Analyzing feature distinctiveness in the processing of living and non-living concepts in Alzheimer’s disease

Semantic feature production norms for manipulable objects

Feature generation tasks and feature databases are important for understanding how knowledge is organized in semantic memory, as they reflect not only the kinds of information that individuals hold about objects but also how objects are conceptually represented. Traditionally, semantic norms focus on a variety of object categories and, as a result, have a small number of concepts per semantic category. Here, our main goal is to create a more fine-grained feature database exclusively for one category of objects-manipulable objects. This database contributes to the understanding of within-category, content-specific processing. To achieve this, we asked 130 participants to freely generate features for 80 manipulable objects and another group of 32 participants to generate action features for the same objects. We then compared our databases with other published semantic norms and found high similarity between them. In our databases, we calculated the similarity between objects in terms of visual, functional, encyclopaedic, and action feature types using Spearman correlation, Baker's gamma index, and cophenetic correlation. We discovered that objects were grouped in a distinctive and meaningful way according to feature type. Finally, we tested the validity of our databases by asking three groups of participants to perform a feature verification experiment while manipulating production frequency. Our results demonstrate that participants can recognize and associate the features of our databases with specific manipulable objects. Participants were faster to verify high-frequency features than low-frequency features. Overall, our data provide important insights into how we process manipulable objects and can be used to further inform cognitive and neural theories of object processing and identification.

Investigating the influence of semantic factors on word retrieval: Reservations, results and recommendations

There is consensus that word retrieval starts with activation of semantic representations. However, in adults without language impairment, relatively little attention has been paid to the effects of the semantic attributes of to-be-retrieved words. This paper, therefore, addresses the question of which item-inherent semantic factors influence word retrieval. Specifically, it reviews the literature on a selection of these factors: imageability, concreteness, number of semantic features, typicality, intercorrelational density, featural distinctiveness, concept distinctiveness, animacy, semantic neighbourhood density, semantic similarity, operativity, valence, and arousal. It highlights several methodological challenges in this field, and has a focus on the insights from studies with people with aphasia where the effects of these variables are more prevalent. The paper concludes that further research simultaneously examining the effects of different semantic factors that are likely to affect lexical co-activation, and the interaction of these variables, would be fruitful, as would suitably scaled computational modelling of these effects in unimpaired language processing and in language impairment. Such research would enable the refinement of theories of semantic processing and word production, and potentially have implications for diagnosis and treatment of semantic and lexical impairments.

Effects of semantic variables on word production in aphasia

Words differ in the complexity of their semantic representations and their relationships to other words and these differences can be operationalised as a variety of semantic variables. The research presented here investigates how word production in aphasia is influenced by six feature-based semantic variables (number of near semantic neighbours, semantic similarity, number of semantic features, typicality, intercorrelational density, and distinctiveness). Previous research has reported inconsistent findings for some of the semantic variables, while others have not been previously studied in aphasia. Spoken picture naming data from a large group of individuals with aphasia with mixed spoken word production impairments (n = 175) and a sub-group who produced few phonological errors (n = 60) was analysed. We examined effects of the semantic variables on overall naming accuracy and on the occurrence of different error types (semantic errors overall, coordinate errors, omissions), while controlling for other psycholinguistic variables using generalised linear mixed effects models and Bayesian correlations. Across analyses, number of semantic features was the most important predictor with a facilitatory main effect on naming accuracy in the sub-group analysis. Number of semantic features, along with typicality and semantic similarity, also predicted error types and in some analyses these effects depended on the integrity of semantic processing. Effects of the semantic variables and their theoretical explanations and implications are discussed in light of previous research and models of word production.

How to carry out conceptual properties norming studies as parameter estimation studies: Lessons from ecology

Conceptual properties norming studies (CPNs) ask participants to produce properties that describe concepts. From that data, different metrics may be computed (e.g., semantic richness, similarity measures), which are then used in studying concepts and as a source of carefully controlled stimuli for experimentation. Notwithstanding those metrics' demonstrated usefulness, researchers have customarily overlooked that they are only point estimates of the true unknown population values, and therefore, only rough approximations. Thus, though research based on CPN data may produce reliable results, those results are likely to be general and coarse-grained. In contrast, we suggest viewing CPNs as parameter estimation procedures, where researchers obtain only estimates of the unknown population parameters. Thus, more specific and fine-grained analyses must consider those parameters' variability. To this end, we introduce a probabilistic model from the field of ecology. Its related statistical expressions can be applied to compute estimates of CPNs' parameters and their corresponding variances. Furthermore, those expressions can be used to guide the sampling process. The traditional practice in CPN studies is to use the same number of participants across concepts, intuitively believing that practice will render the computed metrics comparable across concepts and CPNs. In contrast, the current work shows why an equal number of participants per concept is generally not desirable. Using CPN data, we show how to use the equations and discuss how they may allow more reasonable analyses and comparisons of parameter values among different concepts in a CPN, and across different CPNs.

A practical primer on processing semantic property norm data

Semantic property listing tasks require participants to generate short propositions (e.g., [Formula: see text], [Formula: see text]) for a specific concept (e.g., DOG). This task is the cornerstone of the creation of semantic property norms which are essential for modeling, stimuli creation, and understanding similarity between concepts. Despite the wide applicability of semantic property norms for a large variety of concepts across different groups of people, the methodological aspects of the property listing task have received less attention, even though the procedure and processing of the data can substantially affect the nature and quality of the measures derived from them. The goal of this paper is to provide a practical primer on how to collect and process semantic property norms. We will discuss the key methods to elicit semantic properties and compare different methods to derive meaningful representations from them. This will cover the role of instructions and test context, property preprocessing (e.g., lemmatization), property weighting, and relationship encoding using ontologies. With these choices in mind, we propose and demonstrate a processing pipeline that transparently documents these steps, resulting in improved comparability across different studies. The impact of these choices will be demonstrated using intrinsic (e.g., reliability, number of properties) and extrinsic measures (e.g., categorization, semantic similarity, lexical processing). This practical primer will offer potential solutions to several long-standing problems and allow researchers to develop new property listing norms overcoming the constraints of previous studies.

