Open Journal of Philosophy
2012. Vol.2, No.1, 32-37
Published Online February 2012 in SciRes (
Copyright © 2012 SciRes.
Free Will and Advances in Cognitive Science
Leonid Perlovsky1,2
1Harvard University, Charlestown, USA
Email: leonid@seas.
Received August 10th, 2011; revised September 20th, 2011; accepted S eptember 25th, 2011
Freedom of will is fundamental to morality, intuition of self, and normal functioning of society. However,
science does not provide a clear logical foundation for this idea. This paper considers the fundamental
argument against free will, so called reductionism, and why the choice for dualism against monism, fol-
lows logically. Then, the paper summarizes unexpected conclusions from recent discoveries in cognitive
science. Classical logic turns out not to be a fundamental mechanism of the mind. It is replaced by dy-
namic logic. Mathematical and experimental evidence are considered conceptually. Dynamic logic count-
ers logical arguments for reductionism. Contemporary science of mind is not reducible; free will can be
scientifically accepted along with scientific monism.
Keywords: Free Will; Cognitive Science; Philosophy; Logic; Dynamic Logic; Reductionism; Mind
Most contemporary philosophers and scientists do not be-
lieve that freedom of will exists (Bering, 2010). Scientific ar-
guments against reality of free will can be summarized as fol-
lows (Wikipedia, 2010a). Scientific method is fundamentally
monistic: spiritual events, states, and processes (the mind) are
to be explained based on laws of matter, from material states
and processes in the brain. The basic premise of science is cau-
sality, future states are determined by current states, according
to the laws of physics. If physical laws are deterministic, there
is no free will, since determinism is opposite to freedom. If
physical laws contain probabilistic elements or quantum inde-
terminacy, there is no free will either, since indeterminism and
randomness are opposite from freedom (Lim, 2008; Bielfeldt,
Free will, however, has a fundamental position in many cul-
tures. Morality and judicial systems are base d on free will. De-
nying free will threatens to destroy the entire social fabric of
the society (Rychlak, 1983; Glassman, 1983). Free will also is a
fundamental intuition of self. Most of people on Earth would
rather part with science than with the idea of free will (Bering,
2010). Most people, including many philosophers and scientists,
refuse to accept that their decisions are governed by same laws
of nature as a piece of rock by the road wayside or a leaf flown
by the wind. (e.g. Libet, 1999; Velmans, 2003). Yet reconciling
scientific causality and freedom of will remains an unsolved
Monism and Dualism
The above arguments assume scientific monism: spiritual
states of the mind are produced by material processes in the
brain. It seems scientific monism, accepting the unity of matter
and spirit, fundamentally contradicts freedom of will. This po-
sition of monism denying free will was accepted by B. Spinoza
among many great thinkers (Wikipedia, 2010b). Other great
thinkers could not accept this conclusion, rejected monism and
chose dualism: spiritual and material substances are different in
principle and governed by different laws. Among dualists are R.
Descartes (Wikipedia, 2010c), and D. Chalmers (Wikipedia,
Rejecting monism and accepting dualism (of matter and
spirit) also contradicts the fundamentals of our culture. Dualis-
tic position attempts to separate laws of spirit and matter, how-
ever there is no scientific law to accomplish it. Therefore dual-
ism cannot serve as a foundation of science. The only basis for
separating laws of spirit and matter, it seems, is to accept as
material that which is currently explained by science, and de-
clare spiritual that which seems unexplainable. Any hypothesis
attempting such a separation of spirit and matter at any moment
in history would be falsified by science many times over. The
monistic view that spirit and matter are of the same substance is
not only the basic foundation of science, but also corresponds
to the fundamental theological positions of most world relig-
The set of issues involving free will, monism and dualism,
science, religions, and cultural traditions is difficult to reconcile
(e.g. Chalmers, 1995; Velmans, 2008). The main difficulty is
sometimes summarized as reductionism: if the highest spiritual
values could be scientifically reduced to biological explanations,
eventually they would be reduced to chemistry, to physics, and
there would be no difference between laws governing the mind
and spiritual values on the one hand, and a leaf flown with the
wind on the other.
