Do Objects Look Different After We Learn Their Value?

On the table in front of you is two bottles of vodka: one bottle is Greygoose Vodka and the other, Tesco-branded Vodka. It doesn’t take a seasoned drinker to quickly realise that the bottle of Greygoose is more ‘valuable’ of the two. And if given a free choice between the two, a sensible person should choose the option that is more valuable. Though this value attribution and choice seems simple enough to implement using our supercomputers – our brains – it is significantly harder to rigorously pin down the actual working of neural pathways when we look under the hood.

In particular, cognitive neuroscientists, economists and psychologists are immensely interested in how neural signals in our brains are associated with choice values. Further, how are objects, such as the vodka bottles, actually represented in the brain? The sheer multidimensional coding of the object in the brain is astounding – for instance, there are spatial dimensions, such as shape and colour, but also abstract dimensions, such as monetary worth, cultural significance and social norms, that are coded in the brain (Roe et al., 2012; Drucker, Kerr, & Aguirre, 2009). Where and how this happens in the brain is one of the key research questions in Neuroeconomics.

There is a prevailing view that the decisions we make in the brain can be broken into two stages – first objects are assigned value and then comparisons give rise to choice (Kable & Glimcher, 2009). This would suggest that we can study these two stages in isolation. Here, we look at the first stage (Lebreton et al., 2009).

Research already tells us that when we initially look at an object, neural pathways indicate that there is learning of basic visual dimensions as well as relational associations with more abstract dimensions (Folstein et al., 2012; Connolly et al., 2012). One abstract dimension that is of particular interest to economists, and generally greedy souls such as myself, is the monetary value dimension. An interesting question to consider is this: will an object’s representation in the brain change if it’s associated monetary value changes? And if yes, can this occur even when attention is diverted? Persichetti, Aguirre and Thompson-Schill (2015) highlight the motivation set out above and design an experiment to answer this question.

Using functional magnetic resonance imaging we are able to approximate neural activity in different brain regions. As you can imagine, the wiring and activity in the brain is immensely complex and subject to all sorts of interference. Previous studies that have studied visual value coding have used objects such as the vodka bottles described above – the neural representation of such objects is subject to disturbances by cultural significance or familiarity (classy people drink whiskey, don’t you know?; Rangel, Camerer & Montague, 2008).

To sidestep this problem, this study used novel, moon-like shaped objects that varied on two visual dimensions – shape and colour. The objects also varied in terms of monetary worth and in a way that ensured shape of an object was not a predictor of worth. Also, objects were assigned both positive and negative money values to allow greater scope of investigation – since fMRI experiments don’t come cheap, it was important to get maximum efficiency with least confounds.

Participants’ brains were scanned before and after a training phase where they learnt to associate value with these objects. When tested, thankfully all participants showed that they had learnt the relative value of these novel objects. The main task of interest, however, had nothing to do with monetary value. Participants were asked to decide whether more or less of an object lay on one side of an angled line – a spatial task. This design allowed the researchers to study the neural adaptation of learning the value of objects while performing an unrelated task. Specifically, if the response of neurons in the visual cortex is altered by learning the value of the objects.

The results the study found were both interesting and furthered previous finding. The Early Visual Cortex showed neural adaptation to shape both before and after training – this was expected – as was the fact that there was no neural adaptation to value before training (shape and value were not correlated), but did show adaptation after training. Further, the traditional areas of the brain those are associated with executive functions and store value (LOC, DMPFC, VMPFC) showed adaptation to shape but curiously, not to value.

The latter finding could be a by-product of the main task-relevant dimension being shape and not value. These frontal cortex regions are known to be able to code contextually relevant processes against competing alternatives (Chadick, Zanto & Gazzaley, 2014). The finding, along with the primary EVC finding, suggest that value encoding may happen early in visual processing and the frontal brain regions are only used by the brain when confronted with having to make a choice.

