An ongoing debate about the structure of conceptual space is based on two competing mathematical ... more An ongoing debate about the structure of conceptual space is based on two competing mathematical theories of similarity that make distinct predictions about the structure of mental representations and how to model the representational space they are stored in. These are known as metric (Shepard, 1962) and ultrametric (Tversky, 1977) theories, modeled by multidimensional scaling and additive trees respectively. Turning to the brain to resolve this conflict, we propose a computational framework to assess behavioral and neural data's underlying structure and investigate whether the behaviorally known spaces for colors (metric) and letters (ultrametric) can be reproduced from neural data. Our results show that the metric color wheel can be reproduced from brain area V4, but that neural activations of the letters from extrastriate cortex (V2-V5) are also metric instead of being ultrametric. Finally, we discuss three possibilities for the brain's similarity structure, including a ...
We introduce a new method for decoding neural data from fMRI. It is based on two assumptions, fir... more We introduce a new method for decoding neural data from fMRI. It is based on two assumptions, first that neural representation is distributed over networks of neurons embeded in voxel noise and second that the stimuli can be decoded as learned relations from sets of categorical stimuli. We illustrate these principles with two types of stimuli, color (wavelength) and letters (visual shape), both of which have early visual system response, but at the same time must be learned within a given function or category (color contrast, alphabet). Key to the decoding method is reducing the stimulus cross-correlation by a matched noise voxel sample by normalizing the stimulus voxel matrix thus unmasking a highly discriminative neural profile per stimulus. Projection of this new voxel space (ROI) to a smaller set of dimensions (with e.g., non-metric Multidimensional scaling), the relational information takes a unique geometric form revealing functional relationships between sets of stimuli, defi...
Scale-free networks (SFN) arise from simple growth processes, which can encourage efficient, cent... more Scale-free networks (SFN) arise from simple growth processes, which can encourage efficient, centralized and fault tolerant communication (1). Recently its been shown that stable network hub structure is governed by a phase transition at exponents (>2.0) causing a dramatic change in network structure including a loss of global connectivity, an increasing minimum dominating node set, and a shift towards increasing connectivity growth compared to node growth. Is this SFN shift identifiable in atypical brain activity? The Pareto Distribution (P(D)~D^-\b{eta}) on the hub Degree (D) is a signature of scale-free networks. During resting-state, we assess Degree exponents across a large range of neurotypical and atypical subjects. We use graph complexity theory to provide a predictive theory of the brain network structure. Results. We show that neurotypical resting-state fMRI brain activity possess scale-free Pareto exponents (1.8 se .01) in a single individual scanned over 66 days as we...
The academic achievement gap is a persistent and pernicious educational challenge confounded with... more The academic achievement gap is a persistent and pernicious educational challenge confounded with race and socioeconomic status. The achievement gap persists despite over three decades of interventions and federal, state, and local policies and initiatives meant to close it. We examine the achievement gap in one of the most diverse states in the United States, New Jersey. Across the 480 school districts in New Jersey, the school district racial makeup varies from almost complete segregation to high diversity. Using data‐mining techniques on the statewide standardized test, the 2010–2011 New Jersey Assessment of Skills and Knowledge (NJ ASK) standard language arts literacy and mathematics, we found that test scores increased with increasing diversity. In particular, the achievement gap between Black and White third grade students was lower by more than 60% in racially diverse districts when compared to racially homogeneous districts. This result is consistent with theories of peer sp...
Autism and schizophrenia share overlapping genetic etiology, common changes in brain structure an... more Autism and schizophrenia share overlapping genetic etiology, common changes in brain structure and common cognitive deficits. A number of studies using resting state fMRI have shown that machine learning algorithms can distinguish between healthy controls and individuals diagnosed with either autism spectrum disorder or schizophrenia. However, it has not yet been determined whether machine learning algorithms can be used to distinguish between the two disorders. Using a linear support vector machine, we identify features that are most diagnostic for each disorder and successfully use them to classify an independent cohort of subjects. We find both common and divergent connectivity differences largely in the default mode network as well as in salience, and motor networks. Using divergent connectivity differences, we are able to distinguish autistic subjects from those with schizophrenia. Understanding the common and divergent connectivity changes associated with these disorders may provide a framework for understanding their shared cognitive deficits.