Reductionism and Logic
Physical biology has explained the molecular foundations of
life, DNA and proteins. Cognitive science has explained many
mental processes by material processes in the brain. Yet, mo-
lecular biology is far away from mathematical models relating
processes in the mind to DNA and proteins. Cognitive science
only approaches some foundations of perception and simplest
actions (Perlovsky, 2006a). Nobody ever yet has been able to
scientifically reduce highest spiritual processes and values to
laws of physics. All reductionist arguments and difficulties of
free will discussed above, when applied to highest spiritual
processes, have not been based on mathematical predictive
models with experimentally verifiable predictions—the essence
and hallmark of science. All of these arguments and doubts
were based on logical arguments. Logic has been considered a
fundamental aspect of science since its very beginning and
fundamental to human reason during more than two thousand
years. Yet, no scientist will consider logical argument sufficient,
in absence of predictive scientific models, confirmed by ex-
perimental observations.
In the 1930s a mathematical logician Gödel (1934) discov-
ered fundamental deficiencies of logic. These deficiencies of
logic are well known to scientists and are considered among the
most fundamental mathematical results of the twentieth century.
Nevertheless logical arguments continue to exert powerful in-
fluence on scientists and non-scientists alike. Let me repeat that
most scientists to do not believe in free will. This rejection of
fundamental cultural values and an intuition of self without
scientific evidence seems a glaring contradiction. Sure, there
have to be equally fundamental psychological reasons, most
likely acting unconsciously. The rest of the paper analyzes
these reasons and demonstrates that the discussed doubts are
indeed unfounded. To understand the new arguments we will
look into the recent developments in cognitive science and
mathematical models of the mind.
Recent Cognitive Theories and Data
Attempts to develop mathematical models of the mind (compu-
tational intelligence) have for decades encountered irresolvable
problems related to computational complexity. All developed
approaches, including artificial intelligence, pattern recognition,
neural networks, fuzzy logic and others faced complexity of
computations, the number of operations exceeding the number
of all elementary interactions in the universe (Bellman, 1961;
Minsky, 1975; Winston, 1984; Perlovsky, 1998, 2001, 2006a,
2006b). A mathematical analysis of this complexity problem
related it to the difficulties of logic demonstrated by Gödel
(1934); it turned out that complexity was a manifestation of
Gödelian incompleteness in finite systems, such as computers
or brains, (Perlovsky, 1996, 2001). Difficulties of computa-
tional intelligence turned out related to a most fundamental
mathematical result of the 20th century.
A different type of logic was necessary for overcoming the
difficulty of complexity. Dynamic logic is a process-logic, a
process “from vague to crisp,” from vague statements, condi-
tions, models to crisp ones (Perlovsky, 1987, 1989, 2001,
2006a, 2006b, 2010b; Perlovsky & McManus, 1991). Dynamic
logic is not a collection of static statements (such as “this is a
chair” or “if A then B”); it is a dynamic logic-process. Dynamic
logic was applied to solving a number of engineering problems
that could not have been solved for decades because of the
mathematical difficulties of complexity (Perlovsky, 1989, 1994,
2001, 2004, 2007a, 2007b, 2007c, 2007d, 2010b, 2010d; Per-
lovsky, Chernick, & Schoendorf, 1995; Perlovsky, Schoendorf,
Burdick, & Tye 1997; Perlovsky et al., 1997; Tikhanoff et al.,
2006; Perlovsky & Deming, 2007; Deming & Perlovsky, 2007;
Perlovsky & Kozma, 2007; Perlovsky & Mayorga, 2008; Per-
lovsky & McManus, 1991). These engineering breakthroughs
became possible because dynamic logic mathematically models
perception and cognition in the brain-mind. A basic property of
dynamic logic is that it describes perception and cognition as
processes in which vague (fuzzy) mental representations evolve
into crisp representations. More generally, dynamic logic de-
scribes interaction between bottom-up and top-down signals (to
simplify, signals from sensor organs, and signals from memory).
