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logical Amalgamation of Functional Magnetic Resonance Imaging and

Cognitive Neuroscience

A S Chaudhuri

Introduction

The recent field called neuromarketing (Singer, Tania and Ernst Fehr, 2005) applies the tools

of neuroscience to determine why we like some products over others. Neuroscience explains how

raw brain data is helping researchers open the mysteries of consumer choice. Input concepts

contain:

•Neuroscientists when tracking brain functions (Ramachandran, Vilayanur ,2004) generally

use either electroencephalography (EEG) or functional magnetic resonance imaging (fMRI)

technology. Fluctuations in the electrical activity directly below the scalp is measured by EEG ,

while blood flow throughout the brain is tracked by fMRI.

• Studies have shown activity in that brain (Soldow, Gary F. and Gloria P. Thomas ,1984),

(Sujan, Harish ,1999) and (Marketing Week, London ,2005) area can predict the future

popularity of an experience or a product.

• For businesses planning to outsource neuromarketing services, marketing researchers often

advise (Karmarkar, Uma R.,2012; Karmarkar, Uma R., and Zakary L. Tormala,2012)

seeking out a firm that was founded by a operational scientist, or one that has a strong science

advisory board. This research shows the effect of source certainty that is the level of certainty

expressed by a message source-on arguments. In experiments, consumers receive persuasive

messages from sources of varying expertise and certainty. Across studies, low expertise sources

violate expectancies, stimulate involvement, and promote persuasion when they express

certainty, whereas high expertise sources violate expectancies, stimulate involvement, and

promote persuasion when they express uncertainty.

In the early 1950s, two scientists at McGill University James Olds and Peter Milner(Olds, J.,

and P. Milner, 1954; Olds, J. 1977) discovered the reward centre of the brain with . James

Olds was a postdoctoral fellow at McGill University in 1954. Olds was considered to be

important founders of modern neuroscience. These two researchers inadvertently discovered an

area of the rodent brain dubbed "the pleasure centre," located deep in the nucleus accumbency

systems. When a group of lab rats had the opportunity to stimulate their own pleasure centres via

a electrical current activated by levers, they pressed the lever again and again, hundreds of times

per hour, foregoing food or sleep, until many of them dropped dead from overtiredness. Further

research found pleasure centres exist in human brains, too. James Olds was one of the most

important psychologists of the twentieth century. Indeed, many feel that his discovery of the

"reward" system in the brain is the most important single discovery yet made in the field

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concerned with brain substrates of behavior(Olds, J., Disterhoft, J. F., Segal, M., Kornblith, C.

L., and Hirsh, R.1972). In retrospect, this discovery led to a much-increased understanding of

the brain bases and mechanisms of substance abuse and addict

It is obvious that people are fairly good at expressing what they need, what they desire, or even

how much they will pay for purchase an item. But they aren't very good at understanding where

that value comes from, or how and when it is added by factors like store displays or brands.

Humans are more complicated than rats. But they are largely interested by what makes them feel

good, basically when it comes to their decisions for product purchasing . Consequently, many

major corporations have begun to take special interest in how understanding the human brain can

help them better understand the mindset of consumers. Thus a promising but fast-growing field

called neuromarketing which uses brain-tracking tools to determine why we prefer some

products over others has come into vigorous analysis (Morgan, Robert M. and Shelby D. Hunt

,1994).

People behave fairly accurately while expressing what they want, what they desire, or even how

much they may have to pay for an item But sometimes they appear to be not so good to

understand where that value comes from, or how and when it is subjective by factors like store

displays or brands. It has been researched that neuroscience can help us understand those

clamped elements of the decision process (Karmarkar, Uma R.., 2012). However, there is a clear

difference between the goals of researcher from academia and the goals of a corporation in

utilizing neuroscience.

For marketing researchers from academia work plummets into the category of decision

neuroscience, which is the study of what our brains function when we make choices. Researchers

attempt to understand that process and its implications for behaviour, and draw on concepts and

techniques from neuroscience to conform their research in marketing.

For corporations, on the other hand, the science is a means to an end goal of selling more stuff.

But the tools, once restricted to biomedical research, are largely the same. And Karmarkar et al.

(Karmarkar, Uma R., and Zakary L. Tormala , 2010) expect brain data to play a key role in

future research on consumer choice.

Brain Functions and Neuroscience

Neuroscientists when tracking brain functions generally use either electroencephalography

(EEG) or functional magnetic resonance imaging (fMRI) technology. EEG measures directly

fluctuations in the electrical activity below the scalp, occurring s as a result of neural activity.

Researchers can track the intensity of visceral responses such as anger, lust, disgust, and

excitement by attaching electrodes to subjects' heads and evaluating the electrical patterns of

their brain waves (Nunez PL, Srinivasan R ,1981; Niedermeyer E. and da Silva F.L. ,2004).

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EEG refers to the recording of the brain's spontaneous electrical activity over a short period of

time, usually 20–40 minutes, as recorded from multiple electrodes placed on the scalp.

Diagnostic applications generally focus on the spectral content of EEG, that is, the type of neural

oscillations that can be observed in EEG signals. Despite limited spatial resolution, EEG

continues to be a valuable tool for research and diagnosis, especially when millisecond-range

temporal resolution is required. Derivatives of the EEG technique include evoked

potentials (EP), which involves averaging the EEG activity time-locked to the presentation of a

stimulus of some sort (visual, somatosensory, or auditory). Event-related potentials (ERPs) refer

to averaged EEG responses that are time-locked to more complex processing of stimuli; this

technique is used in cognitive science, cognitive psychology, and psychophysiological research

(Hamalainen, M., Riitta, H., Ilmoniemi, R., Knuutila, J., & Lounasmaa, O. ,1993).

Karmarkar Uma R. (Carmen Nobel, 2012) gives the example of junk-food giant Frito-Lay,

which in 2008 hired a neuromarketing science-based consumer-research firm NeuroFocus, a

Berkeley, California-based company wholly owned by Nielsen Holdings N.V. that claims to

have the tools to tap into your brain to plumb the depths of our minds to look into how

consumers respond to Cheetos, the top-selling brand of cheese puffs in the United States.

Cheetos is a brand of cheese-flavored cornmeal snack made by Frito-Lay, a subsidiary

of PepsiCo. Fritos creator Charles Elmer Doolin invented Cheetos in 1948, and began national

distribution in the U.S. The initial success of Cheetos was a contributing factor to the merger

between The Frito Company and H.W. Lay & Company in 1961 to form Frito-Lay. In 1965

Frito-Lay became a subsidiary of The Pepsi-Cola Company, forming PepsiCo the current owner

of the Cheetos brand.

In 2010, Cheetos was ranked as the top selling brand of cheese puffs in its primary market of the

United States; worldwide the annual retail sales totaled approximately $4 billion. The

original Crunchy Cheetos are still in production but the product line has since expanded to

include 21 different types of Cheetos in North America alone. As Cheetos are sold in more than

36 countries, the flavor and composition is often varied to match regional taste and cultural

preferences--such as Savory American Cream in China, and Strawberry Cheetos in Japan.

Dr Anantha Krishnan Pradeep, CEO of NeuroFocus, presented at the 75th Advertising Research

Foundation (2008) conference the latest innovation: a product called of his company,

NeuroFocus, the Mynd, the world's first portable, wireless electroencephalogram (EEG) scanner.

The skullcap-size device sports dozens of sensors that rest on a subject's head like a crown of

thorns. It covers the entire area of the brain, he explains, so it can comprehensively capture

synaptic waves; but unlike previous models, it doesn't require messy gel. What's more, users can

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capture, amplify, and instantaneously dispatch a subject's brain waves in real time, via Bluetooth,

to another device--a remote laptop, say, an iPhone, or that much-beloved iPad. Over the coming

months, Neuro-Focus plans to give away Mynds to home panelists across the country.

Consumers will be paid to wear them while they watch TV, head to movie theaters, or shop at

the mall. The firm will collect the resulting streams of data and use them to analyze the

participants' deep subconscious responses to the commercials, products, brands, and messages of

its clients. NeuroFocus data crunchers can then identify the products and brands that are the most

appealing (Adam L. Penenberg,2011)

Using EEG technology on a group of willing subjects, the firm determined that consumers

respond strongly to the fact that eating Cheetos turns their fingers orange with residual cheese

dust. An article in the August 2011 issue of Fast Company (already cited), which describes how

the EEG patterns indicated a sense of capricious insurrection that consumers enjoy over the

scruffiness of the product. With data in hand, Frito-Lay moved ahead with an ad campaign called

"The Orange Underground," featuring a series of 30-second TV spots in which the Cheetos

mascot, Chester Cheetah, encourages consumers to commit subversive acts with Cheetos. The

campaign garnered Frito-Lay a 2009 Grand Ogilvy Award from the Advertising Research

Foundation.

The Self-Organized Mapping of EEG

The self-organized mapping of EEG encompasses the real-life picture of human perception.The

stages in our perception of the world have a delicate but powerful influence on later thought

processes; they provide the appropriate links within which our thoughts are framed and they

adapt to many different environments throughout our lives. Understanding the changes in these

links is vital to understanding how our perceptual ability extends, but these changes are often

difficult to quantify in sufficiently complex tasks where objective measures of development are

available. The perceptual learning can be incorporated in neural networks and demonstrate

fundamental changes in these links as a function of decision making skill. These signals are

cognitively grouped together to form perceptual maps that enable rapid picturous categorisation

of complex decision process (Michael Harré, Terry Bossomaier & Allan Snyder, et al.,

2012). Such categories reduce the computational load on our capacity limited thought processes,

they inform our higher cognitive processes and they suggest a framework of perceptual pre-

processing of the compressed representations of sensory perceptions such as Self-Organizing

Maps of EEG that captures the central role of perception in expertise.

Thus we find and compare the structured information, in the form of contextual signals, that is

available to experts and non-experts to assign definite marketing importance. It is argued that

this information is used during implicit learning and subsequent early perceptual processing of

information within a given domain of expertise to aid in fast and accurate categorisation and

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decision-making in complex environments. In particular, these processes enable the reduction of

the dense information perceived in a complex natural environment using the available structured

regularities in EEG maps (Kahneman, D. A, 2003; Turk-Browne, N., Scholl, B., Chun,

M. & Johnson, M., 2009). Furthermore, the integration of these prompts into a cognitive whole

leads to the notion of perceptual networks, the aggregate, sparse representations of the salient

features of the task environment that enables many of the remarkable feats reported in studies of

domain-specific expertise in consumer behavior in retail marketing.

The theory of self-organizing maps (SOM) of EEGs are a direct result of consideration of the

role of perception (Ericsson, K. et al., 1993, 1996) in problem solving, particularly the first

seconds of considering a complex problem. SOM Theory addresses the primacy of perception

and pattern recognition in tasks that previously had been thought to be the domain of conscious

thought processes involving logical reasoning such as search, planning and evaluation. Such

conscious reasoning is characterised as slow, serial and capacity constrained whereas the

perceptual processes obtained from SOM of EEG (Kahneman, D., 2003) considered are fast,

parallel and unconstrained in capacity. Recent work in this area has shown

that conscious perceptual learning can occur in domains as complex as visuals, speech and

mathematics (Kellman, P. & Garrigan, P. , 2009). The perceptual processes can adapt and learn

the complex relationships between visual elements, effectively acting as a pre-processing step

that influences the later stages of SOM of EEG cognition induced in sensory regions of the brain

by extensive mapping.

The Neural Circuitry and the Brain Imaging Techniques

In the past few decades, researchers have learned much about the fundamental workings of the

brain, with tremendous gains in knowledge about the molecules that make it run. Scientists

identified genes for receptor proteins that detect smell and taste. They determined that the stuff

of memories is, literally, a cascade of biochemical changes at the connections, or synapses,

between neurons (Spitzer, N.C. (2012). The voltage-dependent ion channels and

neurotransmitter receptors the mechanisms by which neurons differentiate to achieve the

spectacular complexity of the brain . The ion channel activity participates in signal transduction

that directs subsequent steps of development. The spontaneous transient elevations of

intracellular calcium, generated by ion channels and receptors, control several aspects of

differentiation. The work (Spitzer, N.C. ,2006) is aimed at understanding the roles of electrical

activity in assembly of the nervous system, by analyzing the effects of calcium transients on

neuronal differentiation and determining the molecular mechanisms by which they exert these

effects. Specification of neurotransmitters and selection of transmitter receptors are processes

that depend on patterned spontaneous calcium-dependent electrical activity which has broad

impact on cognitive states and on behavior revealing a partnership of electrical activity and

genetic programs in the assembly of the nervous system (Arroyo, S., Lesser, R.P., Gordon, B.,

Uematsu, S., Jackson, D., Webber, R., 1993).

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Now armed with the human genome and a combination of cutting-edge genetic methods and

brain imaging techniques, lab scientists are exploring the neural circuitry of living animals in

ways they could likely have never dreamed of 20 years ago (Borodinsky, L.N. and Spitzer,

N.C. ,2007). Rather than scrutinizing one or two neurons at a time, they aim to study how

networks or systems of the cells function to influence behavior. Such efforts promise to bridge

the gap between studies of the cognitive powers of the mind, traditionally the turf of

psychologists and linguists and investigations of the physical brain by neurobiologists (Spitzer,

N.C. ,2012) . "We're at the point now... where we can put together these two disciplines and

understand the mind in terms of the operations of the nerve cells in the brain,” says Nicholas

Spitzer, co-director of the Kavli Institute of Brain and Mind at the University of California, San

Diego.

