nils-herrmann/clean_scirepeval huggingface dataset:
EMPATH: A Neural Network that Categorizes Facial Expressions

There are two competing theories of facial expression recognition. Some researchers have suggested that it is an example of categorical perception. In this view, expression categories are considered to be discrete entities with sharp boundaries, and discrimination of nearby pairs of expressive faces is enhanced near those boundaries. Other researchers, however, suggest that facial expression perception is more graded and that facial expressions are best thought of as points in a continuous, low-dimensional space, where, for instance, surprise expressions lie between happiness and fear expressions due to their perceptual similarity. In this article, we show that a simple yet biologically plausible neural network model, trained to classify facial expressions into six basic emotions, predicts data used to support both of these theories. Without any parameter tuning, the model matches a variety of psychological data on categorization, similarity, reaction times, discrimination, and recognition difficulty, both qualitatively and quantitatively. We thus explain many of the seemingly complex psychological phenomena related to facial expression perception as natural consequences of the tasks' implementations in the brain.
Analysis of Oscillations in a Reciprocally Inhibitory Network with Synaptic Depression

We present and analyze a model of a two-cell reciprocally inhibitory network that oscillates. The principal mechanism of oscillation is short-term synaptic depression. Using a simple model of depression and analyzing the system in certain limits, we can derive analytical expressions for various features of the oscillation, including the parameter regime in which stable oscillations occur, as well as the period and amplitude of these oscillations. These expressions are functions of three parameters: the time constant of depression, the synaptic strengths, and the amount of tonic excitation the cells receive. We compare our analytical results with the output of numerical simulations and obtain good agreement between the two. Based on our analysis, we conclude that the oscillations in our network are qualitatively different from those in networks that oscillate due to postinhibitory rebound, spike-frequency adaptation, or other intrinsic (rather than synaptic) adaptational mechanisms. In particular, our network can oscillate only via the synaptic escape mode of Skinner, Kopell, and Marder (1994).
Age of Acquisition in Connectionist Networks

Recently, there has been a resurgence of interest in the role of the Age of Acquisition (AoA) of an item in determining subjects’ reaction time in naming words, objects, and faces. Using the number of epochs required to learn an item as a direct measure of AoA in connectionist networks, Smith, Cottrell & Anderson (in press) have shown that AoA is a stronger predictor of final Sum Squared Error than frequency. In this paper, we replicate Smith et al. using more realistic frequency distributions for the items, and examine why some patterns may be learned earlier than others. First, we have found that the same patterns tend to be learned early and late by networks differing in their initial random weights; hence, the issue is, what property of the patterns determines AoA? We have found that even very weak pattern similarity structure is a strong predictor of AoA when frequency is controlled for. Also, we have found evidence that such a similarity structure may still be an important factor in determining AoA even when pattern frequency is varied.
The Early Word Catches the Weights

The strong correlation between the frequency of words and their naming latency has been well documented. However, as early as 1973, the Age of Acquisition (AoA) of a word was alleged to be the actual variable of interest, but these studies seem to have been ignored in most of the literature. Recently, there has been a resurgence of interest in AoA. While some studies have shown that frequency has no effect when AoA is controlled for, more recent studies have found independent contributions of frequency and AoA. Connectionist models have repeatedly shown strong effects of frequency, but little attention has been paid to whether they can also show AoA effects. Indeed, several researchers have explicitly claimed that they cannot show AoA effects. In this work, we explore these claims using a simple feed forward neural network. We find a significant contribution of AoA to naming latency, as well as conditions under which frequency provides an independent contribution.
Content and cluster analysis: Assessing representational similarity in neural systems

If connectionism is to be an adequate theory of mind, we must have a theory of representation for neural networks that allows for individual differences in weighting and architecture while preserving sameness, or at least similarity, of content. In this paper we propose a procedure for measuring sameness of content of neural representations. We argue that the correct way to compare neural representations is through analysis of the distances between neural activations, and we present a method for doing so. We then use the technique to demonstrate empirically that different artificial neural networks trained by backpropagation on the same categorization task, even with different representational encodings of the input patterns and different numbers of hidden units, reach states in which representations at the hidden units are similar. We discuss how this work provides a rebuttal to Fodor and Lepore's critique of Paul Churchland's state space semantics.
Is All Face Processing Holistic? The View from UCSD