How does the human brain represent simple compositions of constituents: actors, verbs, objects, d... more How does the human brain represent simple compositions of constituents: actors, verbs, objects, directions, and locations? We had subjects view actionsequence videos during neuroimaging (fMRI) sessions and identified lexical descriptions of those videos by decoding the brain representations based only 1 on their fMRI activation patterns. We independently decoded each constituent from a single stimulus presentation and compared the performance of such independent classification to joint classification of the aggregate concepts. Independent classification of constituents performs largely the same as joint classification as measured by accuracy and correlation. The brain regions used for independent constituent classification are largely disjoint and largely cover those used for joint classification. This allows recovery of sentential descriptions of stimulus videos by composing the results of the independent constituent classifiers.
Cognition and behavior emerge from brain network interactions, such that investigating causal int... more Cognition and behavior emerge from brain network interactions, such that investigating causal interactions should be central to the study of brain function. Approaches that characterize statistical associations among neural time series-functional connectivity (FC) methods-are likely a good starting point for estimating brain network interactions. Yet only a subset of FC methods ("effective connectivity") are explicitly designed to infer causal interactions from statistical associations. Here we incorporate best practices from diverse areas of FC research to illustrate how FC methods can be refined to improve inferences about neural mechanisms, with properties of causal neural interactions as a common ontology to facilitate cumulative progress across FC approaches. We further demonstrate how the most common FC measures (correlation and coherence) reduce the set of likely causal models, facilitating causal inferences despite major limitations. Alternative FC measures are suggested to immediately start improving causal inferences beyond these common FC measures.
How does the human brain represent simple compositions of objects, actors, and actions? We had su... more How does the human brain represent simple compositions of objects, actors, and actions? We had subjects view action sequence videos during neuroimaging (fMRI) sessions and identified lexical descriptions of those videos by decoding (SVM) the brain representations based only on their fMRI activation patterns. As a precursor to this result, we had demonstrated that we could reliably and with high probability decode action labels corresponding to one of six action videos (dig, walk, etc.), again while subjects viewed the action sequence during scanning (fMRI). This result was replicated at two different brain imaging sites with common protocols but different subjects, showing common brain areas, including areas known for episodic memory (PHG, MTL, high level visual pathways, etc., i.e., the 'what' and 'where' systems, and TPJ, i.e., 'theory of mind'). Given these results, we were also able to successfully show a key aspect of language compositionality based on simultaneous decoding of object class and actor identity. Finally, combining these novel steps in 'brain reading' allowed us to accurately estimate brain representations supporting compositional decoding of a complex event composed of an actor, a verb, a direction, and an object.
Advances in Neural Information Processing Systems 14, 2002
It has been known that people, after being exposed to sentences generated by an artificial gramma... more It has been known that people, after being exposed to sentences generated by an artificial grammar, acquire implicit grammatical knowledge and are able to transfer the knowledge to inputs that are generated by a modified grammar. We show that a second order recurrent neural network is able to transfer grammatical knowledge from one language (generated by a Finite State Machine) to another language which differ both in vocabularies and syntax. Representation of the grammatical knowledge in the network is analyzed using linear discriminant analysis.
— Aim: This functional magnetic resonance imaging (fMRI) study examined reactivity to alcohol, po... more — Aim: This functional magnetic resonance imaging (fMRI) study examined reactivity to alcohol, polydrug, marijuana and emotional picture cues in students who were referred to a college alcohol and drug assistance program. Methods: The fMRI data of 10 participants (5 females; 5 males) were collected while they viewed standardized emotional and appetitive cues. Results: Positive and negative emotional cues produced greater activity than neutral cues in the expected brain areas. Compared with neutral cues, alcohol cues produced greater brain activation in the right insula, left anterior cingulate, left caudate and left prefrontal cortex (Z = 2.01, 1.86, 1.82, 1.81, respectively; P < 0.05). Drug cues produced significantly greater left prefrontal activity compared with neutral cues, with polydrug cues activating the right insula and marijuana cues activating left anterior cingulate. Conclusions: Students at-risk for alcohol abuse showed neural reactivity to alcohol cues in four brain...