Mental representations in memory, sources of top-down signals,
are vague; during perception and cognition processes they in-
teract with bottom-up signals, and evolve into crisp mental
representations; crispness of the final states correspond to cris-
pness of the bottom-up representations, e.g., retinal images of
objects in front of our eyes. Initial vague representations and
the dynamic logic process from vague to crisp are unconscious;
only the final states, in which top-down representations match
patterns in bottom-up signals, are available to consciousness
and mentally perceived as approximatel y l ogical states.
During recent decades much became known about neural
mechanisms of the mind-brain, especially about mechanisms of
perception at the lower levels of the mental hierarchy (Gross-
berg, 1988). This foundation makes it possible to verify the
vagueness of initial states of mental representations. It is known
that visual imagination is created by top-down signals. If one
closes one’s eyes, and imagines an object, seen just a second
ago, this imagination gives an idea of properties of mental rep-
resentations of the object. The imagined object is vague com-
pared with the object perceived with opened eyes. If we open
our eyes, it seems that we immediately perceive the object
crisply and consciously. However, it is known that it takes ap-
proximately 160 ms to perceive the object crisply and con-
sciously; therefore the neural mechanisms acting during these
160 ms are unconscious. This crude experimental verification
of dynamic logic predictions was confirmed in detailed neuro-
imaging experiments (Bar et al., 2006; Perlovsky, 2009c). Men-
tal representations in memory are vague and less conscious
with closed eyes; with opened eyes they are not conscious.
Opened eyes mask vagueness of initial mental states from our
consciousness. Dynamic logic mathematically models a psy-
chological theory of Perceptual Symbol System (Barsalou,
1999; Perlovsky & Ilin, 2010b). In this theory symbols in the
brain are processes simulating experiences, and they are mathe-
matically modeled by dynamic logic process es .
Hierarchy of the Mind
Attempts to develop mathematical models of the mind. The
mind is organized into an approximate hierarchy. At the lower
levels of the hierarchy we perceive sensory features. We do not
normally experience free will with regard to functioning of our
sensor systems. Higher up the mind perceives objects, still
higher, situations, abstract concepts. Each next higher level
contains more general and more abstract mental representations.
These representations are built (learned) on top of lower level
representations, and correspondingly, representations at every
higher level are vaguer and less conscious (Perlovsky, 2006a,
2006c, 2006d, 2007b, 2007c; Perl ovsky , 2008; Per lovsky , 2010a,
2010c; Mayorga & Perlovsky, 2008). For example, at a lower
level the mind may perceive objects, such as a computer, a
chair, a desk, bookshelves with books; each object is perceived
due to a representation, which organizes perceptual features
into the unified object; at this low level perception mechanisms
function autonomously, mostly unconsciously, and free will is
not experienced. At a higher level, the mind perceives a situa-
tion, say a professor’s office, which is perceived due to a cor-
responding representation as an organized whole made up of
Copyright © 2012 SciRes. 33
Copyright © 2012 SciRes.
objects. We experience free will about, say moving objects and
arranging furniture in our office. At still higher levels the mind
cognizes ideas of a University, or a system of education, due to
representations at corresponding levels. And even if we appre-
ciate that an individual ability of changing educational system
might be limited, st ill we experience free will to think about it.
Whereas in everyday mundane experience we know that our
freedom is limited in many ways, still, at higher levels of the
mind we experience intuitions or ideas of free will and self,
possessing free will.
Many people doubt that free will exists, for the reasons of
scientific causality and reducibility discussed above. Therefore
I remind that even at the level of simple object perception,
mental representations (in absence of actual objects) are vague
and barely conscious. Higher up, on top of several vague and
less conscious levels of the hierarchy, contents of representa-
tions are vague and unconscious. However, believing in free
will, despite severe limitations of our freedom in real life, con-
sciously or unconsciously, is extremely important for individual
survival, for achieving higher goals, and for evolution of cul-
tures (Glassman, 1987; Bielfeldt, 2009). In animal kingdom
“belief in free will” acts instinctively, their psyche is unified.
