Biology subjects

Baker, C. I.

Publications and source records attributed to Baker, C. I..

30 records · Page 2Linked to original sources

bioRxiv2019

Quantifying Aphantasia through drawing: Those without visual imagery show deficits in object but not spatial memory

Congenital aphantasia is a recently characterized experience defined by the inability to form voluntary visual imagery, in spite of intact semantic memory, recognition memory, and visual perception. Because of this specific deficit to visual imagery, aphantasia serves as an ideal population for probing the nature of representations in visual memory, particularly the interplay of object, spatial, and symbolic information. Here, we conducted a large-scale online study of aphantasics and revealed a dissociation in object and spatial content in their memory representations. Sixty-one aphantasics and matched controls with typical imagery studied real-world scene images, and were asked to draw them from memory, and then later copy them during a matched perceptual condition. Drawings were objectively quantified by 2,795 online scorers for object and spatial details. Aphantasics recalled significantly fewer objects than controls, with less color in their drawings, and an increased reliance on verbal scaffolding. However, aphantasics showed incredibly high spatial accuracy, equivalent to controls, and made significantly fewer memory errors. These differences between groups only manifested during recall, with no differences between groups during the matched perceptual condition. This object-specific memory impairment in aphantasics provides evidence for separate systems in memory that support object versus spatial information.

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bioRxiv2019

Distinct representational structure and spatial distribution for visual encoding and recall

During memory recall and visual imagery, reinstatement is thought to occur as an echoing of the neural patterns during encoding. However, the precise information in these recall traces is relatively unknown, with previous work primarily investigating either broad distinctions or specific images, rarely bridging these levels of information. Using ultra-high-field (7T) fMRI with an item-based visual recall task, we conducted an in-depth comparison of encoding and recall along a spectrum of granularity, from coarse (scenes, objects) to mid (e.g., natural, manmade scenes) to fine (e.g., living room, cupcake) levels. In the scanner, participants viewed a trial-unique item, and after a distractor task, visually imagined the initial item. During encoding, we observed decodable information at all levels of granularity in category-selective visual cortex. In contrast, information during recall was primarily at the coarse level with fine level information in some areas; there was no evidence of mid-level information. A closer look revealed segregation between voxels showing the strongest effects during encoding and those during recall, and peaks of encoding-recall similarity extended anterior to category-selective cortex. Collectively, these results suggest visual recall is not merely a reactivation of encoding patterns, displaying a different representational structure and localization from encoding, despite some overlap.

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bioRxiv2019

Intention to learn modulates the impact of reward and punishment on sequence learning

In real-world settings, learning is often characterised as intentional: learners are aware of the goal during the learning process, and the goal of learning is readily dissociable from the awareness of what is learned. Recent evidence has shown that reward and punishment (collectively referred to as valenced feedback) are important factors that influence performance during learning. Presently, however, studies investigating the impact of valenced feedback on skill learning have only considered unintentional learning, and therefore the interaction between intentionality and valenced feedback has not been systematically examined. The present study investigated how reward and punishment impact behavioural performance when participants are instructed to learn in a goal-directed fashion (i.e. intentionally) rather than unintentionally. In Experiment 1, participants performed the serial response time task with reward, punishment, or control feedback and were instructed to ignore the presence of the sequence, i.e., learn unintentionally. Experiment 2 followed the same design, but participants were instructed to intentionally learn the sequence. We found that punishment was significantly beneficial to performance during learning only when participants learned unintentionally, and we observed no effect of punishment when participants learned intentionally. Thus, the impact of feedback on performance may be influenced by goal of the learner.

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bioRxiv2019

Whole-brain MEG decoding of symbolic and non-symbolic number stimuli reveals primarily format-dependent representations

The human brain can rapidly form representations of numerical magnitude, whether presented with symbolic stimuli like digits and words or non-symbolic stimuli like dot displays. Little is known about the relative time course of these symbolic and non-symbolic number representations. We investigated the emergence of number representations for three stimulus formats - digits, words, and dot arrays - by applying multivariate pattern analysis to MEG recordings from 22 participants. We first conducted within-format classification to identify the time course by which individual numbers can be decoded from the MEG signal. Peak classification accuracy for individual numbers in all three formats occurred around 110 ms after stimulus onset. Next, we used between-format classification to determine the time course of shared number representations between stimulus formats. Classification accuracy between formats was much weaker than within format classification, but it was also significant at early time points, around 100 ms for both digit / dot and digit / word comparisons. We then used representational similarity analysis to determine if we could explain variance in the MEG representational geometry using two models: a GIST feature model capturing low-level visual properties and an approximate number model capturing the numerical magnitude of the stimuli. Model RSA results differed between stimulus formats: while the GIST model explained unique variance from 100-300 ms for all number formats, the performance of the approximate number model differed between formats. Together, these results are consistent with the view that distinct, format-specific number representations, moreso than a single \"abstract\" number representation, form the basis of numerical comparison.

