Investigating visual and auditory scene feedback to early visual foveal and peripheral cortex using fMRI

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The cognitive neuroscience of visual working memory

Visual working memory allows us to temporarily maintain and manipulate visual information in order to solve a task. The study of the brain mechanisms underlying this function began more than half a century ago, with Scoville and Milner’s (1957) seminal discoveries with amnesic patients. This timely collection of papers brings together diverse perspectives on the cognitive neuroscience of visual working memory from multiple fields that have traditionally been fairly disjointed: human neuroimaging, electrophysiological, behavioural and animal lesion studies, investigating both the developing and the adult brain

Online Tracking of the Contents of Conscious Perception Using Real-Time fMRI

Perception is an active process that interprets and structures the stimulus input based on assumptions about its possible causes. We use real-time functional magnetic resonance imaging (rtfMRI) to investigate a particularly powerful demonstration of dynamic object integration in which the same physical stimulus intermittently elicits categorically different conscious object percepts. In this study, we simulated an outline object that is moving behind a narrow slit. With such displays, the physically identical stimulus can elicit categorically different percepts that either correspond closely to the physical stimulus (vertically moving line segments) or represent a hypothesis about the underlying cause of the physical stimulus (a horizontally moving object that is partly occluded). In the latter case, the brain must construct an object from the input sequence. Combining rtfMRI with machine learning techniques we show that it is possible to determine online the momentary state of a subject’s conscious percept from time resolved BOLD-activity. In addition, we found that feedback about the currently decoded percept increased the decoding rates compared to prior fMRI recordings of the same stimulus without feedback presentation. The analysis of the trained classifier revealed a brain network that discriminates contents of conscious perception with antagonistic interactions between early sensory areas that represent physical stimulus properties and higher-tier brain areas. During integrated object percepts, brain activity decreases in early sensory areas and increases in higher-tier areas. We conclude that it is possible to use BOLD responses to reliably track the contents of conscious visual perception with a relatively high temporal resolution. We suggest that our approach can also be used to investigate the neural basis of auditory object formation and discuss the results in the context of predictive coding theory

What does semantic tiling of the cortex tell us about semantics?

Recent use of voxel-wise modeling in cognitive neuroscience suggests that semantic maps tile the cortex. Although this impressive research establishes distributed cortical areas active during the conceptual processing that underlies semantics, it tells us little about the nature of this processing. While mapping concepts between Marr's computational and implementation levels to support neural encoding and decoding, this approach ignores Marr's algorithmic level, central for understanding the mechanisms that implement cognition, in general, and conceptual processing, in particular. Following decades of research in cognitive science and neuroscience, what do we know so far about the representation and processing mechanisms that implement conceptual abilities? Most basically, much is known about the mechanisms associated with: (1) features and frame representations, (2) grounded, abstract, and linguistic representations, (3) knowledge-based inference, (4) concept composition, and (5) conceptual flexibility. Rather than explaining these fundamental representation and processing mechanisms, semantic tiles simply provide a trace of their activity over a relatively short time period within a specific learning context. Establishing the mechanisms that implement conceptual processing in the brain will require more than mapping it to cortical (and sub-cortical) activity, with process models from cognitive science likely to play central roles in specifying the intervening mechanisms. More generally, neuroscience will not achieve its basic goals until it establishes algorithmic-level mechanisms that contribute essential explanations to how the brain works, going beyond simply establishing the brain areas that respond to various task conditions

Abstracts of the 2014 Brains, Minds, and Machines Summer School

A compilation of abstracts from the student projects of the 2014 Brains, Minds, and Machines Summer School, held at Woods Hole Marine Biological Lab, May 29 - June 12, 2014.This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216