Similarly this question did not appear in the mind of our early
progenitors. In human mind, for hundreds of thousands of years
belief in free will directed actions of early humans uncon-
sciously. An intuition of free will is a recent cultural achieve-
ment. For example, in Homer Iliad, only Gods possess free will;
100 years later Ulysses demonstrates a lot of free will (Jaynes,
1976). Clearly, conscious contemplation of free will is a cul-
tural construct. It become necessary with evolution and differ-
entiation of consciousness and culture. Majority of cultures
existing today have well developed ideas about free will, reli-
gious and educational systems for installing these ideas in the
minds of every next generation. But does free will really exist?
To answer this question, and even to understand the meaning of
really we will now consider how ideas exist in culture, and how
existence of ideas in cultural consciousness differs from ideas
in individual cognition (cultural consciousness refers to what is
conscious in culture, in its texts, practices, etc.).
Language and Cognition
Cultures accumulate knowledge and transmit it from genera-
tion to generation mostly due to language. Mechanisms of in-
teractions between language and cognition (Perlovsky, 2004,
2007e, 2009a, 2009b; Fontanari & Perlovsky, 2007, 2008a,
2008b; Perlovsky & Ilin, 2010a, 2010b) explain why language
is acquired in childhood, whereas higher cognition requires
much longer. How are correct connections learned between
words and objects, among the multitude of incorrect ones (no
amount of experience would be sufficient to overcome compu-
tational complexity of learning these connections)? Why does
not human-level cognition evolve in animals without language?
What, exactly, are the similarities and differences between lan-
guage and cogniti o n?
According to the given references, these and other properties
of cognition-language interaction are explained due to the
mechanism of the dual model hierarchy (Figure 1). This figure
illustrates the dual hierarchy of the mind, a cognitive hierarchy
from sensory signals, to objects, to situations, to abstract con-
cepts… and a parallel hierarchy of language from words, to
phrases, from concrete to abstract meanings. The dual model
Figure 1.
The dual hierarchy of language and cognition. Language learning is grounded
in surrounding language at all levels of the hierarchy. Learning of embodied
cognitive models is grounded in direct experience of sensory-motor percep-
tions only at the lower levels. At higher levels, their learning from experience
has to be guided by contents of language models. Language representations
are crisp after about age of five; cognitive representations gradually acquire
crisper content throughout life and at high levels remain vague and unconscious.
along with dynamic logic suggests that a newborn brain con-
tains separate place-holders for future representations of lan-
guage and cognitive contents. Initial contents are vague and non-
specific. The newborn mind has no image-representations, say
for chairs, or sound-representations for an English word chair.
Yet connections between placeholders for future cognitive and
language representations are inborn. Inborn connections be-
tween cognitive and language brain areas are not surprising,
Arbib (2005) suggested that such connections existed due to the
mechanism of mirror neurons millions of years before language
ability evolved. Due to these inborn connections, word and
object representations are acquired correctly connected: as one
part of the dual model (a word or object representation) is
learned, becomes crisper and more specific, the other part of the
dual model is learned in correspondence with the first one.
Objects that are directly observed can be learned without lan-
guage (like in animals). However, abstract ideas cannot be di-
rectly observed; they cannot be learned from experience as
useful combinations of objects, because of computational com-
plexity of such learning. Therefore, cognitive representations of
abstract ideas can be learned from experience only due to
guidance by language.
Language can be learned from surrounding language without
real-life experience, because it exists in the surrounding lan-
guage ready-made at all levels of the mind hierarchy. This is
the reason language is acquired in childhood, whereas learning
corresponding cognitive representations requires much experi-
ence. Learning language can proceed fast, because it is grounded
in surrounding language at all hierarchical levels. But cognition
is grounded in direct experience only at the bottom levels of
perception. At higher levels of abstract ideas, learning cognitive
representations from experience is guided by already learned
language representations. Abstract ideas that do not exist in
language (in culture or in personal language) usually cannot be
perceived or cognized and their existence are not noticed, until
first they are learned in language.
