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.

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.

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.

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 compositional 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 the within-concept statistics that reflect how properties correlate with each other across instances of a concept. We ran a classification analysis using graph eigendecomposition to validate these models, and find that these models can successfully discriminate between object concepts. We then computed formal measures from these concept networks and explored their relationship to conceptual structure. We find that diversity coefficients and core-periphery structure can be interpreted as network-based measures of conceptual flexibility and stability, respectively. These results support the feasibility of a concept network framework and highlight its ability to formally capture important characteristics of the conceptual system. BANANA: banana chips, banana pudding, Cavendish banana, fried banana, frozen banana, mashed banana, over-ripe banana, peeled banana, plantain, raw banana, red banana, rotten banana, sliced banana, unripe banana, ripe banana BOTTLE: baby bottle, beer bottle, broken bottle, juice bottle, liquor bottle, medicine bottle, milk bottle, soda bottle, spray bottle, water bottle, wine bottle KEY: car key, key to a city, door key, encryption key, garage key, key to my heart, house key, key card, keyboard key, map key, master key, motorcycle key, office key, padlock key, password, piano key, key to a safe, skeleton key PUMPKIN: pumpkin bar, pumpkin bread, pumpkin candle, canned pumpkin, pumpkin cookie, pumpkin in a field, Halloween pumpkin, Jack-O-Lantern, pumpkin latte, pumpkin muffin, pumpkin pie, pumpkin puree, rotten pumpkin, pumpkin seeds, smashed pumpkin, pumpkin soup, pumpkin spice, whole pumpkin, Thanksgiving pumpkin

Much has been learned about how individual concepts and semantic dimensions are represented in the human brain using methods from the field of cognitive neuroscience; however, the process of conceptual combination, in which a new concept is created from pre-existing concepts, has received far less attention. We discuss theories and findings from cognitive science and cognitive neuroscience that shed light on the processing stages and neural systems that allow humans to form new conceptual combinations. We review systematic and creative applications of cognitive neuroscience methods, including neuroimaging, neuropsychological patients, neurostimulation and behavioral studies that have yielded fascinating insights into the cognitive nature and neural underpinnings of conceptual combination. Studies have revealed important features of the cognitive processes that are central to successful conceptual combination. Furthermore, we are beginning to understand how regions of the semantic system, such as the anterior temporal lobe and angular gyrus, integrate features and concepts, and evaluate the plausibility of potential resulting combinations, bridging work in linguistics and semantic memory. Despite the relative newness of these questions for cognitive neuroscience, the investigations we review give a very strong foundation for ongoing and future work that seeks to fully understand how the human brain can flexibly integrate existing concepts to form new and never-before experienced combinations at will.

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.

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.

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