Diane Beck | University of Illinois at Urbana-Champaign (original) (raw)

Papers by Diane Beck

Journal of Vision, 2010

Abstract What is the underlying nature of conscious awareness? William James observed introspecti... more Abstract What is the underlying nature of conscious awareness? William James observed introspectively that consciousness,“… does not appear to itself chopped into bits… A 'river'or a 'stream'are the metaphors by which it is most naturally described.”(James, 1890). Since ...

Nature Human Behavior, 2019

Scientific research on consciousness is critical to multiple scientific, clinical, and ethical is... more Scientific research on consciousness is critical to multiple scientific, clinical, and ethical issues. The growth of the field could also be beneficial to several areas including neurology and mental health research. To achieve this goal, we need to set funding priorities carefully and address problems such as job creation and potential media misrepresentation.

Journal of vision, 2014

Intertrial effects such as priming of pop-out (PoP) often occur for task-irrelevant dimensions as... more Intertrial effects such as priming of pop-out (PoP) often occur for task-irrelevant dimensions as well as task-relevant dimensions, though to a weaker extent. Here we test the hypothesis that increased priming for task-relevant dimensions is due to greater passive build-up of priming for the task-relevant dimension rather than to an active filtering of task-irrelevant dimensions; if this is the case, then we should observe a positive correlation between the magnitude of task-relevant and task-irrelevant priming. We tested this hypothesis using a pop-out search task in which the task-relevant dimension was orientation and the task-irrelevant dimension was color. We found a strong, positive association between task-relevant and task-irrelevant priming across a large group of participants (N = 100); additionally, we observed increased priming over consecutive repetitions for the task-relevant dimension, whereas task-irrelevant priming was constant across multiple repetitions. As furthe...

Trends in cognitive sciences, 2005

The human ability to detect symmetry has been a topic of interest to psychologists and philosophe... more The human ability to detect symmetry has been a topic of interest to psychologists and philosophers since the 19th century, yet surprisingly little is known about the neural basis of symmetry perception. In a recent fMRI study, Sasaki and colleagues begin to remedy this situation. By identifying the neural structures that respond to symmetry in both humans and macaques, the authors lay the groundwork for understanding the neural mechanisms underlying symmetry perception.

Journal of Cognitive Neuroscience, 2015

■ Objects can be simultaneously categorized at multiple levels of specificity ranging from very b... more ■ Objects can be simultaneously categorized at multiple levels of specificity ranging from very broad ("natural object") to very distinct ("Mr. Woof "), with a mid-level of generality (basic level: "dog") often providing the most cognitively useful distinction between categories. It is unknown, however, how this hierarchical representation is achieved in the brain. Using multivoxel pattern analyses, we examined how well each taxonomic level (superordinate, basic, and subordinate) of real-world object categories is represented across occipitotemporal cortex. We found that, although in early visual cortex objects are best represented at the subordinate level (an effect mostly driven by low-level feature overlap between objects in the same category), this advantage diminishes compared to the basic level as we move up the visual hierarchy, disappearing in object-selective regions of occipitotemporal cortex. This pattern stems from a combined increase in within-category similarity (category cohesion) and between-category dissimilarity (category distinctiveness) of neural activity patterns at the basic level, relative to both subordinate and superordinate levels, suggesting that successive visual areas may be optimizing basic level representations. ■

Frontiers in Human Neuroscience, 2011

PeerJ, 2015

A common goal in biological sciences is to model a complex web of connections using a small numbe... more A common goal in biological sciences is to model a complex web of connections using a small number of interacting units. We present a general approach for dividing up elements in a spatial map based on their connectivity properties, allowing for the discovery of local regions underlying large-scale connectivity matrices. Our method is specifically designed to respect spatial layout and identify locally-connected clusters, corresponding to plausible coherent units such as strings of adjacent DNA base pairs, subregions of the brain, animal communities, or geographic ecosystems. Instead of using approximate greedy clustering, our nonparametric Bayesian model infers a precise parcellation using collapsed Gibbs sampling. We utilize an infinite clustering prior that intrinsically incorporates spatial constraints, allowing the model to search directly in the space of spatially-coherent parcellations. After showing results on synthetic datasets, we apply our method to both functional and structural connectivity data from the human brain. We find that our parcellation is substantially more effective than previous approaches at summarizing the brain's connectivity structure using a small number of clusters, produces better generalization to individual subject data, and reveals functional parcels related to known retinotopic maps in visual cortex. Additionally, we demonstrate the generality of our method by applying the same model to human migration data within the United States. This analysis reveals that migration behavior is generally influenced by state borders, but also identifies regional communities which cut across state lines. Our parcellation approach has a wide range of potential applications in understanding the spatial structure of complex biological networks.

