FROM COGNITIVE SCIENCE TO COGNITIVE NEUROSCIENCE TO NEUROECONOMICS

Economics and Philosophy

24

(2008) 459–471

C

©

Cambridge University Press

doi:10.1017/S0266267108002083

FROM COGNITIVE SCIENCE TO

COGNITIVE NEUROSCIENCE TO

NEUROECONOMICS

TEVEN

R. Q

UARTZ

California Institute of Technology

As an emerging discipline, neuroeconomics faces considerable method-

ological and practical challenges. In this paper, I suggest that these

challenges can be understood by exploring the similarities and dissimilarities

between the emergence of neuroeconomics and the emergence of cognitive

and computational neuroscience two decades ago. From these parallels,

I suggest the major challenge facing theory formation in the neural

and behavioural sciences is that of being under-constrained by data,

making a detailed understanding of physical implementation necessary

for theory construction in neuroeconomics. Rather than following a top-

down strategy, neuroeconomists should be pragmatic in the use of available

data from animal models, information regarding neural pathways and

projections, computational models of neural function, functional imaging

and behavioural data. By providing convergent evidence across multiple

levels of organization, neuroeconomics will have its most promising

prospects of success.

Many neuroscientists incorporating economic theory and methods

into their research would find the intuitions behind Stanley Jevons’s

attempt of 1879 to root economics in the materialist psychophysiology

of his day surprisingly familiar (Jevons 1871, Maas 2005). Based on

psychophysical attempts to discover quantitative laws of sensation,

Jevons’s unflinchingly materialist programme of a “mechanics of utility

and self-interest” bear a striking resemblance to the same research

traditions that led to both modern neuroscience and to the contemporary

project of discovering the neural mechanisms underlying economic

behaviour. It may not be too great an overstatement to note that in one

direction an application of the Fechner–Weber law led to the notion of

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diminishing marginal utility while in the other direction it led to modern

neuroscience in the union of visual psychophysics and neurophysiology

(Kuffler 1953, Hubel and Wiesel 1959). Whatever intriguing parallels there

may have been between the historical roots of neoclassical economics

and modern neuroscience, however, these fundamentally diverged when

economics took its “Paretian turn” (Bruni and Sugden 2007). Pareto’s

attempt to eliminate all psychological notions from the foundation of

economics, replacing laws of sensation with ones regarding abstract choice

or “logical action”, later completed by Hicks, Samuelson and others in

the theory of revealed preference, ultimately made economics a separate,

autonomous and thus supposedly irreducible domain of inquiry.

As this volume illustrates, a century and a quarter after Jevons’s

Theory of Political Economy

, a nascent neuroeconomics is seeking to re-

ground economic behaviour in the current-best materialist understanding

of human behaviour, cognitive neuroscience. Given the structure of

neoclassical economics and its core methodological commitments, it

is not surprising that this project has generated considerable debate,

commentary and critique. While many of these stem from the question of

how neuroeconomics stands in relation to neoclassical economics, others

are the inevitable consequence of new interdisciplinary enterprises, as

distinct disciplinary practices, methods, and standards come into contact

and require integration.

The debates and controversies neuroeconomics has sparked strike me

as reminiscent of those in an earlier episode in the neural and behavioural

sciences, one that occurred approximately 20 years ago. In what follows,

I want to explore the similarities – and dissimilarities – between this

intellectual episode and the emergence of neuroeconomics, as a sort of

historical case study, which I believe can shed light on the current state of

neuroeconomics, its prospects and its challenges.

THE EMERGENCE OF COGNITIVE NEUROSCIENCE

The episode I have in mind is the emergence of cognitive and

computational neuroscience from cognitive science, beginning in the early

to mid-1980s. Before doing so, it is necessary to recap the broadest

contours of cognitive science and its methodological commitments. The

foundations of cognitive science were formed in the 1950s as cognitive

psychologists (e.g. George Miller, Jerome Bruner), artificial intelligence and

computer scientists (e.g. John McCarthy, Marvin Minsky) and linguists

(e.g. Noam Chomsky) reacted against behaviourism by positing that

the mind was a physical symbol processor. According to this view,

the mind’s operations could be characterized at an abstract level of

description, the semantic and algorithmic, which in turn could map

onto multiple physical implementations, later codified as a functionalist

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approach to mind (Putnam 1975). So construed, the semantic level of

description, under which cognition was understood as the manipulation

of symbols with propositional content according to the principle of

rationality, was an autonomous level of description (Pylyshyn 1984).