Modeling causal relationship between memory and craving-related brain networks in non-treatment seeking cocaine smokers using images, a graph theoretic approach
Machine Intelligence and Pattern Recognition, 1986
This paper reports a machine induction program (WITT) which attempts to model human categorizatio... more This paper reports a machine induction program (WITT) which attempts to model human categorization. Properties of categories that human subjects are sensitive to include, best or prototypical members, relative contrasts between putative categories, and polymorphy (niether ne�ary or sufficient features). This approach represents an alternative to traditional Artificial Intelligence (AI) approaches to generalization and conceptual clustering which tend to focus on nec�ry and sufficient feature rules, equivalence classes, and search and match algorithms. The present approach is shown to be more consistent with human categorization while potentially including results produced by more traditional clustering schemes. Applications of this categorization approach are also discussed in the domains of Expert systems and Information retrieval.
Failing to engage in joint attention is a strong marker of impaired social cognition associated w... more Failing to engage in joint attention is a strong marker of impaired social cognition associated with autism spectrum disorder (ASD). The goal of this study was to localize the source of impaired joint attention in individuals with ASD by examining both behavioral and fMRI data collected during various tasks involving eye gaze, directional cuing, and face processing. The tasks were designed to engage three brain networks associated with social cognition (face processing, Theory of Mind, and action understanding). The behavioral results indicate that even high functioning individuals with ASD perform less accurately and more slowly than neurotypical (NT) controls when processing eyes, but not when processing a directional cue (an arrow) that did not involve eyes. Behavioral differences between the neurotypical and ASD groups were consistent with differences in the effective connectivity of FACE, TOM, and ACTION networks. An Independent Multiple-sample Greedy Equivalence
An ongoing debate about the structure of conceptual space is based on two competing mathematical ... more An ongoing debate about the structure of conceptual space is based on two competing mathematical theories of similarity that make distinct predictions about the structure of mental representations and how to model the representational space they are stored in. These are known as metric (Shepard, 1962) and ultrametric (Tversky, 1977) theories, modeled by multidimensional scaling and additive trees respectively. Turning to the brain to resolve this conflict, we propose a computational framework to assess behavioral and neural data's underlying structure and investigate whether the behaviorally known spaces for colors (metric) and letters (ultrametric) can be reproduced from neural data. Our results show that the metric color wheel can be reproduced from brain area V4, but that neural activations of the letters from extrastriate cortex (V2-V5) are also metric instead of being ultrametric. Finally, we discuss three possibilities for the brain's similarity structure, including a ...
We introduce a new method for decoding neural data from fMRI. It is based on two assumptions, fir... more We introduce a new method for decoding neural data from fMRI. It is based on two assumptions, first that neural representation is distributed over networks of neurons embeded in voxel noise and second that the stimuli can be decoded as learned relations from sets of categorical stimuli. We illustrate these principles with two types of stimuli, color (wavelength) and letters (visual shape), both of which have early visual system response, but at the same time must be learned within a given function or category (color contrast, alphabet). Key to the decoding method is reducing the stimulus cross-correlation by a matched noise voxel sample by normalizing the stimulus voxel matrix thus unmasking a highly discriminative neural profile per stimulus. Projection of this new voxel space (ROI) to a smaller set of dimensions (with e.g., non-metric Multidimensional scaling), the relational information takes a unique geometric form revealing functional relationships between sets of stimuli, defi...