It does also make logical sense that the visual sensory system plays a critical role in the valuation process – this could result in faster reaction times in situations of great urgency (Hsieh, Vul & Kanwisher, 2010). Another curious finding of this study was that perhaps the way in which value is encoded in the EVC is dissimilar to the way it is encoded in the frontal regions of the brain.

Ultimately, this study increases our understanding of the way some parts of the brain contribute to the valuation process. More crucially, it shows us that we visually code the objects all around us based on how we value them – with each person potentially seeing the world completely differently. Though still somewhat mysterious, with each passing day our understanding of the brain significantly improves. So when next faced with a choice between Greygoose and Tesco Vodka, you will have a deeper appreciation for what is superficially an ‘obvious’ valuation and a ‘simple’ choice.

Author: Nikhil Ravichandar

Focus Paper: Persichetti, A. S., Aguirre, G. K., & Thompson-Schill, S. L. (2015). Value is the in the eye of the beholder: Early Visual Cortex codes monetary value of objects during a diverted attention task. Journal of Cognitive Neuroscience, 27:5, 893-901.

Chadick, J. Z., Zanto, T. P., & Gazzaley, A. (2014). Structural and functional differences in medial prefrontal cortex underlie distractibility and suppression deficits in ageing. Nature Communications, 5, 4223.

Connolly, A. C., Guntupalli, J. S., Gors, J., Hanke, M., Halchenko, Y. O., Wu, Y.-C., et al. (2012). The representation of biological classes in the human brain. The Journal of Neuroscience, 32, 2608–2618.

Drucker, D. M., Kerr, W. T., & Aguirre, G. K. (2009). Distinguishing conjoint and independent neural tuning for stimulus features with fMRI adaptation. Journal of Neurophysiology, 101, 3310–3324.

Folstein, J. R., Palmeri, T. J., & Gauthier, I. (2012). Category learning increases discriminability of relevant object dimensions in visual cortex. Cerebral Cortex, 23, 814–823.

Hsieh, P.J., Vul, E., & Kanwisher, N. (2010). Recognition alters the spatial pattern of fMRI activation in early retinotopic cortex. Journal of Neurophysiology, 103, 1501–1507.

Kable, J. W., & Glimcher, P. W. (2009). The neurobiology of decision: Consensus and controversy. Neuron, 63, 733–745.

Lebreton, M., Jorge, S., Michel, V., Thirion, B., & Pessiglione, M. (2009). An automatic valuation system in the human brain: Evidence from functional neuroimaging. Neuron, 64, 431–439.

Persichetti, A. S., Aguirre, G. K., & Thompson-Schill, S. L. (2015). Value is the in the eye of the beholder: Early Visual Cortex codes monetary value of objects during a diverted attention task. Journal of Cognitive Neuroscience, 27:5, 893-901.

Rangel, A., Camerer, C., & Montague, P. R. (2008). A framework for studying the neurobiology of value-based decision making. Nature Reviews Neuroscience, 9, 545–556.

Roe, A. W., Chelazzi, L., Connor, C. E., Conway, B. R., Fujita, I., Gallant, J. L., et al. (2012). Toward a unified theory of visual area V4. Neuron, 74, 12–29.

Reinterpreting the Past or Forecasting the Future?

Is the field of economics better at reinterpreting the events of the past  than it is at forecasting the future? Or is it just that we focus on the wrong problems, such as looking at ways to reduce debt when we should be focussing on reducing income inequality. Sure, media and dirty politics make it difficult to take optimal decisions. But is there a way to combat this or reduce its effect? Is state intervention the key that will pull the world out of the ‘patrimonial capitalism’ it seems to be sinking in?
Watch Thomas Piketty, Paul Krugman and Joseph Stiglitz in “The Genius of Economics”, discuss some crucial economic issues.

Keep Calm and Estimate.

Econometrics Projects are due in just over 2 weeks and your brains are probably flooded with data, estimation issues and diagnostic tests! But fear not, we’re here to help. W.E.E has compiled a list of useful resources, which will help you get through the pain. We’ve searched websites, blogs, Youtube and the library, to assemble a list of resources that communicate important econometric concepts and practices, in a simple and concise way. Enjoy!

