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Book ChapterDOI

Cogito componentiter ergo sum

05 Mar 2006-pp 446-453
TL;DR: Evidence that independent component analysis of abstract data such as text, social interactions, music, and speech leads to low level cognitive components is presented.
Abstract: Cognitive component analysis (COCA) is defined as the process of unsupervised grouping of data such that the ensuing group structure is well-aligned with that resulting from human cognitive activity. We present evidence that independent component analysis of abstract data such as text, social interactions, music, and speech leads to low level cognitive components.

Summary (1 min read)

Jump to: [1 Introduction] and [3 Conclusion]

1 Introduction

  • During evolution human and animal visual, auditory, and other primary sensory systems have adapted to a broad ecological ensemble of natural stimuli.
  • It is a fascinating finding in many real world data sets that the label structure discovered by unsupervised learning closely coincides with labels obtained by letting a human or a group of humans perform classification, labels derived from human cognition.
  • A term set is chosen and a document is represented by the vector of term frequencies.
  • LSI/PCA was then performed on the sparsified feature coefficients for visualization.
  • The results seem to indicate that generalizable cognitive components corresponding to the phoneme /ae/ opening the utterances s and f, can be identified using linear component analysis.

3 Conclusion

  • Cognitive component analysis (COCA) has been defined as the process of unsupervised grouping of data such that the ensuing group structure is well-aligned with that resulting from human cognitive activity.
  • It is well-established that information theoretically optimal representations, similar to those found by ICA, are in use in several information processing tasks in human and animal perception.
  • By visualization of data using latent semantic analysis-like plots, the authors have shown that independent components analysis is also relevant for representing semantic structure, in text and other abstract data such as social networks, musical features, and speech.
  • The authors therefore speculate that the cognitive machinery developed for analyzing complex perceptual signals from multi-agent environments may also be used in higher brain function.
  • Hence, the authors hypothesize that independent component analysis –given the right representation– may be a quite generic tool for COCA.

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Cogito componentiter ergo sum
Lars Kai Hansen and Ling Feng
Informatics and Mathematical Modelling,
Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
lkh,lf@imm.dtu.dk, www.imm.dtu.dk
Abstract. Cognitive component analysis (COCA) is defined as the pro-
cess of unsupervised grouping of data such that the ensuing group struc-
ture is well-aligned with that resulting from human cognitive activity.
We present evidence that independent component analysis of abstract
data such as text, social interactions, music, and speech leads to low
level cognitive components.
1 Introduction
During evolution human and animal visual, auditory, and other primary sensory
systems have adapted to a broad ecological ensemble of natural stimuli. This
long-time on-going adaption process has resulted in representations in human
and animal perceptual systems which closely resemble the information theo-
retically optimal representations obtained by independent component analysis
(ICA), see e.g., [1] on visual contrast representation, [2] on visual features in-
volved in color and stereo processing, and [3] on representations of sound fea-
tures. For a general discussion consult also the textbook [4]. The human per-
ceptional system can model complex multi-agent scenery. Human cognition uses
a broad spectrum of cues for analyzing perceptual input and separate individ-
ual signal producing agents, such as speakers, gestures, affections etc. Humans
seem to be able to readily adapt strategies from one perceptual domain to an-
other and furthermore to apply these information processing strategies, such as,
object grouping, to both more abstract and more complex environments, than
have been present during evolution. Given our present, and rather detailed, un-
derstanding of the ICA-like representations in primary sensory systems, it seems
natural to pose the question: Are such information optimal representations rooted
in independence also relevant for modeling higher cognitive functions? We are
currently pursuing a research programme, trying to understand the limitations
of the ecological hypothesis for higher level cognitive processes, such as grouping
abstract objects, navigating social networks, understanding multi-speaker envi-
ronments, and understanding the representational differences between self and
environment.
Wagensberg has pointed to the importance of independence for successful
‘life forms’ [5]
A living individual is part of the world with some identity that tends to
become independent of the uncertainty of the rest of the world

