Decision Neuroscience: Neuroeconomics

(Smith & Huettel, 2010)

This article starts by emphasising the importance of decision making for humans in their everyday life and that neuroscientific methods are helping us to understand this important aspect of human cognition. The aim of neuroscience is to understand the processes involved in different decisions. The authors suggest that previous research has attempted to delineate decision variables and further suggest that the decision variable approach (in the economic field) consists of particular steps, namely

1. Identify a problem of interest
2. Extract the phenomena in away that it can be measured using neuroscientific methods
3. Choose one or more variables that influence decisions
4. Identify brain aspects

The authors continue by stating that they are going to look at the decision variables of value, uncertainty and social interaction.

When discussing values they emphasise that the outcome of a decision necessarily has a value attached to it. They introduce the concept of Reward Prediction Errors (RPE) where the expected value of a decision is not attained because of changed additional information presented to the decision maker after they have made the decision. The dopaminergic system is implicated in forming and updating predictions about rewards and the brain region that is attached to this is the ventral tegmental area (VTA).

The variable of uncertainty is defined as the absence of some desired information. Because cues about future rewards, minimise uncertainty, it is closely connected to reward valuation. In addition, the authors define risky decisions as those where the uncertainty reflects known probabilities. In addition, ambiguity is defined as unknown probabilities.

When discussing the variable of social interaction, the authors describe two main classes of information that are involved in this variable, namely actions (eg. Games) and characteristics (eg. Facial features). Various brain regions that are involved in social interaction decision making are named and described.

The brain, when making value comparisons needs to combine value information from various sources to obtain a value-based result. Based on various research studies that have isolated various brain areas with valuation of different rewards, the authors conclude that the ventromedial PreFrontal Cortex (vmPFC) is the area in which the value information converges. Despite this, other research have identified other non-brain systems that could have a bearing on  value judgements. These include the endowment effect and framing effects.

Whereas decision-making models have traditionally assumed that individuals approach decision making in a similar fashion, Individual difference analysis is discussed by the authors as being of relevance. Subjective value is discussed with reference to different brain regions and it is pointed out that individual measurements and individual traits can add to the information provided by other theories such as the dual process theory. Neural correlates of personality measures have been found by correlating personality traits with brain region activation during decision making. In addition, genetic markers have shown correlation with various brain regions   during decision making.

Having described the virtues of individual differences in the area of neural decision making, the authors enumerate the problems and limitations of this approach. They suggest that “many reports of high correlations between traits and brain activation may be the result of statistical artifacts” They warn against reverse inference in interpreting brain-behaviour relationships but emphasise that these concerns are about the values and not the significance of the correlations found within this area of research.

In their conclusion, the authors question the challenges posed by the cross-disciplinary nature of decision neuroscience and suggest that creating more detailed maps of known decision processes will alleviate some of the uncertainty around decision neuroscience. They make reference to the prevalence of dual systems models of choice in the most common conceptual interpretations of decision neuroscience but that this approach is problematic as it isolates brain areas for particular functions. They acknowledge that decision neuroscience often adopts an experimental approach and the resulting psychological interpretations of the data may not necessarily be generalised to other paradigms. In addition, brain regions typically carry out several functions and computational algorithms have been able to clarify what goes on in these regions and they give several examples of these instances.

One of the problems in decision neuroscience research that the authors have identified, is the integration of conclusions across studies using different neuroscientific methods. They also identify another methodological problem as being the move from correlational to causal models of the mechanisms of decision making.

When discussing practical challenges, the authors refer to several arguments.

1. “The Behavioural Sufficiency Argument”. A model that predicts behaviour will find neural descriptions irrelevant.
2. “The Emergent Phenomenon Argument”. In economic modelling, often complex collective phenomena are involved. Neutral studies which are individually-based could have little bearing on overall collective information

The authors argue that to shape models of decision-making behaviour what will happen is that behaviour will be observed, brain function measured and then new and testable predictions about behaviour will ensue. They suggest that neuroscience will indicate new areas for research (for example understanding variability in decision making) and will run in parallel with behavioural economics and cognitive psychology.

MY NOTES:

1. “To overcome these challenges, future research will likely focus on interpersonal variability in decision making, with the eventual goal of creating biologically plausible models for individual choice.” (p. 1)
2. Good descriptions of ambiguous and risky decision making (pp. 4-5)
3. Areas of brain are given for various actions related to decision making
4. “will neuroscientists simply create more precise maps of known decision processes? Meeting this last challenge, in particular, seems critical for the success of a new discipline of decision neuroscience” (p. 9).
5. Mention is made to models created by gambling experiments in a casino where decisions are made by something called “reinforcement history” (no references given). Check whether “reinforcement history” could have a bearing on what I am (p. 10)
6. “Decision neuroscience studies with human participants typically adopt the methodological conventions of behavioral economics: participants make decisions about real but abstract rewards (e.g., money), they receive full information about the decision scenario (i.e., no deception), and researchers strive to minimize external influences on choice (e.g., no communication with other participants, no other incentives). Over the course of a 1–2hr testing session, a participant might make on the order of 100 independent decisions about information presented using words and numbers.” (p. 12)
7. Possible justification for my study: “the future of decision neuroscience lies in contributing to the development of new, robust, and biologically plausible models of behaviour”. (p. 14)

 

Smith, D. V., & Huettel, S. A. (2010). Decision neuroscience: Neuroeconomics. Wiley Interdisciplinary Reviews: Cognitive Science, 1(6), 854–871. https://doi.org/10.1002/wcs.73