Nuisance Variation Research Articles

Meet JEANIE: A Similarity Measure for 3D Skeleton Sequences via Temporal-Viewpoint Alignment

Video sequences exhibit significant nuisance variations (undesired effects) of speed of actions, temporal locations, and subjects’ poses, leading to temporal-viewpoint misalignment when comparing two sets of frames or evaluating the similarity of two sequences. Thus, we propose Joint tEmporal and cAmera viewpoiNt alIgnmEnt (JEANIE) for sequence pairs. In particular, we focus on 3D skeleton sequences whose camera and subjects’ poses can be easily manipulated in 3D. We evaluate JEANIE on skeletal Few-shot Action Recognition (FSAR), where matching well temporal blocks (temporal chunks that make up a sequence) of support-query sequence pairs (by factoring out nuisance variations) is essential due to limited samples of novel classes. Given a query sequence, we create its several views by simulating several camera locations. For a support sequence, we match it with view-simulated query sequences, as in the popular Dynamic Time Warping (DTW). Specifically, each support temporal block can be matched to the query temporal block with the same or adjacent (next) temporal index, and adjacent camera views to achieve joint local temporal-viewpoint warping. JEANIE selects the smallest distance among matching paths with different temporal-viewpoint warping patterns, an advantage over DTW which only performs temporal alignment. We also propose an unsupervised FSAR akin to clustering of sequences with JEANIE as a distance measure. JEANIE achieves state-of-the-art results on NTU-60, NTU-120, Kinetics-skeleton and UWA3D Multiview Activity II on supervised and unsupervised FSAR, and their meta-learning inspired fusion.

Learning to segment complex vessel-like structures with spectral transformer

This paper demonstrates a novel approach for segmenting complex vessel-like structures from images of retinal vessels, surface cracks, and roadmaps, a challenging task due to nuisance variations in width, curvature, and branching patterns, as well as cluttered backgrounds caused by adverse imaging conditions. We introduce the Spectral Transformer (SpecFormer), a Transformer built from the frequency domain to segment the elongated and linear structured content of images. The idea behind SpecFormer is to take full advantage of the ability of low-frequency components in the Fourier domain to represent the overall structure, global patterns, and smooth variations. Specifically, a Sparse Spectral Neural Operator (SSNO) is proposed to modulate the sparse frequency-concentrated spectrum via learnt frequency-specific filtering, which can well represent the vessel-like structure in the Fourier domain. This operator, as the core component of Dual Attention Block (DAB), is designed in a dual-path way, i.e., self- and scaling-attention paths, to simultaneously capture the long-range dependencies and contextual information of the feature. The complete form of the SpecFormer is built with multiple DABs and modules for patch manipulations and feature fusion. We evaluated the SpecFormer on a wide range of publicly available datasets and achieved consistent improvements over the state-of-the-art (SOTA) methods. Code is available at https://github.com/LouisNUST/Spectral_Transformer.

An empirical Bayes method for differential expression analysis of single cells with deep generative models

Detecting differentially expressed genes is important for characterizing subpopulations of cells. In scRNA-seq data, however, nuisance variation due to technical factors like sequencing depth and RNA capture efficiency obscures the underlying biological signal. Deep generative models have been extensively applied to scRNA-seq data, with a special focus on embedding cells into a low-dimensional latent space and correcting for batch effects. However, little attention has been paid to the problem of utilizing the uncertainty from the deep generative model for differential expression (DE). Furthermore, the existing approaches do not allow for controlling for effect size or the false discovery rate (FDR). Here, we present lvm-DE, a generic Bayesian approach for performing DE predictions from a fitted deep generative model, while controlling the FDR. We apply the lvm-DE framework to scVI and scSphere, two deep generative models. The resulting approaches outperform state-of-the-art methods at estimating the log fold change in gene expression levels as well as detecting differentially expressed genes between subpopulations of cells.

DisP+V: A Unified Framework for Disentangling Prototype and Variation From Single Sample per Person.