In order to fathoming how the whole nervous system functions will require building powerful

computer simulations that can predict the behavior of millions to billions of neurons working

together. The nascent subspecialty of computational neurobiology is thus “a hugely important

domain for the future," says David Van Essen, president of the Society for Neuroscience and a

researcher at Washington University in St. Louis, Missouri(Yarkoni T, Poldrack RA, Van

Essen DC, Wager TD, 2010). The Van Essen lab uses neuroimaging approaches combined with

novel methods of computerized brain mapping and neuroinformatics to explore the functional

organization, connectivity, development. The Human Connectome Project (HCP;

http://www.humanconnectome.org/) involves a large-scale collaborative effort to chart long-

distance connectivity and its variability in healthy adult humans. Van Essen et al. contribution

to the HCP includes the development and application of analysis methods for characterizing

brain connectivity, and the development of a user-friendly platform for data mining of the HCP

datasets that will be made freely available to the neuroscience community (Wager TD, et al.,

2007). In cognitive science of EEG neuroimaging important scientific advances result from the

synthesis and modeling of existing data, in addition to the collection of new data. The overall

behavior of a system as complex cannot readily be inferred from isolated analyses of a few

variables as the human brain. In recognition of these basic principles, a trend has emerged across

disciplines towards the synthesis of data and modeling of the overall behavior of highly

multivariate systems (Poldrack RA., 2006). These approaches build on accumulated evidence

from hundreds or thousands of individual experiments, and provide a ‘bird's eye view’ that

complements the traditional experimental approach.

The explosion of information in the neurosciences demands fresh approaches to data sharing and

data mining. To this end, Van Essen et al. have established the Sums DB database

(http://sumsdb.wustl.edu/sums/) as a repository for many types of neuroimaging data. This

includes a large and freely accessible library representing summary results from thousands of

fMRI, EEG, and structural imaging studies (Van Essen D., 2002).

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Researchers will no doubt be busy for years to come before they can pull together a unifying

theory that explains the miracle of the brain. But step-by-step, they are making headway in many

areas, including these developments on several fascinating fronts. Neuroscientists need a

diagram of the brain’s internal wiring, but mapping neural circuits isn’t easy. The human brain

houses dozens of types of neurons, all intimately intertwined. Each nerve cell is like a tree, with

a head of fine branches known as dendrites that receive messages from several hundred to

thousands of neighbours, and with a complex array of roots that pass the signals on to other cells

across synapses. Small wonder that Santiago Ramon y Cajal famously described the cerebral

cortex as an "impenetrable jungle.” But modern-day researchers can finally see how to survey

that wilderness with some nifty genetic tools.

In the nervous system, a synapse is a structure that permits a neuron to pass an electrical or

chemical signal to another cell (neural or otherwise). Santiago Ramón y Cajal (The Nobel Prize

in Physiology or Medicine 1906) proposed that neurons are not continuous throughout the body,

yet still communicate with each other, an idea known as the neuron doctrine (Ramón y Cajal,

Santiago,1899). Synapses are essential to neuronal function: neurons are cells that are

specialized to pass signals to individual target cells, and synapses are the means by which they

do so. At a synapse, the plasma membrane of the signal-passing neuron comes into close position

with the membrane of the target cell. The cells contain extensive arrays of molecular

machinery that link the two membranes together and carry out the signaling process (Elias, L. J,

& Saucier, D. M.,2005).

Cajal's opus " Histology of the Nervous System of Man and Vertebrates, 2 vols. " (1894-1904),

was made available to the international scientific community in its English translation, by N. and

L.W. Swanson, was published in 1994 by Oxford University Press). Cajal's opus provided the

foundation of modern neuroanatomy, with a detailed description of nerve cell organization in the

central and peripheral nervous system of many different animal species, and was illustrated by

Cajal's renowned drawings, which for decades (and even nowadays) have been reproduced in

neuroscience textbooks (Bentivoglio, M. 1998). .

In addition, Cajal defined "the law of dynamic polarization," stating that the nerve cells are

polarized, receiving information on their cell bodies and dendrites, and conducting information

to distant locations through axons, which turned out to be a basic principle of the functioning of

neural connections. Cajal also made fundamental observations on the development of the

nervous system and its reaction to injuries (his volume "Degeneration and Regeneration of the

Nervous System" translated and edited by R. M. May, London, Oxford University Press, 1928,

has been re-edited by J. DeFelipe and E.G. Jones, Oxford University Press, 1991.

Edward M. Callaway's research is aimed at understanding how neural circuits give rise to

perception and behavior and focuses primarily on the organization and function of neural circuits

in the visual cortex. Relating neural circuits to function in the visual system, where correlations

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between neural activity and perception can be directly tested, provides fundamental insight into

the basic mechanisms by which cortical circuits mediate perception (Kazunari Miyamichi et

al., 2011).. “our brain performs millions of complex computations every second. We are

studying the organization and function of neural circuits in the visual cortex to better understand

how specific neural components contribute to the computations that give rise to visual perception

and to elucidate the basic neural mechanisms that underlie cortical function.” Says Callaway.

Neuroscientists have identified dozens of different neuronal cell types in the brain that work

together in distinct networks. But the circuits are intermingled, and even neighboring neurons of

the same type differ in connectivity and function. Without access to a “wiring diagram” a map of

the neuronal Connections attempting to grasp how the brain lets us understand language,

recognize faces, and schedule our day is akin to trying to discern how a computer chip works

simply by looking at it.

“We still have to hack through some vines here and there, but we have sharper machetes now,”

says neuroscientist Edward Callaway of the Salk Institute for Biological Studies in La Jolla,

California. He and colleagues have invented a method that should make it possible for the first

time to pick any cell in the cortex and then label “every single neuron in the brain that connects

to exactly that one cell,” he says.In addition, neuroscientists are mastering the art of turning

neurons on and off, which will also help with tracing circuits. The standard means of activating

nerve cells is to gently zap them with an electrode, but that stimulates all cells in the area.

Research labs have devised a number of ingenious ways of genetically introducing molecular

switches into neurons that can control their activity more precisely. Lately, neuroscience circles

have been abuzz over one new breakthrough technique in particular: photo-sensitive proteins that

can trigger neurons to respectively fire or shut down within milliseconds when exposed to light.

EEG oscillations reflect repeated variations in the neuronal excitability, with particular frequency

bands reflecting differing spatial scales of brain operation. However, despite decades of clinical

and scientific investigation, there is no unifying theory of EEG organization, and the role of

ongoing activity in sensory processing remains often undecided .The study of Peter Lakatos et

al. (Peter Lakatos et al, 2005) analyzed laminar profiles of synaptic activity of current source

density and multiunit activity, both spontaneous and stimulus-driven, in primary auditory cortex.

The results reveal that the EEG is hierarchically organized (Ulbert I, Halgren E, Heit G, and

Karmos G. , 2001). This oscillatory hierarchy controls baseline excitability and thus stimulus-

related responses in a neuronal ensemble (Freeman WJ and Rogers LJ., 2002). It is proposed

that the hierarchical organization of ambient oscillatory activity allows auditory cortex to

structure its temporal activity pattern so as to optimize the processing of rhythmic inputs

(Buzsaki G and Draguhn A., 2004).

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Stanford University investigators and their collaborators created transgenic mice that produce

channel for perception of light hroughout their brains, without ill effect. The researchers quickly

scanned with blue light over huge regions of an anesthetized rodent’s exposed brain; then, using

electrodes, they monitored responses triggered in other areas. With this strategy, says Stanford

bioengineer and psychiatrist Karl Deisseroth (Deisseroth K, 2012) scientists “can start to map

circuitry much faster than you could before.” In this case, they examined a key neural pathway

involved in processing smells (Deisseroth K., 2011; Deisseroth K, Feng G, Majewska A, et al.

2006; Yizhar O, et al. 2011).

Neuroscientist Karel Svoboda and colleagues at Cold Spring Harbor Laboratory in New York,

and the Howard Hughes Medical Institute’s Janelia Farm campus in Ashburn, Virginia, have

used channel rhodopsin to trace the long neurons that link the two sides of the mammalian brain

through the structure known as the corpus callosum (Huber, D., Gutnisky, D.A., Peron, S.,

O'Connor, D.H., Wiegert, J.S., Tian, L., Oertner, T.G., Looger, L.L., Svoboda, K. , 2012).

By turning on or off parts of a neural loop and watching what happens, researchers hope to learn

how specific complex circuits influence an animal’s behavior. The light-activated methods,

Svoboda says (Svoboda, K. ,2011), “will make a new kind of neurobiology possible.”

Research on natural vision focuses on the acquisition of structured visual information and the

conversion of this information into sophisticated internal representations for controlling behavior

(Fiser, J., Chiu, C., & Weliky, M. , 2004). An integrated approach with three main

components, human visual and learning experiments, computational modeling of learning, and

multi-electrode recording from behaving humans. The recurrent theme of our work is the pursuit

of a statistically based and biologically sound framework to link low-level visual mechanisms

(e.g., adaptation) with the development and learning of higher level complex features and

constancies for efficient visual representations of objects and scenes.Humans learn to understand

their visual environment based on their sensory experience. Despite decades of research, it is still

not clear what representations the brain uses in this process and how it acquires them.

The basic ability EEGs are key in the formation of visual representations from the simplest

levels of luminance changes to the level of conscious memory traces, rules and abstract

knowledge investigating the interaction between learning ability and various perceptual

constraints due to eye movements, clutter, occlusion and other presumably more hardwired

constraints, and the consolidation effect to investigate what visual features humans use for object

recognition (Fiser, J., Bex, P.J., & Makous, W.L., 2003).

The computational modeling work interprets experimental data in a Bayesian framework.

Specifically generative statistical model selection learning can better capture human behavior

observed in the experiments than simple associative learning can (Berkes P, Orbán G, Lengyel

M, Fiser J. , 2011). This suggests that humans interpret their sensory input through an

"unconscious inference" process that follows precisely the statistical structure of the environment

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but aims at the simplest possible internal description of the input which gives framework for

statistically based interpretation of empirical rules, decision making, attention as well as provides

a tightly coupled explanation for visual recognition and visual learning. The implementation of

the scheme cognizance in the brain requires a continuous reciprocal interaction between groups

of elements at different levels of the hierarchical representation encoded in the cortex cortical

bone forms the cortex, or outer shell, of most bones.. This dynamic collective coding is in

contrast with the traditional feed forward view of how visual information is processed in the

cortex. The level of primary visual cortex and at higher areas the representation of visual

information is best described as the activity pattern of cell assemblies rather than a set of

individual feature detectors. The correspondence between evoked neural activity and the

structure of the input signal systematically improved with age. This improvement was linked to a

shift in the dynamics of spontaneous activity. At all ages including the mature human,

correlations in spontaneous neural firing were only slightly modified by visual stimulation,

irrespective of the sensory input. These results suggest that in both the developing and mature

visual cortex, sensory evoked neural activity represents the modulation and triggering of ongoing

circuit dynamics by input signals, rather than directly reflecting the structure of the input signal

itself.

Tracing the Deep History of the Brain

The EEG has been widely used for over 75 yr as a measure of human brain function . However,

because of the dynamic complexity of the EEG, our understanding of its control and functional

significance remains elementary. Modern studies have begun to link specific brain operations to

specific components of the EEG, including “gamma” (Bertrand and Tallon-Baudry 2000;

Engel et al.2001; Fries et al. 2001; Singer and Gray 1995), “theta” (Buzsaki and Draguhn

2004; Chrobak et al. 2000; Kahana et al. 2001), and “alpha” (Makeig et al. 2004; Worden et

al. 2000).

EEG oscillations reflect repeated variations in the neuronal excitability, with particular frequency

bands reflecting differing spatial scales of brain operation. However, despite decades of clinical

and scientific investigation, there is no unifying theory of EEG organization, and the role of

ongoing activity in sensory processing remains often undecided .The study of Peter Lakatos et

al. (Peter Lakatos et al, 2005) analyzed laminar profiles of synaptic activity of current source

density and multiunit activity, both spontaneous and stimulus-driven, in primary auditory cortex.

The results reveal that the EEG is hierarchically organized. This oscillatory hierarchy controls

baseline excitability and thus stimulus-related responses in a neuronal ensemble. It is proposed

that the hierarchical organization of ambient oscillatory activity allows auditory cortex to

structure its temporal activity pattern so as to optimize the processing of rhythmic inputs

(Buzsaki G and Draguhn A., 2004).

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Approximately two million years ago, the brain capacity of our ancient forebears began greatly

increasing, eventually culminating in a brain that today is roughly three times larger than that of

our closest evolutionary cousin. How that transformation happened, and how we acquired our

impressive cognitive abilities, is a mystery that touches on the core of what made us human.

Lately, scientists have been gleaning fresh clues from studying genetic data troves and the

anatomy of human brains at the cellular, molecular and genetic levels. Using computer

algorithms, researchers have compared the whole genomes of humans and identified several

hundred regions containing DNA differences that may have played a role in human evolution.

Because periodically arising genetic changes are what drive evolution, our DNA is a historical

record of deep ancestral secrets.

For instance, biologists at the University of California, Santa Cruz have identified 202 human

DNA segments that underwent rapid changes in the 6 to 7 million years (Kingston, R E and

Tomkin J W, 2006). Most of those regions aren't genes that code for proteins; instead, they are

sequences that appear to regulate when or where certain genes turn on in the body – and some of

those genes may be involved in neuro-development.

