Predictability of investment behavior from brain information measured by functional near-infrared spectroscopy: a bayesian neural network model

A neural basis of rational inattention models: consistency of cognitive cost with the mutual information criterion

The rational inattention model has recently attracted much attention as a promising candidate to model bounded rationality in the research field of decision-making and game theory. However, in contrast to this energetic promotion of the theoretical works, empirical verification of the validity of the RI model has not progressed much. Furthermore, to our knowledge, the central assumption of the RI model, that the amount of mutual information obtained from signals adequately represents the cognitive cost of information, has not been tested from a neuroscientific perspective. The purpose of the present study was to test whether the amount of mutual information adequately represents the cognitive cost of information from a neuroscientific perspective. We proposed a sequential investment task, in which the two main models of RI can be treated simultaneously in a more realistic experimental environment. We used a model-fitting approach to analyze the subjective information cost, and compared the model parameters representing the information cost with the concentration of oxidized hemoglobin in the brain blood. Our results showed that the cost parameter λ of the stochastic choice type model, which fits the behavioral data of the present experiment better than the Kalman filter type model, was significantly positively correlated with the activation status of the rostral prefrontal cortex and dorsolateral prefrontal cortex. The cognitive cost represented by the amount of mutual information employed in the RI model is consistent with the activation of brain regions associated with cognitive cost, and, thus, indirectly supports the assumption of the RI model.

Picking Your Brains: Where and How Neuroscience Tools Can Enhance Marketing Research

The use of neuroscience tools to study consumer behavior and the decision making process in marketing has improved our understanding of cognitive, neuronal, and emotional mechanisms related to marketing-relevant behavior. However, knowledge about neuroscience tools that are used in consumer neuroscience research is scattered. In this article, we present the results of a literature review that aims to provide an overview of the available consumer neuroscience tools and classifies them according to their characteristics. We analyse a total of 219 full-texts in the area of consumer neuroscience. Our findings suggest that there are seven tools that are currently used in consumer neuroscience research. In particular, electroencephalography (EEG) and eye tracking (ET) are the most commonly used tools in the field. We also find that consumer neuroscience tools are used to study consumer preferences and behaviors in different marketing domains such as advertising, branding, online experience, pricing, product development and product experience. Finally, we identify two ready-to-use platforms, namely iMotions and GRAIL that can help in integrating the measurements of different consumer neuroscience tools simultaneously. Measuring brain activity and physiological responses on a common platform could help by (1) reducing time and costs for experiments and (2) linking cognitive and emotional aspects with neuronal processes. Overall, this article provides relevant input in setting directions for future research and for business applications in consumer neuroscience. We hope that this study will provide help to researchers and practitioners in identifying available, non-invasive and useful tools to study consumer behavior.

Exploration and recency as the main proximate causes of probability matching: a reinforcement learning analysis

Research has not yet reached a consensus on why humans match probabilities instead of maximise in a probability learning task. The most influential explanation is that they search for patterns in the random sequence of outcomes. Other explanations, such as expectation matching, are plausible, but do not consider how reinforcement learning shapes people's choices. We aimed to quantify how human performance in a probability learning task is affected by pattern search and reinforcement learning. We collected behavioural data from 84 young adult participants who performed a probability learning task wherein the majority outcome was rewarded with 0.7 probability, and analysed the data using a reinforcement learning model that searches for patterns. Model simulations indicated that pattern search, exploration, recency (discounting early experiences), and forgetting may impair performance. Our analysis estimated that 85% (95% HDI [76, 94]) of participants searched for patterns and believed that each trial outcome depended on one or two previous ones. The estimated impact of pattern search on performance was, however, only 6%, while those of exploration and recency were 19% and 13% respectively. This suggests that probability matching is caused by uncertainty about how outcomes are generated, which leads to pattern search, exploration, and recency.

Near-infrared spectroscopy (NIRS) as a new tool for neuroeconomic research

Over the last decade, the application of neuroscience to economic research has gained in importance and the number of neuroeconomic studies has grown extensively. The most common method for these investigations is fMRI. However, fMRI has limitations (particularly concerning situational factors) that should be countered with other methods. This review elaborates on the use of functional Near-Infrared Spectroscopy (fNIRS) as a new and promising tool for investigating economic decision making both in field experiments and outside the laboratory. We describe results of studies investigating the reliability of prototype NIRS studies, as well as detailing experiments using conventional and stationary fNIRS devices to analyze this potential. This review article shows that further research using mobile fNIRS for studies on economic decision making outside the laboratory could be a fruitful avenue helping to develop the potential of a new method for field experiments outside the laboratory.

Temporal hemodynamic classification of two hands tapping using functional near-infrared spectroscopy

In recent decades, a lot of achievements have been obtained in imaging and cognitive neuroscience of human brain. Brain's activities can be shown by a number of different kinds of non-invasive technologies, such as: Near-Infrared Spectroscopy (NIRS), Magnetic Resonance Imaging (MRI), and ElectroEncephaloGraphy (EEG; Wolpaw et al., 2002; Weiskopf et al., 2004; Blankertz et al., 2006). NIRS has become the convenient technology for experimental brain purposes. The change of oxygenation changes (oxy-Hb) along task period depending on location of channel on the cortex has been studied: sustained activation in the motor cortex, transient activation during the initial segments in the somatosensory cortex, and accumulating activation in the frontal lobe (Gentili et al., 2010). Oxy-Hb concentration at the aforementioned sites in the brain can also be used as a predictive factor allows prediction of subject's investigation behavior with a considerable degree of precision (Shimokawa et al., 2009). In this paper, a study of recognition algorithm will be described for recognition whether one taps the left hand (LH) or the right hand (RH). Data with noises and artifacts collected from a multi-channel system will be pre-processed using a Savitzky-Golay filter for getting more smoothly data. Characteristics of the filtered signals during LH and RH tapping process will be extracted using a polynomial regression (PR) algorithm. Coefficients of the polynomial, which correspond to Oxygen-Hemoglobin (Oxy-Hb) concentration, will be applied for the recognition models of hand tapping. Support Vector Machines (SVM) will be applied to validate the obtained coefficient data for hand tapping recognition. In addition, for the objective of comparison, Artificial Neural Networks (ANNs) was also applied to recognize hand tapping side with the same principle. Experimental results have been done many trials on three subjects to illustrate the effectiveness of the proposed method.