Language grounds and supports learning of the correspond-
ing cognitive representations, similar to the eye supporting
learning of an object representation in the opened-closed eye
experiment. Language serves as inner mental eyes for abstract
ideas. The fundamental difference, however, is that language
“eyes” cannot be closed at will. The crisp and conscious lan-
guage eyes mask vague and barely conscious cognitive repre-
sentations. Therefore we cannot perceive them. If we do not
have necessary experience, our cognitive representations are
vague and unconscious and language representations are taken
for this abstract knowledge. It is obvious with children, but it
also persists through life. Because language contains wealth of
cultural information, we are capable of reasonable judgments,
even without direct life experience.
This discussion is directly relevant to Maimonides’ interpret-
tation of the Original Sin (Levine & Perlovsky, 2008), Adam
was expelled from paradise because he did not want to think,
but ate from the tree of knowledge to acquire existing knowl-
edge ready-made. In terms of Figure 1, he acquired language
knowledge from surrounding language but not in cognitive
representations from his own experience. This discussion is
also directly relevant to the difference between much discussed
(Noble Prize 2002) irrational heuristic decision-making discov-
ered by Tversky & Kahneman (1974, 1981) and decision-
making based on personal experience and careful thinking,
grounded in learning and driven by the knowledge instinct (Le-
vine & Perlovsky, 2008; Perlovsky, Bonniot-Cabanac, & Ca-
banac, 2010). In those cases when life experience is insufficient
and cognitive representations are vague, crisp and conscious
language representations substitute for the cognitive ones. This
substitution is smooth and unconscious, so that we do not no-
tice (without specific scientific training) when we speak from
real life experience, or from language-based knowledge (heu-
ristics). Language-based knowledge accumulates millennial
wisdom and could be very good, but it is not the same as per-
sonal cognitive knowledge combining cultural wisdom with life
experience. It might sound tautologically that we are conscious
only about consciousness, and unconscious about unconscious-
ness. But it is not a tautology that we have no idea of nearly
99% of our mind functioning. Our consciousness jumps from
one tiny conscious and logical island in our mind to another one,
across an ocean of vague unconscious, yet our consciousness
keeps “us” sure that we are conscious all the time, and that
logic is a fundamental mechanism of perception and cognition.
Because of this property of consciousness, even after Gödel,
most scientists have remained sure that logic is the main
mechanism of the mind.
Return now to the question, does free will really exist? Ac-
cording to this paper, the question about whether free will ex-
ists in the sense of resolving the free-will vs determinism de-
bate, this question exists in classical logic, but it does not exist
as a fundamental scientific question. Because of the properties
of mental representations near the top of the mind hierarchy this
question cannot be answered within classical logic.
How can the question about free will be answered within the
developed theory of the mind? Free will does not exist in in-
animate matter. First, free will exists as a cultural concept.
Contents of this concept include all related discussions in cul-
tural texts, literature, poetry, art, in cultural norms. This cultural
knowledge gives the basis for developing corresponding lan-
guage representations in individual minds; language representa-
tions are mostly conscious. Clearly individuals differ by how
much cultural contents they acquire from surrounding language
and culture. The dual model suggests that based on this per-
sonal language representation of free will, every individual
develops his or her personal cognitive representation of this
idea, which assembles his or her related experiences in real life,
language, thinking, acting, into a coherent whole.
Contents of cognitive representation of free will determine
personal thinking, responsibility, will, and actions, which one
exercises in his or her life. Clearly, due to a hierarchy of vague
representations, the concept of free will is far removed from
physical laws controlling molecular interactions. Therefore logi-
cal arguments about reducibility are plainly wrong. Logic is not
a fundamental mechanism of the mind. Mathematical details of
the corresponding cognitive models, supporting experimental
evidence, and future directions of experimental and theoretical
research are discussed in the given references. Among these
directions for future research are experimental verification of
interaction between language and cognition. Psychological and
neuroimaging experiments shall be used to confirm that lan-
guage and cognitive representations are neurally connected
before either of them become crisp; high level abstract ideas are
first become conscious and crisp in language, and then gradu-
ally become conscious and crisp in cognition; language repre-
Copyright © 2012 SciRes. 35
sentations are crisp and conscious long before cognitive repre-
sentations become equally crisp and conscious; the higher up in
the mental hierarchy the vaguer and less conscious are cogni-
tive representations; many abstract cognitive representations
remain vague and unconscious throughout life, even so people
can fluently talk about them. Some of these ideas are being
experimentally tested, and have received partial support.