Thus natural selection favors innovations that increase independence of the agent
in the face of environmental uncertainty, while maximizing the gain from the
predictable aspects of the niche. This view represents a precision of the classical
Darwinian formulation that natural selection simply favors adaptation to given
conditions. Wagensberg points out that recent biological innovations, such as ner-
vous systems and brains are means to decrease the sensitivity to un-predictable
fluctuations. An important aspect of environmental analysis is to be able to rec-
ognize event induced by the self and other agents. Wagensberg also points out
that by creating alliances agents can give up independence for the benefit of
a group, which in turns may increase independence for the group as an entity.
Both in its simple one-agent form and in the more tentative analysis of the group
model, Wagensberg’s theory emphasizes the crucial importance of statistical in-
dependence for evolution of perception, semantics and indeed cognition. While
cognition may be hard to quantify, its direct consequence, human behavior, has a
rich phenomenology which is becoming increasingly accessible to modeling. The
digitalization of everyday life as reflected, say, in telecommunication, commerce,
and media usage allows quantification and modeling of human patterns of activ-
ity, often at the level of individuals. Grouping of events or objects in categories is
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LATENT COMPONENT 4
LATENT COMPONENT 2
Fig. 1. Generic feature distribution produced by a linear mixture of sparse sources
(left) and a typical ‘latent semantic analysis’ scatter plot of principal component pro-
jections of a text database (right). The characteristics of a sparse signal is that it
consists of relatively few large magnitude samples on a background of small signals.
Latent semantic analysis of the so-called MED text database reveals that the semantic
comp onents are indeed very sparse and does follow the laten directions (principal com-
p onents). Topics are indicated by the different markers. In [6] an ICA analysis of this
data set post-processed with simple heuristic classifier showed that manually defined
topics were very well aligned with the independent components. Hence, constituting
an example of cognitive component analysis: Unsupervised learning leads to a label
structure corresponding to that of human cognitive activity.
fundamental to human cognition. In machine learning, classification is a rather

well-understoo d task when based on labelled examples [7]. In this case classifica-
tion belongs to the class of supervised learning problems. Clustering is a closely
related unsupervised learning problem, in which we use general statistical rules
to group objects, without a priori providing a set of labelled examples. It is a
fascinating finding in many real world data sets that the label structure discov-
ered by unsupervised learning closely coincides with labels obtained by letting a
human or a group of humans perform classification, labels derived from human
cognition. We thus define cognitive component analysis (COCA) as unsupervised
grouping of data such that the ensuing group structure is well-aligned with that
resulting from human cognitive activity [8]. This presentation is based on our
earlier results using ICA for abstract data such as text, dynamic text (chat),
web pages including text and images, see e.g., [9–13].
2 Where have we found cognitive components?
Text analysis. Symbol manipulation as in text is a hallmark of human cog-
nition. Salton proposed the so-called vector space representation for statistical
modeling of text data, for a review see [14]. A term set is chosen and a doc-
ument is represented by the vector of term frequencies. A document database
then forms a so-called term-document matrix. The vector space representation
can be used for classification and retrieval by noting that similar documents
are somehow expected to be ‘close’ in the vector space. A metric can be based
on the simple Euclidean distance if document vectors are properly normalized,
otherwise angular distance may be useful. This approach is principled, fast, and
language independent. Deerwester and co-workers developed the concept of la-
tent semantics based on principal component analysis of the term-document
matrix [15]. The fundamental observation behind the latent semantic indexing
(LSI) approach is that similar documents are using similar vocabularies, hence,
the vectors of a given topic could appear as produced by a stochastic process
with highly correlated term-entries. By projecting the term-frequency vectors on
a relatively low dimensional subspace, say determined by the maximal amount
of variance one would be able to filter out the inevitable ‘noise’. Noise should
here be thought of as individual document differences in term usage within a
specific context. For well-defined topics, one could simply hope that a given
context would have a stable core term set that would come out as a eigen ‘di-
rection’ in the term vector space. The orthogonality constraint of co-variance
matrix eigenvectors, however, often limits the interpretability of the LSI rep-
resentation, and LSI is therefore more often used as a dimensional reduction
tool. The representation can be post-processed to reveal cognitive components,
e.g., by interactive visualization schemes [16]. In Figure 1 (right) we indicate
the scatter plot of a small text database. The database consists of documents
with overlapping vocabulary but five different (high level cognitive) labels. The
‘ray’-structure signaling a sparse linear mixture is evident.