A feature-based neurocomputational model of semantic memory

According with a featural organization of semantic memory, this work is aimed at investigating, through an attractor network, the role of different kinds of features in the representation of concepts, both in normal and neurodegenerative conditions. We implemented new synaptic learning rules in order to take into account the role of partially shared features and of distinctive features with different saliency. The model includes semantic and lexical layers, coding, respectively for object features and word-forms. Connections among nodes are strongly asymmetrical. To account for the feature saliency, asymmetrical synapses were created using Hebbian rules of potentiation and depotentiation, setting different pre-synaptic and post-synaptic thresholds. A variable post-synaptic threshold, which automatically changed to reflect the feature frequency in different concepts (i.e., how many concepts share a feature), was used to account for partially shared features. The trained network solved naming tasks and word recognition tasks very well, exploiting the different role of salient versus marginal features in concept identification. In the case of damage, superordinate concepts were preserved better than the subordinate ones. Interestingly, the degradation of salient features, but not of marginal ones, prevented object identification. The model suggests that Hebbian rules, with adjustable post-synaptic thresholds, can provide a reliable semantic representation of objects exploiting the statistics of input features.

Semantic feature degradation and naming performance. Evidence from neurodegenerative disorders

The failure to name an object in Alzheimer's disease (AD) and in the semantic variant of the primary progressive aphasia (sv-PPA) has been generally attributed to semantic memory loss, with a progressive degradation of semantic features. Not all features, however, may have the same relevance in picture naming. We analyzed the relationship between picture naming performance and the loss of semantic features in patients with AD with or without naming impairment, with sv-PPA and in matched controls, assessing the role of distinctiveness, semantic relevance and feature type (sensorial versus non-sensorial) with a sentence verification task. The results showed that distinctive features with high values of semantic relevance were lost only in all patients with naming impairment. The performance on the sensorial distinctive features with high relevance was the best predictor of naming performance only in sv-PPA, while no difference between sensorial and non-sensorial features was found in AD patients.

Semantic memory impairment for biological and man-made objects in individuals with amnestic mild cognitive impairment or late-life depression

OBJECTIVE

Amnestic mild cognitive impairment (aMCI) and late-life depression (LLD) both increase the risk of developing Alzheimer disease (AD). Very little is known about the similarities and differences between these syndromes. The present study addresses this issue by examining the nature of semantic memory impairment (more precisely, object-based knowledge) in patients at risk of developing AD.

METHODS

Participants were 17 elderly patients with aMCI, 18 patients with aMCI plus depressive symptoms (aMCI/D+), 15 patients with LLD, and 29 healthy controls. All participants were aged 55 years or older and were administered a semantic battery designed to assess semantic knowledge for 16 biological and 16 man-made items.

RESULTS

Overall performance of aMCI/D+ participants was significantly worse than the 3 other groups, and performance for questions assessing knowledge for biological items was poorer than for questions relating to man-made items.

CONCLUSION

This study is the first to show that aMCI/D+ is associated with object-based semantic memory impairment. These results support the view that semantic deficits in aMCI are associated with concomitant depressive symptoms. However, depressive symptoms alone do not account exclusively for semantic impairment, since patients with LLD showed no semantic memory deficit.

Functional correlates of preserved naming performance in amnestic Mild Cognitive Impairment

Naming abilities are typically preserved in amnestic Mild Cognitive Impairment (aMCI), a condition associated with increased risk of progression to Alzheimer's disease (AD). We compared the functional correlates of covert picture naming and word reading between a group of aMCI subjects and matched controls. Unimpaired picture naming performance was associated with more extensive activations, in particular involving the parietal lobes, in the aMCI group. In addition, in the condition associated with higher processing demands (blocks of categorically homogeneous items, living items), increased activity was observed in the aMCI group, in particular in the left fusiform gyrus. Graph analysis provided further evidence of increased modularity and reduced integration for the homogenous sets in the aMCI group. The functional modifications associated with preserved performance may reflect, in the case of more demanding tasks, compensatory mechanisms for the subclinical involvement of semantic processing areas by AD pathology.

Revisiting domain-general accounts of category specificity in mind and brain

Theories about the neural basis of semantic knowledge have been strongly influenced by reports that particular semantic categories can be differentially impaired by neuropathology and can differentially activate particular regions of cortex in brain imaging studies. One well-known interpretation of these data is that the brain has evolved distinct functional and anatomical modules for storing and retrieving knowledge about different kinds of things. We review the evidence supporting an alternative view: that category specificity arises from many heterogeneous factors and so tells us little directly about the cognitive and neural architecture of semantic memory. We consider four general hypotheses about domain-general causes of category-specific patterns, their roots in early work, and their reemergence in contemporary research. We argue that there is compelling evidence supporting each hypothesis, and that the different hypotheses together can explain most of the interesting data. We further suggest that such a multifactor domain-general approach to category specificity is appealing partly because it explains the important findings with reference to theoretical claims that are already widely accepted, and partly because it resolves several puzzles that arise under the alternative view. For further resources related to this article, please visit the WIREs website.

CONFLICT OF INTEREST

The authors have declared no conflicts of interest for this article.