Single sample per person face recognition (SSPP FR) is one of the most challenging problems in FR due to the extreme lack of enrolment data. To date, the most popular SSPP FR methods are the generic learning methods, which recognize query face images based on the so-called prototype plus variation (i.e., P+V) model. However, the classic P+V model suffers from two major limitations: 1) it linearly combines the prototype and variation images in the observational pixel-spatial space and cannot generalize to multiple nonlinear variations, e.g., poses, which are common in face images and 2) it would be severely impaired once the enrolment face images are contaminated by nuisance variations. To address the two limitations, it is desirable to disentangle the prototype and variation in a latent feature space and to manipulate the images in a semantic manner. To this end, we propose a novel disentangled prototype plus variation model, dubbed DisP+V, which consists of an encoder-decoder generator and two discriminators. The generator and discriminators play two adversarial games such that the generator nonlinearly encodes the images into a latent semantic space, where the more discriminative prototype feature and the less discriminative variation feature are disentangled. Meanwhile, the prototype and variation features can guide the generator to generate an identity-preserved prototype and the corresponding variation, respectively. Experiments on various real-world face datasets demonstrate the superiority of our DisP+V model over the classic P+V model for SSPP FR. Furthermore, DisP+V demonstrates its unique characteristics in both prototype recovery and face editing/interpolation.

Closing the life-cycle of normative modeling using federated hierarchical Bayesian regression.

Clinical neuroimaging data availability has grown substantially in the last decade, providing the potential for studying heterogeneity in clinical cohorts on a previously unprecedented scale. Normative modeling is an emerging statistical tool for dissecting heterogeneity in complex brain disorders. However, its application remains technically challenging due to medical data privacy issues and difficulties in dealing with nuisance variation, such as the variability in the image acquisition process. Here, we approach the problem of estimating a reference normative model across a massive population using a massive multi-center neuroimaging dataset. To this end, we introduce a federated probabilistic framework using hierarchical Bayesian regression (HBR) to complete the life-cycle of normative modeling. The proposed model provides the possibilities to learn, update, and adapt the model parameters on decentralized neuroimaging data. Our experimental results confirm the superiority of HBR in deriving more accurate normative ranges on large multi-site neuroimaging datasets compared to the current standard methods. In addition, our approach provides the possibility to recalibrate and reuse the learned model on local datasets and even on datasets with very small sample sizes. The proposed method will facilitate applications of normative modeling as a medical tool for screening the biological deviations in individuals affected by complex illnesses such as mental disorders.

Penalized decomposition using residuals (PeDecURe) for feature extraction in the presence of nuisance variables.

Neuroimaging data are an increasingly important part of etiological studies of neurological and psychiatric disorders. However, mitigating the influence of nuisance variables, including confounders, remains a challenge in image analysis. In studies of Alzheimer's disease, for example, an imbalance in disease rates by age and sex may make it difficult to distinguish between structural patterns in the brain (as measured by neuroimaging scans) attributable to disease progression and those characteristic of typical human aging or sex differences. Concerningly, when not properly accounted for, nuisance variables pose threats to the generalizability and interpretability of findings from these studies. Motivated by this critical issue, in this work, we examine the impact of nuisance variables on feature extraction methods and propose Penalized Decomposition Using Residuals (PeDecURe), a new method for obtaining nuisance variable-adjusted features. PeDecURe estimates primary directions of variation which maximize covariance between partially residualized imaging features and a variable of interest (e.g., Alzheimer's diagnosis) while simultaneously mitigating the influence of nuisance variation through a penalty on the covariance between partially residualized imaging features and those variables. Using features derived using PeDecURe's first direction of variation, we train a highly accurate and generalizable predictive model, as evidenced by its robustness in testing samples with different underlying nuisance variable distributions. We compare PeDecURe to commonly used decomposition methods (principal component analysis (PCA) and partial least squares) as well as a confounder-adjusted variation of PCA. We find that features derived from PeDecURe offer greater accuracy and generalizability and lower correlations with nuisance variables compared with the other methods. While PeDecURe is primarily motivated by challenges that arise in the analysis of neuroimaging data, it is broadly applicable to data sets with highly correlated features, where novel methods to handle nuisance variables are warranted.

