus, visual posture information and kinematic capacities are differentially critical to the three types of actions studied here: the kinematic aspect is particularly critical for imitation of meaningless movement, capacity for tool-action posture representations are particularly necessary for pantomimed gestures to the sight of tools, and both capacities inform imitation of tool-related movements. These distinctions enable us to advance traditional accounts of apraxia.

Integrating conceptual knowledge within and across representational modalities

Research suggests that concepts are distributed across brain regions specialized for processing information from different sensorimotor modalities. Multimodal semantic models fall into one of two broad classes differentiated by the assumed hierarchy of convergence zones over which information is integrated. In shallow models, communication within- and between-modality is accomplished using either direct connectivity, or a central semantic hub. In deep models, modalities are connected via cascading integration sites with successively wider receptive fields. Four experiments provide the first direct behavioral tests of these models using speeded tasks involving feature inference and concept activation. Shallow models predict no within-modal versus cross-modal difference in either task, whereas deep models predict a within-modal advantage for feature inference, but a cross-modal advantage for concept activation. Experiments 1 and 2 used relatedness judgments to tap participants' knowledge of relations for within- and cross-modal feature pairs. Experiments 3 and 4 used a dual-feature verification task. The pattern of decision latencies across Experiments 1-4 is consistent with a deep integration hierarchy.

Longitudinal analysis of early semantic networks: preferential attachment or preferential acquisition?

Analyses of adult semantic networks suggest a learning mechanism involving preferential attachment: A word is more likely to enter the lexicon the more connected the known words to which it is related. We introduce and test two alternative growth principles: preferential acquisition-words enter the lexicon not because they are related to well-connected words, but because they connect well to other words in the learning environment-and the lure of the associates-new words are favored in proportion to their connections with known words. We tested these alternative principles using longitudinal analyses of developing networks of 130 nouns children learn prior to the age of 30 months. We tested both networks with links between words represented by features and networks with links represented by associations. The feature networks did not predict age of acquisition using any growth model. The associative networks grew by preferential acquisition, with the best model incorporating word frequency, number of phonological neighbors, and connectedness of the new word to words in the learning environment, as operationalized by connectedness to words typically acquired by the age of 30 months.

ConceptBuilder: an open-source software tool for measuring, depicting, and quantifying causal models

Measurements of people's causal and explanatory models are frequently key dependent variables in investigations of concepts and categories, lay theories, and health behaviors. A variety of challenges are inherent in the pen-and-paper and narrative methods commonly used to measure such causal models. We have attempted to alleviate these difficulties by developing a software tool, ConceptBuilder, for automating the process and ensuring accurate coding and quantification of the data. In this article, we present ConceptBuilder, a multiple-use tool for data gathering, data entry, and diagram display. We describe the program's controls, report the results of a usability test of the program, and discuss some technical aspects of the program. We also describe ConceptAnalysis, a companion program for generating data matrices and analyses, and ConceptViewer, a program for viewing the data exactly as drawn.