Bayesian decoding of brain images

Friston, Karl J.; Chu, Carlton; Mourão-Miranda, Janaı́na; Hulme, Oliver J.; Rees, Geraint; Penny, Will D.; Ashburner, John

doi:10.1016/j.neuroimage.2007.08.013

Cited by 180 publications

(234 citation statements)

References 51 publications

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“…We used a Bayesian model-based decoding method, MVB, for all decoding analyses (21)(22)(23)). An MVB model maps multivariate voxel responses to a psychological target variable (e.g., individual memories) using a hierarchical approach known as parametric empirical Bayes.…”

Section: Methodsmentioning

confidence: 99%

CA3 size predicts the precision of memory recall

Chadwick

Bonnici

Maguire

2014

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

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There is enduring interest in why some of us have clearer memories than others, given the substantial individual variation that exists in retrieval ability and the precision with which we can differentiate past experiences. Here we report novel evidence showing that variation in the size of human hippocampal subfield CA3 predicted the amount of neural interference between episodic memories within CA3, which in turn predicted how much retrieval confusion occurred between past memories. This effect was not apparent in other hippocampal subfields. This shows that subtle individual differences in subjective mnemonic experience can be accurately gauged from measurable variations in the anatomy and neural coding of hippocampal region CA3. Moreover, this mechanism may be relevant for understanding memory muddles in aging and pathological states.O ur memories often contain overlapping elements, because they tend to feature the same people and places that form the cornerstones of our lives. Nevertheless, we are generally able to recall many of these past experiences as distinct episodes, although we are not all equally adept at doing so. There is substantial individual variation in retrieval ability and the precision with which we can differentiate past events (1, 2). This is most acute as we age and in conditions such as dementia, where confusion about the past is often evident (2). There is keen interest, therefore, in elucidating the neural mechanisms that allow us to recollect numerous life experiences despite a high degree of intermemory similarity.We know little about how this is achieved in humans, but theoretical models propose that computations within hippocampal subfields facilitate the efficient storage and retrieval of similar memories (3-7). When we experience an event, pattern separation leads to the formation of a distinct neural representation within region CA3 (8-11). At retrieval, a previously stored memory representation within CA3 can be reactivated through the process of pattern completion (12, 13). However, when episodes are highly similar, the CA3 neuronal representations may not be completely distinct, leading to partial overlap (14). It is therefore not clear precisely what the limits of CA3 pattern separation might be. Here we directly tested the capacity of human CA3 to maintain distinct episodic representations in the presence of overlapping elements. We further investigated whether variation in this ability provides an explanatory account of individual differences in the precision of episodic memory retrieval. ResultsWe combined high-resolution functional MRI (14) (fMRI) with an ultra-high resolution structural MRI scanning protocol that permitted the separate identification of CA1, CA3, dentate gyrus (DG), and subiculum (15, 16). Stimuli were created by filming two brief action events against a green-screen background. Each event was then superimposed onto the same two spatial contexts, creating four movie clips that included every combination of the two events and the two contexts (17) (Fig...

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Section: Methodsmentioning

confidence: 99%

CA3 size predicts the precision of memory recall

Chadwick

Bonnici

Maguire

2014

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

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“…Subsequently, parameter (contrast-) images were calculated for each participant and entered into a second-level Bayesian analysis. This analysis, compared with null hypothesis significance, is highly reliable in small-group statistics with high within-subject variability caused by outliers [Friston and Penny, 2003;Friston et al, 2008;Neumann and Lohmann, 2003;Penny et al, 2005]. Given the high anatomical and physiological variability of the subjects, robustness against outliers is of basic importance for tools analyzing fMRI data.…”

Section: Experimental Design and Proceduresmentioning

confidence: 99%

The impact of iconic gestures on foreign language word learning and its neural substrate

Macedonia¹,

Müller²,

Friederici³

2011

Human Brain Mapping

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Vocabulary acquisition represents a major challenge in foreign language learning. Research has demonstrated that gestures accompanying speech have an impact on memory for verbal information in the speakers' mother tongue and, as recently shown, also in foreign language learning. However, the neural basis of this effect remains unclear. In a within-subjects design, we compared learning of novel words coupled with iconic and meaningless gestures. Iconic gestures helped learners to significantly better retain the verbal material over time. After the training, participants' brain activity was registered by means of fMRI while performing a word recognition task. Brain activations to words learned with iconic and with meaningless gestures were contrasted. We found activity in the premotor cortices for words encoded with iconic gestures. In contrast, words encoded with meaningless gestures elicited a network associated with cognitive control. These findings suggest that memory performance for newly learned words is not driven by the motor component as such, but by the motor image that matches an underlying representation of the word's semantics.

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“…In general terms, the algorithm aims to find a mapping between the dimensions for the A:B relation and those for the C:D relation that minimizes a distance measure defined over the weight distributions. Because the full search space for this correspondence problem scales exponentially with the number of dimensions, we employ a greedy search algorithm (Friston et al, 2008), a type of procedure designed to make locally optimal choices with the hope of approximating the global optimum. More specifically, the algorithm develops a one-to-one mapping between dimensions sequentially on the basis of the overall "importance" of dimensions.…”

Section: Model Evaluation On Generalization Testmentioning

confidence: 99%

Bayesian analogy with relational transformations.

Chen

Holyoak

2012

Psychological Review

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How can humans acquire relational representations that enable analogical inference and other forms of high-level reasoning? Using comparative relations as a model domain, we explore the possibility that bottom-up learning mechanisms applied to objects coded as feature vectors can yield representations of relations sufficient to solve analogy problems. We introduce Bayesian analogy with relational transformations (BART) and apply the model to the task of learning first-order comparative relations (e.g., larger, smaller, fiercer, meeker) from a set of animal pairs. Inputs are coded by vectors of continuousvalued features, based either on human magnitude ratings, normed feature ratings (De Deyne et al., 2008), or outputs of the topics model (Griffiths, Steyvers, & Tenenbaum, 2007). Bootstrapping from empirical priors, the model is able to induce first-order relations represented as probabilistic weight distributions, even when given positive examples only. These learned representations allow classification of novel instantiations of the relations and yield a symbolic distance effect of the sort obtained with both humans and other primates. BART then transforms its learned weight distributions by importance-guided mapping, thereby placing distinct dimensions into correspondence. These transformed representations allow BART to reliably solve 4-term analogies (e.g., larger:smaller::fiercer:meeker), a type of reasoning that is arguably specific to humans. Our results provide a proof-of-concept that structured analogies can be solved with representations induced from unstructured feature vectors by mechanisms that operate in a largely bottom-up fashion. We discuss potential implications for algorithmic and neural models of relational thinking, as well as for the evolution of abstract thought. Keywords: analogy, relation learning, generalization, Bayesian modelsSupplemental materials: http://dx.doi.org/10.1037/a0028719.supp One of the hallmarks of human reasoning is the ability to form representations of relations between entities and then to reason about the higher order relations between these relations. Whereas concepts such as larger and smaller, for example, are first-order relations, potentially derivable by comparing features of individual objects, a relation such as opposite is a higher order relation between relations (Gentner, 1983). The capacity to represent and reason with higher order relations has been considered central to human analogical thinking

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Bayesian decoding of brain images

Cited by 180 publications

References 51 publications

CA3 size predicts the precision of memory recall

CA3 size predicts the precision of memory recall

The impact of iconic gestures on foreign language word learning and its neural substrate

Bayesian analogy with relational transformations.

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