Revealing nonlinear neural decoding by analyzing choices

Sensory data about most natural task-relevant variables are entangled with task-irrelevant nuisance variables. The neurons that encode these relevant signals typically constitute a nonlinear population code. Here we present a theoretical framework for quantifying how the brain uses or decodes its nonlinear information. Our theory obeys fundamental mathematical limitations on information content inherited from the sensory periphery, describing redundant codes when there are many more cortical neurons than primary sensory neurons. The theory predicts that if the brain uses its nonlinear population codes optimally, then more informative patterns should be more correlated with choices. More specifically, the theory predicts a simple, easily computed quantitative relationship between fluctuating neural activity and behavioral choices that reveals the decoding efficiency. This relationship holds for optimal feedforward networks of modest complexity, when experiments are performed under natural nuisance variation. We analyze recordings from primary visual cortex of monkeys discriminating the distribution from which oriented stimuli were drawn, and find these data are consistent with the hypothesis of near-optimal nonlinear decoding.

Statistical assessment of feeding corn with higher oil content to piglets in the starter phase

The aim of this study was to assess the digestibility coefficients (DC) of corn [maize] with an oil content above 3.46% and its effects on the performance of piglets when fed as dry grain (DG) and as rehydrated corn grain silage (RCGS). In Experiment I, 15 piglets (22.51 + 2.39 kg) were allocated to a reference diet (RD) and to two test diets in which corn in the RD was replaced with DG or RCGS. There were five replications of each treatment. Experiment II involved 36 piglets (14.76 ± 2.72 kg), which were assigned to a control diet with common corn grain and to diets in which DG or RCGS replaced the common corn. There were six replications of each treatment. Data were analysed with four statistical models. Model 1 included only the effect of treatment. Model 2 was similar to Model 1 but included initial bodyweight as a covariate. Model 3 was similar to model 1 but included the interaction of diet and period. Model 4 was similar to Model 3 but included the covariate. The more complicated models were generally preferred to Model 1 as they controlled more of the nuisance variation. Feeding a diet that contained RCGS reduced feed intake and improved feed conversion ratio (FCR).

AutoBayes: Automated Bayesian Graph Exploration for Nuisance- Robust Inference

Learning data representations that capture task-related features, but are invariant to nuisance variations remains a key challenge in machine learning. We introduce an automated Bayesian inference framework, called AutoBayes, that explores different graphical models linking classifier, encoder, decoder, estimator and adversarial network blocks to optimize nuisance-invariant machine learning pipelines. Auto Bayes also enables learning disentangled representations, where the latent variable is split into multiple pieces to impose various relationships with the nuisance variation and task labels. We benchmark the framework on several public datasets, and provide analysis of its capability for subject-transfer learning with/without variational modeling and adversarial training. We demonstrate a significant performance improvement with ensemble learning across explored graphical models.

Invariant Deep Compressible Covariance Pooling for Aerial Scene Categorization

Learning discriminative and invariant feature representation is the key to visual image categorization. In this article, we propose a novel invariant deep compressible covariance pooling (IDCCP) to solve nuisance variations in aerial scene categorization. We consider transforming the input image according to a finite transformation group that consists of multiple confounding orthogonal matrices, such as the D4 group. Then, we adopt a Siamese-style network to transfer the group structure to the representation space, where we can derive a trivial representation that is invariant under the group action. The linear classifier trained with trivial representation will also be possessed with invariance. To further improve the discriminative power of representation, we extend the representation to the tensor space while imposing orthogonal constraints on the transformation matrix to effectively reduce feature dimensions. We conduct extensive experiments on the publicly released aerial scene image data sets and demonstrate the superiority of this method compared with state-of-the-art methods. In particular, with using ResNet architecture, our IDCCP model can reduce the dimension of the tensor representation by about 98% without sacrificing accuracy (i.e., <0.5%).

Untargeted metabolomics profiling and hemoglobin normalization for archived newborn dried blood spots from a refrigerated biorepository

Archived dried blood spots (DBS) following newborn screening are an attractive resource for interrogating early-life biology using untargeted metabolomics. Therefore, they have the potential to substantially aid etiological studies, particularly for rare and low-frequency childhood diseases and disorders. However, metabolite quantification in DBS is hindered by variation sources not present in serum and plasma samples such as the hematocrit effect and unknown initial blood volumes. Hemoglobin (Hb) is an appropriate correlate for hematocrit in experimentally-generated DBS punches. However, since many biorepositories worldwide archive DBS at 4−5 °C, there is a need to validate the utility of Hb for DBS archived under refrigeration. We evaluated two simple spectroscopic methods for measuring Hb in DBS stored at 4 +/− 2 °C for up to 21 years, obtained from the newborn screening program at the Karolinska University Hospital, Sweden. Spearman correlation analysis and Akaike Information Criterion model selection found that measurement of a Hb sodium lauryl sulfate complex at 540 nm better described nuisance variation than Hb measured at 404 nm, or using age of spot alone. This is the first study to profile metabolites and to propose a normalization factor for metabolite measurements from DBS archived for decades at 4 °C.