The reason for the autonomy of this level of description was in the

claim that there were generalizations at the semantic level (in terms of

propositional attitudes such as beliefs) for which there was no unitary

physical-level description. As a consequence, cognitive science was

argued to be irreducible to the physical sciences. In particular, since the

semantic level of description could be implemented in different substrates,

neural-level evidence seemed largely irrelevant to the explanation of

mind.

While cognitive science was a productive research programme, it was

not without limitations, some of which in retrospect were fundamental.

For example, its research strategy of mapping cognitive tasks onto

computational models proved to be severely under-constrained. For

any set of behavioural data (e.g. a working memory task) there were

many behaviourally equivalent computational models. The problem of

model selection was compounded by the fact that the entire class of

models being utilized incorporated unrealistic assumptions, such as

unbounded resources, which in turn were justified on the basis of

distinctions, such as performance/competence in linguistics, that struck

many as problematic. By the mid-1980s, this functionalist cognitive science

began to be challenged by a group of researchers in connectionism or

parallel distributed processing (Rumelhart and McClelland 1986). These

researchers challenged the autonomy of cognitive science from the physical

sciences, and particularly cognitive science’s central claim that the level

of implementation was irrelevant to the explanation of cognition and

behaviour. In particular, connectionism embraced a model of computation

that had its roots in the perceptron linear classifiers of Rosenblatt (1958)

and others. Whereas neural network research had been unpopular due to

the computational limitations (Minsky and Papert 1969) explored for the

case of Rosenblatt’s single-layer perceptrons (and incorrectly conjectured

would hold for more complex networks), the discovery, or re-discovery

as the history is complex (Werbos 1994), of the back-propagation learning

algorithm created tremendous excitement over the prospect of ‘brain-like’

computation.

Beginning around 1986, there was a dramatic realignment of research

in the neural and behavioural sciences. The reasons for this are complex,

but a few key points are worth highlighting here. First, neural computation

provided important explanatory links between the high-level symbolic

explanations of cognitive psychology and the low-level, mechanistic

explanations of neuroscience. Thus, psychologists working in areas as

diverse as visual psychophysics, memory, and learning were drawn to

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these modelling efforts as a way to constrain explanation. This led,

ultimately, to cognitive neuroscience, which has in a remarkably short

time become the standard approach to cognition (Gazzaniga 2004). In

the other direction, neuroscience had amassed a tremendous amount of

data regarding single neurons, from the dynamics of single ion channels

and receptors using patch clamp techniques to the level of receptive

field properties, but had few techniques or methods to integrate this

information into theories of neural circuit function, large-scale theories

of brain function, or even detailed models of single-neuron processing,

such as dendritic integration. Computational modelling offered a way to

integrate this information into such large-scale theories, and was thus

rapidly integrated into neuroscience, in fact, creating a new sub-field of

computational or theoretical neuroscience (Dayan and Abbott 2001).

THE CRITIQUE FROM WITHIN: NEOCLASSICAL ECONOMICS’

REACTION TO NEUROECONOMICS

As an outsider looking into the debate within economics, many of the

debates between what I will refer to loosely as mainstream economics

and the proponents of neuroeconomics strike me as deeply reminiscent

of those between cognitive science and connectionism. For example, the

proponents of “mindless” economics (Gul and Pesendorfer 2008) charge

neuroeconomists with making a category mistake by supposing that neural

evidence could in principle be relevant to economics, as they “address

different types of empirical evidence”.

A similar argument was made by cognitive scientists against the

relevance of neural evidence by claiming that neural and symbolic levels

of description were autonomous, so any attempt to constrain one level

of description by evidence from another amounted to a conceptual

confusion (Pylyshyn 1984). As a graduate student during the 1980s

working on the relationship between cognitive and neural development, I

can recall psychologists looking quizzically at my suggestion that cognitive

processes described as learning could participate in the construction of

the neural circuits underlying that learning (Quartz and Sejnowski 1997).

One pointed out that the suggestion was a non-starter, as Chomsky had

stipulated an a priori “stationarity” principle making this impossible.