Youtube it!

STATAcorp LP: Lots of useful videos on how to use STATA


Sayed Hossain: Slower paced guided videos. Videos available for STATA and EVIEWS.

Burkey Academy: Theory and Practical examples of OLS, Panel data models and Fixed Effects.

Ben Lambert: A simple Khan Academy style approach to Econometric theory. Great for intuition! Mostly basic approach but some helpful stuff

Websites and Blogs

Mostly Harmless Econometrics: blog to accompany the book.

Econometrics by Simulation: PHD student demonstrates application of STATA and R.

Econometrics Sense: exactly what is says on the tin!

Talkstats: Online Forum for all things Econometrics and Statistics

Books (all available at the Library/Online)

“A Guide to Modern Econometrics “ by Marno Verbeek
Offers more concise and simplified explanations of Econometric concepts.

“A New Introduction to Multiple Time Series Analysis” by Helmut Lutkepohl.
A more advanced approach to Time Series Econometric theory.

“Microeconometrics Using Stata” by Colin Cameron and Pravin Trivedi
“Introduction to Time Series Using Stata” by Sean Becketti
Both books offer applied examples of time series analysis using STATA. Theory, STATA and examples all in one place, what more could you want?

Homegrown: Warwick University resources

Jeremy Smith: The father of Econometrics at Warwick. He has a range of great resources to ease your STATA woes!



Econometrics 2: Final year Econometrics module, goes back to the basics.Includes lecture notes and useful STATA code from class exercises.

(a) Econometrics 2: Microeconometrics:

(b) Econometrics 2: Time Series

Microcredit: The Grameen Bank

Microcredit is an important poverty alleviation tool which is based on providing small loans without collateral to the impoverished, who otherwise would be dependent on local moneylenders who charge very high interest rates for capital. The debate over the effectiveness of micro credit has been going on for quite a while with no sign of ending soon. This blogpost seeks to provide a brief introduction to microcredit by specifically focussing on Grameen Bank, we lay out its model in brief and finally list out the major criticisms against microcredit.

The Grameen Bank

The Grameen Bank, which is generally considered the first modern microcredit institution, was founded in 1983 in Bangladesh that has succeeded in providing credit, without collateral, to millions of poor people and recording very low default rates along with very high recovery rates. Grameen Bank creates a market niche for the low income groups who do not have alternative sources of credit at the rate of interest charged. Grameen’s creation of market niche is also due to its implicit targeting of women amongst the poor. This is not just because women form the majority of poor people in rural areas but also because they are seen as having the biggest influence in attempting to reduce poverty and are more reliable in making repayments. Lending to women has become a core principle for most microcredit organizations – indeed, some lend exclusively to women.

The Model: Group Lending

The Grameen Bank operates in a credit market that is characterized by imperfect information and imperfect enforcement. Lending entails high risk of loan default, but formal lenders are not part of the community and hence, do not have ability as well as means to collect information about the borrowers and their activities.

The Grameen Bank tackles this problem by offering group-based credit as opposed to traditional individual lending, where an individual’s access to credit is tied to group responsibility and repayment behaviour. It uses peer pressure to monitor and enforce contracts, provides an incentive structure for the borrowers to repay, and helps screen good borrowers from bad ones, all of which would be costly for the bank to accomplish otherwise.


Criticisms of Microcredit

  • Microcredit has not had a positive impact on gender relationships.
  • Microcredit imposes interest rates that are too high.
  • Microcredit has driven poor households into a debt trap.
  • Microcredit does not alleviate poverty or improve health and education.
  • Microcredit constitutes “privatization of welfare”.

The first randomized evaluation of microcredit, however showed mixed results: there was no effect on household expenditure, gender equity, education or health, but the number of new businesses increased by one third compared to a control group.