In lab experiments, the research team found that one DNA region, named Human accelerated

regions (HARs) (Pollard KS, Salama SR, Lambert N, Lambot MA, Coppens S, Pedersen JS,

Katzman S, King B, Onodera C, Siepel A, Kern AD, Dehay C, Igel H, Ares M Jr,

Vanderhaeghen P, Haussler D ,2006) is active in neurons, which organize the initial formation

of the neocortex. Although that discovery is exciting, the scientists have much to learn about

Human accelerated regions (HARs) 's function in the brain before reaching any conclusion about

its potential role in human evolution, says computational biologist Katherine Pollard (K.S.

Pollard , 2009 and K.S. Pollard, S. Dudoit, M.J. van der Laan 2005),who now works at the

University of California, Davis.

Indeed, understanding the early development of the cerebral cortex is another important source

of information for deciphering possible mechanisms of how evolution built a bigger and more

intricate brain (Imamura F, Ayoub AE, Rakic P, Greer CA.2011, Dominguez MH, Rakic

P.2009). “We want to figure out how it was done, and the secret is in individual cells, in how

they behave during embryonic development,” says Pasko Rakic, director of the Kavli Institute

for Neuroscience at Yale University. Working at the molecular and genetic level, his lab has

been studying how neurons born deep inside the brain know exactly where to go as they migrate

upward to form the six layers of the neocortex.

An early proposition (Bishop 1933) suggested that impulsive EEG reflects recurring variation of

cortical excitability. Although the relationship of the EEG to neuronal activity was relatively

neglected over the prevailing years, recent studies have rekindled interest in this topic.

Intracellular recordings in EEG provide a striking demonstration of neuronal

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membranepotentials undergoing slow rhythmic shifts between depolarized and hyperpolarized

states (Sanchez-Vives and McCormick 2000). Other recent findings have pointed to an

underlying structure to the EEG spectrum. There is gathering evidence that ongoing cortical

activity has an effect on sensory processing (Fiser et al. 2004; Massimini et al. 2003).

This study (Massimini et al. 2003) provides a way to organize these important findings. First,

there is a hierarchical structure to the EEG, by the phase of a lower frequency oscillation at each

oscillatory frequency the amplitude is modulated. This structure seems to extend from slow

waves up through the gamma frequencies, although technical constraints in this study precluded

quantitative assessment of the interrelationship of delta and very slow oscillations. The

intracellular recordings in vitro suggest that layer 5 pyramidal cells play a key role in organizing

and promoting slow oscillations in cortical neurons (Sanchez-Vives and McCormick 2000).

A second key aspect of findings (Peter Lakatos, Ankoor S. Shah, Kevin H. Knuth, Istvan

Ulbert, George Karmos,and Charles E. Schroeder, 2005) is that like the slow oscillation, the

higher frequency oscillations reflect determined excitability variations in cortical ensembles.

This is reflected in local neuronal firing, which is clearly related to the phase of delta, theta, and

gamma oscillations.

Finally, the ambient oscillatory activity has significant effects on stimulus processing (i.e.,

stimulus-related activity) during which stimulus response is enhanced or suppressed. The facts

that impulsive and event-related oscillations occur in the same frequency bands are both in phase

and have similar laminar distributions implies that the same neural circuitry is used.

However, whether the oscillatory hierarchy present in spontaneous activity is preserved in

stimulus-related activity remains an important question for future studies in neurological

sensitivity modeling as may be required in neuro decision making. The above findings have

important implications for cortical processing of natural acoustic and visual stimuli. While

stimulus processing clearly is structured by the ambient context (Arieli et al. 1996), the onset of

a sound and vision can instantly reset the phase of the ambient delta oscillation, which

effectively phase-locks the entire hierarchical structure of oscillatory activity to the stimulus.

Thus on cortical processing effects of ambient activity should be dramatic for complex rhythmic

inputs that are typical of a natural environment. The brain is described as a system of

chronological stations leading from an object that has a fixed internal representation to the

behavioral response. According to this conventional view of feature detection, each element can

be characterized to a specific stimulus by a fixed response (Arieli A ,1992). Even during well-

defined cognitive tasks, successive brain responses to repeated identical stimulations are highly

variable due to the ongoing cortical activity: even in the absence of external sensory input,

cortical activity exhibits highly structured, internally driven ongoing (spontaneous) waves of

13

activity (including in the sensory areas) (Kenet T, Bibitchkov D, Tsodyks M, Grinvald A,

ArieliA, 2003). For example, resetting of the ambient oscillatory hierarchy should be

enormously useful in processing sounds and vision that occur with rhythmic components. It so

happens that for humans, the temporal structure of numerous biologically relevant stimuli (Singh

and Theunissen, 2003) fit this pattern remarkably well.

From Molecules to Memory

One of the strangest and most wondrous things in the universe is the wrinkled lump in every

person’s head: the human brain. Weighing about three pounds for the average adult, within the

brain are 100 billion neurons that give us the ability to see, smell and move, as well as think,

weep, talk and read. Furthermore, all we experience and remember – in essence, every little thing

that makes us who we are – is rooted in the neocortex, the seat of the "thinking" brain.

Understanding how such a miracle is possible is the vast mission of the relatively young field of

neuroscience and the nascent field of neuromarketing .

In the past few decades, researchers have learned much about the fundamental workings of the

brain, with tremendous gains in knowledge about the molecules that make it run. Scientists

identified genes for receptor proteins that detect smell and taste. They determined that the stuff

of memories is, literally, a cascade of biochemical changes at the connections, or synapses,

between neurons. And belying an old view that the nervous system is hardwired from birth,

experts found that its cells retain some capacity to adapt and reorganize in response to

experience.

The main action happens at the connections between neurons. In the first hour of memory

formation, neurotransmitters are released, receptors congregate and the signals that cross the

synapse are boosted. Most scientists believe that ultimately, it is an overall persistent

strengthening of synaptic activity that lays down a long-term memory.

Without the brain's knack for remembering, you would have no learning and no autobiography,

crafting who you become. Our memories are what make us each unique, says neurobiologist

Roger Nicoll of the University of California, San Francisco, winner of the 2012 Edward M.

Scolnick Prize in Neuroscience. “What identifies you is nothing other than storage of events and

places and people." Roger Nicoll is interested in elucidating the cellular and molecular

mechanisms underlying learning and memory in the human brain. Long-term potentiation (LTP),

a phenomenon in which brief repetitive activity causes a long lasting (many weeks) enhancement

in the strength of synaptic transmission, is generally accepted to be a key cellular substrate for

learning and memory. His lab uses a combination of electrophysiological and molecular

techniques to elucidate the molecular basis of LTP. It has been have found that LTP involves the

rapid activity-dependent trafficking of glutamate receptors to the synapse through

aminomethylphosphonic acid (AMPA) which intervene fast synaptic transmission in the central

14

nervous system (CNS) (Tzingounis AV, Nicoll RA., 2006; Nicoll RA, Alger BE, 2004; Wilson

RI, Nicoll RA., 2002; Anders S. Kristensen1 & Stephen F. Traynelis, 2005) . AMPA

receptors that are cationic channels allowing the passage of Na+ and K+ and therefore have

an equilibrium potential near 0 mV. Nicoll’s main research goal for almost three decades has

been to understand, at the cellular and molecular level, how electrical activity reshapes the

brain’s connections. Much of his work has involved a form of synaptic plasticity known as long-

term potentiation (LTP), an experimental procedure by which a burst of high-frequency electrical

stimulation can induce a lasting increase in synaptic strength. LTP is most commonly studied

within the hippocampus, a brain structure that is crucial for memory. Since its discovery in

1966, a growing body of evidence has indicated that the LTP reflects the natural mechanism by

which experience leads to the formation and storage of new memories. Neuroscientists have

detailed the basic, initial biochemical steps that convert perceptions of the world into permanent

recollections of facts and occurrences. “It’s absolutely incredible how far we’ve come,” says

Nicoll (Milstein AD, Nicoll RA., 2009). Support for that idea comes from a decades-old

observation that, when hippocampal cells are rapidly bombarded with electrical zaps, neurons on

the receiving end of the stimulated cells’ synapses respond with a long-lasting jump in firing

activity. But the theory that this so-called long-term potentiation (LTP) underlies real-life

memory encoding has been tough to prove.

Todd Sacktor and many scientists are now focusing on later stages of memory formation

(Pastalkova, E.,Serrano, P., Pinkhasova, D.,Wallace, E., Fenton, A. A., and Sacktor, T. C.,

2006). In people, as years pass, the hippocampus is apparently no longer needed to sustain a

recollection, which instead becomes embedded in neurons distributed across the neocortex.

Scientists know little about this consolidation process.

On another front, neurobiologists are unraveling the molecular underpinnings of working

memory, the mental scratchpad that makes it possible to retain a phone number long enough to

dial it. Working memory depends on a network of cells, housed in the brain’s prefrontal cortex,

that all trigger each other to fire persistently to hold onto that number. Recent research has

shown that certain molecules, called Hydrogen cyanide channels, control whether this neural

network is functioning. The channels are like tiny gates in a neuron’s cell membrane that let

charged molecules flow through. When the channels are open, they weaken the ability of a

neuron to receive information from other cells, and thus disconnect the circuit, says Amy

Arnsten, a neurobiologist at the Kavli Institute of Neuroscience at Yale (Arnsten, A.F.T., 2004 ;

Southwick, S., Rasmusson, A., Barron, X., Arnsten, A.F.T, 2005).

The Inexplicable riddle of consciousness

Some of life’s secrets seem so formless and inconceivable as to disregard any attempt at inquiry.

Such is the great riddle of consciousness (Gerald Maurice Edelman,1990). Where does it come

from? How can electrical buzzing of physical brain cells produce nonphysical sensations of pain

15

or the emotion of savoring the redness of a rose? What accounts for the conscious and the

essentially private state of being you? Although it is obvious to researchers that consciousness

arises from the brain in the 1990s, however, Nobel laureates Gerald Edelman (The Nobel Prize

in Physiology or Medicine 1972) began pushing for serious biological investigations (Gerald

Maurice Edelman, 1990, 1993, 2004, 2006, Gerald Maurice Edelman, Giulio Tononi, 2000).

Gerald Maurice Edelman's theories are entrenched in neurology. In fact, he insists that this is the

only foundation for a successful theory of consciousness: the answers are not to be found in

quantum physics, philosophical speculation, or computer programming.

For Edelman the structure of the brain is a key factor. The neurons in the brain wire themselves

up in complex and distinctive patterns during growth. No two people are wired the same way.

The neurons do come to compose a number of structures, however. They form groups which

tend to fire together, and for Edelman these groups are the basic operating unit of the brain. The

other main structures are maps and mapping not just sensory inputs, but each other and other

kinds of neuronal activity. The whole system is bound together by re-entrant connections

The principle which makes this structure work is Neuronal Group Selection, or Neural

Darwinism. Some patterns are reinforced by experience, while many others are eliminated in a

selective process which resembles evolution. Edelman draws an analogy with the immune

system, which produces a huge variety of random antibodies: those which link successfully to a

foreign substance reproduce rapidly. This explains how the body can quickly produce antibodies

for substances it has never encountered before (and indeed for substances which never existed in

the previous history of the planet): and in an analogous way the Theory of Neuronal Group

Selection (TNGS) explains how the brain can recognise objects in the world without having a

huge inherited catalogue of patterns, and without a scale model to do the recognising for it.

The re-entrant connections between neuronal groups in different parts of the brain co-

ordinate impressions from the different senses to provide a consistent continuous experience; but

re-entry is also the basic mechanism of recategorisation, the fundamental process by which the

brain carves up the world into different things and recognises those it has encountered before.

Edelman is noted for his theory of consciousness, which he has documented in a trilogy of

technical books, and in several subsequent books written for a general audience including Bright

Air, Brilliant Fire (1992), A Universe of Consciousness (2001, with Giulio Tononi), Wider than

the Sky (2004) and Second Nature: Brain Science and Human Knowledge (2007).

In Second Nature Edelman defines human consciousness as being:

"... what you lose on entering a dreamless deep sleep ... deep anesthesia or coma ... what you

regain after emerging from these states. [The] experience of a unitary scene composed variably

of sensory responses ... memories ... situatedness ... "

16

The first of Edelman's technical books, Neural Darwinism (1987) explores his theory

of memory that is built around the idea of plasticity in the neural network in response to the

environment. The second book, Topobiology (1988), proposes a theory of how the original

neuronal network of a newborn's brain is established during development of the embryo. The

Remembered Present (1990) contains an extended exposition of his theory of consciousness.

Edelman proposes a biological theory of consciousness, based on his studies of the immune

system. He explicitly locates his theory within Charles Darwin's Theory of Natural Selection,

citing the key tenets of Darwin's population theory, which postulates that individual variation

within species provides the basis for the natural selection that eventually leads to the evolution of

new species. He rejects dualism and also dismisses newer hypotheses such as the so-called

'computational' model of consciousness, which liken the brain's functions to the operations of a

computer.

Edelman argues that the mind and consciousness are wholly material and purely biological

phenomena, arising from highly complex cellular processes within the brain, and that the

development of consciousness and intelligence can be satisfactorily explained by Darwinian

theory. In Edelman's view, human consciousness depends on and arises from the uniquely

complex physiology of the human brain:the vast number of neurons and associated cells in the

brain almost infinitely complex physiological variations in neurons (even of the same general

type) and in their connections with other cells the massive multiple parallel reentrant connections

between individual cells, and between larger neuronal groups, and so on, up to entire functional

regions and beyond. Edelman's theory of neuronal group selection, also known as Neural

Darwinism, has three basic tenets; Developmental Selection, Experiential Selection and

Reentry.