Social networks. The ability to understand social networks is critical to hu-
mans. Is it possible that the simple unsupervised scheme for identification of
independent components could play a role in this human capacity? To investi-
gate this issue we have initiated an analysis of a well-known social network of
some practical importance. The so-called actor network is a quantitative rep-
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EIGENCAST 3
EIGENCAST 5
Fig. 2. The so-called actor network quantifies the collaborative pattern of 382.000
actors participating in almost 128.000 movies. For visualization we have projected
the data onto principal components (LSI) of the actor-actor co-variance matrix. The
eigenvectors of this matrix are called ‘eigencasts’ and they represent characteristic
communities of actors that tend to co-appear in movies. The network is extremely
sparse, so the most prominent variance components are related to near-disjunct sub-
communities of actors with many common movies. However, a close up of the coupling
b etween two latent semantic components (the region (0, 0)) reveals the ubiquitous
signature of a sparse linear mixture: A pronounced ‘ray’ structure emanating from
(0,0). The ICA components are color coded. We speculate that the cognitive machinery
developed for handling of independent events can also be used to locate independent
sub-communities, hence, navigate complex social networks.
resentation of the co-participation of actors in movies, for a discussion of this
network, see e.g., [17]. The observation model for the network is not too different
from that of text. Each movie is represented by the cast, i.e., the list of actors.
We have converted the table of the about T = 128.000 movies with a total
of J = 382.000 individual actors, to a sparse J × T matrix. For visualization
we have projected the data onto principal components (LSI) of the actor-actor
co-variance matrix. The eigenvectors of this matrix are called ‘eigencasts’ and
represent characteristic communities of actors that tend to co-appear in movies.
The sparsity and magnitude of the network means that the components are dom-
inated by communities with very small intersections, however, a closer look at
such scatter plots reveals detail suggesting that a simple linear mixture model in-
deed provides a reasonable representation of the (small) coupling between these
relative trivial disjunct subsets, see Figure 2. Such insight may be used for com-

puter assisted navigation of collaborative, peer-to-peer networks, for example in
the context of search and retrieval.
Musical genre. The growing market for digital music and intelligent music
services creates an increasing interest in modeling of music data. It is now feasible
to estimate consensus musical genre by supervised learning from rather short
music segments, say 5-10 seconds, see e.g., [18], thus enabling computerized
handling of music request at a high cognitive complexity level. To understand
the possibilities and limitations for unsupervised modeling of music data we here
visualize a small music sample using the latent semantic analysis framework.
The intended use is for a music search engine function, hence, we envision that
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Fig. 3. We represent three music tunes (genre labels: heavy metal, jazz, classical)
by their spectral content in overlapping small time frames (w = 30msec, with an overlap
of 10msec, see [18], for details). To make the visualization relatively independent of
‘pitch’, we use the so-called mel-cepstral representation (MFCC, K = 13 coefficients
pr. frame). To reduce noise in the visualization we have ‘sparsified’ the amplitudes. This
was achieved simply by keeping coefficients that belonged to the upper 5% magnitude
p ercentile. The total number of frames in the analysis was F = 10
5
. Latent semantic
analysis provided unsupervised subspaces with maximal variance for a given dimension.
We show the scatter plots of the data of the first 1-5 latent dimensions. The scatter
plots below the diagonal have been ‘zoomed’ to reveal more details of the ICA ‘ray’
structure. For interpretation we have coded the data points with signatures of the three
genres involved: classical (), heavy metal (diamond), jazz (+). The ICA ray structure
is striking, however, note that the situation is not one-to-one (ray to genre) as in the
small text databases. A component (ray) quantifies a characteristic musical ‘theme’
at the temporal level of a frame (30msec), i.e., an entity similar to the ‘phoneme’ in
sp eech.
a largely text based query has resulted in a few music entries, and the algorithm
is going to find the group structure inherent in the retrieval for the user. We

Citations
More filters
01 Sep 2010
TL;DR: The thesis thus combines elements of machine learning with aspects of cognitive semantics that could potentially be utilized in applications ranging from media information retrieval and business related sentiment analysis to cognitive neuroscience.
Abstract: Though one might think of media as an audiovisual stream of consciousness, we frequently encode frames of video sequences and waves of sound into strings of text. Language allows us to both share the internal representations of what we perceive as mental concepts, as well as categorizing them as distinct states in the continuous ebb and flow of emotions underlying consciousness. Whether it being a soundscape of structured peaks or tiny black characters lined up across a page, we rely on syntax for parsing sequences of symbols, which based on hierarchically nested structures allow us to express and share the meaning contained within a sentence or a melodic phrase. As both low-level semantic structure of texts and our affective responses can be encoded in words, a simplified cognitive model can be constructed which uses LSA latent semantic analysis to emulate how we perceive the emotional context of media based on lyrics, synopses, subtitles, blogs or web pages associated with the content. In the proposed model the bottom-up generated sensory input is a matrix of tens of thousands of words co-occurring within multiple contexts, that are in turn represented as vectors in a semantic space of reduced dimensionality. While top-down, patterns of emotional categorization emerge by defining term vector distances to affective adjectives, that constrain the latent semantic structures according to the neurophysiological dimensions of valence and arousal. The thesis thus combines elements of machine learning with aspects of cognitive semantics that could potentially be utilized in applications ranging from media information retrieval and business related sentiment analysis to cognitive neuroscience.