ABSTRACT The purpose of categorization is to identify generalizable classes of objects whose memb... more ABSTRACT The purpose of categorization is to identify generalizable classes of objects whose members can be treated equivalently. Within a category, however, some exemplars are more representative of that concept than other members of the same category (Rosch 1973, Rosch & Mervis 1975). This typicality effect usually manifests as increased speed of recognition, as well as lower error rates for verifying category membership of the more typical item. Despite these behavioral effects, little is known about how typicality influences the neural representation of objects from the same category. To address this question, we performed an fMRI experiment in which participants were shown color photographs from 128 subordinate-level object categories grouped into 16 basic-level categories (4 species of animals, 4 types of plants, 4 transportation modalities, and 4 classes of musical instruments). Typicality for each subordinate within its basic category was assessed behaviorally. We analyzed neural responses in early visual areas and object-, scene-, and face-selective areas: V1, V2, V3v, hV4, LOC, TOS, PPA, RSC, FFA. For each brain area, we computed separate similarity matrices (Kriegeskorte et al. 2008) for the most and least prototypical halves of the category set. In V1, V2, and LOC, the most typical exemplars from a basic category had a more similar representation and were more distinct from prototypes of other basic categories (using a category boundary effect measure) than the least typical exemplars. Furthermore, a subsequent analysis showed that in LOC, but not in early visual areas, the most typical exemplars also correlated better than the least typical with the average response elicited by other exemplars. Our results suggest that neural representation differs for typical and less typical object exemplars. More specifically, typicality may be correlated to neural distance between categories in LOC, with highly typical members maximizing dissimilarity to instances of other categories.

Journal of Vision, 2010

Abstract What is the underlying nature of conscious awareness? William James observed introspecti... more Abstract What is the underlying nature of conscious awareness? William James observed introspectively that consciousness,“… does not appear to itself chopped into bits… A 'river'or a 'stream'are the metaphors by which it is most naturally described.”(James, 1890). Since ...

Nature Human Behavior, 2019

Scientific research on consciousness is critical to multiple scientific, clinical, and ethical is... more Scientific research on consciousness is critical to multiple scientific, clinical, and ethical issues. The growth of the field could also be beneficial to several areas including neurology and mental health research. To achieve this goal, we need to set funding priorities carefully and address problems such as job creation and potential media misrepresentation.

Journal of vision, 2014

Intertrial effects such as priming of pop-out (PoP) often occur for task-irrelevant dimensions as... more Intertrial effects such as priming of pop-out (PoP) often occur for task-irrelevant dimensions as well as task-relevant dimensions, though to a weaker extent. Here we test the hypothesis that increased priming for task-relevant dimensions is due to greater passive build-up of priming for the task-relevant dimension rather than to an active filtering of task-irrelevant dimensions; if this is the case, then we should observe a positive correlation between the magnitude of task-relevant and task-irrelevant priming. We tested this hypothesis using a pop-out search task in which the task-relevant dimension was orientation and the task-irrelevant dimension was color. We found a strong, positive association between task-relevant and task-irrelevant priming across a large group of participants (N = 100); additionally, we observed increased priming over consecutive repetitions for the task-relevant dimension, whereas task-irrelevant priming was constant across multiple repetitions. As furthe...

Trends in cognitive sciences, 2005

The human ability to detect symmetry has been a topic of interest to psychologists and philosophe... more The human ability to detect symmetry has been a topic of interest to psychologists and philosophers since the 19th century, yet surprisingly little is known about the neural basis of symmetry perception. In a recent fMRI study, Sasaki and colleagues begin to remedy this situation. By identifying the neural structures that respond to symmetry in both humans and macaques, the authors lay the groundwork for understanding the neural mechanisms underlying symmetry perception.