There has been a great deal of progress, as well as controversy, in understanding how complex objects, in particular, human faces, are processed by the cortex. At the same time, sophisticated neural network models have been developed that do many of the same tasks required by these cortical areas. Such simplifying models allow us to explore hypotheses concerning relatively complex domains such as face processing. In this chapter, we give a somewhat idiosyncratic history of the development of neural network models of face processing, concentrating on work at UCSD, and show how these models have led to a novel hypothesis concerning processing of facial expression. While our models have suggested a role for holistic representations of faces in identification, general local features appear to be important in recognition of expression. Is all face processing holistic? 3
SIZE ANGLE SIZE ANGLE SIZE ANGLE SIZE ANGLE CATEGORY INSTANCES DEGRADED STIMULUS STIMULUS WORKING MEMORY RULE

Instructed category learning studies have shown that categorization practice on a fixed set of labeled training exemplars can cause learners to violate explicitly provided categorization instructions. We have previously proposed a connectionist account of this exemplar-based interference effect — an account which predicts that individuals who display initial difficulty in the application of a categorization rule will exhibit greater exemplar-based interference than good rule-followers. In this paper, we report on a study of human instructed category learning performance intended to test this prediction of the model, and we provide the results of additional connectionist simulations which are fit to the human experimental data.
Early detection of Basal Cell Carcinoma using Optical Coherence Tomography and Pattern Recognition Techniques

Basal cell carcinoma (BCC) is the most common tumor in light-skinned humans. BCC is induced at least in part by ultraviolet irradiation and with the aging of the American population, these tumors will continue to cause significant morbidity. Due to their limited metastatic potential, BCC are usually not fatal. However, because of their propensity to invade and destroy surrounding tissues, early treatment is essential. Especially in the face, one of the common sites for this tumor, destruction of surrounding tissues such as the eye or nose is a major problem. In the face, the treatment of choice is excisional surgery. Since BCC usually invades the tissues surrounding the main tumor mass without causing clinically visible changes, determination of the margins of the tumor becomes difficult. This has led to the development of Mohs micrographic surgery (MMS). In this type of surgery the main tumor is removed and then the edges of the excised tissue are processed for histological examination. If the tumor can still be detected histologically at the edges of the excised tissue, more tissue is removed until the margins are " clear ". Because of the time needed to mark the excised tissue and process the frozen sections for histological examination, MMS is a time consuming and expensive procedure. The specific aim of this proposal is to combine a new imaging technology-Optical Coherence To-mography-with the proven success of pattern classification techniques and apply these to the diagnosis and early detection of BCC. OCT is a non-invasive, rapid technique for imaging slices of tissue at an unprecedented resolution of 10 micrometers or less. This allows the imaging of the fine structures of the skin necessary for determining tumor margins. Our research will develop procedures for 1) acquiring images of skin at a depth of WHATEVER [ask Zhonping] 2) Compare the images obtained by OCT at the tumor's margin with the tumor margins defined by histological assessment during MMS. 3) Evaluate the ability of pattern recognition techniques for automating the diagnosis.
User lenses—achieving 100% precision on frequently asked questions

The concept of a “user lens” is introduced. The lens is a sequence of linear transformations used to reweight the vectors which represent documents or queries in information retrieval systems. It is trained automatically via relevance data provided by the user. Experiments verify the lens can improve performance on training data while not degrading test data performance, and that larger lenses result in nearly perfect performance on the training set. The lens provides a mechanism for automatically capturing long-term, user-specific information about an improved representation scheme for document vectors.
Prosopagnosia in modular neural network models.

There is strong evidence that face processing in the brain is localized. The double dissociation between prosopagnosia, a face recognition de cit occurring after brain damage, and visual object agnosia, di culty recognizing other kinds of complex objects, indicates that face and non-face object recognition may be served by partially independent neural mechanisms. In this chapter, we use computational models to show how the face processing specialization apparently underlying prosopagnosia and visual object agnosia could be attributed to 1) a relatively simple competitive selection mechanism that, during development, devotes neural resources to the tasks they are best at performing, 2) the developing infant's need to perform subordinate classi cation (identi cation) of faces early on, and 3) the infant's low visual acuity at birth. Inspired by de Schonen and Mancini's (1998) arguments that factors like these could bias the visual system to develop a specialized face processor, and Jacobs and Kosslyn's (1994) experiments in the mixtures of experts (ME) modeling paradigm, we provide a preliminary computational demonstration of how this theory accounts for the double dissociation between face and object processing. We present two feed-forward computational models of visual processing. In both models, the selection mechanism is a gating network that mediates a competition between modules attempting to classify input stimuli. In Model I, when the modules are simple unbiased classi ers, the competition is su cient to achieve enough of a specialization that damaging one module impairs the model's face recognition more than its object recognition, and damaging the other module impairs the model's object recognition more than its face recognition. In Model II, however, we bias the modules by providing one with low spatial frequency information and the other with high spatial frequency information. In this case, when the model's task is subordinate classi cation of faces and superordinate classi cation of objects, the low spatial frequency network shows an even stronger specialization for faces. No other combination of tasks and inputs shows this strong specialization. We take these results as support for the idea that something resembling a face processing \module" could arise as a natural consequence of the infant's developmental environment without being innately speci ed.
Adaptive combination of evidence for information retrieval