1 citations


Cites result from "Cogito componentiter ergo sum"

  • ...This indicates that core elements of lyrical music appear to be treated in a fashion similar to those of language [18], which is in turn supported by EEG `electroencephalograhy' studies showing that language and music compete for the same neural resources when processing syntax and semantics [19]....

    [...]

01 Jan 2006
TL;DR: These evidences confirmed that ICA is relevant for representing semantic structure, in text and social networks and musical features; more strikingly for representing information embedded in speech signals, such as phoneme, gender, speaker identity, and even height.
Abstract: Cognitive Science has attracted a new level of prosperity ‘consciousness’ of engineers during recent decades. One of the reasons is that people begin to wonder whether statistically optimal representations by a variety of machine learning methods are aligned with human cognitive activities. The evolution of human perception system and cognition system is a long-time adaptation process and an on-going interaction between natural environments and natural selection. During the course of charting family trees of various species, evolutionary biologists intended to distinguish between "primitive" and "derived" features. Primitive features group alliances agents; and derived features are prone to enlarge the difference among individuals within a specific agent. Wagensberg linked the difference of individuals to the importance of independence for successful ‘life forms’: A living individual is part of the world with some identity that tends to become independent of the uncertainty of the rest of the world. Wagensberg also points out that by creating alliances agents can give up independence for the benefit of a group, which in turns may increase independence for the group as an entity. Our independence hypothesis has been inspired by intriguing facts from using independent component analysis (ICA) algorithm. As a consequence of evolution, human perception system can model complex multi-agent scenery. Humans’ ability of using a broad spectrum of cues for analyzing perceptual input and identification of individual agents, has been studied and furthermore stimulated in computers. The resulting theoretically optimal representations achieved by using a variety of ICA closely resembles representations found in human perceptual systems on visual contrast detection, on visual features involved in color and stereo processing, and on representations of sound features. As a consequence, COgnitive Component Analysis (COCA) was first brought to bear in 2005: the process of unsupervised grouping of data such that the resulting group structure is well-aligned with that resulting from human cognitive activity. We investigated the independent cognitive component hypothesis, which asks the question: Do humans also use these information theoretically optimal ‘ICA’ methods in more generic and abstract data analysis. COCA has been applied to broad topics to review low-level cognitive components. These evidences confirmed that ICA is relevant for representing semantic structure, in text and social networks and musical features; more strikingly for representing information embedded in speech signals, such as phoneme, gender, speaker identity, and even height. Human learns strategies from one perceptual domain to another, and apply them in more or less distinct categories, such as events or objects grouping. In machine learning, the label structures found by unsupervised learning using some real world data sets, are consistent with the labels derived from human cognition. ‘Ray’-structures in latent semantic analysis-like plots, are the key COCA phenomena. Evidences have been found that ‘ray’-structures discovered by COCA, which are understood as the human cognition labels, coincide with labels of the samples in the relevant feature space. The fact that structures by COCA are aligned with labels structures, highlights the possibility of using unlabeled data in supervised learning methods.

1 citations


Cites background from "Cogito componentiter ergo sum"

  • ...[24, 8], has shown that the spatial receptive field properties match sparse representations, and by maximizing statistical independence and sparseness of the representations, the resulting receptive field properties share similarities with those of cortical neurons [66]....

    [...]

  • ...They anticipated the predictive power of abstract unsupervised learning techniques [8]....

    [...]

01 Jan 2008
TL;DR: A new pair of models are introduced, which directly employ the independent hypothesis, and it is found that the supervised and unsupervised learning provide similar representations measured by the classification similarity at different levels.
Abstract: This paper explores the generality of COgnitive Component Analysis (COCA), which is defined as the process of unsupervised grouping of data such that the ensuing group structure is well-aligned with that resulting from human cognitive activity. The hypothesis of COCA is ecological: the essentially independent features in a context defined ensemble can be efficiently coded using a sparse independent component representation. Our devised protocol aims at comparing the performance of supervised learning (invoking cognitive activity) and unsupervised learning (statistical regularities) based on similar representations, and the only difference lies in the human inferred labels. Inspired by the previous research on COCA, we introduce a new pair of models, which directly employ the independent hypothesis. Statistical regularities are revealed at multiple time scales on phoneme, gender, age and speaker identity derived from speech signals. We indeed find that the supervised and unsupervised learning provide similar representations measured by the classification similarity at different levels.