The arguments about the effectiveness of micro-finance in alleviating global poverty will continue. They often reflect the different political values brought to bear on the whole field of world development.
Useful Links for further reading:

  1. Wikipedia article on microcredit:
  1. A more detailed blog on Microcredit and the Grameen Bank:
  1. Article by David Roodman from The Washington Post explaining the limits of microfinance in helping people out of poverty

Hello everyone! WEE is delighted to inform that we’re now extending our aim to exchange and explore ideas in the field of economics to an online platform. We invite you all to send economics related articles to be published on our blog. These could be opinion pieces written by you or just something interesting you came across. The idea is to offer high quality articles that can stimulate debates and discussions. We look forward to your entries.


For the inaugural blog post of the WEE, we thought it would be a good idea to give a short summary of Colin Camerer’s collaboration paper: ‘Irrational exuberance and neural crash warning signals during endogenous experimental market bubbles’.

As is the trend with Neuroeconomics, the title of most published papers seem both intimidating and inaccessible to the economist not trained in the dark arts of neurobiology. However, that’s where this blog post (and Wikipedia, really) comes in. We lay out a brief motivation of the paper, their experimental methodology and an even briefer discussion of results; our own aim being to make papers such as this more accessible.

It is quite easy to think of many instances where groups of people misalign value to assets, leading to price bubbles such that the price rises far above fundamental values. There are many psychological reasons that have been suggested in the past (and as the paper notes) such as ‘euphoria’, ‘irrational exuberance’, ‘animal spirits’ etc. However, we don’t really have a good idea of how to incorporate these ideas elegantly into our models. Also, the connection between neural responses and behavioural acts in such situations has so far not been studied owing to technological constraints. fMRI technology allows us to study neural activity non-invasively and is surprisingly much more acceptable than inserting electrodes into conscious, human brains.

The motivation of the paper is to get a better idea of the behavioural and neurological processes at play in human subjects in an experimental asset market. An experimental setup such as this affords the experimenter the ability to define a risky asset that has an unambiguous fundamental value – something that is extremely rare in field data. The market was so defined that endogenous price bubbles could exist and with 2-3 participants, out of 20 in each session, hooked up to a functional MRI machine. The neural activity of the participants along with the market price fluctuations, behavioural acts (buying/selling) and earnings were recorded. Do take a look at the exact experimental methodology in the paper – those interested in finance will find it quite interesting.

Well, what did they find? Before we get there, it might be good to describe the relevant brain regions in brief. The Nucleus Accumbens “…has a significant role in the cognitive processing of motivation, pleasure, and reward and reinforcement learning, and hence has significant role in addiction.” And, the anterior insular cortex is an area that deals with emotional awareness and is active during negative emotional states and bodily discomfort.

[Aside: this type of information is easily available on Wikipedia and a great starting point when dealing with unfamiliar terms associated with Neuroeconomics]

The study found that the activity in the nucleus accumbens (NAcc) tracks the price bubble and aggregating the NAcc activity across participants predicted future prices changes and crashes. Furthermore, there were significant differences between the brain activity of the highest and lowest earners. The lowest earners tended to buy as a function of the NAcc activity. However, the highest earners had activity in the anterior insular cortex that preceded the price peak. Given that the highest earners tended to sell before the bubble burst, this could be a neural early warning signal in such people!

These are exciting times for the study of Neuroeconomics and the study of decision making as a whole. Perhaps a more thorough understanding of brain activity, the biological constraints of our neural hardware and the underlying processes at play can help us build better predictive models of human behaviour. The physical manifestations of decisions might serve as guideposts in our unrelenting quest to understand how, what and why people make the decisions they do.

Hopefully this blog post has motivated you to read the original paper in all its glory (it’s only 6 pages), critique it and given you confidence to venture a look at other similar papers.

Useful links:

  1. Original Paper:
  1. Marr’s 3 Levels of Analysis:
  1. What is fMRI?
  1. An interesting perspective on how Neuroeconomics can tell us about economics: