Biology subjects

Snoek, L.

Publications and source records attributed to Snoek, L..

7 recordsLinked to original sources

bioRxiv2023

Human visual cortex and deep convolutional neural network care deeply about object background

Deep convolutional neural networks (DCNNs) are able to predict brain activity during object categorization tasks, but factors contributing to this predictive power are not fully understood. Our study aimed to investigate the factors contributing to the predictive power of DCNNs in object categorization tasks. We compared the activity of four DCNN architectures with electroencephalography (EEG) recordings obtained from 62 human subjects during an object categorization task. Previous physiological studies on object categorization have highlighted the importance of figure-ground segregation - the ability to distinguish objects from their backgrounds. Therefore, we set out to investigate if figure-ground segregation could explain DCNNs predictive power. Using a stimuli set consisting of identical target objects embedded in different backgrounds, we examined the influence of object background versus object category on both EEG and DCNN activity. Crucially, the recombination of naturalistic objects and experimentally-controlled backgrounds creates a sufficiently challenging and naturalistic task, while allowing us to retain experimental control. Our results showed that early EEG activity (<100ms) and early DCNN layers represent object background rather than object category. We also found that the predictive power of DCNNs on EEG activity is related to processing of object backgrounds, rather than categories. We provided evidence from both trained and untrained (i.e. random weights) DCNNs, showing figure-ground segregation to be a crucial step prior to the learning of object features. These findings suggest that both human visual cortex and DCNNs rely on the segregation of object backgrounds and target objects in order to perform object categorization. Altogether, our study provides new insights into the mechanisms underlying object categorization as we demonstrated that both human visual cortex and DCNNs care deeply about object background. Author summaryOur study aimed to investigate the factors contributing to the predictive power of deep convolutional neural networks (DCNNs) on EEG activity in object recognition tasks. We compared the activity of four DCNN architectures with human neural recordings during an object categorization task. We used a stimuli set consisting of identical target objects embedded in different phase-scrambled backgrounds. The distinction between object backgrounds and object categories allows us to investigate the influence of either factor for human subjects and DCNNs. Surprisingly, we found that both human visual processing and early DCNNs layers dedicate a large proportion of activity to processing object backgrounds instead of object category. Furthermore, this shared ability to make object backgrounds (and not just object category) invariant is largely the reason why DCNNs are predictive of brain dynamics in our experiment. We posit this shared ability to be an important solution for object categorization. Finally, we conclude that DCNNs, like humans, care deeply about object backgrounds.

neuroscience↗

bioRxiv2022

A critical test of deep convolutional neural networks ability to capture recurrent processing in the brain using visual masking

Recurrent processing is a crucial feature in human visual processing supporting perceptual grouping, figure-ground segmentation, and recognition under challenging conditions. There is a clear need to incorporate recurrent processing in deep convolutional neural networks (DCNNs) but the computations underlying recurrent processing remain unclear. In this paper, we tested a form of recurrence in deep residual networks (ResNets) to capture recurrent processing signals in the human brain. Though ResNets are feedforward networks, they approximate an excitatory additive form of recurrence. Essentially, this form of recurrence consists of repeating excitatory activations in response to a static stimulus. Here, we used ResNets of varying depths (reflecting varying levels of recurrent processing) to explain electroencephalography (EEG) activity within a visual masking paradigm. Sixty-two humans and fifty artificial agents (10 ResNet models of depths - 4, 6, 10, 18 and 34) completed an object categorization task. We show that deeper networks (ResNet-10, 18 and 34) explained more variance in brain activity compared to shallower networks (ResNet-4 and 6). Furthermore, all ResNets captured differences in brain activity between unmasked and masked trials, with differences starting at [~]98ms (from stimulus onset). These early differences indicated that EEG activity reflected pure feedforward signals only briefly (up to [~]98ms). After [~]98ms, deeper networks showed a significant increase in explained variance which peaks at [~]200ms, but only within unmasked trials, not masked trials. In summary, we provided clear evidence that excitatory additive recurrent processing in ResNets captures some of the recurrent processing in humans. Significance statementThe challenge of modeling recurrent processes is not trivial and the operationalization of recurrent processing is highly contested. In this paper, we tested the ability of deep residual networks (ResNets) to explain recurrent processes in the human brain. Though ResNets are feedforward networks, they have been shown to equate operations in recurrent neural networks. In this study, we show that deeper networks explained more variance in brain activity than shallower networks. However, all networks still performed far from the noise ceiling. Thus, we conclude that recurrent processing in ResNets captures a form of recurrent processing in humans though other types of recurrent processing (inhibition, multiplicative) that are not present in current regular deep neural networks (alexnet, cornet, resnet) are necessary for building better visual models.