Cites methods from "Cogito componentiter ergo sum"

  • ...Thus far, ICA has been used to model the ray structure and to represent the semantic structure in text, the communities in social networks, and other abstract data, e.g. music (Hansen et al., 2005; Hansen & Feng, 2006) and speech (Feng & Hansen, 2006)....

    [...]

References
More filters
Journal ArticleDOI
15 Oct 1999-Science
TL;DR: A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
Abstract: Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.

33,771 citations

Book
01 Jan 1995
TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
Abstract: From the Publisher: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modelling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks, and Bayesian techniques and their applications. Designed as a text, with over 100 exercises, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.

19,056 citations

Book ChapterDOI
TL;DR: The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue.
Abstract: Publisher Summary This chapter provides an account of different neural network architectures for pattern recognition. A neural network consists of several simple processing elements called neurons. Each neuron is connected to some other neurons and possibly to the input nodes. Neural networks provide a simple computing paradigm to perform complex recognition tasks in real time. The chapter categorizes neural networks into three types: single-layer networks, multilayer feedforward networks, and feedback networks. It discusses the gradient descent and the relaxation method as the two underlying mathematical themes for deriving learning algorithms. A lot of research activity is centered on learning algorithms because of their fundamental importance in neural networks. The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue. It closes with the discussion of performance and implementation issues.

13,033 citations


"Cogito componentiter ergo sum" refers background in this paper

  • ...well-understood task when based on labelled examples [7]....

    [...]

Journal ArticleDOI
TL;DR: A new method for automatic indexing and retrieval to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries.
Abstract: A new method for automatic indexing and retrieval is described. The approach is to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries. The particular technique used is singular-value decomposition, in which a large term by document matrix is decomposed into a set of ca. 100 orthogonal factors from which the original matrix can be approximated by linear combination. Documents are represented by ca. 100 item vectors of factor weights. Queries are represented as pseudo-document vectors formed from weighted combinations of terms, and documents with supra-threshold cosine values are returned. initial tests find this completely automatic method for retrieval to be promising.

12,443 citations


"Cogito componentiter ergo sum" refers background in this paper

  • ...Deerwester and co-workers developed the concept of latent semantics based on principal component analysis of the term-document matrix [15]....

    [...]

Book
18 May 2001
TL;DR: Independent component analysis as mentioned in this paper is a statistical generative model based on sparse coding, which is basically a proper probabilistic formulation of the ideas underpinning sparse coding and can be interpreted as providing a Bayesian prior.
Abstract: In this chapter, we discuss a statistical generative model called independent component analysis. It is basically a proper probabilistic formulation of the ideas underpinning sparse coding. It shows how sparse coding can be interpreted as providing a Bayesian prior, and answers some questions which were not properly answered in the sparse coding framework.

8,333 citations

Frequently Asked Questions (11)
Q1. What contributions have the authors mentioned in the paper "Cogito componentiter ergo sum" ?

The authors present evidence that independent component analysis of abstract data such as text, social interactions, music, and speech leads to low level cognitive components. 

A metric can be based on the simple Euclidean distance if document vectors are properly normalized, otherwise angular distance may be useful. 

By projecting the term-frequency vectors on a relatively low dimensional subspace, say determined by the maximal amount of variance one would be able to filter out the inevitable ‘noise’. 

The vector space representation can be used for classification and retrieval by noting that similar documents are somehow expected to be ‘close’ in the vector space. 

The authors are currently pursuing a research programme, trying to understand the limitations of the ecological hypothesis for higher level cognitive processes, such as grouping abstract objects, navigating social networks, understanding multi-speaker environments, and understanding the representational differences between self and environment. 

To make the visualization relatively independent of ‘pitch’, the authors use the so-called mel-cepstral representation (MFCC, K = 13 coefficients pr. frame). 

The fundamental observation behind the latent semantic indexing (LSI) approach is that similar documents are using similar vocabularies, hence, the vectors of a given topic could appear as produced by a stochastic process with highly correlated term-entries. 

The eigenvectors of this matrix are called ‘eigencasts’ and represent characteristic communities of actors that tend to co-appear in movies. 

The authors thus define cognitive component analysis (COCA) as unsupervised grouping of data such that the ensuing group structure is well-aligned with that resulting from human cognitive activity [8]. 

It is a fascinating finding in many real world data sets that the label structure discovered by unsupervised learning closely coincides with labels obtained by letting a human or a group of humans perform classification, labels derived from human cognition. 

It is now feasible to estimate consensus musical genre by supervised learning from rather short music segments, say 5-10 seconds, see e.g., [18], thus enabling computerized handling of music request at a high cognitive complexity level.