Research outline February

 

The morning after the night before… investigating the post-decision neural activity of time-restricted, sequential decisions using psychophysiological methods

Introduction

Decision making is a key human task that occurs virtually every second of our lives. From autonomous decisions to considered decisions, humans are dependent on decision making to survive. The focus of the proposed study will be on the topic of neural decision making (ie. What occurs in the brain when decision making occurs), namely how neural activity reflects decision-making behaviour. The emphasis of the study will be on post-decision neuronal activity in the context of financial share trading. The study will argue that post-decision neuronal activity can be established using psychophysiological measurements and aims to establish if this activity has a bearing on future decisions.

Theoretical background

The academic literature is replete with articles on neural decision making and what happens within the brain at the moment of choice, in the context of a given decision (Balleine, 2007; Lee & Seo, 2016; Newsome, Britten, & Movshon, 1989; Steinemann, 2017). This literature draws on algorithmic computational models such as signal detection theory (Gold & Shadlen, 2007), the concept of decision thresholds (Domenech & Dreher, 2010), diffusion models (Ratcliff, Smith, Brown, & McKoon, 2016), sequential analysis (Domenech & Dreher, 2010) and reaction time (Milosavljevic, Malmaud, Huth, Koch, & Rangel, 2010). Statistical probability theory plays an important part in many of these theories as probability is taken as one of the variables included in the relevant algorithms to calculate such things as decision threshold. These approaches generally use fMRI and electroencephalography to track and measure neural responses to experimentally-posed (mostly binary) decisions (Heitz, 2007).  A search of the literature, however, finds limited research on the neural activity that occurs as a result of a decision, post the moment-of-action (which can be said to be the observable evidence of a decision, such as clicking a button). The moment-of-action is arguably important in decision making, as it may contribute to moderating future decisions, by adding to memory, contributing to learning and reshaping current emotions and expectations as can be concluded by applying a Hebbian approach or the Somatic Marker Theory (Damasio, 1996). Indeed, human endeavours and survival success are arguably built not as much on current decisions, but more on learning from past decisions.

Method

The question arises, then, what neural activity occurs after the moment of action and how this neural activity influences future decision making. Attempting to understand this relationship, is the expected contribution to knowledge of the proposed study.

This question will be addressed experimentally by recruiting 10-20 experienced financial traders (on a voluntary basis and with due ethical consideration) and asking them to make binary decisions (buy or sell) on the share markets, but using dummy (but still real time) accounts. The decisions they make will be placed under time pressure and they will be required to make several sequential trades, again within a given time period. The market itself will prove whether their decision was good or bad.  The participants will be monitored using various psychophysiological devices such as an eye tracker, electroencephalographic device, galvanic skin response and more. The data from these devices will be used to determine the neural activity and physiological responses during and post decision, with the focus on how good (or bad) decisions shape their future decisions. While the traders will not be subject to direct risk, as professional traders, it is argued that their professional pride will ensure that they strive to make the most money possible from each trade. Verbal protocols will be used post experiment to better understand their decision making. As each trader will be making decisions in very different circumstances from other traders (i.e. the markets will have changed from one experiment to the next), the study will not be comparative in nature across participants. In essence, this will be a qualitative study, but supported with considerable quantitative data. As part of the experiment, baseline data will be obtained prior to the start of the experiment and pre/post decision data will be compared with each other (and with the baseline).

The experiment may be hypothesis driven, with the proposal that neural activity decreases over several successful decisions, but increases over several unsuccessful decisions. At this early stage it is proposed that the experiment will comprise at least 10-20 participants.

References

Balleine, B. W. (2007). The Neural Basis of Choice and Decision Making. Journal of Neuroscience, 27(31), 8159–8160.

Damasio, A. R. (1996). The Somatic Marker Hypothesis and the possible functions of the prefrontal cortex. Philosophical Transactions of the Royal Society of London, 351, 1413–1420.

Domenech, P., & Dreher, J.-C. (2010). Decision Threshold Modulation in the Human Brain. Journal of Neuroscience, 30(43), 14305–14317.

Gold, J. I., & Shadlen, M. N. (2007). The Neural Basis of Decision Making. Annual Review of Neuroscience, 30(1), 535–574.

Heitz, R. P. (2007). Neural Correlates of Speed-Accuracy Tradeoff: an Electrophysiological Analysis. Dissertation Abstracts International, Ann Arbor.

Lee, D., & Seo, H. (2016). Neural Basis of Strategic Decision Making. Trends in Neurosciences, 39(1), 40–48.

Milosavljevic, M., Malmaud, J., Huth, A., Koch, C., & Rangel, A. (2010). The Drift Diffusion Model Can Account for the Accuracy and Reaction Time of Value-Based Choices Under High and Low Time Pressure. SSRN Electronic Journal, 5(6), 437–449.

Newsome, W. T., Britten, K. H., & Movshon, J. A. (1989). Neuronal correlates of a perceptual decision. Nature, 341, 52–54.

Ratcliff, R., Smith, P. L., Brown, S. D., & McKoon, G. (2016). Diffusion Decision Model: Current Issues and History. Trends in Cognitive Sciences, 20(4), 260–281.

Steinemann, N. (2017). Perceptual decision making in humans: Neural correlates along the sensorimotor hierarchy, (November), 2017–2018.