This paper addressed a fundamental philosophical issue of
how one could scientifically accept an idea of free will, while
humans are collections of atoms and molecules having no free-
dom. For centuries this consideration has been propelled by
logic toward the idea of reductionism, logically denying a pos-
sibility of free will. We explained belief in logic in many scien-
tists and philosophers, even in those well familiar with Göde-
lian theory, by fundamental properties of consciousness: we are
conscious only about logical or near logical states of the mind.
We resolved this difficulty by pointing out that logic, although
prominent in consciousness, is not a fundamental mechanism of
the mind. Dynamic logic, proven experimentally, models the
human mind as an approximate hierarchy of vaguer and vaguer
representations. This model eliminates logical arguments of
reductionism (supporting those scientists denying it earlier) and
supports the agreement between free will, scientific monism,
and science.
Arbib, M. (2005). From monkey-like action recognition to human lan-
guage: An evolutionary framework for neurolinguistics. Behavioral
and Brain Sciences, 28, 105-167. doi:10.1017/S0140525X05000038
Bar, M. et al. (2006). Top-down facilitation of visual recognition. PNAS,
103, 449-454. doi:10.1073/pnas.0507062103
Barsalou, L. (1999). Perceptual symbol systems. Behavioral and Brain
Sciences, 22, 577-660.
Bellman, R. (1961). Adaptive control processes. Princeton, NJ: Prince-
ton University Press.
Bering, J. (2010). Scientists say free will probably doesn’t exist, but
urge: “Don’t stop believing!” Scientific American Mind. URL (last
checed 6 April 2010).
Bielfeldt, D. (2009). Freedom and neurobiology: Reflections on free
will, language, and political power. Zygon, 44, 999-1002.
Chalmers, D. J. (1995). Facing up to the problem of consciousness.
Journal of Consciousness Studies, 2, 200- 219.
Deming, R., & Perlovsky, L. (2007). Concurrent multi-target localiza-
tion, data association, and navigation for a swarm of flying sensors,
Information Fusion, 8, 316-330. doi:10.1016/j.inffus.2005.11.001
Fontanari, J., & Perlovsky, L. (2007). Evolving compositionality in
evolutionary language games. IEEE Transactions on Evolutionary
Computations, 11, 758-769. doi:10.1109/TEVC.2007.892763
Fontanari, J., & Perlovsky, L. (2008a). How language can help dis-
crimination in the neural modeling fields framework. Neural Net-
works, 21, 250-256. doi:10.1016/j.neunet.2007.12.007
Fontanari, J., & Perlovsky, L. (2008b). A game theoretical approach to
the evolution of structured communication codes. Theory in Biosci-
ences, 127, 205-214. doi:10.1007/s12064-008-0024-1
Glassman, R. (1983). Free will has a neural substrate: Critique of Jo-
seph F. Rychlak’s discovering free will and personal responsibility.
Zygon, 18, 17-82. doi:10.1111/j.1467-9744.1983.tb00498.x
Gödel, K. (1934). Kurt Gödel collected works, I. In S. Feferman et al.,
(Ed.), Cambridge, MA: Oxford University Press.
Grossberg, S. (1988). Neural networks and natural intelligence. Cam-
bridge, MA: MIT Press.
Jaynes, J. L. (1976). The origin of consciousness in the breakdown of
the bicameral mind. Boston, M A: Houghton Mifflin Co.
