Relational and lexical similarity in analogical reasoning and recognition memory: Behavioral evidence and computational evaluation.

We examined the role of different types of similarity in both analogical reasoning and recognition memory. On recognition tasks, people more often falsely report having seen a recombined word pair (e.g., flower: garden) if it instantiates the same semantic relation (e.g., is a part of) as a studied word pair (e.g., house: town). This phenomenon, termed relational luring, has been interpreted as evidence that explicit relation representations-known to play a central role in analogical reasoning-also impact episodic memory. We replicate and extend previous studies, showing that relation-based false alarms in recognition memory occur after participants encode word pairs either by making relatedness judgments about individual words presented sequentially, or by evaluating analogies between pairs of word pairs. To test alternative explanations of relational luring, we implemented an established model of recognition memory, the Generalized Context Model (GCM). Within this basic framework, we compared representations of word pairs based on similarities derived either from explicit relations or from lexical semantics (i.e., individual word meanings). In two experiments on recognition memory, best-fitting values of GCM parameters enabled both similarity models (even the model based solely on lexical semantics) to predict relational luring with comparable accuracy. However, the model based on explicit relations proved more robust to parameter variations than that based on lexical similarity. We found this same pattern of modeling results when applying GCM to an independent set of data reported by Popov, Hristova, and Anders (2017). In accord with previous work, we also found that explicit relation representations are necessary for modeling analogical reasoning. Our findings support the possibility that explicit relations, which are central to analogical reasoning, also play an important role in episodic memory.

Individual Differences in the Neural Localization of Relational Networks of Semantic Concepts.

Semantic concepts relate to each other to varying degrees to form a network of zero-order relations, and these zero-order relations serve as input into networks of general relation types as well as higher order relations. Previous work has studied the neural mapping of semantic concepts across domains, although much work remains to be done to understand how the localization and structure of those architectures differ depending on various individual differences in attentional bias toward different content presentation formats. Using an item-wise model of semantic distance of zero-order relations (Word2vec) between stimuli (presented both in word and picture forms), we used representational similarity analysis to identify individual differences in the neural localization of semantic concepts and how those localization differences can be predicted by individual variance in the degree to which individuals attend to word information instead of pictures. Importantly, there were no reliable representations of this zero-order semantic relational network when looking at the full group, and it was only through considering individual differences that a stable localization difference became evident. These results indicate that individual differences in the degree to which a person habitually attends to word information instead of picture information substantially affects the neural localization of zero-order semantic representations.

Developing a neurally informed ontology of creativity measurement.

A central challenge for creativity research-as for all areas of experimental psychology and cognitive neuroscience-is to establish a mapping between constructs and measures (i.e., identifying a set of tasks that best captures a set of creative abilities). A related challenge is to achieve greater consistency in the measures used by different researchers; inconsistent measurement hinders progress toward shared understanding of cognitive and neural components of creativity. New resources for aggregating neuroimaging data, and the emergence of methods for identifying structure in multivariate data, present the potential for new approaches to address these challenges. Identifying meta-analytic structure (i.e., similarity) in neural activity associated with creativity tasks might help identify subsets of these tasks that best reflect the similarity structure of creativity-relevant constructs. Here, we demonstrated initial proof-of-concept for such an approach. To build a model of similarity between creativity-relevant constructs, we first surveyed creativity researchers. Next, we used NeuroSynth meta-analytic software to generate maps of neural activity robustly associated with tasks intended to measure the same set of creativity-relevant constructs. A representational similarity analysis-based approach identified particular constructs-and particular tasks intended to measure those constructs-that positively or negatively impacted the model fit. This approach points the way to identifying optimal sets of tasks to capture elements of creativity (i.e., dimensions of similarity space among creativity constructs), and has long-term potential to meaningfully advance the ontological development of creativity research with the rapid growth of creativity neuroscience. Because it relies on neuroimaging meta-analysis, this approach has more immediate potential to inform longer-established fields for which more extensive sets of neuroimaging data are already available.

Individual differences in white and grey matter structure associated with verbal habits of thought.

Modality specific encoding habits account for a significant portion of individual differences reflected in functional activation during cognitive processing. Yet, little is known about how these habits of thought influence long-term structural changes in the brain. Traditionally, habits of thought have been assessed using self-report questionnaires such as the visualizer-verbalizer questionnaire. Here, rather than relying on subjective reports, we measured habits of thought using a novel behavioral task assessing attentional biases toward picture and word stimuli. Hypothesizing that verbal habits of thought are reflected in the structural integrity of white matter tracts and cortical regions of interest, we used diffusion tensor imaging and volumetric analyses to assess this prediction. Using a whole-brain approach, we show that word bias is associated with increased volume in several bilateral language regions, in both white and grey matter parcels. Additionally, connectivity within white matter tracts within an a priori speech production network increased as a function of word bias. These results demonstrate long-term structural and morphological differences associated with verbal habits of thought.

