Representational geometry: integrating cognition, computation, and the brain

Kriegeskorte, Nikolaus; Kievit, Rogier A.

doi:10.1016/j.tics.2013.06.007

Cited by 820 publications

(928 citation statements)

References 104 publications

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“…We used a representational similarity analysis approach, which uses the neural pattern similarity between pairs of stimuli to infer the representational similarity (22). This method is therefore wellsuited for assessing neural overlap between semantic representations (23)(24)(25), as the degree of overlap should be directly reflected in the representational similarity.…”

Section: Resultsmentioning

confidence: 99%

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Semantic representations in the temporal pole predict false memories

Chadwick

Anjum

Kumaran

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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Recent advances in neuroscience have given us unprecedented insight into the neural mechanisms of false memory, showing that artificial memories can be inserted into the memory cells of the hippocampus in a way that is indistinguishable from true memories. However, this alone is not enough to explain how false memories can arise naturally in the course of our daily lives. Cognitive psychology has demonstrated that many instances of false memory, both in the laboratory and the real world, can be attributed to semantic interference. Whereas previous studies have found that a diverse set of regions show some involvement in semantic false memory, none have revealed the nature of the semantic representations underpinning the phenomenon. Here we use fMRI with representational similarity analysis to search for a neural code consistent with semantic false memory. We find clear evidence that false memories emerge from a similarity-based neural code in the temporal pole, a region that has been called the "semantic hub" of the brain. We further show that each individual has a partially unique semantic code within the temporal pole, and this unique code can predict idiosyncratic patterns of memory errors. Finally, we show that the same neural code can also predict variation in true-memory performance, consistent with an adaptive perspective on false memory. Taken together, our findings reveal the underlying structure of neural representations of semantic knowledge, and how this semantic structure can both enhance and distort our memories.false memory | semantic | temporal pole | fMRI | pattern similarity E ach of us has a vast store of semantic knowledge that we apply to incoming sensory data to extract meaning from the world around us. Semantic representations are capable of capturing important structural features of the world at many different levels of abstraction, which allows for rapid and flexible responses to a diverse array of environmental challenges. This preexisting knowledge structure guides ongoing cognition, which usually aids performance, but under some circumstances can lead us into error (1-3). A striking example is the widely studied DRM (Deese, Roediger, and McDermott) false-memory illusion (4, 5). In a typical DRM task, subjects are asked to memorize a set of words such as "snow," "winter," "ice," and "warm." After a delay, subjects will typically falsely remember having seen the semantically related word "cold." It is widely agreed that this memory illusion is driven by the semantic relatedness between words contained in the encoding list (e.g., "snow") and falsely remembered words that were not actually presented (e.g., "cold"). As such, it is thought that each list item automatically, but weakly, activates the semantically related concept (Fig. 1A). This activation leads to memory confusion, either through a cumulative priming of the related lure (5, 6) or the encoding of the semantic overlap as a "gist" memory (3), resulting in a false memory unless the error is detected by some internal monitoring p...

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Section: Resultsmentioning

confidence: 99%

“…We used a searchlight representational similarity analysis (22,28) to search for brain regions containing the predicted neural code. Representational similarity analysis uses the neural pattern similarity between pairs of stimuli to infer the representational similarity.…”

Section: Methodsmentioning

confidence: 99%

Semantic representations in the temporal pole predict false memories

Chadwick

Anjum

Kumaran

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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“…7A). The use of such approaches within neuroimaging is growing, with development and application of techniques such as representational similarity analysis (RSA) [Kriegeskorte and Kievit, 2013], and the use of supervised classification algorithms together with cross‐validation [Hastie et al, 2001], often referred to as a decoding [Haxby et al, 2014; King and Dehaene, 2014; Quian Quiroga and Panzeri, 2009]. Our new GCMI estimator allows us to address these issues within the unified framework of information theory.…”

Section: Discussionmentioning

confidence: 99%

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

Ince

Giordano

Kayser

et al. 2016

Human Brain Mapping

265

298

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We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open‐source Matlab and Python code implementing the new methods accompanies this article. Hum Brain Mapp 38:1541–1573, 2017. © 2016 Wiley Periodicals, Inc.

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“…Using multivariate pattern classification (Carlson et al, 2013;Cichy et al, 2014;Isik et al, 2014) and representational similarity analysis (Kriegeskorte, 2008;Kriegeskorte and Kievit, 2013;Cichy et al, 2014) on millisecond-resolved magnetoencephalography data (MEG), we identified a marker of scene size around 250 ms, preceded by and distinct from an early signal for lower-level visual analysis of scene images at ~100ms. Furthermore, we demonstrated that the scene size marker was independent of both low-level image features (i.e.…”

Section: The Temporal Dynamics Of Spatial Layout Processingmentioning

confidence: 99%

Dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks

Cichy

Khosla

Pantazis

et al. 2015

Preprint

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Human scene recognition is a rapid multistep process evolving over time from single scene image to spatial layout processing. We used multivariate pattern analyses on magnetoencephalography (MEG) data to unravel the time course of this cortical process. Following an early signal for lowerlevel visual analysis of single scenes at ~100 ms, we found a marker of real-world scene size, i.e. spatial layout processing, at ~250 ms indexing neural representations robust to changes in unrelated scene properties and viewing conditions. For a quantitative model of how scene size representations may arise in the brain, we compared MEG data to a deep neural network model trained on scene classification. Representations of scene size emerged intrinsically in the model, and resolved emerging neural scene size representation. Together our data provide a first description of an electrophysiological signal for layout processing in humans, and suggest that deep neural networks are a promising framework to investigate how spatial layout representations emerge in the human brain.

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Representational geometry: integrating cognition, computation, and the brain

Cited by 820 publications

References 104 publications

Semantic representations in the temporal pole predict false memories

Semantic representations in the temporal pole predict false memories

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

Dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks

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