Structure shapes the representation of a novel category.

Concepts contain rich structures that support flexible semantic cognition. These structures can be characterized by patterns of feature covariation: Certain features tend to cluster in the same items (e.g., feathers, wings, can fly). Existing computational models demonstrate how this kind of structure can be leveraged to slowly learn the distinctions between categories, on developmental timescales. However, it is not clear whether and how we leverage feature structure to quickly learn a novel category. We thus investigated how the internal structure of a new category is first extracted from experience, with the prediction that feature-based structure would have a rapid and broad influence on the learned category representation. Across three experiments, novel categories were designed with patterns of feature associations determined by carefully constructed graph structures, with Modular graphs-exhibiting strong clusters of feature covariation-compared against Random and Lattice graphs. In Experiment 1, a feature inference task using verbal stimuli revealed that Modular structure broadly facilitated category learning. Experiment 2 replicated this effect in visual categories. In Experiment 3, a statistical learning paradigm revealed that this Modular benefit relates to high-level structure rather than pairwise feature associations and persists even when category structure is incidental to the task. A neural network model was readily able to account for these effects, suggesting that correlational feature structure may be encoded within rapidly learned, distributed category representations. These findings constrain theories of category representation and link theories of category learning with structure learning more broadly. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Constructing complex social categories under uncertainty.

Conceptual combination is the act of building complex concepts from simpler ones. Although research has examined how inferences about compound objects (e.g., fuzzy chair) are produced from their constituent concepts, little is known about the combinatorial processes that produce inferences about compound social categories (e.g., Irish musician). Using a computational approach, we investigated the relationship between ratings of 25 nationality-occupation combinations and ratings of their constituent concepts along the attribute dimensions of warmth and competence. We found that people incorporate uncertainty into their perceptions of compound social categories. Further, people are more likely to use a linear combination strategy when they are more certain about the attributes of the constituents and less familiar with the combination. Conversely, when social combinations are more familiar, their judged attributes deviate further from the predictions of a combinatorial model and are shared across participants, suggesting that stereotype-based knowledge plays a central role in the representation of complex social groups. Twenty-five non-human animal combinations (e.g., circus snake) serve as a comparison and were rated on size and ferocity. We found evidence that familiarity has different effects on the strategies used to combine person concepts and animal concepts, pointing to the possible existence of both common and distinct mechanisms for constructing social and non-social categories.

Feature Uncertainty Predicts Behavioral and Neural Responses to Combined Concepts.

The cognitive and neural structure of conceptual knowledge affects how concepts combine in language and thought. Examining the principles by which individual concepts (e.g., diamond, baseball) combine into more complex phrases (e.g., "baseball diamond") can illuminate not only how the brain combines concepts but also the key ingredients of conceptual structure. Here we specifically tested the role of feature uncertainty in the modulation of conceptual brightness evoked by adjective-noun combinations (e.g., "dark diamond") in male and female human subjects. We collected explicit ratings of conceptual brightness for 45 noun concepts and their "dark" and "light" combinations, resulting in a measure reflecting the degree of conceptual brightness modulation in each noun concept. Feature uncertainty was captured in an entropy measure, as well as in a predictive Bayesian model of feature modulation. We found that feature uncertainty (i.e., entropy) and the Bayesian model were both strong predictors of these behavioral effects. Using fMRI, we observed the neural responses evoked by the concepts and combinations in a priori ROIs. Feature uncertainty predicted univariate responses in left inferior frontal gyrus, and multivariate responses in left anterior temporal lobe were predicted by degree of conceptual brightness modulation. These findings suggest that feature uncertainty is a key ingredient of conceptual structure, and inform cognitive neuroscience theories of conceptual combination by highlighting the role of left inferior frontal gyrus and left anterior temporal lobe in the process of flexible feature modulation during comprehension of complex language.SIGNIFICANCE STATEMENT The meaning of a word depends on the words surrounding it. The challenge of understanding how flexible meaning emerges in language can be simplified by studying adjective-noun phrases. We tested whether the uncertainty of a feature (i.e., brightness) in a given noun concept (e.g., diamond) influences how the adjective and noun concepts combine. We analyzed feature uncertainty using two probabilistic measures, and found that feature uncertainty predicted people's explicit interpretations of adjective-noun phrases (e.g., "dark diamond"). Using fMRI, we found that combined concepts evoked responses in left inferior frontal gyrus and left anterior temporal lobe that related to our measures of feature modulation and uncertainty. These findings reveal the cognitive and neural processes supporting conceptual combination and complex language use.

Semantic Search as Pattern Completion across a Concept.

What role does the hippocampus play in semantic memory? In a recent paper, Cutler et al. use a vector space model of semantics to characterize semantic search deficits in hippocampal amnesia. We relate their findings to properties of the hippocampal neural code and to controversies regarding hippocampal contributions to cognition.

Implementing a concept network model.