An Item Analysis of the Trauma Inventory for Partners of Sex Addicts

The Trauma Inventory for Partners of Sex Addicts (TIPSA) represents a pioneering attempt to measure trauma in the partners of sex addicts. In this study, we utilized a large sample of TIPSA responses to examine the impact of rescoring empirically disordered response options on error variance and scale reliability. Results suggested that rescoring by collapsing categories in response to empirical response option disordering has a mixed and marginal impact on error variance and scale reliability, and may not be beneficial from a practical standpoint in terms of reducing nuisance variation. Importantly, results also support the conclusion that measurement and diagnosis are distinct endeavors that serve two separate purposes.

Population and Harvest Dynamics of Midcontinent Sandhill Cranes

ABSTRACTSandhill cranes (Antigone canadensis) inhabiting the midcontinent of North America have been hunted since the 1960s under management goals of maintaining abundance, retaining geographic distribution, and maximizing sustainable harvest. Some biologists have raised concerns regarding harvest sustainability because sandhill cranes have lower reproductive rates than other game birds. We summarized demographic information in an age‐structured matrix model to better understand population dynamics and harvest. Population indices and recovered harvest since the early 1980s suggest midcontinent sandhill cranes have experienced an average long‐term annual growth of 0.9%; meanwhile, harvest has increased 1.8% annually. Adult survival and recruitment rates estimated from field data required modest adjustments (1–3%) so that model‐derived growth rates matched growth estimated from a long‐term survey (0.887 adult survival and 0.199 females/breeding female). Considering 0.9% long‐term annual growth, sandhill cranes could be harvested at a rate of 6.6% if harvest was additive to natural mortality (assumed to be 0.05) or 11.3% if harvest mortality compensated for natural mortality. Life‐history characteristics for long‐lived organisms and demographic evidence suggested that hunter harvest was primarily additive. Differential harvest rates of segments of sandhill cranes in the midcontinent population derived from differential exposure to hunting suggested potentially unsustainable harvest for greater sandhill cranes (A. c. tabida) from 2 breeding segments. Overall, demographic evidence suggests that the harvest of sandhill cranes in the midcontinent population has been managed sustainably. Monitoring activities that reduce nuisance variation and estimate vital and harvest rates by subspecies would support continued management of sandhill cranes that are of interest to hunters and bird watchers. Published 2020. This article is a U.S. Government work and is in the public domain in the USA.

Correcting nuisance variation using Wasserstein distance.

Profiling cellular phenotypes from microscopic imaging can provide meaningful biological information resulting from various factors affecting the cells. One motivating application is drug development: morphological cell features can be captured from images, from which similarities between different drug compounds applied at different doses can be quantified. The general approach is to find a function mapping the images to an embedding space of manageable dimensionality whose geometry captures relevant features of the input images. An important known issue for such methods is separating relevant biological signal from nuisance variation. For example, the embedding vectors tend to be more correlated for cells that were cultured and imaged during the same week than for those from different weeks, despite having identical drug compounds applied in both cases. In this case, the particular batch in which a set of experiments were conducted constitutes the domain of the data; an ideal set of image embeddings should contain only the relevant biological information (e.g., drug effects). We develop a general framework for adjusting the image embeddings in order to “forget” domain-specific information while preserving relevant biological information. To achieve this, we minimize a loss function based on distances between marginal distributions (such as the Wasserstein distance) of embeddings across domains for each replicated treatment. For the dataset we present results with, the only replicated treatment happens to be the negative control treatment, for which we do not expect any treatment-induced cell morphology changes. We find that for our transformed embeddings (i) the underlying geometric structure is not only preserved but the embeddings also carry improved biological signal; and (ii) less domain-specific information is present.