In Developmental selection the formation of the gross anatomy of the brain is controlled by

genetic factors, but in any individual the connectivity between neurons at the synaptic level and

their organisation into functional neuronal groups is determined by somatic selection during

growth and development. This process generates tremendous variability in the neural circuitry

like the fingerprint or the iris, no two people will have precisely the same synaptic structures in

any comparable area of brain tissue. Their high degree of functional plasticity and the

extraordinary density of their interconnections enables neuronal groups to self-organise into

many complex and adaptable "modules". These are made up of many different types of neurons

which are typically more closely and densely connected to each other than they are to neurons in

other groups.

In Experimental selection overlapping the initial growth and development of the brain, and

extending throughout an individual's life, a continuous process of synaptic selection occurs

within the diverse repertoires of neuronal groups. This process may strengthen or weaken the

connections between groups of neurons and it is constrained by value signals that arise from the

17

activity of the ascending systems of the brain, which are continually modified by successful

output. Experiential selection generates dynamic systems that can 'map' complex spatio-temporal

events from the sensory organs, body systems and other neuronal groups in the brain onto other

selected neuronal groups. Edelman argues that this dynamic selective process is directly

analogous to the processes of selection that act on populations of individuals in species, and he

also points out that this functional plasticity is imperative, since not even the vast coding

capability of entire human genome is sufficient to explicitly specify the astronomically complex

synaptic structures of the developing brain.[17]

Reentry the third principle of Edelman's thesis is the concept of reentrant signaling between

neuronal groups , the neural circuitry. He defines reentry as the ongoing recursive dynamic

interchange of signals that occurs in parallel between brain maps, and which continuously

interrelates these maps to each other in time and space. Edelman demonstrates spontaneous

group formation among neurons with re-entrant connections. Reentry depends for its operations

on the intricate networks of massively parallel reciprocal connections within and between

neuronal groups, which arise through the processes of developmental and experiential selection

outlined above. Edelman describes reentry as "a form of ongoing higher-order selection ... that

appears to be unique to animal brains" and that "there is no other object in the known universe so

completely distinguished by reentrant circuitry as the human brain".

Crick and Caltech neuroscientist Christof Koch argued the problem could be tackled by breaking

it down into smaller research questions (Crick F, Koch C 1990, 1995a, 1995b , 1995c and

Koch C. and Hepp K. 2006). One fascinating approach has been to ask how the mind becomes

conscious of certain information while apparently ignoring other stimuli that shower the senses.

For instance, it’s well known that if one of your eyes is presented with a photo of, say, a house

while the other eye sees a photo of a face, the two images do not blend. You alternately perceive

only either picture for a few seconds each even as each retina “sees” the same image all the

while. A similar effect happens while gazing with both eyes at an outline of a 3-D cube, which

flips between facing leftward and rightward. Such optical illusions are called bistable visual

patterns. In bistable vision perception oscillates automatically between two mutually exclusive

states (Sabine Windmann, , Michaela Wehrmann, 2006).

The prefrontal cortex might influence this process either by maintaining the dominant pattern

while protecting it against the competing representation, or by facilitating perceptual switches

between the two competing representations (Mitchell, J. F., Stoner, G. R., & Reynolds, J. H.

2004 ; Moore, T., & Armstrong, K. M. 2003).

In numerous experiments scientists have monitored the brain’s responses to a bistable image.

Neural areas that initially process visual data fire constantly, showing no differences when

conscious perception shifts from one image to the other. But something interesting happens in

18

the higher visual processing regions (Kanwisher, N., & Wojciulik, E. (2000; Leopold, D. A.,

Wilke, M., Maier, A., & Logothetis, N. K. 2002). Such findings supported the idea that a

subset of brain cells which Crick and Koch called “neuronal correlates of consciousness” (Crick

F, Koch C ,1990) are specialized to transmit selective visual signals to the mind’s consciousness

(Rees G. and Frith C. 2007).

Using t magnetic stimulation, researchers can apply magnetic pulses to the brain and then use

EEG recordings to monitor electrical activity across the cerebral cortex. The studies are helping

Tononi fine-tune a theory that views consciousness as an integrated system of information, with

parts of the cortex and underlying thalamus ideally suited for managing the integration (Laurey,

S.and Tononi, G. , 2009). Although overall progress in the field of consciousness is slow, he

says, a growing number of scientists are now using the best neuroscientific tools to inquire

questions about consciousness

Observational Control, Object Perception in conscious Processes using fMRI

The primary source of observational control in object perception is the prefrontal cortex. This

region is involved in the maintenance of goal-related information as well as in observational

selection and set shifting. Recent analyses (Windmann Sabine et al., 2006) have highlighted the

role of top-down processes during elementary visual processes as illustrated in bistable vision

where perception automatically oscillates between two mutually limited states. This influence the

process either by maintaining the leading pattern while protecting it against the competing

representation using perceptual switches between the two competing representations. Humans

are able to control perceptual switches in the hold condition. These results suggest that the

presentation is necessary to bias the selection of visual representations in accord with current

goals for maintaining selected information active that is continuously available in the

environment.

Cognitive neuroscience aims to map mental processes onto brain function, which attempts to

answer the question of what “mental processes” exist and how they relate to the tasks and

objects that are used to influence and measure them. The increasing progress in cognitive

neuroscience requires a more systematic approach to represent the mental entities that are being

mapped to brain function and the tasks used to control and determine mental processes.

(Poldrack RA,et al. , 2011) describe a new open collaborative project that aims to provide a

knowledge base for cognitive neuroscience, called the Cognitive Atlas (accessible online at

http://www.cognitiveatlas.org), and outline how this project has the prospective to drive novel

discoveries about both mind and brain in matters of decision and perception.

The real life reasoning is fundamental to science, human culture, and the solution of problems in

daily life. It starts with domains and yields a logically necessary conclusion that is not

unambiguous in the premises. Fangmeier Thomas et al. 2006 investigated the neurocognitive

19

processes underlying coherent thinking with event-related functional magnetic resonance

imaging. The researchers specifically focused on three temporally separable phases: (1) the

premise processing phase, (2) the premise integration phase, and (3) the validation phase in

which humans decide whether a conclusion logically follows from the premises. The distinct

patterns of cortical activity during these phases with initial activation shifting to the prefrontal

cortex was found along with the reasoning process. Activity in these latter regions was specific

to reasoning. The phenomenon of stabilized retinal stimuli to fade and become replaced by their

backgroundis a good example of central brain mechanisms that can selectively add or delete

information to/from the retinal input. Importantly, such cortical mechanisms may overlap with

those that are used more generally in visual perception. In order to identify cortical areas that

contribute to the perception, researchers (Mendola J. D., et al., 2006) used functional magnetic

resonance imaging to image activity in the visual cortex while subjects experienced perception.

The results of investigations lead to propose that perceptual filling-in suggests high-level control

mechanisms to reconcile competing percepts, and alters the normal image-related signals at the

first stages of cortical processing. The overall pattern of activation in resembles is seen as of

perceptually bistable stimuli, including binocular rivalry, indicating common control

mechanisms.

It is necessary in everyday life to selectively adapt our behavior to different situations and tasks.

in cognitive psychology, Such adaptive behavior in cognitive psychology can be investigated

with the task-switching model. The functional magnetic resonance imaging (fMRI) study may be

set out to investigate processes that are relevant when participants can decide by their own which

task to perform and chose a better objective (Matthew F S Rushworth, Timothy E J Behrens

,2008). It may be expected to find prolonged reaction times as well as higher activations within

the cortex for the choice conditions compared to the no-choice condition (Susanne Karch, et al.

2010). The fMRI results revealed a significant activation difference for the choice conditions

versus the no-choice condition. These activations revealed no selection-specific difference

between three and two choices. The analysis (Forstmann Birte U., 2006) showed that the

activation is associated with higher task-dependent response when participants can select a task

and objective.

Evidence Derived from fMRI and Dynamic Causal Modeling (Thorsten Plewan, etal. , 2012)

the human visual system converts identically sized retinal stimuli into different-sized

perceptions. The strength of this perception can be expressed as the difference between physical

and erceived reality. Accordingly, imaginative strength reflects how strong a representation is

transformed along its track from a retinal image up to a conscious perception. It has been

investigated that changes of effective connectivity between brain areas supporting these

transformation processes to further illustrate the neural underpinnings of imaginations. Dynamic

causal modeling was employed to investigate cognitive interactions between visual streams to

model bidirectional connections at areas most accurately modeled for the underlying network

20

dynamics. directly related to size transformation activation processing task-related supervisory

functions. Over the last 20 years, fMRI has revolutionized cognitive neuroscience. It is hoped

that fMRI in cognitive neuroscience might include increased methodological rigor, an increasing

focus on connectivity and pattern analysis with greater focus on selective inference powered by

open databases, and increased use of computational models to describe underlying processes.

The eye-tracking approach of an fMRI is used to examine the mechanisms involved in learning

to understand an object in a scene. This has suggested a role for effective visual sampling and

prior experience in the development of mature object perception. integrating across variable

sampled experiences to persuade perceptual change. It has been found with fMRI that relative to

the Control condition, participants in the training condition were significantly more likely to

change their percept from “disconnected” to “connected,” an as indexed by pre-training and post-

training test performance. This pattern was not restricted to participants who changed their

initial “disconnected” object perception. Neuroimaging data (Lauren L. Emberson and Dima

Amso, 2012)suggest an involvement of the ongoing regular experience to enable changes in a

modal completion.

Some brain areas preferentially process information from a particular sensory process.

Awareness to perceptible stimuli depends on the temporal frequency of stimulation as observed

in images of fMRI (Per F. Nordmark, et al., 2012).Whole-brain analysis revealed an effect of

motivation frequency in the visual cortex. The blood oxygen level dependent fMRI (BOLD) response

in the auditory cortex was stronger during stimulation at hearable frequencies (20 and 100 Hz)

whereas the response in the visual cortex was suppressed at infrasonic frequencies (3 Hz).

Regardless of which hand was stimulated, the frequency-dependent effects were lateralized

to the left auditory cortex and the right visual cortex.Furthermore, the frequency-dependent

effects in both areas were removed when the participants performed a visual task while receiving

identical tangible stimulation as in the perceptible threshold-tracking task. As such the brain

areas contribute to sensory processing by performing specific computations regardless of input

in physical phenomenon.

Magnetic resonance imaging (MRI) has fast become an important tool in clinical medicine and

biological research. Its functional variant (functional magnetic resonance imaging; fMRI) is

widely used method for studying the neural basis of human cognition and brain mapping with

sufficient knowledge of the physiological foundation of the fMRI signal to interpret the data with

respect to neural activity. This paper reviews the basic principles of The blood-oxygen-level-

dependent (BOLD) fMRI signal elicits the neural activity during sensory stimulation

(Logothetis, N. K., Pauls, J., Augath, M., Trinath, T. & Oelter-mann, A. 2001).

Depending on the temporal characteristics of the stimulus of BOLD responses a strong corre-

lation was found between the neural activity measured with microelectrodes and the BOLD

signal averaged over a small area around the microelectrode tips. The BOLD signal has higher

21

significant with the neural activity, indicating that human fMRI combined with traditional

statistical methods successfully provides the reliability of the neuronal activity. To understand

the contribution of spatio-temporal fMRI responses the statistical analysis of signal has been

observed with tools of systems analysis to predict the fMRI responses. These findings, together

with an analysis of the neural signals, indicate that the BOLD signal primarily measures the input

and processing of neuronal information within a region and not the output signal transmitted to

other brain regions (Nikos K. Logothetis, 2002).

The MRI has optimized diagnostics and enabled us to monitor therapeutics, providing not only

clinically essential information but also insight into the basic mechanisms of brain function and

malfunction. Its recently developed functional variant, fMRI has had an similar impact in a

number of different research disciplines ranging from developmental biology to cognitive

psychology.In the neurosciences, imaging techniques are indispensable. Understanding how the

brain functions requires not only a comprehension of the physiological workings of its individual

elements, that is its neurons, but also demands a detailed map of its functional architecture and a

description of the connections between populations of neurons, the networks that underlying

behaviour. The neural origin of the BOLD contrast mechanism of fMRI will concentrate on the

application of MRI to the study on the emphasis to be placed on fMRI at high spatio-temporal

resolution and its combination with electrophysiological measurements (Heeger, D. J. & Ress,

D. 2002).

Frequency modulation (FM) is an acoustic feature of nearly all complex sounds. Directional FM

sweeps are especially pervasive in speech, music, animal vocalizations, and other natural sounds.

Although the existence of FM-selective cells in the auditory cortex of humans Using fMRI and

Multivariate Pattern Classification has been documented (Hui Hsieh.,2012). Multivariate

pattern analysis may be used to identify cortical selectivity for direction of a multitone FM

sweep. This method distinguishes one pattern of neural activity from another within the same

FM, even when overall level of activity is similar, allowing for direct identification of FM-

specialized networks. Standard contrast analysis showed that despite robust activity in auditory

cortex, no clusters of activity were associated with up versus down sweeps. Multivariate pattern

analysis classification, however, identified two brain regions as selective for FM direction, the

right primary auditory cortex on the supra-temporal plane and the left anterior region of the

superior temporal plane. directly demonstrating the existence of FM directional selectivity in the

human auditory cortex.