The same concept can mean different things or be instantiated in different forms, depending on context, suggesting a degree of flexibility within the conceptual system. We propose that a feature-based network model can be used to capture and predict this flexibility. We modeled individual concepts (e.g., BANANA, BOTTLE) as graph-theoretical networks, in which properties (e.g., YELLOW, SWEET) were represented as nodes and their associations as edges. In this framework, networks capture within-concept statistics that reflect how properties relate to one another across instances of a concept. We extracted formal measures of these networks that capture different aspects of network structure, and explored whether a concept's network structure relates to its flexibility of use. To do so, we compared network measures to a text-based measure of semantic diversity, as well as to empirical data from a figurative-language task and an alternative-uses task. We found that network-based measures were predictive of the text-based and empirical measures of flexible concept use, highlighting the ability of this approach to formally capture relevant characteristics of conceptual structure. Conceptual flexibility is a fundamental attribute of the cognitive and semantic systems, and in this proof of concept we reveal that variations in concept representation and use can be formally understood in terms of the informational content and topology of concept networks.

Finding features, figuratively.

Object concepts refer to unique clusters of properties that can be selectively activated or inhibited depending on what information is currently relevant. This conceptual "stretching" enables limitless new meanings to be generated, and figurative language provides a useful framework in which to study this conceptual flexibility. Here we probe the cognitive and neural mechanisms underlying the comprehension of novel metaphors as a means of understanding the conceptual flexibility inherent to language processing more generally. We show that novel metaphor comprehension involves the activation or inhibition of conceptual properties that are either relevant or irrelevant to the metaphor, and that left inferior frontal gyrus is recruited in this process, supporting a role for this region in the fine-tuning of conceptual meaning. Our results are consistent with a flexible, compositional account of conceptual structure in which semantic control mechanisms operate over conceptual properties during figurative language comprehension in order to create context-dependent meaning.

A meta-analysis of fMRI decoding: Quantifying influences on human visual population codes.

Information in the human visual system is encoded in the activity of distributed populations of neurons, which in turn is reflected in functional magnetic resonance imaging (fMRI) data. Over the last fifteen years, activity patterns underlying a variety of perceptual features and objects have been decoded from the brains of participants in fMRI scans. Through a novel multi-study meta-analysis, we have analyzed and modeled relations between decoding strength in the visual ventral stream, and stimulus and methodological variables that differ across studies. We report findings that suggest: (i) several organizational principles of the ventral stream, including a gradient of pattern granulation and an increasing abstraction of neural representations as one proceeds anteriorly; (ii) how methodological choices affect decoding strength. The data also show that studies with stronger decoding performance tend to be reported in higher-impact journals, by authors with a higher h-index. As well as revealing principles of regional processing, our results and approach can help investigators select from the thousands of design and analysis options in an empirical manner, to optimize future studies of fMRI decoding.

Competition between Mutually Exclusive Object States in Event Comprehension.

Successful language comprehension requires one to correctly match symbols in an utterance to referents in the world, but the rampant ambiguity present in that mapping poses a challenge. Sometimes the ambiguity lies in which of two (or more) types of things in the world are under discussion (i.e., lexical ambiguity); however, even a word with a single sense can have an ambiguous referent. This ambiguity occurs when an object can exist in multiple states. Here, we consider two cases in which the presence of multiple object states may render a single-sense word ambiguous. In the first case, one must disambiguate between two states of a single object token in a short discourse. In the second case, the discourse establishes two different tokens of the object category. Both cases involve multiple object states: These states are mutually exclusive in the first case, whereas in the second case, these states can logically exist at the same time. We use fMRI to contrast same-token and different-token discourses, using responses in left posterior ventrolateral prefrontal cortex (pVLPFC) as an indicator of conflict. Because the left pVLPFC is sensitive to competition between multiple, incompatible representations, we predicted that state ambiguity should engender conflict only when those states are mutually exclusive. Indeed, we find evidence of conflict in same-token, but not different-token, discourses. Our data support a theory of left pVLPFC function in which general conflict resolution mechanisms are engaged to select between multiple incompatible representations that arise in many kinds of ambiguity present in language.

A cortical network for the encoding of object change.

Understanding events often requires recognizing unique stimuli as alternative, mutually exclusive states of the same persisting object. Using fMRI, we examined the neural mechanisms underlying the representation of object states and object-state changes. We found that subjective ratings of visual dissimilarity between a depicted object and an unseen alternative state of that object predicted the corresponding multivoxel pattern dissimilarity in early visual cortex during an imagery task, while late visual cortex patterns tracked dissimilarity among distinct objects. Early visual cortex pattern dissimilarity for object states in turn predicted the level of activation in an area of left posterior ventrolateral prefrontal cortex (pVLPFC) most responsive to conflict in a separate Stroop color-word interference task, and an area of left ventral posterior parietal cortex (vPPC) implicated in the relational binding of semantic features. We suggest that when visualizing object states, representational content instantiated across early and late visual cortex is modulated by processes in left pVLPFC and left vPPC that support selection and binding, and ultimately event comprehension.