Journal of Cognitive Neuroscience, 2015

■ Objects can be simultaneously categorized at multiple levels of specificity ranging from very b... more ■ Objects can be simultaneously categorized at multiple levels of specificity ranging from very broad ("natural object") to very distinct ("Mr. Woof "), with a mid-level of generality (basic level: "dog") often providing the most cognitively useful distinction between categories. It is unknown, however, how this hierarchical representation is achieved in the brain. Using multivoxel pattern analyses, we examined how well each taxonomic level (superordinate, basic, and subordinate) of real-world object categories is represented across occipitotemporal cortex. We found that, although in early visual cortex objects are best represented at the subordinate level (an effect mostly driven by low-level feature overlap between objects in the same category), this advantage diminishes compared to the basic level as we move up the visual hierarchy, disappearing in object-selective regions of occipitotemporal cortex. This pattern stems from a combined increase in within-category similarity (category cohesion) and between-category dissimilarity (category distinctiveness) of neural activity patterns at the basic level, relative to both subordinate and superordinate levels, suggesting that successive visual areas may be optimizing basic level representations. ■

Frontiers in Human Neuroscience, 2011

PeerJ, 2015

A common goal in biological sciences is to model a complex web of connections using a small numbe... more A common goal in biological sciences is to model a complex web of connections using a small number of interacting units. We present a general approach for dividing up elements in a spatial map based on their connectivity properties, allowing for the discovery of local regions underlying large-scale connectivity matrices. Our method is specifically designed to respect spatial layout and identify locally-connected clusters, corresponding to plausible coherent units such as strings of adjacent DNA base pairs, subregions of the brain, animal communities, or geographic ecosystems. Instead of using approximate greedy clustering, our nonparametric Bayesian model infers a precise parcellation using collapsed Gibbs sampling. We utilize an infinite clustering prior that intrinsically incorporates spatial constraints, allowing the model to search directly in the space of spatially-coherent parcellations. After showing results on synthetic datasets, we apply our method to both functional and structural connectivity data from the human brain. We find that our parcellation is substantially more effective than previous approaches at summarizing the brain's connectivity structure using a small number of clusters, produces better generalization to individual subject data, and reveals functional parcels related to known retinotopic maps in visual cortex. Additionally, we demonstrate the generality of our method by applying the same model to human migration data within the United States. This analysis reveals that migration behavior is generally influenced by state borders, but also identifies regional communities which cut across state lines. Our parcellation approach has a wide range of potential applications in understanding the spatial structure of complex biological networks.

ABSTRACT The purpose of categorization is to identify generalizable classes of objects whose memb... more ABSTRACT The purpose of categorization is to identify generalizable classes of objects whose members can be treated equivalently. Within a category, however, some exemplars are more representative of that concept than other members of the same category (Rosch 1973, Rosch & Mervis 1975). This typicality effect usually manifests as increased speed of recognition, as well as lower error rates for verifying category membership of the more typical item. Despite these behavioral effects, little is known about how typicality influences the neural representation of objects from the same category. To address this question, we performed an fMRI experiment in which participants were shown color photographs from 128 subordinate-level object categories grouped into 16 basic-level categories (4 species of animals, 4 types of plants, 4 transportation modalities, and 4 classes of musical instruments). Typicality for each subordinate within its basic category was assessed behaviorally. We analyzed neural responses in early visual areas and object-, scene-, and face-selective areas: V1, V2, V3v, hV4, LOC, TOS, PPA, RSC, FFA. For each brain area, we computed separate similarity matrices (Kriegeskorte et al. 2008) for the most and least prototypical halves of the category set. In V1, V2, and LOC, the most typical exemplars from a basic category had a more similar representation and were more distinct from prototypes of other basic categories (using a category boundary effect measure) than the least typical exemplars. Furthermore, a subsequent analysis showed that in LOC, but not in early visual areas, the most typical exemplars also correlated better than the least typical with the average response elicited by other exemplars. Our results suggest that neural representation differs for typical and less typical object exemplars. More specifically, typicality may be correlated to neural distance between categories in LOC, with highly typical members maximizing dissimilarity to instances of other categories.