The neural and cognitive basis of expository text comprehension.

As science and technology rapidly progress, it becomes increasingly important to understand how individuals comprehend expository technical texts that explain these advances. This study examined differences in individual readers' technical comprehension performance and differences among texts, using functional brain imaging to measure regional brain activity while students read passages on technical topics and then took a comprehension test. Better comprehension of the technical passages was related to higher activation in regions of the left inferior frontal gyrus, left superior parietal lobe, bilateral dorsolateral prefrontal cortex, and bilateral hippocampus.

Similarities and differences in the neural representations of abstract concepts across English and Mandarin.

Recent research suggests there is a neural organization for representing abstract concepts that is common across English speakers. To investigate the possible role of language on the representation of abstract concepts, multivariate pattern analytic (MVPA) techniques were applied to fMRI data to compare the neural representations of 28 individual abstract concepts between native English and Mandarin speakers. Factor analyses of the activation patterns of the 28 abstract concepts from both languages characterized this commonality in terms of a set of four underlying neurosemantic dimensions, indicating the degree to which a concept is verbally represented, internal to the person, contains social content, and is rule-based.

The neuroscience of advanced scientific concepts.

Cognitive neuroscience methods can identify the fMRI-measured neural representation of familiar individual concepts, such as apple, and decompose them into meaningful neural and semantic components. This approach was applied here to determine the neural representations and underlying dimensions of representation of far more abstract physics concepts related to matter and energy, such as fermion and dark matter, in the brains of 10 Carnegie Mellon physics faculty members who thought about the main properties of each of the concepts. One novel dimension coded the measurability vs.

Neural Representations of Procedural Knowledge.

Although declarative concepts (e.g., ) have been shown to be identifiable from their functional MRI (fMRI) signatures, the correspondence has yet to be established for executing a complex procedure such as tying a knot.

Neural Representations of Abstract Concepts: Identifying Underlying Neurosemantic Dimensions.

The abstractness of concepts is sometimes defined indirectly as lacking concreteness, this view provides little insight into their cognitive or neural basis. Multivariate pattern analytic techniques applied to functional magnetic resonance imaging data were used to characterize the neural representations of 28 individual abstract concepts. A classifier trained on the concepts' neural signatures reliably decoded their neural representations in an independent subset of data for each participant.

Converging measures of neural change at the microstructural, informational, and cortical network levels in the hippocampus during the learning of the structure of organic compounds.

The critical role of the hippocampus in human learning has been illuminated by neuroimaging studies that increasingly improve the detail with which hippocampal function is understood. However, the hippocampal information developed with different types of imaging technologies is seldom integrated within a single investigation of the neural changes that occur during learning. Here, we show three different ways in which a small hippocampal region changes as the structures and names of a set of organic compounds are being learned, reflecting changes at the microstructural, informational, and cortical network levels.

Brain reading and behavioral methods provide complementary perspectives on the representation of concepts.

The advent of brain reading techniques has enabled new approaches to the study of concept representation, based on the analysis of multivoxel activation patterns evoked by the contemplation of individual concepts such as animal concepts. The present fMRI study characterized the representation of 30 animal concepts. Dimensionality reduction of the multivoxel activation patterns underlying the individual animal concepts indicated that the semantic building blocks of the brain's representations of the animals corresponded to intrinsic animal properties (e.

Commonalities and differences in the neural representations of English, Portuguese, and Mandarin sentences: When knowledge of the brain-language mappings for two languages is better than one.

This study extended cross-language semantic decoding (based on a concept's fMRI signature) to the decoding of sentences across three different languages (English, Portuguese and Mandarin). A classifier was trained on either the mapping between words and activation patterns in one language or the mappings in two languages (using an equivalent amount of training data), and then tested on its ability to decode the semantic content of a third language. The model trained on two languages was reliably more accurate than a classifier trained on one language for all three pairs of languages.

A brain-based account of "basic-level" concepts.

This study provides a brain-based account of how object concepts at an intermediate (basic) level of specificity are represented, offering an enriched view of what it means for a concept to be a basic-level concept, a research topic pioneered by Rosch and others (Rosch et al., 1976). Applying machine learning techniques to fMRI data, it was possible to determine the semantic content encoded in the neural representations of object concepts at basic and subordinate levels of abstraction.

Predicting the brain activation pattern associated with the propositional content of a sentence: Modeling neural representations of events and states.

Even though much has recently been learned about the neural representation of individual concepts and categories, neuroimaging research is only beginning to reveal how more complex thoughts, such as event and state descriptions, are neurally represented. We present a predictive computational theory of the neural representations of individual events and states as they are described in 240 sentences. Regression models were trained to determine the mapping between 42 neurally plausible semantic features (NPSFs) and thematic roles of the concepts of a proposition and the fMRI activation patterns of various cortical regions that process different types of information.

