5.- MATERIALES Y MÉTODOS
FRECUENCIA PORCENTAJE
In this research project, our aim was to conduct a comprehensive evaluation of the relative explanatory power of four different forms subjective valuation.
To compare four value transformation rules, each of which captures a different subjective valuation mechanism, we used a popular cognitive model, the DDM and its extension, the aDDM, to derive predictions about choice behaviour in two choice experiments with trinary choice sets, one with complex stimuli (movie posters), and another one with a perceptual task (rapidly updating sequences of numbers).
We tested the relative explanatory power of these value transformation rules in three separate tests. In the first two tests, we used a model fitting ap-proach (based on simulations in the first test, and approximated choice probabil-ities in the second test), using choice and eye-tracking data from Experiment 1.
The results showed that the global maximum rule was the best fitting subjective transformation rule for the majority of participants, followed by the local maxi-mum rule. In addition, the results from the two tests were remarkably similar, alleviating concerns about employing a simulations-based estimation approach in the context of a stochastic model.
In the third test, we compared the qualitative patterns of choice propor-tions using data from Experiment 2 and 1. The results from the two comparison methods were broadly in line, both supporting the view that while subjective valuation mostly reflects the absolute magnitude of the options under consider-ation, to a lesser extent, it is also affected by the relative value of the options (within the local context).
These findings are consistent with insights from research investigating the neural correlates of value-based decision making. Results from numerous neuroe-conomic studies strongly support the view that the orbifrontal cortex (OFC) is the brain region where subjective value encoding takes place in economic choices
(for a review see Padoa-Schioppa & Conen, 2017). More importantly, it has been suggested that there is a group of neurons in the OFC with two fundamental properties that are likely to be directly responsible for simultaneous absolute and relative value sensitivity in economic valuation.
For example, Padoa-Schioppa and Assad (2008) have shown that such neurons exhibit menu invariance, meaning that the value assigned to each option under consideration is independent from the value of the rest of the available alternatives, reflecting the absolute value of the option (global maximum in our experiments). Menu invariance gives rise to preference transitivity, ensuring that preferences are stable across the wide range of contexts the decision maker might encounter. In addition, there is ample evidence that neuronal firing rates adapt to the range of available values (e.g., Padoa-Schioppa, 2009; Louie et al., 2013).
Such adaptation is widespread in sensory systems, and is a natural consequence of biophysical constraints. Interestingly, although the most commonly proposed form of adaptation in the neuroeconomic literature is range adaptation (e.g., Soltani et al., 2012), our data suggests that normalising by the maximum on the current trials fares better than a range normalisation approach at predicting choice.
There exist other ways to model context sensitivity within the sequential sampling framework. Another approach to investigate context dependency could have been to directly model changes in the drift rate. This could be done within an experiment where the range of values encountered are manipulated block by block (e.g., as done in Mullett & Tunney, 2013), and the model fitting approach can evaluate the explanatory power of various forms of context dependency.
In addition, we could have investigated mixtures of models, for example, a hybrid model, where the subjective value is partly affected by the absolute value and partly affected by the local maximum transformation. Such investi-gations would have required estimation an additional, mixing parameter, which determines the degree to which each rule affects the subjective value.
Alternatively, instead of changing the input values of the accumulation equation, we could have focused on how these values are integrated and incor-porated into the accumulation process (described in Equations 2.4–2.6). This is what Teodorescu, Moran, and Usher (2016) did in a somewhat similar in-vestigation to ours. Specifically, they contrasted relative and absolute evidence processing in a sequential sampling framework by comparing an independent race model (capturing absolute value processing, where the input is the abso-lute value of the options), with a DDM model (where differences of input values govern the accumulation process).
In their experiment, participants were instructed to choose the brighter out of two, fluctuating grey patches, with a fixed mean brightness. They focused on two manipulations: in an additive-boost condition, they added the same con-stant to both means, preserving the difference between the two mean brightness, whereas in the multiplicative-boost condition, they multiplied both means by the same constant, preserving the ratio of the two means. These conditions are direct equivalents to our add a constant, and multiply by a constant choice set manipulations.
Interestingly, they found that no “pure” (either entirely absolute or rela-tive) accumulation model could account for the data, and thus they propose two distinct types of models that can account for this pattern: a DDM model where the noise in the process is a function of the intensity of the inputs, and a leaky competing accumulator model (LCA; Usher & McClelland, 2001), where simul-taneous absolute and relative value sensitivity is a result of lateral inhibition.
While our approach is different from that of Teodorescu et al. (2016), our results are similar, as they both suggest that subjective valuation is sensitive to both the absolute and relative magnitudes of objective values. This dovetails with findings from neuroeconomic research that suggests that OFC neurons ex-hibit both menu invariance and range adaptation. Taken together, these results point to a sequential sampling model with some form of hybrid value
transfor-mation rule. As an ever increasing amount of research focuses on understanding the neural basis of economic decision making, insights from this field will no doubt inform and greatly advance the explanatory power of cognitive models of choice in the future.