DeeplyTough: Learning Structural Comparison of Protein Binding Sites.

Protein pocket matching, or binding site comparison, is of importance in drug discovery. Identification of similar binding pockets can help guide efforts for hit-finding, understanding polypharmacology, and characterization of protein function. The design of pocket matching methods has traditionally involved much intuition and has employed a broad variety of algorithms and representations of the input protein structures. We regard the high heterogeneity of past work and the recent availability of large-scale benchmarks as an indicator that a data-driven approach may provide a new perspective. We propose DeeplyTough, a convolutional neural network that encodes a three-dimensional representation of protein pockets into descriptor vectors that may be compared efficiently in an alignment-free manner by computing pairwise Euclidean distances. The network is trained with supervision (i) to provide similar pockets with similar descriptors, (ii) to separate the descriptors of dissimilar pockets by a minimum margin, and (iii) to achieve robustness to nuisance variations. We evaluate our method using three large-scale benchmark datasets, on which it demonstrates excellent performance for held-out data coming from the training distribution and competitive performance when the trained network is required to generalize to datasets constructed independently. DeeplyTough is available at https://github.com/BenevolentAI/DeeplyTough.

Fall Age-Ratio Survey Design for Eastern Population Greater Sandhill Cranes

AbstractFor migratory birds, wildlife managers can use the recruitment rate of young into an adult population to model population dynamics, manage harvest, or evaluate habitat. Few recruitment estimates exist for Eastern Population greater sandhill cranes Antigone canadensis tabida, and estimates are outdated and local in scale. Wildlife managers can use age ratios as an index to recruitment, and surveying at fall staging areas is efficient and cost effective. We created a systematic survey design for surveying eastern population crane age ratios in the Kankakee River valley (2013–2015) in northwestern Indiana and the south-central counties of Michigan (2014–2015). Using logistic regression, we investigated factors that may cause spatial and temporal variation in age ratios, including flock size, timing within season, state, year, random vs. incidental survey routes, and observer. We stratified our selection of survey routes using a core area of high crane abundance, but do not recommend stratifying due to the added complexity and low utility. Observers determined the age of 53,371 cranes and found that the proportion of juveniles in Eastern Population crane flocks (π = 0.113, 95% CI: 0.105 – 0.122) was similar to previous estimates for the population. Proportion of juveniles was greater for south-central Michigan than for the Kankakee River valley, decreased with flock size, increased with lateness in the season, and varied among observers. Our design accounted for both ecological sources of variation in age ratios as well as nuisance variation. We recommend that future surveys use our design as part of a monitoring plan for Eastern Population cranes to support harvest management of the population and ensure that future survey results are comparable across years.

A Machine Learning Analysis of Seasonal and Cyclical Sales in Weekly Scanner Data

This paper analyzes weekly scanner data collected for 108 groups at the county level between 2006 and 2014. The data display multi-dimensional weekly seasonal effects that are not exactly periodic but are cross-sectionally dependent. Existing univariate procedures are imperfect and yield adjusted series that continue to display strong seasonality upon aggregation. We suggest augmenting the univariate adjustments with a panel data step that pools information across counties. Machine learning tools are then used to remove the within-year seasonal variations. A demand analysis of the adjusted budget shares finds three factors: one that is trending, and two cyclical ones that are well aligned with the level and change in consumer confidence. The effects of the Great Recession vary across locations and product groups, with consumers substituting towards home cooking away from non-essential goods. The adjusted data also reveal changes in spending to unanticipated shocks at the local level. The data are thus informative about both local and aggregate economic conditions once the seasonal effects are removed. The two-step methodology can be adapted to remove other types of nuisance variations provided that these variations are cross-sectionally dependent. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Facial Expression Recognition Using Hierarchical Features With Deep Comprehensive Multipatches Aggregation Convolutional Neural Networks

Facial expression recognition (FER) has long been a challenging task in computer vision. In this paper, we propose a novel method, named deep comprehensive multipatches aggregation convolutional neural networks (CNNs), to solve the FER problem. The proposed method is a deep-based framework, which mainly consists of two branches of the CNN. One branch extracts local features from image patches while the other extracts holistic features from the whole expressional image. In the model, local features depict expressional details and holistic features characterize the high-level semantic information of an expression. We aggregate both local and holistic features before making classification. These two types of hierarchical features represent expressions in different scales. Compared with most current methods with single type of feature, the model can represent expressions more comprehensively. Additionally, in the training stage, a novel pooling strategy named expressional transformation-invariant pooling is proposed for handling nuisance variations, such as rotations, noises, etc. Extensive experiments are conducted on the famous the Extended Cohn-Kanade (CK+) dataset and the Japanese Female Facial Expression (JAFFE) database expression datasets, where the recognition results obtained.

