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Research at the CSLB

Neurocognitive accounts of conceptual knowledge

Introduction

How is meaning represented and processed in the brain? To address this fundamental question, we develop cognitive models of conceptual knowledge, based on semantic feature statistics, and investigate how conceptual knowledge is processed, with a focus on how concrete concepts and visual objects are represented in the brain. Through combining cognitive theory and neurobiological models, our research endeavour reflects a neurocognitive approach. Our current research builds upon the hierarchical model of object processing in the ventral stream where increasingly anterior regions represent increasingly complex information about objects, and while activity in the posterior fusiform is sufficient to support coarse semantic representations, anteromedial aspects of the stream are required for the integration of more fine-grained conceptual knowledge supporting subtle distinctions between similar objects. Building on this model, our research asks how such meaningful representations emerge over time, how meaningful information is processed and represented in the brain and how this can be embodies in cognitive models of conceptual knowledge.

The evolution of meaningful object knowledge

Recognising and understanding what visual objects are is a critical ability if we are to relate and interact with the world, and requires the ability to extract meaningful semantic information from our visual perceptions. While it's generally appreciated that our knowledge about visual objects progresses along a course-to-fine time-course, exactly how the brain transforms this visual information into a more abstract, meaningful representations is unclear. Our research aims to uncover how such meaning evolves from vision over time - as neural signals propagate and reverberate through the cortex in a dynamic and interactive manner, and when different forms of perceptual and semantic information are expressed in neural signals. To address these issues we utilise a variety of behavioural and neuroimaging techniques - including MEG and fMRI, coupled with multivariate statistical analyses and connectivity measures.

Recurrent interactions between the left anterior temporal and posterior fusiform increase when more specific semantic information is required. This is shown through increased phase-locking between these regions during basic (e.g. tiger) compared to domain naming (i.e. living or nonliving; left), and increased activity in the anterior temporal peaking ~200 ms and posterior fusiform peaking ~250 ms (right). Adapted from Clarke, Taylor & Tyler (2011) Journal of Cognitive Neuroscience, 23(8), 1887-1899. (Click image to see full-size).

Conceptual knowledge as measured by property norms

We are currently working on developing models of how the brain represents conceptual knowledge. To this end we are developing a new set of property norms from which we can calculate various statistics about the way that concept knowledge is represented. In particular we are interested in the relationship between features of concepts that occur rarely or often and how this interacts with the co-occurrence of features. We hope that these norms will be the most extensive collected in British English participants.

Multivariate analyses of models of conceptual representations

How do conceptual representations arise in the brain? Although a seemingly effortless process, object recognition remains a much debated issue in cognitive neuroscience. This work mainly focuses on the testing of different models, such as the Conceptual Structure Account (CSA), which can account for neural activity (as acquired by fMRI) during object naming in healthy/normal subjects. We make extensive usage of multivariate statistics in imaging data analysis which can afford considerable analytical power in addressing these questions.

The neural basis of object processing: an ICA approach

Naming an object involves several processes including low-level visual feature analysis, accessing the object's meaning, producing the name, and selection/control processes. It remains unclear as to whether these processes are supported by distinct neural networks and how these networks interact as a function of an object's properties and related processing demands. We investigated these issues in an fMRI object naming experiment using group independent component analysis, attempting to separate distinct neural networks supporting different cognitive functions and further understand the relationships among these networks.

Perceptual and conceptual processing in brain-damaged populations

This research project focuses on relating behavioural measures of perceptual and conceptual processes to patterns of neural integrity in brain-damaged populations, with a particular focus on the ventral stream. The ventral stream is a network of regions believed to be crucial for translating visual input into a meaningful representation that can be understood and acted upon. Our aim is to better understand how regions along the ventral stream support perceptual and conceptual processes.

Functional neuroimaging studies using healthy individuals have shown that regions in the anteromedial aspects of the ventral stream, such as the peri- and entorhinal cortices, are increasingly involved when making fine-grained distinctions between objects. These regions are particularly responsive when naming living things, which have a relatively high number of shared features (e.g. like eyes and legs), but relatively few distinguishing features (e.g. a tiger's stripes), making the recognition of living things more reliant on these fine-grained processes supported by the anteromedial temporal cortex. Further, damage to the anteriomedial temporal cortex selectively impairs (1) the recognition of living things and (2) the ability to integrate information from different sensory modalities (i.e. vision and hearing).

This research project aims to extend this work through collecting behavioural data using an extensive test battery from brain-damaged volunteers, that assesses a range of perceptual (e.g. sorting shapes by size or identifying objects at odd angles) and conceptual abilities (e.g. deciding whether certain objects belonged in groups together or answering questions about the features of an object). We aim to answer a number of specific questions by relating our volunteers' test scores to patterns of brain damage:

1. Does damage to different parts of the ventral stream differentially impair perceptual and conceptual processing?
2. Does damage to the anteromedial temporal cortex leave perceptual processing intact while selectively impairing
   1. the more fine-grained processing of objects?
   2. recognition of living vs. nonliving things?
3. Do perceptual and conceptual abilities depend specifically upon the left or right hemisphere?
4. Which abilities require both hemispheres, and which can be supported by either hemisphere?

Regions in anteromedial temporal lobe where damage impairs integration of a concept's features across visual and auditory modalities. The graph shows that damage has a greater influence on performance for living things (red) than nonliving things (black). From Taylor et al. (2009) Brain, 132, 671-683.

Further reading

	Tyler, L. K., & Moss, H. E. (2001). Towards a distributed account of conceptual knowledge. Trends in Cognitive Sciences, 5(6), 244-252.
	Tyler, L. K., Stamatakis, E. A., Bright, P., Acres, K., Abdallah, S., Rodd, J. M., & Moss, H. E. (2004). Processing objects at different levels of specificity. Journal of Cognitive Neuroscience, 16(3), 351-362.
	Taylor, K.I., Stamatakis, E.A. & Tyler, L.K.(2009). Cross-modal integration of object features: voxel-based correlations in brain-damaged patients. Brain, 132, 671-683.
	Clarke, A., Taylor, K.I., & Tyler, L.K. (2011). The evolution of meaning: Spatiotemporal dynamics of visual object recognition Journal of Cognitive Neuroscience, 23(8), 1887-1899.
	Taylor, K.I., Devereux, B.J. & Tyler, L.K. (2011). Conceptual structure: Towards an integrated neuro-cognitive account. Language and Cognitive Processes, 26(9), 1368-1401.