Levine, D., & Perlovsky, L. (2008). Neuroscientific insights on biblical
myths: Simplifying heuristics versus careful thinking: Scientific
analysis of millennial spiritual issues. Zygon, Journal of Science and
Religion, 43, 797-821.
Libet B. (1999). Do we have free will? Journal of Consciousness Stud-
ies, 6, 47-57.
Lim, D. (2008). Did my neurons make me do it? Philosophical and
neurobiological perspectives on moral responsibility and free will.
Zygon, 43, 748-753. doi:10.1111/j.1467-9744.2008.00953.x
Mayorga, R., & Perlovsky, L. (Eds.) (2008). Sapient systems. London:
Minsky, M. (1975). A framework for representing knowledge. In P. H.
Winston (Ed.), The psychology of computer vision. New York:
McGraw-Hill Book.
Perlovsky, L. (1987). Multiple sensor fusion and neural networks.
DARPA Neural Network Study.
Perlovsky, L. (1989). Cramer-rao bounds for the estimation of normal
mixtures. Pattern Recognition Letters, 10, 141-148.
Perlovsky, L. (1994). A model based neural network for transient signal
processing. Neural Netwo rks, 7, 565-572.
Perlovsky, L. (1996). Gödel theorem and semiotics. Proceedings of the
Conference on Intelligent Systems and Semiotics ’96, Gaithersburg,
2, 14-18.
Perlovsky, L. (1998). Conundrum of combinatorial complexity. IEEE
Transactions on Pattern Analysis and Machine Intelligence, 20,
666-670. doi:10.1109/34.683784
Perlovsky, L. (2001). Neural networks and intellect: Using model
based concepts. New York: Oxford University Press.
Perlovsky, L. (2004). Integrating language and cognition. IEEE Con-
nections, 2, 8-12.
Perlovsky, L. (2006a). Toward physics of the mind: Concepts, emotions,
consciousness, and symbols. Physics of Life Reviews, 3, 23-55.
Perlovsky, L. (2006b). Neural networks, fuzzy models and dynamic
logic. In R. Köhler, & A. Mehler (Ed.), Aspects of automatic text
analysis (Festschrift in Honor of Burghard Rieger) (pp. 363-386).
Heidelberg: Springer. doi:10.1007/978-3-540-37522-7_17
Perlovsky, L. (2006c) . Music—The first principle. Musical Theater.
Perlovsky, L. (2006d). Joint evolution of cognition, consciousness, and
music. Lectures in Musicology, School of Music. Columbus: Ohio
State University.
Perlovsky, L. (2007a). Neural dynamic logic of consciousness: The
knowledge instinct. In R. Kozma, & L. Perlovsky (Ed.), Neurody-
namics of higher-level cognition and consciousness. Heidelberg: Sp-
ringer-Verlag. doi:10.1007/978-3-540-73267-9_5
Perlovsky, L. (2007b). Cognitive high level information fusion. Infor-
mation Sciences, 177, 2099-2118. doi:10.1016/j.ins.2006.12.026
Perlovsky, L. (2007c). Modeling field theory of higher cognitive func-
tions. In A. Loula, R. Gudwin, & J. Queiroz (Ed.), Artificial cogni-
tion systems (pp. 64-105). Hershey, PA: Idea Gro up .
Perlovsky, L. (2007d). Evolution of languages, consciousness, and
cultures. IEEE Computational Intelligence Magazine, 2, 25-39.
Perlovsky, L. (2007e). Symbols: Integrated cognition and language. In
R. Gudwin, & J. Queiroz (Eds.), Semiotics and intelligent systems
development (pp. 121-151 ). Hershey, PA: Idea Group.
Perlovsky, L. (2008). Sapience, consciousness, and the knowledge
instinct. (Prolegomena to a physical theory). In R. Mayorga, & L. I.
Perlovsky, (Ed s.), Sapient systems. London: Springer.
Perlovsky, L. (2009a). Language and cognition. Neural Networks, 22,
247-257. doi:10.1016/j.neunet.2009.03.007
Perlovsky, L. (2009b). Language and emotions: Emotional sapir-whorf
hypothesis. Neural Networks, 22, 518-526.