Many of the claims made by proponents of mindless economics regarding

the autonomy of economics have the same flavour of the Chomskyian

whose commitments to model-theoretic principles are so severe that they

rule out entire domains of potential empirical support or disconfirmation.

Indeed, it is hard to otherwise understand how Gul and Pesendorfer

(2008) could make the claim that “neuroscience evidence cannot refute

economic models because the latter make no assumptions and draw no

conclusions about the physiology of the brain” other than by invoking

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a strong autonomy thesis. While economic theory may make no

explicit

assumptions about the physiology of the brain, it is not the case that

it makes no predictions that could either be confirmed or disconfirmed

by neuroscience. For example, financial decision theory specifies the

minimal parameters necessary for rational choice under uncertainty,

expected reward and risk as variance of reward (Markowitz 1952).

While financial decision theory makes no explicit predictions regarding

neural correlates of these parameters, it does by implication make the

testable prediction that neural activity will correlate separately with

these two parameters given the appropriate task. A possible rejoinder

is the claim that the theory does not make such a prediction since

the function of theory is not to postulate terms that may or may not

correspond to entities in the world, but rather to provide

as if

explanations

whose sufficiency is determined by instrumental considerations, such

as predictive capacity (Friedman 1953). While a consideration of realist

vs. instrumentalist conceptions of economic methodology is beyond the

scope of this paper (Lagueux 1994), it is worth noting that instrumentalist

interpretations of the symbolic level of cognitive science (Dennett 1987)

are generally regarded as failed programmes, which may lack internal

coherence (Baker 1989). Furthermore, while an instrumentalist may deny

that a correspondence (or lack thereof) between theoretical terms and

real-world entities is the proper measure of a theory, the issue of

such correspondence is nonetheless still an empirical one that may be

tested.

In a recent study, we investigated this issue and found that brain

activation in human striatum correlated with reward and risk and were

differentiated both spatially and temporally and arose in the absence of

learning, motivation or salience confounds (Preuschoff

et al.

2006). To us, it

was striking that the implicit predictions of financial decision theory were

confirmed in neural activity. There was no a priori reason why this should

be the case, and such a confirmation strengthens the theory beyond that

provided by an

as if

instrumentalism.

The more general point I want to make is that the autonomy thesis of

cognitive science proved to be a major barrier to progress in that field.

It created a methodological isolationism within cognitive science that

resulted in its failure to incorporate neuroscience into its approach, even

though the case for such integration should have been clear. The result was

not only that its theories remained highly under-constrained and it lacked

a means to adjudicate among multiple equivalent theories, but it created an

intellectual inflexibility that ultimately led to its obsolescence. If there is a

cautionary tale for mindless economics contained in this historical episode

it is that strong autonomy claims often lead to methodological isolationism

and inflexibility, particularly in the face of growing methodological

alternatives.

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It may be worth noting that one reason for this methodological

isolationism is often the perception that the integration of evidence

from another field will be primarily disconfirming. Connectionists

presented their models primarily in this disconfirming mode. In particular,

connectionists argued that the syntactic transformations that cognitive

science supposed were the core operations of a symbol system (a finite

set of syntactic transformation rules applied to a finite set of symbols

is essentially the definition of a grammar and language of thought,

respectively) would be eliminated in a connectionist scheme (Rumelhart

and McClelland 1986). While this claim may have been an important

part of the revolutionary fervor of connectionism, in retrospect it was a

relatively minor element of the programme with increasing links between

connectionist models and semantic levels of representation (Rogers and

McClelland 2004). Had cognitive science been more methodologically

pragmatic, it likely could have absorbed these new developments rather

than being displaced. Much neuroeconomics is likewise presented as

primarily disconfirming to mainstream economics. Whether this will in

fact be the case remains to be seen.