Scale-free networks (SFN) arise from simple growth processes, which can encourage efficient, cent... more Scale-free networks (SFN) arise from simple growth processes, which can encourage efficient, centralized and fault tolerant communication (1). Recently its been shown that stable network hub structure is governed by a phase transition at exponents (>2.0) causing a dramatic change in network structure including a loss of global connectivity, an increasing minimum dominating node set, and a shift towards increasing connectivity growth compared to node growth. Is this SFN shift identifiable in atypical brain activity? The Pareto Distribution (P(D)~D^-\b{eta}) on the hub Degree (D) is a signature of scale-free networks. During resting-state, we assess Degree exponents across a large range of neurotypical and atypical subjects. We use graph complexity theory to provide a predictive theory of the brain network structure. Results. We show that neurotypical resting-state fMRI brain activity possess scale-free Pareto exponents (1.8 se .01) in a single individual scanned over 66 days as we...
The academic achievement gap is a persistent and pernicious educational challenge confounded with... more The academic achievement gap is a persistent and pernicious educational challenge confounded with race and socioeconomic status. The achievement gap persists despite over three decades of interventions and federal, state, and local policies and initiatives meant to close it. We examine the achievement gap in one of the most diverse states in the United States, New Jersey. Across the 480 school districts in New Jersey, the school district racial makeup varies from almost complete segregation to high diversity. Using data‐mining techniques on the statewide standardized test, the 2010–2011 New Jersey Assessment of Skills and Knowledge (NJ ASK) standard language arts literacy and mathematics, we found that test scores increased with increasing diversity. In particular, the achievement gap between Black and White third grade students was lower by more than 60% in racially diverse districts when compared to racially homogeneous districts. This result is consistent with theories of peer sp...
Autism and schizophrenia share overlapping genetic etiology, common changes in brain structure an... more Autism and schizophrenia share overlapping genetic etiology, common changes in brain structure and common cognitive deficits. A number of studies using resting state fMRI have shown that machine learning algorithms can distinguish between healthy controls and individuals diagnosed with either autism spectrum disorder or schizophrenia. However, it has not yet been determined whether machine learning algorithms can be used to distinguish between the two disorders. Using a linear support vector machine, we identify features that are most diagnostic for each disorder and successfully use them to classify an independent cohort of subjects. We find both common and divergent connectivity differences largely in the default mode network as well as in salience, and motor networks. Using divergent connectivity differences, we are able to distinguish autistic subjects from those with schizophrenia. Understanding the common and divergent connectivity changes associated with these disorders may provide a framework for understanding their shared cognitive deficits.
How does the human brain represent simple compositions of constituents: actors, verbs, objects, d... more How does the human brain represent simple compositions of constituents: actors, verbs, objects, directions, and locations? We had subjects view actionsequence videos during neuroimaging (fMRI) sessions and identified lexical descriptions of those videos by decoding the brain representations based only 1 on their fMRI activation patterns. We independently decoded each constituent from a single stimulus presentation and compared the performance of such independent classification to joint classification of the aggregate concepts. Independent classification of constituents performs largely the same as joint classification as measured by accuracy and correlation. The brain regions used for independent constituent classification are largely disjoint and largely cover those used for joint classification. This allows recovery of sentential descriptions of stimulus videos by composing the results of the independent constituent classifiers.
Cognition and behavior emerge from brain network interactions, such that investigating causal int... more Cognition and behavior emerge from brain network interactions, such that investigating causal interactions should be central to the study of brain function. Approaches that characterize statistical associations among neural time series-functional connectivity (FC) methods-are likely a good starting point for estimating brain network interactions. Yet only a subset of FC methods ("effective connectivity") are explicitly designed to infer causal interactions from statistical associations. Here we incorporate best practices from diverse areas of FC research to illustrate how FC methods can be refined to improve inferences about neural mechanisms, with properties of causal neural interactions as a common ontology to facilitate cumulative progress across FC approaches. We further demonstrate how the most common FC measures (correlation and coherence) reduce the set of likely causal models, facilitating causal inferences despite major limitations. Alternative FC measures are suggested to immediately start improving causal inferences beyond these common FC measures.