The ubiquity of the World Wide Web and the growth in both size and availability of collections of free text in general argues for smarter and more effective techniques for finding relevant documents. We explore one class of IR system algorithms (or “search engines”) based on combining the lists of documents returned by other IR systems, attempting to test the adage “two heads are better than one.” These algorithms also have the critical feature that the they are adaptive, taking feedback from the user and using it to change the way they combine the component search engines. We explore two specific neural network based models: the Linear Combination (LC), which scores a document as a weighted sum of the scores the document received from the component systems, and the Adaptive Combination of Evidence (ACE) model, which modifies how much it listens to any one of the search engines based on the semantic content of the document. Via extensive experiments combining a wide range of search engines from the standardized TREC database, we discover not only which types of IR tasks each combination model is best suited for, but also what types of IR systems it makes sense to combine. We also characterize the amount of gain achievable by adding progressively more search engines to the combination, and describe methods which will ensure moderate to extensive levels of improvement.
Why johnny can read: the role of statistical regularities in reading and connectionist modeling

OF THE DISSERTATION Why Johnny Can Read: The Role of Statistical Regularities in Reading and Connectionist Modeling by Jeanne Claire Milostan Doctor of Philosophy in Cognitive Science and Computer Science University of California, San Diego, 1999 Professor Garrison W. Cottrell, Chair As is true for almost all computational modeling of higher brain functions, debate currently rages over models which address the ability to read. Three current models (the single-route connectionist system of (Plaut et al., 1996), the Dual-Route Cascade of (Coltheart et al., 1993), and the connectionist Dual-Process model of (Zorzi et al., 1998b)) are examined in relation to various human reading data. An apparently serial e ect seen in irregular words, where early irregularities cause more slowing in pronunciation time than late irregularities, is reanalyzed with respect to enemy/friend ratios, and the results of subject experiments are presented. The work of (Plaut et al., 1996) is extended to handle multisyllabic words, modeling the e ect seen in the human studies, and thus showing that the position-of-irregularity e ect is not a serial phenomena after all. xv Chapter
PCA = Gabor for Expression Recognition

We show that Gabor filter representations give quantitatively indistinguishable results for classification of facial expressions as local PCA representations, in contrast to other recent work. We also show that a simple discriminant analysis automatically locates regions roughly corresponding to relevant Facial Actions. Finally, we in troduce a method that typically boosts generalization performance 9% by "peeking" at all of the unlabeled training patt erns before classifying them.
Predicting the performance of linearly combined IR systems

We introduce a new technique for analyzing combination models. The technique allows us to make qualitative conclusions about which IR systems should be combined. We achieve this by using a linear regression to accurately (T ’ = 0.98) predict the performance of the combined system based on quantitative measurements of individual component systems taken from TREC5. When applied to a linear model (weighted sum of relevance scores), the technique supports several previously suggested hypotheses: one should maximize both the individual systems’ performances and the overlap of relevant documents between systems, while minimizing the overlap of nonrelevant documents. It also suggests new conclusions: both systems should distribute scores similarly, but not rank relevant documents similarly. It furthermore suggests that the linear model is only able to exploit a fraction of the benefit possible from combination. The technique is general in nature and capable of pointing out the strengths and weaknesses of any given combination approach.
Using d 0 to Optimize Rankings

We introduced0 as an optimization measure for ranking problems, such as information retrieval (IR). d0 is an intuitive, differentiable measure which is somewhat correlated with standard IR measures of performance. A set of experiments is described in which the weight used to combine the results of two IR systems is chosen by optimizing d0. This technique works about as well as optimizing “exact precision,” a traditional IR performance measure. These results suggest the use of d0 f r training more complex, neural net models for solving ranking problems.
Eigenfaces for Familiarity