Parts and Wholes in Scene Processing

During natural vision, our brains are constantly exposed to complex, but regularly structured, environments. Real-world scenes are defined by typical part–whole relationships, where the meaning of the whole scene emerges from configurations of localized information present in individual parts of the scene. Such typical part–whole relationships suggest that information from individual scene parts is not processed independently, but that there are mutual influences between the parts and the whole during scene analysis. Here, we review recent research that used a straightforward, but effective approach to study such mutual influences: By dissecting scenes into multiple arbitrary pieces, these studies provide new insights into how the processing of whole scenes is shaped by their constituent parts and, conversely, how the processing of individual parts is determined by their role within the whole scene. We highlight three facets of this research: First, we discuss studies demonstrating that the spatial configuration of multiple scene parts has a profound impact on the neural processing of the whole scene. Second, we review work showing that cortical responses to individual scene parts are shaped by the context in which these parts typically appear within the environment. Third, we discuss studies demonstrating that missing scene parts are interpolated from the surrounding scene context. Bridging these findings, we argue that efficient scene processing relies on an active use of the scene's part–whole structure, where the visual brain matches scene inputs with internal models of what the world should look like

Neural Foundations of Mental Simulation: Future Prediction of Latent Representations on Dynamic Scenes

Humans and animals have a rich and flexible understanding of the physical world, which enables them to infer the underlying dynamical trajectories of objects and events, plausible future states, and use that to plan and anticipate the consequences of actions. However, the neural mechanisms underlying these computations are unclear. We combine a goal-driven modeling approach with dense neurophysiological data and high-throughput human behavioral readouts to directly impinge on this question. Specifically, we construct and evaluate several classes of sensory-cognitive networks to predict the future state of rich, ethologically-relevant environments, ranging from self-supervised end-to-end models with pixel-wise or object-centric objectives, to models that future predict in the latent space of purely static image-based or dynamic video-based pretrained foundation models. We find strong differentiation across these model classes in their ability to predict neural and behavioral data both within and across diverse environments. In particular, we find that neural responses are currently best predicted by models trained to predict the future state of their environment in the latent space of pretrained foundation models optimized for dynamic scenes in a self-supervised manner. Notably, models that future predict in the latent space of video foundation models that are optimized to support a diverse range of sensorimotor tasks, reasonably match both human behavioral error patterns and neural dynamics across all environmental scenarios that we were able to test. Overall, these findings suggest that the neural mechanisms and behaviors of primate mental simulation are thus far most consistent with being optimized to future predict on dynamic, reusable visual representations that are useful for embodied AI more generally.Comment: 17 pages, 6 figure

Decoding the Recognition of Occluded Objects in the Human Brain

The dynamics of object recognition are intricate, particularly when under challenging visual conditions, such as occlusion. Current models of vision often fall short in explaining the human visual system's remarkable ability to represent occluded objects. Previous studies have predominantly employed simple shapes as occluders, limiting the understanding of real-world occlusion scenarios. Chapter 2 delves into neural representations by investigating occlusion with realistic stimuli—objects occluding other objects. Using event-related fMRI, participants engaged in a one-back task while viewing objects in isolation, occluded by other objects, or cut out by silhouettes. Decoding analyses in early visual cortex (EVC) revealed a reliance on visible features, while inferotemporal cortex (IT) exhibited robust representations, incorporating both visible and inferred features. Competition effects across multiple objects were evident in EVC but notably weaker in IT, highlighting IT's capacity to disentangle neural responses amidst competing stimuli. Chapter 3 expands the exploration to behavioural aspects, unveiling the impact of occlusion magnitude on recognition difficulty. IT displayed a linear increase of beta weights in processing allocation with recognition difficulty. Behaviourally, unoccluded conditions showed enhanced accuracy and faster response times, with unique recognition patterns emerging when objects served as both occluders and occluded objects. Chapter 4 uses fMRI to examine the theoretical perspective of predictive processing, employing expectation suppression in EVC during occlusion, motivated by high-level occlusion responses found previously. Multivariate pattern analysis indicated an expectation suppression effect aligning with the sharpening account of predictive processing. The concluding chapter synthesises these findings, emphasising the practical and theoretical implications. Notably, the thesis underscores the importance of utilising ecological visual information in visual neuroscience studies and highlights the differing capabilities of EVC and IT. Collectively, our research contributes valuable insights into the neural mechanisms underlying object recognition in challenging visual conditions, paving the way for future research avenues