Stimulus repetition often leads to facilitated processing, resulting in neural decreases and faster

repetition. Such repetition-related effects have been accredited to the facilitation of repeated

cognitive processes and/or the retrieval of previously encoded stimulus–response bindings. The

spatial and temporal resolutions of fMRI and EEG has been respectively utilized to examine a

22

long-lag classification priming paradigm that required response repetitions or reversals at

multiple levels of response representation. A repetition effect has been observed in

occipital/temporal cortex (fMRI) where stimulus onset (EEG) was time-locked and strong to

switches in response, together with a repetition effect in (fMRI) ( Aidan J. Horner et al. , 2012).

The response-sensitive effect occurred even when changing from object names to object pictures

between repetitions, suggesting that stimulus–response bindings can code abstract

representations of stimuli. Most importantly, we found evidence for Interference effects of

response-sensitive bindings were retrieved with increased neural activity.

Participants in two fMRI experiments named pictures with superimposed disturbances that were

high or low in frequency or varied in terms of age of acquisition (Greig I. de Zubicar, 2012).

Pictures superimposed with low-frequency words were named more slowly by participants than

those superimposed with high-frequency words, and late-acquired words interfered with picture

naming to a greater extent than early-acquired words with self-monitoring system in picture–

word interference.

The fMRI studies have often used confidence ratings as an index of memory strength to

investigate potentially recognition memory responses. Confidence ratings correlated with

memory strength reflect sources of changeability in terms of applying decision criteria including

task-irrelevant item effects. The fMRI analyses of correct old responses on the basis of

subjective confidence ratings or estimates from single- versus dual-process recognition memory

models have been conducted. The effect of highlighting attention on spaced repetitions at study

has proven as enhanced recognition memory performance. The patterns of activity indicates that

fMRI signals associated with subjective confidence ratings reflect additional sources of

variability. The results are reliable with predictions of single-process models of identification

memory (Greig I. de Zubicaray et al., 2011).

Self-projection, the capacity to re-experience the personal past and to mentally infer another

person's perspective has been linked to be associated with inferences about one's own self. In the

fMRI studies it has been examined that self-projection using a novel camera technology, which

employs a sensor and timer to automatically take hundreds of photographs when worn, in order

to create dynamic visuo-spatial signals taken from a first-person perspective (Peggy L. St.

Jacques et al., 2011).This allowed to ask participants to self-project into the personal past or into

the life of another person. We predicted that self-projection to the personal past would elicit

greater activity in self-projection revealed task-related functional connectivity analysis to

contributed to the network linked to memory processes.

The functional magnetic resonance imaging studies have identified brain regions associated with

different forms of memory(Brass, M., Derrfuss,et al. 2005). Working memory has been

associated primarily with the bilateral prefrontal and parietal regions; semantic memory with the

23

left prefrontal and temporal regions; episodic memory encoding with the left prefrontal and

medial temporal regions; episodic memory retrieval with the right prefrontal, posterior midline

and medial temporal regions; and skill learning with the motor, parietal, and subcortical regions.

Recent studies (Cabeza, R.,et al. , 2000) have provided higher specificity, by dissociating the

neural correlates of different subcomponents of complex memory tasks, and the cognitive roles

of different subregions of larger brain areas.

The fMRI neuroimaging is obtained introspectively through memory recall. Consequently,

several confounding factors may guide the accuracy of fMRI reports including forgetting,

reconstruction mechanisms, verbal description of difficulties. The researchers must be well

aware of these limitations and should minimize them by using suitable strategies when collecting

or analyzing fMRI data. The modern brain imaging techniques have emerged as major tools to

better understand the neural mechanisms of cognitive consciousness, perceptual and emotional

characteristics with valuable and unique information about brain functions. The recent

neuroimaging studies, in particular functional MRI studies showed that it is now possible to

capture more transient, dynamic changes of brain activity with a high anatomical

resolution(Corbetta, M., & Shulman, G. L. 2002). While these advanced imaging methods will

undoubtedly contribute to redefining the links between brain processes and the varieties of

cognition experiences. Another important challenge for future studies will be to systematically

investigate changes in brain activity and mental content across all audio-visual cognitive states

and achieve a detailed characterization of the neural constraints affecting the daily oscillations of

human conscious experiences. Such an integrated framework for the study of human cognitive

process is necessary to accommodate the diversity and increasing sophistication of modern

neuroimaging research and to improve our understanding of the neuroanatomy and functions of

consciousness.

High-resolution maps of genome-wide gene expression have been available for mice for a few

years, but only relatively coarse equivalents have been published for the human brain because of

the challenges presented by the 1,000-fold increase in size and the limited availability and

quality of postmortem tissue. Now Michael Hawrylycz and colleagues at the Allen Institute for

Brain Science in Seattle, Washington, have used laser microdissection and microarrays to assess

900 precise subdivisions in brains from two healthy men with 60,000 gene-expression probes.

The resulting atlas, freely available at www.brain-map.org, allows comparisons between humans

and other animals, and will facilitate studies of human neurological and psychiatric behaviours.

Neuromarketing a subset of Neuroeconomics is a new highly promising approach to

understanding the neurobiology of decision making and how it affects cognitive social

interactions between humans and societies/economies. This book(Paul W. Glimcher et al,

2008)is the first edited reference to examine the science behind neuroeconomics/neuromarketing,

24

including how it influences human behavior and societal decision making from a behavioral

economics point of view. Presenting a truly interdisciplinary approach, the book presents

research from neuroscience, psychology , and behavioral economics, and includes chapters by all

the major figures in the field, including two Economics nobel laureates. Carefully edited for a

cohesive presentation of the material, the book is also a great textbook to be used in the many

newly emerging graduate courses on Neuroeconomics in Neuroscience, Psychology, and

Economics graduate schools This groundbreaking work is sure to become the standard reference

source for this growing area of research.

An fMRI Study of the Cognitive Regulation of Emotions, Thinking and Feelings

For mental and physical health the ability to cognitively regulate emotional responses to events is

important The functional magnetic resonance imaging is employed to examine the neural

systems used review scenes in unemotional terms with subjective experience. Neural correlates

of appraisal are increased activation of the lateral and medial prefrontal regions and decreased

activation of the amygdala which is a groups of nuclei located deep within the medial temporal

lobes of the brain in complex human vertebrates and medial orbito-frontal cortex (Jackson, D.

C.,et al. 2000). The prefrontal cortex is involved in constructing reappraisal strategies that can

modulate activity in multiple emotion processing systems.

Taking help of a vast array of coping skills humans are extraordinarily adaptable creatures who ,

can successfully manage situation in even the most trying of circumstances. Shakespeare’s

(1998/1623, p. 216) Hamlet observed, ‘‘there is nothing either good or bad, but thinking makes

it so’’ is one of the most remarkable of these skills. Hamlet’s message is clear. By changing the

way we think we can change the way we feel thereby lessening the emotional consequences of

otherwise worrying experience. The cognitive transformation of emotional experience is an

unpleasant stimulus in unemotional terms which reduces negative affect with few of the

physiological, cognitive, or social costs associated with other emotion-regulatory strategies,such

as the inhibition of emotion-expressive behavior (Jackson, D. C.,et al. 2000; Gross, J. J., 1998,

2002).The functional magnetic resonance imaging (fMRI) elucidates the neural bases of

reappraisal. The neural systems involved in the cognitive control of emotion would involve

processing dynamics similar to those in implicated in other forms of cognitive control.

Reappraisal involves reinterpreting the meaning of an emotional event; for example, creating an

alternative scenario or adopting a different attitude (Gross, 2002; Ochsner et al., 2004). It is the

basis of cognitive rehabilitation (Frewen et al., 2008), has been found to be more beneficial than

suppressing emotions (Ochsner et al., 2002) can be instructed or and varies widely across

individuals (Gross and John, 2003).Emotional responses are often quick adaptive responses

that help us successfully address challenges that arise in our environment . However, in some

context , otherwise adaptive emotional responses may be inappropriate because they are either

ill-timed or are of intensity for the particular situation at hand. Healthy adaption therefore

25

requires the ability to regulate our emotion. fMRI can assist in creating an emotion regulatory

networks (Kevin N. Ochsner et al., 2002)

The Neural Groundworks of Perception: The Real World and the fMRI Scanner

Our knowledge of the neural groundworks of perception is largely built upon studies employing

2-dimensional (2D) planar images.An effect commonly observed using 2D images having slow

event-related functional imaging in humans may be examined to show whether neural

populations have a characteristic repetition-related change in haemodynamic response for real-

world 3-dimensional (3D) objects. Surprisingly, however, repetition effects were weak, if not

absent on trials involving the 3D objects. These results suggest that The neural mechanisms

involved in processing real objects are distinct from those that arise when a2D representation of

the same items is met. These introductory results suggest the need for research to widen our

understanding of the neural mechanisms underlying human vision with ecologically valid stimuli

in the imaging designs (Jacqueline C. Snow,et al, 2011).

By almost all functional magnetic resonance imaging (fMRI) studies the 2-dimensional (2D)

pictures of objects of human neural substrates have been examined. We interact with real 3-

dimensional (3D) objects far more often than 2D representations even pictures are same in

everyday life since we have little difficulty in distinguishing between the two. By almost all

functional magnetic resonance imaging (fMRI) studies the 2-dimensional (2D) pictures of

objects of human neural substrates have been examined. We interact with real 3-dimensional

(3D) objects far more often than 2D representations even pictures are same in everyday life

since we have little difficulty in distinguishing between the two. Investigation with fMRI help us

examine whether real-world objects involving the large body of evidence pertaining to human

neural processing of pictorial stimuli. The processing of object shape in the brains of humans is

broadly distributed across a number of cortical areas spanning both the dorsal and ventral visual

pathways known as lateral occipital complex (LOC) (Kanwisher N. G. et al., 1996; Malach

R., et al. 1995).

By using the technique of functional magnetic resonance imaging. the stages of integration

leading from local feature analysis to object recognition were explored in human visual cortex as

evidence for object-related activation. Compared to a wide range of texture patterns LOC shows

preferential activation to images of objects. By using the technique of functional magnetic

resonance imaging. the stages of integration leading from local feature analysis to object

recognition were explored in human visual cortex as evidence for object-related activation.

Compared to a wide range of texture patterns LOC shows preferential activation to images of

objects. This activation was not caused by a global difference in The Fourier spatial frequency

content of objects versus texture images produce enhanced LOC activation compared to textures

matched in power spectra. A conspicuous demonstration that activity in LOC is uniquely

26

correlated to object detect ability in which digitized objects increase their recognizability

leading to significant enhancement of LOC activation (Konen C. S. & Kastner S, 2008). Thus,

objects varying extensively are activated y in their recognizability (e.g., famous faces, common

objects, and unfamiliar three-dimensional abstract sculptures) to a similar degree. These lead to

object recognition in human visual cortex resulting in showing s evidence for an intermediate

link in the chain of processing stages.

Neural coding within object-selective cortex beyond simple fMRI subtraction designs has been

investigated using comparisons between repeated vs. unrepeated objects ( Squire L. R., et

al.. 1992; Stern C. E., et al.. 1996; Buckner R. L., et al..1998: Wiggs C. L. & Martin

A.,1998). The characteristic reduction in haemodynamic response with stimulus repetition has

been variously referred is a robust effect in which neurons within infero-temporal cortex show

reduced firing rates as a result of stimulus repetition. Repetition designs have become a popular

methodological approach that contrast with standard mapping techniques in their ability to probe

neural selectivity in higher-order visual areas with traditional fMRI designs (Krekelberg B.,et

al., 2006). In the field of object perception, repetition designs have perhaps most commonly

been used to determine whether object-selective neural populations are response invariant to

image transformations such as changes in viewpoint size or elucidation (Grill-Spector K., et

al..1999).

An fMRI Trial Sequence f Stimulus Items

The choice of 2D stimuli to study object recognition has been largely one of expedient and

experimental control. A flat screen presentation of 2D images basically requires projection of the

images through a mirror while the participant can lie comfortably in the favourable position. The

control of image parameters (e.g., size, depth, timing) is straightforward Many additional

challenges arising in the presentation of real world 3D stimuli have been solved in 2D fMRI

research on grasping and reaching where 3D objects are required to bring forth normal object-

directed actions(Cavina-Pratesi C., et al.. 2010).

An fMR repetition pattern may be used to examine both the overall level of activation and

repetition-based effects in the framework of real-world 3D objects compared to 2D pictures. It is

expected to have clear activation and repetition effects within the areas identified across prior

studies for both motivated classes. However, the main question was how similar these effects

would be for 3D objects. It has been found that the overall level of activation as well as the

strength of repetition effects for the richer, real-world 3D objects are at least equal to, if not

greater than, those for 2D pictures (Ishai A., Ungerleider L. G., Martin A., Schouten J. L. &

Haxby J. V. , 1999). Neurophysiology research has characterized several areas with 3D object-

selective responses(Verhoef B. E., Vogels R. & Janssen P. , 2010) for which human

homologues have been proposed (Culham J. C. & Valyear K. F. ,2006). These areas are

postulated to be involved in the extraction of shape for visual transformations associated with

27

the control of action. The human anatomical structural areas show fMR-adaptation for studying

the functional properties of human cortical neurons (Grill-Spector K, Malach R 2001). Such

areas may be expected to show larger responses and stronger repetition effects in the context of

real-world objects.