How does the human brain represent simple compositions of objects, actors, and actions? We had su... more How does the human brain represent simple compositions of objects, actors, and actions? We had subjects view action sequence videos during neuroimaging (fMRI) sessions and identified lexical descriptions of those videos by decoding (SVM) the brain representations based only on their fMRI activation patterns. As a precursor to this result, we had demonstrated that we could reliably and with high probability decode action labels corresponding to one of six action videos (dig, walk, etc.), again while subjects viewed the action sequence during scanning (fMRI). This result was replicated at two different brain imaging sites with common protocols but different subjects, showing common brain areas, including areas known for episodic memory (PHG, MTL, high level visual pathways, etc., i.e., the 'what' and 'where' systems, and TPJ, i.e., 'theory of mind'). Given these results, we were also able to successfully show a key aspect of language compositionality based on simultaneous decoding of object class and actor identity. Finally, combining these novel steps in 'brain reading' allowed us to accurately estimate brain representations supporting compositional decoding of a complex event composed of an actor, a verb, a direction, and an object.
Advances in Neural Information Processing Systems 14, 2002
It has been known that people, after being exposed to sentences generated by an artificial gramma... more It has been known that people, after being exposed to sentences generated by an artificial grammar, acquire implicit grammatical knowledge and are able to transfer the knowledge to inputs that are generated by a modified grammar. We show that a second order recurrent neural network is able to transfer grammatical knowledge from one language (generated by a Finite State Machine) to another language which differ both in vocabularies and syntax. Representation of the grammatical knowledge in the network is analyzed using linear discriminant analysis.
— Aim: This functional magnetic resonance imaging (fMRI) study examined reactivity to alcohol, po... more — Aim: This functional magnetic resonance imaging (fMRI) study examined reactivity to alcohol, polydrug, marijuana and emotional picture cues in students who were referred to a college alcohol and drug assistance program. Methods: The fMRI data of 10 participants (5 females; 5 males) were collected while they viewed standardized emotional and appetitive cues. Results: Positive and negative emotional cues produced greater activity than neutral cues in the expected brain areas. Compared with neutral cues, alcohol cues produced greater brain activation in the right insula, left anterior cingulate, left caudate and left prefrontal cortex (Z = 2.01, 1.86, 1.82, 1.81, respectively; P < 0.05). Drug cues produced significantly greater left prefrontal activity compared with neutral cues, with polydrug cues activating the right insula and marijuana cues activating left anterior cingulate. Conclusions: Students at-risk for alcohol abuse showed neural reactivity to alcohol cues in four brain...
Modeling causal relationship between memory and craving-related brain networks in non-treatment seeking cocaine smokers using images, a graph theoretic approach
Machine Intelligence and Pattern Recognition, 1986
This paper reports a machine induction program (WITT) which attempts to model human categorizatio... more This paper reports a machine induction program (WITT) which attempts to model human categorization. Properties of categories that human subjects are sensitive to include, best or prototypical members, relative contrasts between putative categories, and polymorphy (niether ne�ary or sufficient features). This approach represents an alternative to traditional Artificial Intelligence (AI) approaches to generalization and conceptual clustering which tend to focus on nec�ry and sufficient feature rules, equivalence classes, and search and match algorithms. The present approach is shown to be more consistent with human categorization while potentially including results produced by more traditional clustering schemes. Applications of this categorization approach are also discussed in the domains of Expert systems and Information retrieval.
Failing to engage in joint attention is a strong marker of impaired social cognition associated w... more Failing to engage in joint attention is a strong marker of impaired social cognition associated with autism spectrum disorder (ASD). The goal of this study was to localize the source of impaired joint attention in individuals with ASD by examining both behavioral and fMRI data collected during various tasks involving eye gaze, directional cuing, and face processing. The tasks were designed to engage three brain networks associated with social cognition (face processing, Theory of Mind, and action understanding). The behavioral results indicate that even high functioning individuals with ASD perform less accurately and more slowly than neurotypical (NT) controls when processing eyes, but not when processing a directional cue (an arrow) that did not involve eyes. Behavioral differences between the neurotypical and ASD groups were consistent with differences in the effective connectivity of FACE, TOM, and ACTION networks. An Independent Multiple-sample Greedy Equivalence
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Papers by Stephen Hanson