NEUROECONOMICS WITHIN NEUROSCIENCE

As the lead papers illustrate, a major driving force in the formation

of neuroeconomics has been by behavioural economists looking to

neuroscience to inform and constrain economic theory. In some respects,

this accords with a similar trajectory of cognitive psychologists toward

integrating neuroscience over the last few decades, who increasingly

looked to neural constraints on behavioural data. However, another

driving force that is relatively unexplored in the lead papers is in the

other direction, namely from neuroscientists, who are eager to incorporate

portions of economic theory into neuroscience. In many respects, the

integration of economics into neuroscience is a natural extension of

the incorporation of computational models into neuroscience in the late

1980s. In particular, a result of that integration was a major shift in how

neuroscientists think about the brain and the organization of behaviour

more generally. In cognitive science, the dominant view of the mind was

as an abstract symbol processor, whose state transformations coincided

with cognition as exemplified in such capacities as language production

and problem-solving. The more basic view that minds/brains evolved

as systems to navigate environments according to satisfying needs, that

is, to find reward and avoid punishment, was marginalized in cognitive

science. This was probably due to the fact that cognitive science grew

out of a rejection of behaviourism, as exemplified in Chomsky’s review

of Skinner’s

Verbal Behavior

(Chomsky 1959). To this day, discussions

of reward learning and conditioning are still often met by cognitive

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465

psychologists suspiciously as attempts to reintroduce behaviourist notions

that had, according to the view, been dismissed long ago.

Within neuroscience, however, by the early 1990s computational

accounts of reinforcement learning provided the theoretical insights

necessary to develop a novel approach to the function of a key neural

system that until then had been intensively studied but poorly understood.

Specifically, the midbrain dopamine system, a collection of small clusters

of cells that project widely to the cortex, had long been implicated in

reward, motivated behaviour and addiction, but the specific functional

role of this system remained a mystery (Koob and Swerdlow 1988). In

part, this was due to the fact that, unlike many other neural systems,

the projections of this system were diffuse and non-specific, making it

problematic to know how such a system could be involved in anything

more than altering a relatively global feature of neural processing, such as

gain. The temporal difference model of reinforcement learning required

such a signal to broadcast a valuation signal for reward learning (Sutton

and Barto 1998). As this model was applied to the mammalian midbrain

dopamine system and to homologous systems in insects, it demonstrated

that these reinforcement learning algorithms were computationally more

powerful than behaviourist ones, bringing about an important shift in

thinking about the centrality of reward processing to neural systems

(Montague

et al.

1995, 1996). This was central to a rapid growth of

interest among neuroscientists more generally in the area of neural

valuation.

The interest in valuation also led to a growing interest among

neuroscientists in the role of emotion in neural processing, as emotions

are typically regarded in neuroscience as reflective of reward processing,

which in turn led to the growing appreciation of the role of emotions

in decision-making. This was seen most strikingly in the neuroscience

literature of the time in neurological patients whose impairment in

emotional processing was accompanied by major changes in their decision-

making and social interaction, which helped spawn a growing social

neuroscience (Damasio 1994). As this work progressed, however, it

became apparent to many neuroscientists that neuroscience lacked well-

developed methods to probe reward processing, decision-making and

social interaction. Hence, neuroscientists increasingly looked to economics,

not only for quantitative models of decision-making under uncertainty but

also for quantitative models of social interaction, particularly behavioural

game theory (Sanfey

et al.

2003; King-Casas

et al.

2005).

The influx of neuroscientists into neuroeconomics is a major

driving force behind its growth and may well represent the majority

of researchers who regard themselves as engaged in neuroeconomic

research. It is interesting to note that out of cognitive science grew

two overlapping but somewhat distinct fields: from the top-down grew

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cognitive neuroscience while from the bottom-up grew computational

neuroscience. The distinctions between these fields lie in differing research

agendas, an ordering of major open questions, increasing specialization,

along with sufficiency conditions on theory formation. It remains an open

question what disciplinary form neuroeconomics will take, whether it will

become an entity distinct from economics and neuroscience departments

(as was the case with cognitive science), whether it will fragment into

specializations within economics and neuroscience, or whether it will

take some other form. In this regard, it is worth noting that the goals

of neuroscientists within neuroeconomics are distinct from those of

economists for a variety of reasons, which may order their research agenda

in ways that diverge from their economic collaborators and may shape the

ultimate form neuroeconomics takes. For example, whereas economists

within neuroeconomics face the economic critique that their work is too

reductive, from the perspective of neuroscience it may not be reductive

enough, particularly work at the functional imaging level. For this reason,

many neuroscientists are eager to make links with neurophysiology

(some neuroeconomics labs currently employ both neurophysiology and

fMRI) or other lower-level methods in an effort to uncover underlying

neural mechanisms. A likely consequence of this is that the number of

animal models of economic behaviour will grow. While some economic

collaborators may embrace these developments, it may also sharpen

the external criticism of neuroeconomics as it moves further away from

traditional focuses. Relatedly, a research priority for many neuroscientists

will be to apply neuroeconomic methods to clinical populations, such

as subjects with bipolar disorder, schizophrenia, and autism. Indeed, the

methods of neuroeconomics promise to shed important light on these

illnesses and disorders, and are currently being utilized among these

populations (Paulus 2007). These research priorities may not correspond

to those of economists.