Perlovsky, L. (2009c). “Vague-to-crisp” neural mechanism of percep-
tion. IEEE Tra ns ac tio ns o n N eu ra l Networks, 20, 1363-1367.
Copyright © 2012 SciRes.
Copyright © 2012 SciRes. 37
Perlovsky, L. (2010a). Intersections of mathematical, cognitive, and
aesthetic theories of mind. Psychology of Aesthetics, Creativity, and
the Arts, 4, 11-17. doi:10.1037/a0018147
Perlovsky, L. (2010b) . Neural mechanisms of the mind, aristotle, zadeh,
& fMRI. IEEE Transactions on Neural Networks, 21, 718-733.
Perlovsky, L. ( 2010c). The mind is not a kludge. Skeptic, 15, 50-55.
Perlovsky, L. (2010d).
Perlovsky, L., Bonniot, C., Marie, C., & Cabanac, M. (2010). Curiosity
and pleasure. Webmed Central Psychology, 1, WMC001275.
Perlovsky, L., Chernick, J. l., & Schoendorf, W. (1995). Multi-sensor
ATR and identification friend or foe using MLANS. Neural Net-
works, 8, 1185-1200. doi:10.1016/0893-6080(95)00078-X
Perlovsky, L., & Deming, R. (2007). Neural networks for improved
tracking. IEEE Transactions on Neural Networks, 18, 1854-18 57.
Perlovsky, L., & Ilin, R. (2010a). Neurally and mathematically moti-
vated architecture for language and thought. Special issue “brain and
language architectures: Where we are now?” The Open Neuroimag-
ing Journal, 4, 70-80. doi:10.2174/1874440001004020070
Perlovsky, L., & Ilin, R. (2010b). Grounded symbols in the brain,
computational foundations for perceptual symbol system. Webmed-
Central Psychology, 1, WMC001357.
Perlovsky, L., & Kozma, R. (Eds.) (2007). Neurodynamics of higher-
level cognition and consciousness. Heidelberg: Springer-Verlag.
Perlovsky, L., & Mayorga, R. (2008). Preface. In R. Mayorga, & L. I.
Perlovsky (Eds.), Sapient systems. London: Springe r.
Perlovsky, L., & McManus, M. (1991). Maximum likelihood neural
networks for sensor fusion and adaptive classification. Neural Net-
works, 4, 89-102. doi:10.1016/0893-6080(91)90035-4
Perlovsky, L., Plum, C., Franchi, P., Tichovolsky, E., Choi, D., & Wei-
jers, B. (1997). Einsteinian neural network for spectrum estimation.
Neural Networks, 10, 1541-1546.
Perlovsky, L., Schoendorf, W., Burdick, B., & Tye, D. (1997). Mo-
del-based neural network for target detection in SAR images. IEEE
Transactions on Image Processing, 6, 203-216.
Rychlak, J. (1983). Free will as transcending the unidirectional neural
substrate. Zygon, 18, 439-442.
Tikhanoff, V., Fontanari, J., Cangelosi, A., & Perlovsky, L. (2006).
Language and cognition integration through modeling field theory:
Category formation for symbol grounding. Book Series in Computer
Science, 4131. Heidelberg: Springer.
Tversky, A., & Daniel, K. (1974). Judgment under uncertainty: Heuris-
tics and biases. Science, 185, 1124-1131.
Tversky, A,, and Daniel, K. (1981). The framing of decisions and the
rationality of choice. Science, 211, 453-458.
Velmans, M. (2003). Preconscious free will. Journal of Consciousness
Studies, 10, 42-61.
Velmans, M. (2008). Reflexive monism. Journal of Consciousness
Studies, 15, 5-50.
Wikipedia (2010a). Free will.
Wikipedia (2010b). Baruch spinoz a.
Wikipedia (2010c). René descartes.
Wikipedia (2010d). David chalmers. David_Chalmers
Winston, P. (1984) Artificial intelligence (2nd ed.). Reading, MA: