31 research outputs found
What Makes a Pattern? Matching Decoding Methods to Data in Multivariate Pattern Analysis
Research in neuroscience faces the challenge of integrating information across different spatial scales of brain function. A promising technique for harnessing information at a range of spatial scales is multivariate pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) data. While the prevalence of MVPA has increased dramatically in recent years, its typical implementations for classification of mental states utilize only a subset of the information encoded in local fMRI signals. We review published studies employing multivariate pattern classification since the technique’s introduction, which reveal an extensive focus on the improved detection power that linear classifiers provide over traditional analysis techniques. We demonstrate using simulations and a searchlight approach, however, that non-linear classifiers are capable of extracting distinct information about interactions within a local region. We conclude that for spatially localized analyses, such as searchlight and region of interest, multiple classification approaches should be compared in order to match fMRI analyses to the properties of local circuits
Much Easier Said Than Done: Falsifying the Causal Relevance of Linear Decoding Methods
Linear classifier probes are frequently utilized to better understand how
neural networks function. Researchers have approached the problem of
determining unit importance in neural networks by probing their learned,
internal representations. Linear classifier probes identify highly selective
units as the most important for network function. Whether or not a network
actually relies on high selectivity units can be tested by removing them from
the network using ablation. Surprisingly, when highly selective units are
ablated they only produce small performance deficits, and even then only in
some cases. In spite of the absence of ablation effects for selective neurons,
linear decoding methods can be effectively used to interpret network function,
leaving their effectiveness a mystery. To falsify the exclusive role of
selectivity in network function and resolve this contradiction, we
systematically ablate groups of units in subregions of activation space. Here,
we find a weak relationship between neurons identified by probes and those
identified by ablation. More specifically, we find that an interaction between
selectivity and the average activity of the unit better predicts ablation
performance deficits for groups of units in AlexNet, VGG16, MobileNetV2, and
ResNet101. Linear decoders are likely somewhat effective because they overlap
with those units that are causally important for network function.
Interpretability methods could be improved by focusing on causally important
units.Comment: 6 pages, 3 figures, to be published in I Can't Believe It's Note
Better Workshop at NeurIPS 202
Recommended from our members
No group differences in traditional economics measures of loss aversion and framing effects in bipolar I disorder
Hervorming Sociale Regelgevin
Identifying Brain Network Topology Changes in Task Processes and Psychiatric Disorders
Hervorming Sociale Regelgevin
Recommended from our members
A Nexus Model of Restricted Interests in Autism Spectrum Disorder
Restricted interests (RIs) in autism spectrum disorder (ASD) are clinically impairing interests of unusual focus or intensity. They are a subtype of restricted and repetitive behaviors which are one of two diagnostic criteria for the disorder. Despite the near ubiquity of RIs in ASD, the neural basis for their development is not well understood. However, recent cognitive neuroscience findings from nonclinical samples and from individuals with ASD shed light on neural mechanisms that may explain the emergence of RIs. We propose the nexus model of RIs in ASD, a novel conceptualization of this symptom domain that suggests that RIs may reflect a co-opting of brain systems that typically serve to integrate complex attention, memory, semantic, and social communication functions during development. The nexus model of RIs hypothesizes that when social communicative development is compromised, brain functions typically located within the lateral surface of cortex may expand into social processing brain systems and alter cortical representations of various cognitive functions during development. These changes, in turn, promote the development of RIs as an alternative process mediated by these brain networks. The nexus model of RIs makes testable predictions about reciprocal relations between the impaired development of social communication and the emergence of RIs in ASD and suggests novel avenues for treatment development.</p
Nucleus Accumbens Mediates Relative Motivation for Rewards in the Absence of Choice
To dissociate a choice from its antecedent neural states, motivation associated with the expected outcome must be captured in the absence of choice. Yet, the neural mechanisms that mediate behavioral idiosyncrasies in motivation, particularly with regard to complex economic preferences, are rarely examined in situations without overt decisions. We employed functional magnetic resonance imaging in a large sample of participants while they anticipated earning rewards from two different modalities: monetary and candy rewards. An index for relative motivation toward different reward types was constructed using reaction times to the target for earning rewards. Activation in the nucleus accumbens (NAcc) and anterior insula (aINS) predicted individual variation in relative motivation between our reward modalities. NAcc activation, however, mediated the effects of aINS, indicating the NAcc is the likely source of this relative weighting. These results demonstrate that neural idiosyncrasies in reward efficacy exist even in the absence of explicit choices, and extend the role of NAcc as a critical brain region for such choice-free motivation
Flash-lag chimeras: the role of perceived alignment in the composite face effect
Spatial alignment of different face halves results in a configuration that mars the recognition of the identity of either face half (). What would happen to the recognition performance for face halves that were aligned on the retina but were perceived as misaligned, or were misaligned on the retina but were perceived as aligned? We used the 'flash-lag' effect () to address these questions. We created chimeras consisting of a stationary top half-face initially aligned with a moving bottom half-face. Flash-lag chimeras were better recognized than their stationary counterparts. However when flashed face halves were presented physically ahead of moving halves thereby nulling the flash-lag effect, recognition was impaired. This counters the notion that relative movement between the two face halves per se is sufficient to explain better recognition of flash-lag chimeras. Thus, the perceived spatial alignment of face halves (despite retinal misalignment) impairs recognition, while perceived misalignment (despite retinal alignment) does not
Recommended from our members
Neural Mechanisms of Reward Prediction Error in Autism Spectrum Disorder.
Few studies have explored neural mechanisms of reward learning in ASD despite evidence of behavioral impairments of predictive abilities in ASD. To investigate the neural correlates of reward prediction errors in ASD, 16 adults with ASD and 14 typically developing controls performed a prediction error task during fMRI scanning. Results revealed greater activation in the ASD group in the left paracingulate gyrus during signed prediction errors and the left insula and right frontal pole during thresholded unsigned prediction errors. Findings support atypical neural processing of reward prediction errors in ASD in frontostriatal regions critical for prediction coding and reward learning. Results provide a neural basis for impairments in reward learning that may contribute to traits common in ASD (e.g., intolerance of unpredictability).</p