For these and other reasons, neuroscientists will likely be less

concerned than their economics colleagues with the question, “is it good

economics?” For neuroscientists, their interests will lie in whether the use

of economic models and tools helps them make progress as measured by

neuroscientific standards, whether neuroscientific journals publish their

findings, and whether neuroscientific funding sources fund their research.

They may be less moved by the concern that the research meets the

expectations of economics, and this divergence may create impediments

to interdisciplinary collaboration.

IS NEUROECONOMICS A SUCCESS?

The early days of cognitive neuroscience and computational neuroscience

were, in retrospect, replete with non-starters, overly ambitious agendas,

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and unfulfilled promissory notes. It would, however, have been extremely

premature to have judged the success or failure of either new discipline

based on those early days. Likewise, it strikes me as extremely premature

to judge neuroeconomics on the basis of its current results. Rather, the

lessons of cognitive neuroscience and computational neuroscience, both

of which are unquestionably extremely productive enterprises today, is

that new interdisciplinary enterprises require a gestation period in which

investigators learn the practices, standards and vocabulary of disciplines,

and likely most importantly young investigators and graduate students

are trained in both.

It is likely that these disciplinary differences account for Harrison’s

charge that the lack of publicly available fMRI data is a ‘dark secret’

of the field. Neuroscience, like many other disciplines such as physics,

has not traditionally made raw experimental data publicly available

or, for that matter, generally available to researchers within the field.

My own speculation is that this tradition in neuroscience grew out of

neurophysiology, in which a single experiment often took years of often

heroic effort to complete, particularly if it involved primate behaviour.

Rather, like the standard in most other experimental fields, replication was

at the level of experimental parameters, not data-sharing. There have been,

however, ‘neuroinformatic’ attempts to make raw fMRI data available,

such as the fMRI Data Center (http://www.fmridc.org/f/fmridc).

Whether these are actually useful datasets is, I think, an open question,

as raw fMRI data typically requires minute details of the experimental

parameters to be useful. There is also the additional complication that to

release fMRI data would likely require a revision of standard informed

consent protocols authorizing its release even in anonymized form, as the

relationship between fMRI data and medical information continues to be

a major issue (such as incidental findings).

THE ROLE OF FMRI IN NEUROECONOMICS

There is no doubt that functional magnetic resonance imaging has played

a prominent role in the emergence of neuroeconomics. It is worth noting,

however, that in a 1988 perspective on cognitive neuroscience, Churchland

and Sejnowski’s widely reproduced figure of the spatial and temporal

resolution of the experimental techniques of cognitive neuroscience did

not include fMRI (and has been subsequently updated) (Churchland

and Sejnowski 1988). In other words, cognitive neuroscience emerged

prior to fMRI and relied instead on computational links across levels of

organization. fMRI has become a workhorse of cognitive neuroscience

because it represents the best tradeoff in terms of spatial and temporal

resolution of the (limited) non-invasive probes of human brain function.

Prior to its development, there was virtually no non-invasive probe

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for studying the human brain (electroencephalogy was available, but

had extremely poor spatial resolution). In the absence of such non-

invasive human probes, neuroscience relied on animal model systems,

which proved enormously successful for investigating such areas as the

cellular basis of learning, but for many key human domains there are

no corresponding animal models (e.g. language, strategic interaction

involving theory of mind). While fMRI has rapidly developed, it

nonetheless has basic limitations, including the temporal properties of

the haemodynamic response, its relation to underlying physiological

events, and limited spatial resolution. For these reasons, neuroscientists

typically regard it as one to be utilized as a convergent technique alongside

evidence from other neural sources, including data from animals models

and computational accounts (whereas fMRI has generated a great deal

of enthusiasm outside neuroscience, among many neuroscientists it is

considered an extremely limited tool). Increasingly, as neuroeconomics

research progresses, it will need to integrate these convergent methods

alongside fMRI, as indeed is already occurring (O’Doherty

et al.

