Integrating theoretical models with functional neuroimaging

Pratte, Michael S.; Tong, Frank

doi:10.1016/j.jmp.2016.06.008

Cited by 5 publications

(3 citation statements)

References 78 publications

(135 reference statements)

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“…One innovation is to make the target of decoding a model measure, such as item familiarly according to a cognitive model [4]. The four quadrants shown in Figure 1 are not an exhaustive taxonomy of how to relate models to the BOLD response (for a more complete treatment, see [25,26]).…”

Section: Linking Cognitive Models To the Bold Responsementioning

confidence: 99%

Model-based fMRI analysis of memory

Love

2020

Current Opinion in Behavioral Sciences

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Recent advances in Model-based fMRI approaches enable researchers to investigate hypotheses about the time course and latent structure in data that were previously inaccessible. Cognitive models, especially when validated on multiple datasets, allow for additional constraints to be marshalled when interpreting neuroimaging data. Models can be related to BOLD response in a variety of ways, such as constraining the cognitive model by neural data, interpreting the neural data in light of behavioural fit, or simultaneously accounting for both neural and behavioural data. Using cognitive models as a lens on fMRI data is complementary to popular multivariate decoding and representational similarity analysis approaches. Indeed, these approaches can realise greater theoretical significance when situated within a model-based approach. HighlightsCognitive models can formalise theories to make assumptions and predictions clearer.Cognitive models offer additional constraints when interpreting the BOLD response.Models can go from behaviour to BOLD, vice versa, or address both simultaneously.Model-based approaches can incorporate pattern similarity and decoding approaches.

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Section: Linking Cognitive Models To the Bold Responsementioning

confidence: 99%

Model-based fMRI analysis of memory

Love

2020

Current Opinion in Behavioral Sciences

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“…Several authors have recently discussed "what constitutes an explanation?" A common theme is to invoke Marr's levels of analysis [30,21,37,13,4,31,35]. The first level, computational theory, is concerned with the 'what' and 'why'.…”

Section: The Goal Of Information-based Neuroimage Analysismentioning

confidence: 99%

How can deep learning advance computational modeling of sensory information processing?

Thompson,

Bengio,

Formisano

et al. 2018

Preprint

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Deep learning, computational neuroscience, and cognitive science have overlapping goals related to understanding intelligence such that perception and behaviour can be simulated in computational systems. In neuroimaging, machine learning methods have been used to test computational models of sensory information processing. Recently, these model comparison techniques have been used to evaluate deep neural networks (DNNs) as models of sensory information processing. However, the interpretation of such model evaluations is muddied by imprecise statistical conclusions. Here, we make explicit the types of conclusions that can be drawn from these existing model comparison techniques and how these conclusions change when the model in question is a DNN. We discuss how DNNs are amenable to new model comparison techniques that allow for stronger conclusions to be made about the computational mechanisms underlying sensory information processing.

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“…Pratte and Tong (2017) highlight a number of salient examples linking cognitive models and functional brain imaging data using a model-based cognitive neuroscience approach. Their selective review spans a broad range of core topics in perception and cognition, including visual perception (Brouwer & Heeger, 2011), attention (Pratte, Ling, Swisher, & Tong, 2013), long-term memory (Kragel, Morton, & Polyn, 2015), categorization (Mack et al, 2013), and cognitive control (Ide, Shenoy, Yu, & Li, 2013).…”

Section: Overviewmentioning

confidence: 99%

Model-based cognitive neuroscience

Palmeri

Love

Turner

2017

Journal of Mathematical Psychology

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This special issue explores the growing intersection between mathematical psychology and cognitive neuroscience. Mathematical psychology, and cognitive modeling more generally, has a rich history of formalizing and testing hypotheses about cognitive mechanisms within a mathematical and computational language, making exquisite predictions of how people perceive, learn, remember, and decide. Cognitive neuroscience aims to identify neural mechanisms associated with key aspects of cognition using techniques like neurophysiology, electrophysiology, and structural and functional brain imaging. These two come together in a powerful new approach called model-based cognitive neuroscience, which can both inform cognitive modeling and help to interpret neural measures. Cognitive models decompose complex behavior into representations and processes and these latent model states can be used to explain the modulation of brain states under different experimental conditions. Reciprocally, neural measures provide data that help constrain cognitive models and adjudicate between competing cognitive models that make similar predictions about behavior. As examples, brain measures are related to cognitive model parameters fitted to individual participant data, measures of brain dynamics are related to measures of model dynamics, model parameters are constrained by neural measures, model parameters or model states are used in statistical analyses of neural data, or neural and behavioral data are analyzed jointly within a hierarchical modeling framework. We provide an introduction to the field of model-based cognitive neuroscience and to the articles contained within this special issue.

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Integrating theoretical models with functional neuroimaging

Cited by 5 publications

References 78 publications

Model-based fMRI analysis of memory

Model-based fMRI analysis of memory

How can deep learning advance computational modeling of sensory information processing?

Model-based cognitive neuroscience

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