An adaptation index (AI) which estimates response difference between Repeat and Different

conditions relative to the overall fMRI response to a given stimulus (Konen C. S. & Kastner S.,

2008) shows consistency of observation in fMRI BOLD response on each stimulus type and

region of interest (ROI). The slow event-related fMRI has been to show contrast repetition-

related changes in fMRI responses to 2D pictures of objects with real-world 3D exemplars.

Whereas presentation of 2D pictures elicited strong repetition-related changes in the BOLD

response (Schacter D. L., et al.. 1995)and within this area there has been marked variability

across participants in the relative magnitude of the BOLD response. In the ROI analyses

significant 2D repetition effects are observed and BOLD response patterns are highly consistent

across observers. Accordingly, whole-brain analyses revealed robust repetition effects for 2D

objects.

Neurophysiological studies have identified neurons that are sensitive to shapes defined by

binocular disparity within early visual area. In fMRI studies for stereo displays involving planar

shapes to show that responses within LOC are identical despite changes in the stereoscopic depth

of the shape showing equivalent BOLD responses depicting identical silhouette shapes where a

2D silhouette has followed by a stereo silhouette image (so that the shape appeared to lie in front

of the fixation plane). These findings imply that object shape is processed similarly within LOC,

whether the shape is depicted in a purely 2D format or with additional stereo cues (Mur M., et

al., 2010). Face recognition that requires distinguishable neuronal representations of individual

faces is a complex cognitive process. The functional magnetic resonance imaging (fMRI) studies

performed blood-oxygen level--dependent (BOLD) fMRI measurements using the ‘‘fMRI-

adaptation’’ technique has suggested the existence of face-identity representations in face-

selective regions. These results remind us that fMRI stimulus-change effects can have a range of

causes and do not provide conclusive evidence for a neuronal representation of the changed

stimulus property.

The stimulus objects in these studies simply have defined figure from ground and provided

information about the outer contours of the shape (i.e., first-order stereo). Unlike real objects,

they contained no information about inherent curvature or shape (i.e., second-order stereo).

Binocular perceptions of picture indicate that it is completely flat whereas monocular perceptions

such as shading, texture gradients, occlusion, spherical highlights, and other pictorial perceptions

signify a 3D representation. It is possible that classical repetition and release effects typically

observed in picture viewing may be attributable to processes associated with resolving such

depth perceptions conflict.

28

An additional processing is required to decipher object identity from 2D pictures. Results from

fMRI raise the stimulating suggestion that the presence of real-world objects invokes

qualitatively different computations to those illustrated by 2D images. Researchers in the field of

behavioral psychophysics have expressed long-standing concern about the extent to which

pictures of objects capture the properties of their real-world ecological validity as to their

appropriateness as stimuli with which to examine the nature of human object perception (Marr,

D., 1982). The images consist merely of patterns of light arising from a 2D projection surface of

real objects of physical substances with a definite texture, reflectance, colour and shape. An

object placed within arm's length affords reaching, grasping, and manipulation. Indeed, fMRI

studies demonstrate that information is critical for the visual control of grasping and

manipulation (Gallivan, J. P.et al., 2009).

The Voxel Analysis of Functional Magnetic Resonance Imaging (fMRI)

The analysis of functional magnetic resonance imaging (fMRI) data is a difficult procedure. The

large data sets are computationally difficult to control and special modeling techniques are

necessary to deal with sequential correlation. The statistical models used to analyze fMRI data

require many steps starting with raw data and ending with achievable results for evaluation

(Jeanette A. Mumford and Russell A. Poldrack, 2007) . Fortunately there are easy-to-use

software packages that allow users to input their data and choose certain modeling options

toconduct data analyses.The disadvantage of the data analysis ‘black box’ is that users are often

not aware why certain types of models are used and the purpose of different modeling options to

describe the model used to analyze group fMRI data.

The proper model for group fMRI data is a mixed model of the two-stage summary statistics

approach. A mixed model is necessary to extrapolate results beyond the study sample. The two-

stage summary statistics approach of this model reduces the computational burden of analyzing

the large volumes of data collected in fMRI studies. The different models are necessary under

each of these data collection topics. In order to understand the models and how they differ, it is

necessary to understand the two different effects that can be specified in a model: fixed and

random. When defining effects as being fixed or random one must consider how the data were

collected, what inferences are of interest and to which population inferences will be applied.

Under the incorrect data collection description, a fixed effects model is used to carry out

inference on the overall mean opinion change fixed effect and gives a mean estimate In the case

of group fMRI data, the data for a single volumetric pixel (voxel) consist of time series from

multiple subjects, where each time series is a group of data specific to a particular subject. Each

point in an fMRI time series is not randomly selected from a random subject, but an entire time

series is selected from random subjects. The distributions of fMRI time series between subjects

can be very different, with some subjects activating more and/or having more variability in their

29

signal than others. Since the goal of most fMRI studies is to apply the inferences beyond the

study sample, a mixed effects model accounting for between- and within-subject variability, is

the appropriate model used on group fMRI data having small estimated variances. The

characteristic mixed model used by statisticians to analyze multiple time series from multiple

subjects is a one-stage all-in-one approach that includes all subjects’ data concurrently (Verbeke

and Molenberghs, 2000). This type of model is computationally difficult to use on fMRI data,

which consist of time series in excess of 100 time points for each of 100 000 or more voxels. To

apply the mixed model to a voxel of data, the all-in-one model is broken up into two stages of

modeling known as the two-stage summary statistics model (Holmes and Friston, 1998).

In the case of fMRI data it is attempted to obtain subject specific signal size parameters and

within-subject variance. fMRI time series are very noisy, with noise contributions from the

subject (cardiac, respiratory noise, head motion, etc.) as well as the scanner.The objective is to

create a model that captures both the noise structure and the fMRI signal. fMRI data analysis

software offers many options to deal with these complications including highpass filters, lowpass

filters and correlation estimation (or whitening) to model or reduce the noise and hemodynamic

response function convolution to improve the model of the fMRI signal. Classical hemodynamic

monitoring is based on the invasive measurement of systemic, pulmonary arterial and venous

pressures, and of cardiac output. In reality, since the fMRI signal is a measurement of

hemodynamic change, there is a delay and the response to an event and the model must reflect

this. The noise of the time series starts with the low frequency drift which appears in the time

series as a downward trend over time. The highpass filter is designed to reduce this type of noise

by passing the high frequency noise and reducing the low frequency noise. Software packages

handle this issue different ways.Specific group fMRI modeling assumptions of different software

packages and how they differ are available (Mumford and Nichols ,2006). Since fMRI data

consist of over 100 000 time series that can each be at least 100 time points long, data are

analyzed in a voxel-wise fashion and the mixed model is broken into two stages, where single

subjects are analyzed at the first level and group analyses are carried out at the second level.

Convolution and highpass filtering tend to improve the fit of the model. Modeling the positive

correlation of fMRI data reduces bias in the variance estimates (Nichols and Hayasaka ,2003).

References

Morgan, Robert M. and Shelby D. Hunt (1994), “The Commitment–Trust Theory of

Relationship Marketing,” Journalof Marketing, 58 (July), 20–38.

Pinker, Steve (1997), How the Mind Works. New York: W.W.Norton.

Ramachandran, Vilayanur (2004), A Brief Tour of Human Consciousness. New York: PI Press.

30

Shiv, Baba, Antoine Bechara, Irwin Levin, Joseph W. Alba, James R. Bettman, Laurette Dube, et

al. (2005), “Decision Neuroscience,” Marketing Letters, 16 (3–4), 375–86.

Singer, Tania and Ernst Fehr (2005), “The Neuroeconomics of Mind Reading and Empathy,”

Neuroscientific Foundations of Economic Decision-Making, 95 (2), 340–45.

Soldow, Gary F. and Gloria P. Thomas (1984), “Relational Communication: Form Versus

Content in the Sales Interaction,” Journal of Marketing, 48 (October), 84–93.

Sujan, Harish (1999), “Optimism and Street-Smarts: Identifying and Improving Salesperson

Intelligence,” Journal of Personal Selling & Sales Management, 19 (3), 17–33.

Marketing Week, London (2005), "Neuromarketing: brain scam or valuable tool?," in Marketing

Week Vol. February 3, 2005.

Zak, Paul J. (2004), "Neuroeconomics," Phil. Trans. R. Soc. Lond. B, 359, 1737-48.

Karmarkar, Uma R. "Note on Neuromarketing." Harvard Business School Note 512-031,2012.

Karmarkar, Uma R., and Zakary L. Tormala. "Believe Me, I Have No Idea What I Am Talking

About: The Effects of Source Certainty on Consumer Involvement and Persuasion." Journal of

Consumer Research 36, no. 6 (2010): 1033-1049.

1954 Olds, J., and P. Milner. Positive reinforcement produced by electrical stimulation of septal

area and other regions of rat brain. J. Comp. Physiol. Psychol. 47:419-27.

1972 Olds, J., Disterhoft, J. F., Segal, M., Kornblith, C. L., and Hirsh, R.: Learning centres of rat

brain mapped by measuring latencies of conditioned unit responses. J. Neurophysiol. 35 202-19.

Olds, J. Drives and Reinforcements: Behavioral Studies of Hypothalamic Functions. New York:

Raven Press, 1977.

Nunez PL, Srinivasan R (1981). Electric fields of the brain: The neurophysics of EEG. Oxford

University Press

Niedermeyer E. and da Silva F.L. (2004). Electroencephalography: Basic Principles, Clinical

Applications, and Related Fields. Lippincot Williams & Wilkins.ISBN 0-7817-5126-8.

Hamalainen, M., Riitta, H., Ilmoniemi, R., Knuutila, J., & Lounasmaa, O. (1993).

Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of

the working human brain. Reviews of Modern Physics, 65(2), 414-497.

Adam L. Penenberg, NeuroFocus Uses Neuromarketing to Hack Your Brain, Fast Company

Magazine , AUGUST 8, 2011

31

Carmen Nobel, What Neuroscience Tells Us About Consumer Desire, Published: March 26,

2012, Harvard Business School | Working Knowledge.

Michael Harré, Terry Bossomaier & Allan Snyder, et al.,

The Perceptual Cues that Reshape Expert Reasoning Scientific eports 2, doi:10.1038/srep00502,

11 July 2012

Kahneman, D. A perspective on judgment and choice: Mapping bounded rationality. Am

Psychol 58,697 (2003).

Turk-Browne, N., Scholl, B., Chun, M. & Johnson, M. Neural evidence of statistical learning:

efficient detection of visual regularities without awareness. J Cognitive Neurosci 21, 1934–

1945 (2009).

Ericsson, K. & Lehmann, A. Expert and exceptional performance: Evidence of maximal

adaptation to task constraints. Ann Rev Psychol 47, 273–305 (1996).

Ericsson, K., Krampe, R. & Tesch-Römer, C. The role of deliberate practice in the acquisition of

expert performance. Psychol Rev 100, 363 (1993).

Kellman, P. & Garrigan, P. Perceptual learning and human expertise. Phys Life Rev 6, 53–

84 (2009).

Arroyo, S., Lesser, R.P., Gordon, B., Uematsu, S., Jackson, D., Webber, R., 1993. Functional

significance of the mu rhythm of human cortex: an electrophysiologic study with subdural

electrodes. Electroencephalogr.Clin. Neurophysiol. 87, 76– 87.

Spitzer, N.C. (2012) Activity-dependent neurotransmitter respecification. Nature Reviews

Neurosci. 13: 94-106.

Spitzer, N.C. (2006). Electrical activity in early neuronal development. Nature 444:707-712.

Borodinsky, L.N. and Spitzer, N.C. (2007). Activity-dependent neurotransmitter-receptor

matching at the neuromuscular junction. Proc. Natl. Acad. Sci. 104:335-340.

Yarkoni T, Poldrack RA, Van Essen DC, Wager TD., Cognitive neuroscience 2.0: building a

cumulative science of human brain function. Trends Cogn Sci. 2010 Nov;14(11):489-96. Epub

2010 Sep 29.

Kober H, Wager TD. Meta-analysis of neuroimaging data. Wiley Interdisc Rev Cogn.

Sci.2010;1:293–300.

32

Biswal B, et al. Toward discovery science of human brain function. Proc Natl Acad Sci U S

A.2010;107:4734–4739.

Gardner D, et al. The neuroscience information framework: a data and knowledge environment

for neuroscience. Neuroinformatics. 2008;6:149–160.

Luo X, et al. Neuroimaging informatics tools and resources clearinghouse (NITRC) resource

announcement. Neuroinformatics. 2009;7:55–56.

Van Horn JD, et al. The Functional Magnetic Resonance Imaging Data Center (fMRIDC): the

challenges and rewards of largeñscale databasing of neuroimaging studies. Philos Trans R Soc

Lond, Ser B: Biol Sci. 2001;356:1323.

Wager TD, et al. Elements of functional neuroimaging. In: Cacioppo JT, et al.,

editors. Handbook of Psychophysiology. 3rd. 2007. pp. 19–55.

Poldrack RA. Can cognitive processes be inferred from neuroimaging data. Trends Cogn

Sci.2006;10:59–63.

Van Essen D. Windows on the brain: the emerging role of atlases and databases in

neuroscience.Curr Opin Neurobiol. 2002;12:574–579.

Elias, L. J, & Saucier, D. M. (2005). Neuropsychology: Clinical and Experimental Foundations.

Boston: Pearson

Ramón y Cajal, Santiago (1899). Comparative Study of the Sensory Areas of the Human Cortex.