2006).

IS NEUROECONOMICS A RADICAL NEW THEORY OR AN

INCREMENTAL CHANGE?

Harrison’s interesting discussion of what constitutes an economic agent

in relation to the work of Don Ross (Ross 2005), and whether this

notion remains unitary, incorporates a dual process theory, or something

else raises an important set of issues. The explanatory vocabulary of

cognitive neuroscience, and by extension, neuroeconomics, is couched

firmly in the familiar language of “folk psychology”, whereby human

behaviour is explained in terms of intentional states, such as propositional

attitudes, and (largely) semantically coherent transitions among those

states (confirming to a principle of rationality). However, it was always

the suggestion of cognitive neuroscience that a more complete theory

may radically revise this framework. There are some strong indications

that this framework is already under pressure, as traditional categories

such as reason and emotion appear not to map onto neural systems in

an unproblematic manner, and our functional interpretation of neural

structures remains highly simplistic (Pessoa 2008). Further, it is important

to bear in mind that while neuroscience has made remarkable advances,

many of its most fundamental questions remain unanswered. To mention

just a few, the information-processing capacity of single neurons, the

nature of the neural code (how neurons code/decode information), how

information is integrated within small neighbourhoods of neurons, to say

nothing of long-range communication, remain fundamental questions.

As progress is made on these fundamental issues within neuroscience,

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469

it is likely that they will necessitate large-scale revision of higher-level

theories.

WHAT METHODOLOGICAL DIRECTION SHOULD

NEUROECONOMICS TAKE?

Going back to Jevons and Pareto, although both sought to establish a very

different foundation for economics, they both shared deep methodological

commitments. In particular, both adopted the common methodological

principles of their day, John Stuart Mill’s concrete deductive method,

whereby from a small set of obvious laws (Jevons’s example is that a greater

gain is preferred to a smaller one) one may deduce complex economic

phenomena. These methodological commitments are clearly in evidence

throughout the axiomatic approach of neoclassical economics.

Neuroeconomics clearly requires a distinct methodology as an

experimental discipline and one in which there as yet appears to be

few organizing principles around which neuroscience can be organized.

Harrison, based on a discussion of (Glimcher 2004), considers the

possibility that neuroeconomics adopt the top-down methodology

David Marr (1982) developed, whereby a problem is decomposed

along three distinct levels at which an information processing system

must be understood, the computational, the algorithmic, and the

implementational. These three levels also correspond to a methodology,

whereby a computational level precedes the algorithmic, and so on. Marr’s

methodology, however, grew out of many of the core commitments of

cognitive science (indeed, Marr cites Chomsky’s (1959) theory of universal

grammar as paradigmatic of the computational level). In retrospect, Marr’s

proposed methodology turned out to be a failure even within his own

area of vision (Churchland

et al.

1994). From the computational level, it

seemed obvious that the computational goal of vision is to provide an

accurate internal representation of the external world. This, combined

with Marr’s modular decomposition of a task into discrete sub-tasks

(combined with Fodor’s claim that modules operate in a bottom-up

fashion), led Marr to the notion that the goal of vision was to produce

what he called a 2

- D sketch through a hierarchically organized

series of computations decomposed into sub-problems (such as shape

from shading, motion, etc.), that were subsequently integrated, and then

interfaced with visual cognition. This not only mistook the ecological goals

of vision (whereby some visual circuits bypass cortical centres altogether

and project directly to spinal cord indicating the goal of vision is not simply

to replicate the visual world, but to link visual information with adaptive

action such as predator evasion), but also neglected the non-hierarchical

nature of visual information-processing, and the non-modular nature

of cortical information-processing (Sporns and Zwi 2004). As a matter

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of discovery, then, a detailed understanding of the underlying physical

implementation is necessary for theory construction in neuroeconomics.

For these reasons, neuroeconomists should be pragmatic in the use

of available data from animal models, information regarding neural

pathways and projections, computational models of neural function,

functional imaging, and behavioural data. As cognitive science illustrates,

the major challenge facing theory formation in the neural and behavioural

sciences is that of being under-constrained by data, and it is precisely

in the capacity of cognitive neuroscience to provide convergent evidence

across multiple levels of organization that neuroeconomics has the most

promising prospects.

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