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bioRxiv2019

Memorability of photographs in subjective cognitive decline and mild cognitive impairment: implications for cognitive assessment

INTRODUCTIONImpaired long-term memory is a defining feature of Mild Cognitive Impairment (MCI). We tested whether this impairment is item-specific, limited to some memoranda whereas some remain consistently memorable.\n\nMETHODSWe conducted item-based analyses of long-term visual recognition memory. 394 participants (healthy controls (HC), Subjective Cognitive Decline (SCD), and MCI) in the multicentric DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE) were tested with images from a pool of 835 photographs.\n\nRESULTSWe observed consistent memorability for images in HCs, SCDs, and MCI, predictable by a neural network trained on another healthy sample. Looking at memorability differences between groups, we identified images that could successfully categorize group membership with higher success and a substantial image reduction than the original image set.\n\nDISCUSSIONIndividuals with SCD and MCI show consistent memorability for specific items, while other items show significant diagnosticity. Certain stimulus features could optimize diagnostic assessment, while others could support memory.

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bioRxiv2019

Distinct subdivisions of human medial parietal cortex are recruited differentially for memory recall of places and people

Human medial parietal cortex (MPC) is implicated in multiple cognitive processes including memory recall, visual scene processing and navigation. It is also considered a core component of the default mode network. Here, we combine fMRI data across three independent experiments to demonstrate distinct subdivisions of MPC that are selectively recruited during memory recall of either specific places or specific people. First, distinct regions of MPC were identified on the basis of differential functional connectivity with medial and lateral regions of anterior ventral temporal cortex (VTC). Second, these same medial regions exhibited differential responses to the visual presentation of different stimulus categories, with clear preferences for scenes and faces, respectively. Third, and most critically, these regions were selectively recruited during either place or people memory recall. These subdivisions also showed a striking relationship with ventral and dorsal divisions of the default mode network. Taken together, these data reveal distinct subdivisions within MPC for the recall of places and people and moreover, suggest that the organizing principle defining the medial-lateral axis of VTC is reflected in MPC, but in the context of memory recall.

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bioRxiv2019

THINGS: A database of 1,854 object concepts and more than 26,000 naturalistic object images

In recent years, the use of a large number of object concepts and naturalistic object images has been growing enormously in cognitive neuroscience research. Classical databases of object concepts are based mostly on a manually-curated set of concepts. Further, databases of naturalistic object images typically consist of single images of objects cropped from their background, or a large number of uncontrolled naturalistic images of varying quality, requiring elaborate manual image curation. Here we provide a set of 1,854 diverse object concepts sampled systematically from concrete picturable and nameable nouns in the American English language. Using these object concepts, we conducted a large-scale web image search to compile a database of 26,107 high-quality naturalistic images of those objects, with 12 or more object images per concept and all images cropped to square size. Using crowdsourcing, we provide higher-level category membership for the 27 most common categories and validate them by relating them to representations in a semantic embedding derived from large text corpora. Finally, by feeding images through a deep convolutional neural network, we demonstrate that they exhibit high selectivity for different object concepts, while at the same time preserving variability of different object images within each concept. Together, the THINGS database provides a rich resource of object concepts and object images and offers a tool for both systematic and large-scale naturalistic research in the fields of psychology, neuroscience, and computer science.

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bioRxiv2018

Reward and punishment differentially recruit cerebellum and medial temporal lobe to facilitate skill memory retention

Reward and punishment shape behavior, but the mechanisms underlying their effect on skill learning are not well understood. Here, we tested whether the functional connectivity of premotor cortex (PMC), a region known to be critical for learning of sequencing skills, is altered after training by reward or punishment given during training. Resting-state fMRI was collected in two experiments before and after participants trained on either a serial reaction time task (SRTT; n = 36) or force-tracking task (FTT; n = 36) with reward, punishment, or control feedback. In each experiment, training-related change in PMC functional connectivity was compared across feedback groups. In both tasks, reward and punishment differentially affected PMC functional connectivity. On the SRTT, participants trained with reward showed an increase in functional connectivity between PMC and cerebellum as well as PMC and striatum, while participants trained with punishment showed an increase in functional connectivity between PMC and medial temporal lobe connectivity. After training on the FTT, subjects trained with control and reward showed increases in PMC connectivity with parietal and temporal cortices after training, while subjects trained with punishment showed increased PMC connectivity with ventral striatum. While the results from the two experiments overlapped in some areas, including ventral pallidum, temporal lobe, and cerebellum, these regions showed diverging patterns of results across the two tasks for the different feedback conditions. These findings suggest that reward and punishment strongly influence spontaneous brain activity after training, and that the regions implicated depend on the task learned.