Bentivoglio, M. 1998. Life and discoveries of Santiago Ramón y Cajal Nobelprize.org. Retrieved

February 2, 2008

Kazunari Miyamichi, Fernando Amat, Farshid Moussavi, Chen Wang, Ian Wickersham,

Nicholas R. Wall, Hiroki Taniguchi, Bosiljka Tasic, Z. Josh Huang, Zhigang He, Edward M.

Callaway, Mark A. Horowitz & Liqun Luo, Cortical representations of olfactory input by trans-

synaptic tracing, ature, 472, 191–196 ,(14 April 2011) ,doi:10.1038/nature09714

Peter Lakatos, Ankoor S. Shah, Kevin H. Knuth, Istvan Ulbert, George Karmos,and Charles E.

Schroeder, An Oscillatory Hierarchy Controlling Neuronal Excitability and Stimulus Processing

in the Auditory Cortex, J Neurophysiol 94: 1904–1911, 2005.First published May 18, 2005;

doi:10.1152/jn.00263.2005

Buzsaki G and Draguhn A. Neuronal oscillations in cortical networks.Science 304: 1926–1929,

2004.

33

Deisseroth K. Optogenetics and psychiatry: applications, challenges, and opportunities. Biol

Psychiatry. Jun 15, 2012;71(12):1030-2.

Deisseroth K, Feng G, Majewska A, Ting AE, Miesenböck G, Schnitzer MJ (2006): Next-

generation optical technologies for illuminating genetically-targeted brain circuits. J Neurosci

26:10380 –10386.

Deisseroth K (2011): Optogenetics. Nat Methods 8:26 –29.

Arieli, A., Sterkin, A., & Aertsen, A. (1996). Dynamics of ongoing activity: Explanation of the

large variability in evoked cortical responses. Science, 273:1868.

Kenet T, Bibitchkov D, Tsodyks M, Grinvald A, ArieliA(2003) Spontaneously emerging cortical

representations of visual attributes. Nature

425: 954-956.

Arieli A (1992) Novel strategies to unravel mechanisms of cortical function: From Macro- to

Micro-electrophysiological recordings. In: Information Processing in the Cortex (Ed. A. Aertsen

and V. Braitenberg). Springer-Verlag, Berlin-Heidelberg-New York, p. 123-138

Singh N and Theunissen FE, Modular spectra of natural sounds and ethological theories of

auditory processing , Journal of the Acoustical Society of America 114:3394-3411.

Yizhar O, Fenno LE, Davidson TJ, Mogri M, Deisseroth K (2011): Optogenetics

in neural systems. Neuron 71:9 –34.

Ulbert I, Halgren E, Heit G, and Karmos G. Multiple microelectroderecording system for human

intracortical applications. J Neurosci Methods, 106: 69–79, 2001.

Freeman WJ and Rogers LJ. Fine temporal resolution of analytic phase reveals episodic

synchronization by state transitions in gamma EEGs.J Neurophysiol 87: 937–945, 2002.

Huber, D., Gutnisky, D.A., Peron, S., O'Connor, D.H., Wiegert, J.S., Tian, L., Oertner, T.G.,

Looger, L.L., Svoboda, K. (2012) Multiple dynamic representations in the motor cortex during

sensorimotor learning. Nature 484, 473-481.

Svoboda, K. (2011). The past, present, and future of single neuron

reconstruction. Neuroinformatics 9:97-8

Fiser, J., Chiu, C., & Weliky, M. (2004). Small modulation of ongoing cortical dynamics by

sensory input during natural vision. Nature, 431: 573-578.

Berkes P, Orbán G, Lengyel M, Fiser J. Spontaneous cortical activity reveals hallmarks of an

optimal internal model of the environment.Science. 2011 Jan 7;331(6013):83-7.

34

Fiser, J., Bex, P.J., & Makous, W.L. (2003). Contrast conservation in human vision. Vision

Research, 43: 2637-2648

Fu KM, Shah AS, O’Connell MN, McGinnis T, Eckholdt H, Lakatos P,Smiley J, and Schroeder

CE. Timing and laminar profile of eye-position effects on auditory responses in primate auditory

cortex. J Neurophysiol 92:3522–3531, 2004.

Bertrand O and Tallon-Baudry C. Oscillatory gamma activity in humans: a possible role for

object representation. Int J Psychophysiol 38: 211–223,2000.

Engel AK, Fries P, and Singer W. Dynamic predictions: oscillations and synchrony in top-down

processing. Nat Rev Neurosci 2: 704–716, 2001.

Singer W and Gray CM. Visual feature integration and the temporal correlation hypothesis. Annu

Rev Neurosci 18: 555–586, 1995

Buzsaki G and Draguhn A. Neuronal oscillations in cortical networks.Science 304: 1926–1929,

2004.

Bishop G. Cyclic changes in excitability of the optic pathway of the rabbit. Am J Physiol 103:

213–224, 1933.

Sanchez-Vives MV and McCormick DA. Cellular and network mechanisms of rhythmic

recurrent activity in neocortex. Nat Neurosci 3: 1027–1034,2000.

Chrobak JJ and Buzsaki G. Gamma oscillations in the entorhinal cortex of the freely behaving

rat. J Neurosci 18: 388–398, 1998.

Kahana MJ, Seelig D, and Madsen JR. Theta returns. Curr Opin Neurobiol11: 739–744, 2001.

Makeig S, Debener S, Onton J, and Delorme A. Mining event-related brain dynamics. Trends

Cogn Sci 8: 204–210, 2004.

Massimini M, Rosanova M, and Mariotti M. EEG slow (approximately 1Hz) waves are

associated with nonstationarity of thalamo-cortical sensory processing in the sleeping human. J

Neurophysiol 89: 1205–1213, 2003.

Worden MS, Foxe JJ, Wang N, and Simpson GV. Anticipatory biasing of visuospatial attention

indexed by retinotopically specific alpha-band electroencephalography increases over occipital

cortex. J Neurosci 20: RC63,2000.

Kingston, R E and Tomkin J W, 2006, Transcriptional regulation by Trithorax group proteins in

Epigenetics (edited by C. D. Allis, T Jenuwein and D. Reinberg) Cold Spring Harhor Laboratory

Press, University of California, Santa Cruz

35

Pollard KS, Salama SR, Lambert N, Lambot MA, Coppens S, Pedersen JS, Katzman S, King B,

Onodera C, Siepel A, Kern AD, Dehay C, Igel H, Ares M Jr, Vanderhaeghen P, Haussler D

(2006-08-16). "An RNA gene expressed during cortical development evolved rapidly in

humans". Nature 443 (7108): 167–172. doi:10.1038/nature05113

K.S. Pollard (2009). What makes us human? Scientific American, May: 44-49

K.S. Pollard, S. Dudoit, M.J. van der Laan (2005). Multiple Testing Procedures: R multtest

Package and Applications to Genomics., in Bioinformatics and Computational Biology Solutions

Using R and Bioconductor, R. Gentleman, V. Carey, W. Huber, R. Irizarry, S. Dudoit (Editors).

Springer (Statistics for Biology and Health Series), pp. 251-272.

Imamura F, Ayoub AE, Rakic P, Greer CA. Timing of neurogenesis is a determinant of olfactory

circuitry. Nat Neurosci. 2011 Mar;14(3):331-7.

Dominguez MH, Rakic P. Language evolution: The importance of being human.Nature. 2009

Nov 12;462(7270):169-70.

Fries P, Reynolds JH,Rorie AE, and Desimone R. Modulation of oscillatory neuronal

synchronization by selective visual attention. Science 291: 1560–1563, 2001

Tzingounis AV, Nicoll RA. Arc/Arg3.1: linking gene expression to synaptic plasticity and

memory. Neuron. 2006 Nov 9; 52(3):403-7.

Nicoll RA, Alger BE. The brain's own marijuana. Sci Am. 2004 Dec; 291(6):68-75.

Tomita S, Chen L, Kawasaki Y, Petralia RS, Wenthold RJ, Nicoll RA, Bredt DS. Functional

studies and distribution define a family of transmembrane AMPA receptor regulatory proteins. J

Cell Biol. 2003 May 26; 161(4):805-16.

Wilson RI, Nicoll RA. Endocannabinoid signaling in the brain. Science. 2002 Apr 26;

296(5568):678-82.

Milstein AD, Nicoll RA., TARP modulation of synaptic AMPA receptor trafficking and gating

depends on multiple intracellular domains. Proc Natl Acad Sci U S A. 2009 Jul 7;

106(27):11348-51.

Pastalkova, E., Serrano, P., Pinkhasova, D., Wallace, E., Fenton, A. A., and Sacktor, T. C.

(2006). Storage of spatial information by the maintenance mechanism of LTP. Science 313,

1141-1144.

Anders S. Kristensen1 & Stephen F. Traynelis ,Neuroscience: An intrusive chaperone,

Nature 435, 1042-1043 (23 June 2005) | doi:10.1038/4351042a

36

Arnsten, A.F.T., Molecular pharmacology and the treatment of Tourette’s Syndrome and

Attention Deficit-Hyperactivity Disorder. In Neuroscience, Molecular Medicine and the

Therapeutic Transformation of Neurology, S.G. Waxman, Ed.. Academic Press, pp. 183-192,

2004

Southwick, S., Rasmusson, A., Barron, X., Arnsten, A.F.T., Neurobiological and neurocognitive

alterations in PTSD inNeuropsychology of PTSD: Biological, Cognitive and Clinical

Perspectives J.J. Vasterling and C.R. Brewin, eds. Guilford Publications, NY, pp. 27-58, 2005

Gerald Maurice Edelman, Neural Darwinism: The Theory of Neuronal Group Selection (Basic

Books, New York 1987). ISBN 0-19-286089-5

Gerald Maurice Edelman,The Remembered Present: A Biological Theory of

Consciousness (Basic Books, New York 1990). ISBN 0-465-06910-X

Gerald Maurice Edelman,Bright Air, Brilliant Fire: On the Matter of the Mind (Basic Books,

1992, Reprint edition 1993). ISBN 0-465-00764-3

Gerald Maurice Edelman,The Brain, Edelman and Jean-Pierre Changeux, editors, (Transaction

Publishers, 2000). ISBN 0-7658-0717-3

Gerald Maurice Edelman, Giulio Tononi,A Universe of Consciousness: How Matter Becomes

Imagination (Basic Books, 2000, Reprint edition 2001). ISBN 0-465-01377-5

Gerald Maurice Edelman,Wider than the Sky: The Phenomenal Gift of Consciousness (Yale

Univ. Press 2004) ISBN 0-300-10229-1

Gerald Maurice Edelman,Second Nature: Brain Science and Human Knowledge (Yale

University Press 2006) ISBN 0-300-12039-7.

Crick F, Koch C (1990) Towards a neurobiological theory of consciousness. Sem

Neurosci 2:263-275.

Crick F, Koch C (1995a) Are we aware of neural activity in primary visual

cortex? Nature 375:121-123.

Crick F, Koch C (1995b) Cortical areas in visual awareness - Reply. Nature 377:294-295.

Crick F, Koch C (1995c) Why neuroscience may be able to explain consciousness. Sci

Am 273:84-85.

Koch C. and Hepp K. (2006) Quantum mechanics and higher brain functions: Lessons from

quantum computation and neurobiology. Nature 440: 611–2

37

Rees G. and Frith C. (2007) Methodologies for identifying the neural correlates of

consciousness. In: The Blackwell Companion to Consciousness. Velmans M and Schneider S,

eds., pp. 553–66. Blackwell: Oxford, UK.

Kanwisher, N., & Wojciulik, E. (2000). Visual attention:Insights from brain imaging. Nature

Reviews Neuroscience, 1, 91–100.

Leopold, D. A., Wilke, M., Maier, A., & Logothetis, N. K. (2002). Stable perception of visually

ambiguous patterns.Nature Neuroscience, 5, 506–609.

Mitchell, J. F., Stoner, G. R., & Reynolds, J. H. (2004). Object-based attention determines

dominance in binocular rivalry. Nature, 429, 410–413.

Moore, T., & Armstrong, K. M. (2003). Selective gating of visual signals by microstimulation of

frontal cortex. Nature, 421, 370–373

Sabine Windmann, , Michaela Wehrmann, and et al. Role of the Prefrontal Cortex in Attentional

Control over Bistable Vision, Journal of Cognitive Neuroscience , March 2006, Vol. 18, No. 3,,

pp. 456–471

Laurey, S.; Tononi, G. (2009). The Neurology of Consciousness: Cognitive Neuroscience and

Neuropathology. Academic Press.

Windmann Sabine, Wehrmann Michaela, Calabrese Pasquale and Güntürkün Onur, Role of the

Prefrontal Cortex in Attentional Control over Bistable Vision, Journal of Cognitive Neuroscience

March 2006, Vol. 18, No. 3, Pages 456-471

Fangmeier Thomas, Knauff Markus, Ruff Christian C. Sloutsky Vladimir, fMRI Evidence for a

Three-Stage Model of Deductive Reasoning, Journal of Cognitive Neuroscience, March 2006,

Vol. 18, No. 3, Pages 320-334.