A previous experiment tested subjects’ new/old judgments of previously-studied faces, distractors, and morphs betw e n pairs of studied parents. We examine the extent to which models based on principal component analysis (eigenfaces) can predict human recognition of studied faces and false alarms to the distractors and morphs. We also compare eigenface model s to the predictions of previous models based on the positions of faces in a multidimensional “face space” derived from a multidimensional scaling (MDS) of human similarity ratings. W e find that the error in reconstructing a test face from its posi tion in an “eigenface space” provides a good overall predict ion of human familiarity ratings. However, the model has difficulty accounting for the fact that humans false alarm to morp hs with similar parents more frequently than they false alarm t o morphs with dissimilar parents. We ascribe this to the limit ations of the simple reconstruction error-based model. We th en outline preliminary work to improve the fine-grained fit with in the eigenface-based modeling framework, and discuss the re sults’ implications for exemplarand face space-based mod els of face processing.
A neural network model for facial affect classification

This thesis presents an automated emotion recognition model that is capable of identifying six basic emotions (happy, surprise, sad, angry, fear, disgust) achieving an 86% generalization rate on novel face images. Faces are represented to the model by projecting local feature regions around the eyes and mouth on to principal components of random image blocks. The model is an ensemble of neural network classifiers that learn to recognize emotion by incrementally adjusting their responses to positive examples of the emotions. 
The model exhibits substantial agreement with humans in the confusions shown by the model between responses to portrayed emotions when averaged over many examples. When model and human face judgments are examined using multi-dimensional scaling, they result in a circular distribution of responses clustered by emotion in the same relative sequence. Comparison of the rankings derived from ordering the entries in the confusion matrix of humans and the model by emotion also shows significant agreement. This is true even when similarity information is masked from the model during training. 
The model reproduces behavior seen in human subjects when they are presented with interpolated face images between two emotion prototypes of a single face. Categorical perception is observed in humans in this situation. The neural network model generates similar behavior when evaluating these images. It also exhibits the same type of category structure in that its judgments about the mix of emotions present in the interpolated images mirrors that of humans. 
Three conclusions are drawn from these results. First, discrete training with expressive emotion prototypes (as compared to training with continuous stimuli) is sufficient to learn an emotion space that captures many human characteristics. Second, local unbiased features, concentrated in regions rich in variations (eyes and mouth) provide for better generalization than do either global features or local features specifically tuned to those regions. Finally, we conclude that categorical perception (CP) in human emotion responses to continuous face stimuli, can be learned by a simple classifier. This suggests that CP is not an intrinsic property of biological processing but is the result of learning in a distributed, non-linear decision system.
Facial Memory Is Kernel Density Estimation (Almost)

We compare the ability of three exemplar-based memory models, each using three different face stimulus representations, to account for the probability a human subject responded "old" in an old/new facial memory experiment. The models are 1) the Generalized Context Model, 2) SimSample, a probabilistic sampling model, and 3) MMOM, a novel model related to kernel density estimation that explicitly encodes stimulus distinctiveness. The representations are 1) positions of stimuli in MDS "face space," 2) projections of test faces onto the "eigenfaces" of the study set, and 3) a representation based on response to a grid of Gabor filter jets. Of the 9 model/representation combinations, only the distinctiveness model in MDS space predicts the observed "morph familiarity inversion" effect, in which the subjects' false alarm rate for morphs between similar faces is higher than their hit rate for many of the studied faces. This evidence is consistent with the hypothesis that human memory for faces is a kernel density estimation task, with the caveat that distinctive faces require larger kernels than do typical faces.
Optimizing similarity using multi-query relevance feedback

We propose a novel method for automatically adjusting parameters in ranked-output text retrieval systems to improve retrieval performance. A ranked-output text retrieval system implements a ranking function which orders documents, placing documents estimated to be more relevant to the user's query before less relevant ones. The system adjusts its parameters to maximize the match between the system's document ordering and a target ordering. The target ordering is typically given by user feedback on a set of sample queries, but is more generally any document preference relation. We demonstrate the utility of the approach by using it to estimate a similarity measure (scoring the relevance of documents to queries) in a vector space model of information retrieval. Experimental results using several collections indicate that the approach automatically finds a similarity measure which performs equivalently to or better than all “classic” similarity measures studied. It also performs within 1% of an estimated optimal measure (found by exhaustive sampling of the similarity measures). The method is compared to two alternative methods: A Perceptron learning rule motivated by Wong and Yao's (1990) Query Formulation method, and a Least Squared learning rule, motivated by Fuhr and Buckley's (1991) Probabilistic Learning approach. Though both alternatives have useful characteristics, we demonstrate empirically that neither can be used to estimate the parameters of the optimal similarity measure.