Mental models use common neural spatial structure for spatial and abstract content.

Mental models provide a cognitive framework allowing for spatially organizing information while reasoning about the world. However, transitive reasoning studies often rely on perception of stimuli that contain visible spatial features, allowing the possibility that associated neural representations are specific to inherently spatial content. Here, we test the hypothesis that neural representations of mental models generated through transitive reasoning rely on a frontoparietal network irrespective of the spatial nature of the stimulus content. Content within three models ranges from expressly visuospatial to abstract. All mental models participants generated were based on inferred relationships never directly observed. Here, using multivariate representational similarity analysis, we show that patterns representative of mental models were revealed in both superior parietal lobule and anterior prefrontal cortex and converged across stimulus types. These results support the conclusion that, independent of content, transitive reasoning using mental models relies on neural mechanisms associated with spatial cognition.

Individual differences in encoded neural representations within cortical speech production network.

When two individuals view the same item, they do not necessarily perceive an item in the same way. If an individual is presented with a stimulus to be recalled later, the information that is encoded is dependent on the features of the stimulus to which one attends. Past studies have shown that, on the group level, verbal and visual information (e.g., words and pictures) are encoded in disparate regions of the brain. However, this account conflates external and internal representational formats, and it also neglects individual differences in attention. In this study, we examined neural and behavioral patterns associated with individual differences in attention to verbal representations-both external and internal. We found that the encoded neural representation of semantic content (meaningful words and pictures) varied as a function of individual differences in verbal attention, independent of the stimulus presentation format. Individuals who demonstrated an attentional bias toward words showed similar multivariate BOLD activity patterns within an a priori speech production network when encoding object names as when encoding pictures of objects. This result indicates that these individuals encode both words and pictures verbally. These effects were not found for non-semantic stimuli (pronounceable non-words and nonsense pictures). Importantly, as expected, no individual differences in neural representation were found in a separate network of regions known to process semantic content independent of format. These results highlight inter-individual divergence and convergence in internal representations of encoded semantic content. SIGNIFICANCE STATEMENT: This study shows how tendencies to attend to word representations is associated with individual differences in encoded neural representations. Individuals who selectively attend to words instead of pictures process semantically meaningful information in language regions of the brain, regardless of whether the information was originally presented as a word or a picture. Though all participants encoded words and pictures similarly in regions that are known to represent domain-general semantic information, only the individuals who were biased towards word representations additionally processed both words and pictures in modality-specific verbal regions. These results demonstrate both the convergence and divergence between individuals that occurs during encoding of meaningful information.

Putting the pieces together: Generating a novel representational space through deductive reasoning.

How does the brain represent a newly-learned mental model? Representational similarity analysis (RSA) has revealed the neural basis of common representational spaces learned early in development, such as categories of natural kinds. This study uses RSA to examine the neural implementation of a newly-learned mental model-i.e., a representational space created through deductive reasoning-and study the structure of previously found parietal activity in reasoning tasks. Specifically, all the information in this mental model could only be obtained through abstract transitive reasoning, as there were no predictive differences between observable features in the stimuli, and stimuli were counterbalanced across participants. Participants were shown unfamiliar face portraits paired with names and asked to learn about the height of each person pictured in the portraits through comparison to other individuals in the set. Participants learned the relative heights only of adjacent pairs in the set and then used transitive reasoning to generate a linear ranking of heights (e.g., "Matthew is taller than Thomas; Thomas is taller than Andrew; therefore Matthew is taller than Andrew"). During fMRI, participants recalled the approximate height of each individual based on these inferences. Using a predictive model based on the relative heights of the set of individuals, RSA revealed three brain regions in the right hemisphere that encoded this newly-learned representational space, located within the intraparietal sulcus, precuneus, and inferior frontal gyrus. These findings demonstrate the value of RSA for analyzing structure within patterns of activity and support theories asserting that logical reasoning recruits spatial processing mechanisms.

Verbal and visual cognition: Individual differences in the lab, in the brain, and in the classroom.

In many ways, individuals vary in their thought processes, and in their cognitive strengths and weaknesses. Among the findings revealed by individual differences research, one major dividing line highlighted recurrently by decades of experimental studies is that between linguistically-mediated cognitive operations (verbal cognition), versus cognition, which primarily operates on visual - or visuospatial - representations (visual cognition). In this article, we review findings from three research areas-cognitive abilities, working memory, and task strategies-focusing on individual differences in verbal and visual cognition. In each area we highlight behavioral, neuroimaging, and classroom-based findings, bridging the perspectives of these different methodologies.