Neural representations of the concepts in simple sentences: Concept activation prediction and context effects.

Although it has been possible to identify individual concepts from a concept's brain activation pattern, there have been significant obstacles to identifying a proposition from its fMRI signature. Here we demonstrate the ability to decode individual prototype sentences from readers' brain activation patterns, by using theory-driven regions of interest and semantic properties. It is possible to predict the fMRI brain activation patterns evoked by propositions and words which are entirely new to the model with reliably above-chance rank accuracy.

Commonality of neural representations of sentences across languages: Predicting brain activation during Portuguese sentence comprehension using an English-based model of brain function.

The aim of the study was to test the cross-language generative capability of a model that predicts neural activation patterns evoked by sentence reading, based on a semantic characterization of the sentence. In a previous study on English monolingual speakers (Wang et al., submitted), a computational model performed a mapping from a set of 42 concept-level semantic features (Neurally Plausible Semantic Features, NPSFs) as well as 6 thematic role markers to neural activation patterns (assessed with fMRI), to predict activation levels in a network of brain locations.

Identifying thematic roles from neural representations measured by functional magnetic resonance imaging.

The generativity and complexity of human thought stem in large part from the ability to represent relations among concepts and form propositions. The current study reveals how a given object such as rabbit is neurally encoded differently and identifiably depending on whether it is an agent ("the rabbit punches the monkey") or a patient ("the monkey punches the rabbit"). Machine-learning classifiers were trained on functional magnetic resonance imaging (fMRI) data evoked by a set of short videos that conveyed agent-verb-patient propositions.

Neural Representations of Physics Concepts.

We used functional MRI (fMRI) to assess neural representations of physics concepts (momentum, energy, etc.) in juniors, seniors, and graduate students majoring in physics or engineering. Our goal was to identify the underlying neural dimensions of these representations.

Modality-independent representations of small quantities based on brain activation patterns.

Machine learning or MVPA (Multi Voxel Pattern Analysis) studies have shown that the neural representation of quantities of objects can be decoded from fMRI patterns, in cases where the quantities were visually displayed. Here we apply these techniques to investigate whether neural representations of quantities depicted in one modality (say, visual) can be decoded from brain activation patterns evoked by quantities depicted in the other modality (say, auditory). The main finding demonstrated, for the first time, that quantities of dots were decodable by a classifier that was trained on the neural patterns evoked by quantities of auditory tones, and vice-versa.

Aberrant functioning of the theory-of-mind network in children and adolescents with autism.

Background: Theory-of-mind (ToM), the ability to infer people's thoughts and feelings, is a pivotal skill in effective social interactions. Individuals with autism spectrum disorders (ASD) have been found to have altered ToM skills, which significantly impacts the quality of their social interactions. Neuroimaging studies have reported altered activation of the ToM cortical network, especially in adults with autism, yet little is known about the brain responses underlying ToM in younger individuals with ASD.

Diminished neural adaptation during implicit learning in autism.

Neuroimaging studies have shown evidence of disrupted neural adaptation during learning in individuals with autism spectrum disorder (ASD) in several types of tasks, potentially stemming from frontal-posterior cortical underconnectivity (Schipul et al., 2012). The aim of the current study was to examine neural adaptations in an implicit learning task that entails participation of frontal and posterior regions.

Structural and functional neuroplasticity in human learning of spatial routes.

Recent findings with both animals and humans suggest that decreases in microscopic movements of water in the hippocampus reflect short-term neuroplasticity resulting from learning. Here we examine whether such neuroplastic structural changes concurrently alter the functional connectivity between hippocampus and other regions involved in learning. We collected both diffusion-weighted images and fMRI data before and after humans performed a 45min spatial route-learning task.

Monitoring the growth of the neural representations of new animal concepts.

Although enormous progress has recently been made in identifying the neural representations of individual object concepts, relatively little is known about the growth of a neural knowledge representation as a novel object concept is being learned. In this fMRI study, the growth of the neural representations of eight individual extinct animal concepts was monitored as participants learned two features of each animal, namely its habitat (i.e.

Physics instruction induces changes in neural knowledge representation during successive stages of learning.

Incremental instruction on the workings of a set of mechanical systems induced a progression of changes in the neural representations of the systems. The neural representations of four mechanical systems were assessed before, during, and after three phases of incremental instruction (which first provided information about the system components, then provided partial causal information, and finally provided full functional information). In 14 participants, the neural representations of four systems (a bathroom scale, a fire extinguisher, an automobile braking system, and a trumpet) were assessed using three recently developed techniques: (1) machine learning and classification of multi-voxel patterns; (2) localization of consistently responding voxels; and (3) representational similarity analysis (RSA).