Mendola J. D., Conner I. P., Sharma S., Bahekar A. and Lemieux S., fMRI Measures of

Perceptual Filling-in in the Human Visual Cortex, Journal of Cognitive Neuroscience ,March

2006, Vol. 18, No. 3, Pages 363-375

Forstmann Birte U., Brass Marcel, Koch Iring, von Cramon D. Yves, Voluntary Selection of

Task Sets Revealed by Functional Magnetic Resonance Imaging, Journal of Cognitive

Neuroscience, March 2006, Vol. 18, No. 3, Pages 388-398

38

Poldrack RA, Kittur A, Kalar D, Miller E, Seppa C, Gil Y, Parker DS, Sabb FW and Bilder RM

(2011) The cognitive atlas: toward a knowledge foundation for cognitive neuroscience. Front.

Neuroinform. 5:17.

Thorsten Plewan, Ralph Weidner, Simon B. Eickhoff, and Gereon R. Fink, Ventral and Dorsal

Stream Interactions during the Perception of the Müller-Lyer Illusion: Evidence Derived from

fMRI and Dynamic Causal Modeling, October 2012, Vol. 24, No. 10, Pages 2015-2029, Journal

of Cognitive Neuroscience.

Lauren L. Emberson and Dima Amso, 2012, Learning to Sample: Eye Tracking and fMRI

Indices of Changes in Object Perception, Journal of Cognitive Neuroscience, October 2012, Vol.

24, No. 10, Pages 2030-2042

Susanne Karch, Tobias Thalmeier, Jürgen Lutz, Anja Cerovecki,Markus Opgen-Rhein, Bettina

Hock, Gregor Leicht, Kristina Hennig-Fast, Thomas Meindl, Michael Riedel, Christoph

Mulert, Oliver Pogarell. (2010) Neural correlates (ERP/fMRI) of voluntary selection in adult

ADHD patients. European Archives of Psychiatry and Clinical Neuroscience 260:5, 427-440

Online publication date: 1-Aug-2010.

Per F. Nordmark, J. Andrew Pruszynski, and Roland S. Johansson, BOLD Responses to Tactile

Stimuli in Visual and Auditory Cortex Depend on the Frequency Content of Stimulation, Journal

of Cognitive Neuroscience, October 2012, Vol. 24, No. 10, Pages 2120-2134

Matthew F S Rushworth, Timothy E J Behrens. (2008) Choice, uncertainty and value in

prefrontal and cingulate cortex. Nature Neuroscience 11:4, 389-397 ,Online publication date: 1-

Apr-2008.

Nikos K. Logothetis, The neural basis of blood-oxygen-level-dependent functional magnetic

resonance Imaging signal, Phil. Trans. R. Soc. Lond. B , DOI 10.1098/rstb.2002.1 114 ,

01TB020E.1-35

Logothetis, N. K., Pauls, J., Augath, M., Trinath, T. & Oelter-mann, A. 2001 Neurophysiological

investigation of the basis of the fMRI signal, Nature 412, 150-157.

Heeger, D. J. & Ress, D. 2002 What does fMRI tell us about neuronal activity? Nature Rev.

Neurosci. 3, 142-151

Hui Hsieh, Paul Fillmore, Feng Rong, Gregory Hickok, and Kourosh Saberi, FM-selective

Networks in Human Auditory Cortex Revealed Using fMRI and Multivariate Pattern

Classification, Journal of Cognitive Neuroscience, September 2012, Vol. 24, No. 9, Pages 1896-

1907

39

Aidan J. Horner and Richard N. Henson, Incongruent Abstract Stimulus–Response Bindings

Result in Response Interference: fMRI and EEG Evidence from Visual Object Classification

Priming, Journal of Cognitive Neuroscience, February 2012, Vol. 24, No. 2, Pages 482-495

Greig I. de Zubicara, Michele Miozzo, Kori Johns, Niels O. Schille, and Katie L. McMahon,

Independent Distractor Frequency and Age-of-Acquisition Effects inPicture–Word Interference:

fMRI Evidence for Post-lexical and Lexical Accounts according to Distractor Type, Journal of

Cognitive Neuroscience February 2012, Vol. 24, No. 2, Pages 482-495.

Greig I. de Zubicaray, Katie L. McMahon, Simon Dennis and John C. Dunn, Memory Strength

Effects in fMRI Studies: A Matter of Confidence, Journal of Cognitive Neuroscience, February

2012, Vol. 24, No. 2, Pages 482-495

Peggy L. St. Jacques, Martin A. Conway, Matthew W. Lowder, and Roberto Cabeza, Watching

My Mind Unfold versus Yours: An fMRI Study Using a Novel Camera Technology to Examine

Neural Differences in Self-projection of Self versus Other Perspectives, Journal of Cognitive

Neuroscience, June 2011, Vol. 23, No. 6, Pages 1275-1284

Cabeza, R., Nyberg, L, Neural bases of learning and memory: Functional neuroimaging

evidence (Review ) , Current Opinion in Neurology, Volume 13, Issue 4, 2000, Pages 415-

421.

Brass, M., Derrfuss, J., Forstmann, B., & von Cramon, D. Y. (2005). The role of the inferior

frontal junction area in cognitive control. Trends in Cognitive Sciences, 9(7), 314–316.

Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in

the brain. Nature Reviews Neuroscience, 3(3), 201–215.

Paul W. Glimcher (Editor), Ernst Fehr (Editor), Colin Camerer (Editor), Russell Alan

Poldrack (Editor), Neuroeconomics: Decision Making and the Brain Academic Press, October 1,

2008 | ISBN-10: 0123741769 | ISBN-13: 978-0123741769 | Edition: 1

Jacqueline C. Snow, Charles E. Pettypiece, Teresa D. McAdam, Adam D. McLean,Patrick W.

Stroman, + et al.,Bringing the real world into the fMRI scanner: Repetition effects for

pictures versus real objects ,Scientific Reports 1, Nature Publishing Group,

doi:10.1038/srep00130, 26 October 2011.

Kanwisher N. G., Chun M. M., McDermott J. & Ledden P. J. Functional imaging of human

visual recognition. Cogn. Brain Res. 5, 55–67 (1996).

40

Konen C. S. & Kastner S. Two hierarchically organized neural systems for object information

in human visual cortex. Nat. Neurosci. 11, 224–231 (2008).

Schacter D. L., et al.. Brain regions associated with retrieval of structurally coherent visual

information. Nature 376, 587–590 (1995).

Amedi A., Jacobson G., Hendler T., Malach R. & Zohary E. Convergence of visual and tactile

shape processing in the human lateral occipital complex. Cereb. Cortex 12, 1202–1212 (2002).

Mur M., Ruff D. A., Bodurka J., Bandettini P. A. & Kriegeskorte N. Face-identity change

activation outside the face system: "release from adaptation" may not always indicate neuronal

selectivity. Cereb. Cortex 20, 2027–2042 (2010).

Malach R., et al.. Object-related activity revealed by functional magnetic resonance imaging

in human occipital cortex. Proc. Natl. Acad. Sci. USA 92, 8135–8139 (1995).

Konen C. S. & Kastner S. Two hierarchically organized neural systems for object information in

human visual cortex. Nat. Neurosci. 11, 224–231 (2008).

Squire L. R., et al.. Activation of the hippocampus in normal humans: a functional anatomical

study of memory. Proc. Natl. Acad. Sci. USA 89, 1837–1841 (1992).

Stern C. E., et al.. The hippocampal formation participates in novel picture encoding: evidence

from functional magnetic resonance imaging. Proc. Natl. Acad. Sci. USA 93, 8660–8665

(1996).

Buckner R. L., et al.. Functional-anatomic correlates of object priming in humans revealed by

rapid presentation event-related fMRI. Neuron 20, 285–296 (1998).

Wiggs C. L. & Martin A. Properties and mechanisms of perceptual priming. Current

Opinion in Neurobiology, 8, 227–233 (1998).

Krekelberg B., Boynton G. M. & van Wezel R. J. A. Adaptation: from single cells to BOLD

signals.Trends Neurosci. 29, 250–256 (2006).

41

Grill-Spector K., et al.. Differential processing of objects under various viewing conditions in the

human lateral occipital complex. Neuron 24, 187–203 (1999).

Cavina-Pratesi C., et al.. Functional magnetic resonance imaging reveals the neural

substrates of arm transport and grip formation in reach-to-grasp actions in humans. J.

Neurosci. 30, 10306–10323 (2010).

Ishai A., Ungerleider L. G., Martin A., Schouten J. L. & Haxby J. V. Distributed

representation of objects in the human ventral visual pathway. Proc. Natl. Acad. Sci.

U.S.A. 96, 9379–9384 (1999).

Verhoef B. E., Vogels R. & Janssen P. Contribution of inferior temporal and posterior

parietal activity to three-dimensional shape perception. Curr. Biol. 20, 909–913 (2010).

Culham J. C. & Valyear K. F. Human parietal cortex in action. Curr. Opin.

Neurobiol. 16, 205–212 (2006).

Grill-Spector K, Malach R (2001) fMR-adaptation: a tool for studying the functional

properties of human cortical neurons. Acta Psychol (Amst) 107:293-321.

Silver M. A., Ress D. & Heeger D. J. Topographic maps of visual spatial attention in

human parietal cortex. J. Neurophysiol. 94, 1358–1371 (2005).

Marsolek C. J. , et al.. Identifying objects impairs knowledge of other objects: a relearning

explanation for the neural repetition effect. Neuroimage 49, 1919–1932 (2010).

Marr, D. Vision (Freeman, San Francisco, 1982).

Gallivan, J. P., Cavina-Pratesi, C. & Culham, J. C. Is that within reach? fMRI reveals that the

human superior parieto-occipital cortex encodes objects reachable by the hand. J.

Neurosci. 29, 4381–4391(2009).

Hiraoka, K., Suzuki, K., Hirayama, K. & Mori, E. Visual agnosia for line drawings and

silhouettes without apparent impairment of real-object recognition: a case report. Behav.

Neurol. 21, 187–192(2009).

42

Bushong, B., King, L. M., Camerer, C. F. & Rangel, A. Pavlovian Processes in Consumer

Choice: The Physical Presence of a Good Increases Willingness-to-Pay. Am. Econ.

Rev. 100, 1556–1571(2010).

Vinberg, J. & Grill-Spector, K. Representation of shapes, edges, and surfaces across multiple

cues in the human visual cortex. J. Neurophysiol. 99, 1380–1393 (2008).

Sawamura, H., Orban, G. A. & Vogels, R. Selectivity of neuronal adaptation does not match

response selectivity: a single-cell study of the FMRI adaptation paradigm. Neuron 49, 307–

318 (2006).

Proffitt, D. R. An Action Specific Approach to Spatial Perception. in Embodiment, Ego-Space,

and Action (ed. Klatzky R. L., , MacWhinney B., & Behrmann M., eds. ) 177–201 (Psychology

Press, New York, 2008).

Jackson, D. C., Malmstadt, J. R., Larson, C. L., & Davidson, R. J. (2000). Suppression and

enhancement of emotional responses to unpleasant pictures. Psychophysiology, 37, 515–522.

Gross, J. J. (1998). Antecedent- and response-focused emotion regulation: Divergent

consequences for experience,expression, and physiology. Journal of Personality and

Social Psychology, 74, 224–237.

Gross, J. J. (2002). Emotion regulation: Affective, cognitive, and social consequences.

Psychophysiology, 9, 281–291.

Shakespeare,W. (1998/1623). The Oxford Shakespeare:Hamlet. New York: Oxford University

Press.

H. Lee et al. Amygdala–prefrontal coupling underlies individual differences in emotion

regulation, NeuroImage 62 (2012) 1575–1581

Ochsner, K.N., Bunge, S.A., Gross, J.J., Gabrieli, J.D.E., 2002. Rethinking feelings: an

fMRI study of the cognitive regulation of emotion. J. Cogn. Neurosci. 14 (8),

1215–1229.

Ochsner, K.N., Ray, R.D., Cooper, J.C., Robertson, E.R., Chopra, S., Gabrieli, J.D.E., Gross,

J.J., 2004. For better or for worse: neural systems supporting the cognitive downand

up-regulation of negative emotion. NeuroImage 23 (2), 483–499.

Frewen, P.A., Dozois, D.J.A., Lanius, R.A., 2008. Neuroimaging studies of psychological

interventions for mood and anxiety disorders: empirical and methodological review.

Clin. Psychol. Rev. 28 (2), 228–246.

Gross, J.J., John, O.P., 2003. Individual differences in two emotion regulation processes:

43

implications for affect, relationships, and well-being. J. Pers. Soc. Psychol. 85 (2),

348–362.

Kevin N. Ochsner, Silvia A. Bunge, James J. Gross, and John D. E. Gabrieli, , Rethinking

Feelings: An fMRI Study of the Cognitive Regulation of Emotion, 2002 Massachusetts Institute

of Technology Journal of Cognitive Neuroscience 14:8, pp. 1215–1229

Jeanette A. Mumford and Russell A. Poldrack, Modeling group fMRI data, Oxford Journals

Medicine Social Cognitive & Affective NeurosciVolume 2, Issue 3, 2007,Pp. 251-257

Verbeke, G., Molenberghs, G. (2000). Linear Mixed Models for Longitudinal Data. New York:

Springer Verlag.

Holmes, A., Friston, K. (1998). Generalisability, random effects and population inference.

Neuroimage, 7, S754.

Mumford, J., Nichols, T. (2006). Modeling and inference of multisubject fMRI data: using

mixed-effects analysis for joint analysis. IEEE Engineering in Medicine and Biology Magazine,

25(2), 42–51.

Nichols, T.E., Hayasaka, S. (2003). Controlling the familywise error rate in functional

neuroimaging: a comparative review. Statistical Methods in Medical Research, 12(5), 419–46.