neuroscience↗

bioRxiv2020

The Amsterdam Open MRI Collection, a set ofmultimodal MRI datasets for individual differenceanalyses

We present the Amsterdam Open MRI Collection (AOMIC): three datasets with multimodal (3T) MRI data including structural (T1-weighted), diffusion-weighted, and (resting-state and task-based) functional BOLD MRI data, as well as detailed demographics and psychometric variables from a large set of healthy participants (N = 928, N = 226, and N = 216). Notably, task-based fMRI was collected during various robust paradigms (targeting naturalistic vision, emotion perception, working memory, face perception, cognitive conflict and control, and response inhibition) for which extensively annotated event-files are available. For each dataset and data modality, we provide the data in both raw and preprocessed form (both compliant with the Brain Imaging Data Structure), which were subjected to extensive (automated and manual) quality control. All data is publicly available from the Openneuro data sharing platform.

neuroscience↗

bioRxiv2020

Religious Belief and Cognitive Conflict Sensitivity: A Preregistered fMRI Study

In the current preregistered fMRI study, we investigated the relationship between religiosity and behavioral and neural mechanisms of conflict processing, as a conceptual replication of the study by Inzlicht et al. (2009). Participants (N = 193) performed a gender-Stroop task and afterwards completed standardized measures to assess their religiosity. As expected, the task induced cognitive conflict at the behavioral level and at a neural level this was reflected in increased activity in the anterior cingulate cortex (ACC). However, individual differences in religiosity were not related to performance on the Stroop task as measured in accuracy and interference effects, nor to neural markers of response conflict (correct responses vs. errors) or informational conflict (congruent vs. incongruent stimuli). Overall, we obtained moderate to strong evidence in favor of the null hypotheses that religiosity is unrelated to cognitive conflict sensitivity. We discuss the implications for the neuroscience of religion and emphasize the importance of designing studies that more directly implicate religious concepts and behaviors in an ecologically valid manner.

neuroscience↗

bioRxiv2019

Choosing to view morbid information involves reward circuitry

People often seek out stories, videos or images that detail death, violence or harm. Considering the ubiquity of this behavior, it is surprising that we know very little about the neural circuits involved in choosing negative information. Here we show that choosing intensely negative stimuli engages similar brain regions as those that support extrinsic incentives and \"regular\" curiosity. Participants made choices to view negative and positive images, based on negative (e.g., a soldier kicks a civilian against his head) and positive (e.g., children throw flower petals at a wedding) verbal cues. We hypothesized that the conflicting, but relatively informative act of choosing to view a negative image, resulted in stronger activation of reward circuitry as opposed to the relatively uncomplicated act of choosing to view a positive stimulus. Indeed, as preregistered, we found that choosing negative cues was associated with activation of the striatum, inferior frontal gyrus, anterior insula, and anterior cingulate cortex, both when contrasting against a passive viewing condition, and when contrasting against positive cues. These findings nuance models of decision-making, valuation and curiosity, and are an important starting point when considering the value of seeking out negative content.