Automated Contouring of Contrast and Non-Contrast CT Liver Images with Fully Convolutional Neural Networks

Liver contouring can be particularly labor-intensive based on size and unique patient liver shape. While model-based segmentation methods have shown promise in the past, they can be relatively slow (> 10 minutes), and not robust to unique patient anatomy. Fully-convolutional neural networks have shown great promise in the ability to accurately segment 2D images with multiple classes in short time periods and robustness against nuisance variations. We hypothesize that fully-convolutional networks (FCNs) can be used to rapidly and to accurately contour the Manual contours of the liver were delineated on the CT scans of 58 patients who had colorectal liver metastases (CLM) by a trained radiologist. A previously trained FCN, the Visual Geometry Group (VGG)-16 network, was adapted for rapid segmentation of the liver. A skip-layer architecture, similar to that found in the popular ‘U-net’ design, was implemented in order to improve output segmentation results. Various batch sizes, learning rates, and filters were investigated before deciding on the final model. For validation, 161 contrast-enhanced image sets released by the Medical Imaging Computing and Computer Assisted Intervention (MICCAI) challenges were obtained, as well as 10 non-contrast-enhanced image sets from the institution. The contrast-enhanced image sets came specifically from the abdominal challenge (n=30) and liver tumor segmentation challenge (LiTs, n=131). As a final test, 26 patients with clinically defined liver contours were compared by a blinded diagnostic radiologist. Upon the presentation of the two contours, responses were categorized based on the contour preference, and clinical usability. The final model was selected based on its performance in dice similarity coefficient (DSC) scores. The average DSC and standard deviations for the validation groups were as follows: μAbdomen:0.93, σAbdomen:0.02, μLiTS:0.92, σLiTS:0.05, and μNon-Contrast:0.95, σNon-Contrast:0.006. Of the contours generated from this model compared to 26 clinically defined contours, 50% (13/26) were preferred to the previously created contour, 54% (14/26) would be clinically useful without edits, 92% (24/26) useable or useable with minor edits (<1 minute of work), and 8% (2/26) required major edits. Liver contours were predicted, post-processed, and transferred to the planning system within 1 minute using an Intel i5 processor (3.3 GHz) and 16 GB RAM. These results suggest that the developed FCN can rapidly and accurately contour the liver on CT scans in a manner similar to those contoured in our clinic. Beyond the scope of DSC, on a small subset of cases these contours have been shown to be clinically useful and even preferable to currently created manual contours, warranting further investigation. For the two cases of the major edits, these patients had extensive disease, which was not seen in the training data, and will be investigated further in future iterations of the model.

Empiricism without magic: transformational abstraction in deep convolutional neural networks

In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, often appealing to a faculty of abstraction. Rationalists have frequently complained, however, that empiricists never adequately explained how this faculty of abstraction actually works. In this paper, I tie these two questions together, to the mutual benefit of both disciplines. I argue that the architectural features that distinguish DCNNs from earlier neural networks allow them to implement a form of hierarchical processing that I call “transformational abstraction”. Transformational abstraction iteratively converts sensory-based representations of category exemplars into new formats that are increasingly tolerant to “nuisance variation” in input. Reflecting upon the way that DCNNs leverage a combination of linear and non-linear processing to efficiently accomplish this feat allows us to understand how the brain is capable of bi-directional travel between exemplars and abstractions, addressing longstanding problems in empiricist philosophy of mind. I end by considering the prospects for future research on DCNNs, arguing that rather than simply implementing 80s connectionism with more brute-force computation, transformational abstraction counts as a qualitatively distinct form of processing ripe with philosophical and psychological significance, because it is significantly better suited to depict the generic mechanism responsible for this important kind of psychological processing in the brain.