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bioRxiv2017

The temporal evolution of conceptual object representations revealed through models of behavior, semantics and deep neural networks

Visual object representations are commonly thought to emerge rapidly, yet it has remained unclear to what extent early brain responses reflect purely low-level visual features of these objects and how strongly those features contribute to later categorical or conceptual representations. Here, we aimed to estimate a lower temporal bound for the emergence of conceptual representations by defining two criteria that characterize such representations: 1) conceptual object representations should generalize across different exemplars of the same object, and 2) these representations should reflect high-level behavioral judgments. To test these criteria, we compared magnetoencephalography (MEG) recordings between two groups of participants (n = 16 per group) exposed to different exemplar images of the same object concepts. Further, we disentangled low-level from high-level MEG responses by estimating the unique and shared contribution of models of behavioral judgments, semantics, and different layers of deep neural networks of visual object processing. We find that 1) both generalization across exemplars as well as generalization of object-related signals across time increase after 150 ms, peaking around 230 ms; 2) behavioral judgments explain the most unique variance in the response after 150 ms. Collectively, these results suggest a lower bound for the emergence of conceptual object representations around 150 ms following stimulus onset.

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bioRxiv2017

Distinct contributions of functional and deep neural network features to scene representation in brain and behavior

Inherent correlations between visual and semantic features in real-world scenes make it difficult to determine how different scene properties contribute to neural representations. Here, we assessed the contributions of multiple properties to scene representation by partitioning the variance explained in human behavioral and brain measurements by three feature models whose inter-correlations were minimized a priori through stimulus preselection. Behavioral assessments of scene similarity reflected unique contributions from a functional feature model indicating potential actions in scenes as well as high-level visual features from a deep neural network (DNN). In contrast, similarity of cortical responses in scene-selective areas was uniquely explained by mid- and high-level DNN features only, while an object label model did not contribute uniquely to either domain. The striking dissociation between functional and DNN features in their contribution to behavioral and brain representations of scenes indicates that scene-selective cortex represents only a subset of behaviorally relevant scene information.

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bioRxiv2017

Deconstructing multivariate decoding for the study of brain function

Multivariate decoding methods were developed originally as tools to enable accurate predictions in real-world applications. The realization that these methods can also be employed to study brain function has led to their widespread adoption in the neurosciences. However, prior to the rise of multivariate decoding, the study of brain function was firmly embedded in a statistical philosophy grounded on univariate methods of data analysis. In this way, multivariate decoding for brain interpretation grew out of two established frameworks: multivariate decoding for predictions in real-world applications, and classical univariate analysis based on the study and interpretation of brain activation. We argue that this led to two confusions, one reflecting a mixture of multivariate decoding for prediction or interpretation, and the other a mixture of the conceptual and statistical philosophies underlying multivariate decoding and classical univariate analysis. Here we attempt to systematically disambiguate multivariate decoding for the study of brain function from the frameworks it grew out of. After elaborating these confusions and their consequences, we describe six, often unappreciated, differences between classical univariate analysis and multivariate decoding. We then focus on how the common interpretation of what is signal and noise changes in multivariate decoding. Finally, we use four examples to illustrate where these confusions may impact the interpretation of neuroimaging data. We conclude with a discussion of potential strategies to help resolve these confusions in interpreting multivariate decoding results, including the potential departure from multivariate decoding methods for the study of brain function.\n\nHighlightsO_LIWe highlight two sources of confusion that affect the interpretation of multivariate decoding results\nC_LIO_LIOne confusion arises from the dual use of multivariate decoding for predictions in real-world applications and for interpretation in terms of brain function\nC_LIO_LIThe other confusion arises from the different statistical and conceptual frameworks underlying classical univariate analysis to multivariate decoding\nC_LIO_LIWe highlight six differences between classical univariate analysis and multivariate decoding and differences in the interpretation of signal and noise\nC_LIO_LIThese confusions are illustrated in four examples revealing assumptions and limitations of multivariate decoding for interpretation\nC_LI

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bioRxiv2017

Representational dynamics of task context and its influence on visual object processing

Despite the importance of an observers goals in determining how a visual object is categorized, surprisingly little is known about how humans process the task context in which objects occur and how it may interact with the processing of objects. Using magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI) and multivariate techniques, we studied the spatial and temporal dynamics of task and object processing. Our results reveal a sequence of separate but overlapping task-related processes spread across frontoparietal and occipitotemporal cortex. Task exhibited late effects on object processing by selectively enhancing task-relevant object features, with limited impact on the overall pattern of object representations. Combining MEG and fMRI data, we reveal a parallel rise in task-related signals throughout the cerebral cortex, with an increasing dominance of task over object representations from early to higher visual areas. Collectively, our results reveal the complex dynamics underlying task and object representations throughout human cortex.

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