neuroscience↗

bioRxiv2018

How to control for confounds in decoding analyses of neuroimaging data

Over the past decade, multivariate pattern analyses and especially decoding analyses have become a popular alternative to traditional mass-univariate analyses in neuroimaging research. However, a fundamental limitation of decoding analyses is that the source of information driving the decoder is ambiguous, which becomes problematic when the to-be-decoded variable is confounded by variables that are not of primary interest. In this study, we use a comprehensive set of simulations and analyses of empirical data to evaluate two techniques that were previously proposed and used to control for confounding variables in decoding analyses: counterbalancing and confound regression. For our empirical analyses, we attempt to decode gender from structural MRI data when controlling for the confound brain size. We show that both methods introduce strong biases in decoding performance: counterbalancing leads to better performance than expected (i.e., positive bias), which we show in our simulations is due to the subsampling process that tends to remove samples that are hard to classify; confound regression, on the other hand, leads to worse performance than expected (i.e., negative bias), even resulting in significant below-chance performance in some scenarios. In our simulations, we show that below-chance accuracy can be predicted by the variance of the distribution of correlations between the features and the target. Importantly, we show that this negative bias disappears in both the empirical analyses and simulations when the confound regression procedure performed in every fold of the cross-validation routine, yielding plausible model performance. From these results, we conclude that foldwise confound regression is the only method that appropriately controls for confounds, which thus can be used to gain more insight into the exact source(s) of information driving ones decoding analysis.\n\nHIGHLIGHTSO_LIThe interpretation of decoding models is ambiguous when dealing with confounds;\nC_LIO_LIWe evaluate two methods, counterbalancing and confound regression, in their ability to control for confounds;\nC_LIO_LIWe find that counterbalancing leads to positive bias because it removes hard-to-classify samples;\nC_LIO_LIWe find that confound regression leads to negative bias, because it yields data with less signal than expected by chance;\nC_LIO_LIOur simulations demonstrate a tight relationship between model performance in decoding analyses and the sample distribution of the correlation coefficient;\nC_LIO_LIWe show that the negative bias observed in confound regression can be remedied by cross-validating the confound regression procedure;\nC_LI

neuroscience↗

bioRxiv2017

Porcupine: a visual pipeline tool for neuroimaging analysis

The field of neuroimaging is rapidly adopting a more reproducible approach to data acquisition and analysis. Data structures and formats are being standardised and data analyses are getting more automated. However, as data analysis becomes more complicated, researchers often have to write longer analysis scripts, spanning different tools across multiple programming languages. This makes it more difficult to share or recreate code, reducing the reproducibility of the analysis. We present a tool, Porcupine, that constructs ones analysis visually and automatically produces analysis code. The graphical representation improves understanding of the performed analysis, while retaining the flexibility of modifying the produced code manually to custom needs. Not only does Porcupine produce the analysis code, it also creates a shareable environment for running the code, in the form of a Docker image. Together, this forms a reproducible way of constructing, visualising and sharing ones analysis. Currently, Porcupine links to Nipype functionalities, which in turn accesses most standard neuroimaging analysis tools. With Porcupine, we bridge the gap between a conceptual and an implementational level of analysis and thus create reproducible and shareable science. We give the researcher a better oversight of their pipeline, both while developing and communicating their work. This will reduce the threshold at which less expert users can generate reusable pipelines. We provide a wide range of examples and documentation, as well as installer files for all platforms on our website: https://timvanmourik.github.io/Porcupine. Porcupine is free, open source, andreleased under the GNU General Public License v3.0.\n\nAuthor SummaryThe neuroimaging community is fervently debating that its reproducibility and transparency should be improved, but it is a challenging problem as to how to accomplish this. We here propose a tool, Porcupine, to aid in this process by more easily creating shareable workflows for analysing neuroimaging data. The conceptual understanding of a pipeline is improved by means of the graphical interface, and it automatically produces the code to perform the analysis and to create a sharing environment. This retains full flexibility to modify the script afterwards but in principle produces readily executable code for an end-to-end analysis. Porcupine currently links to all Nipype functionality, but is designed to be extendable to other workflow packages in neuroimaging and beyond. Porcupine is free and is released under the GNU General Public License.

neuroscience↗