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.

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