JP5467453B2 - Method and apparatus for discriminating human behavior in investment behavior - Google Patents

Description

The present invention relates to a method and apparatus for discriminating human behavior in investment behavior .

  Conventionally, experiments have been conducted to try to elucidate the decision-making process using the brain information of the subject. For example, Patent Document 1 describes a device that controls the operation of an electric wheelchair based on user's brain information, and measures the user's brain information using NIRS (Near InfraRed Spectroscopy).

JP 2007-20882 A

  However, since these experiments attempt to predict human decision making after measuring the entire brain, there is a problem that a large-scale facility is required, and introduction costs and maintenance costs become very high. In addition, because the measurement environment is fixed and body movement restrictions are severe, physical and mental stress is given to the subjects. For this reason, it is difficult to apply to other than experiments, that is, to daily life, and it has been put to practical use.

  The present invention has been made in view of the above points, and provides a method and apparatus for predicting human decision making while suppressing introduction costs and maintenance costs, and not subjecting a subject to physical or mental stress. The purpose is to do.

(1) A method for constructing a prediction model for predicting user behavior in investment behavior, the step of measuring brain activity information of a specific part by a non-invasive brain activity measurement unit, and the measured brain activity information And constructing the prediction model from the vertical direction from 10% to 20% between the nasal root Nz and the parietal region Cz in the direction of the origin Fpz to the parietal region Cz in the international 10-20 method. A main part A2 having an axial width and a lateral axis width of approximately 4% to 12.5% between the left anterior pinna point T3 and the right pinna point T4 in the direction from the origin Fpz to the left pinna point T3; The vertical axis width from the lower part 10% to the upper part 20% between the nose root part Nz and the parietal part Cz from the origin Fpz to the parietal part Cz direction, and the left anterior pinna point T3 and the right anterior pinna point T4 direction from the origin Fpz Left anterior point T3 and right anterior point T4 The auxiliary site B1 having a horizontal axis width of about 4% of each, the vertical axis width of 10% below the nasal root Nz and the parietal portion Cz from the origin Fpz to the parietal portion Cz direction, and the left auricle from the origin Fpz In the direction of the front point T3 and the right anterior point T4, the auxiliary part B2 having a horizontal axis width of 10% between the left anterior point T3 and the right anterior point T4, or from the origin Fpz to the parietal portion Cz. The vertical axis width of 20% between the nasal root Nz and the parietal portion Cz, and from the origin Fpz in the direction of the left auricular anterior point T3 to 20% from the left auricular point T3 and the right auricular point T4 to approximately 28%. The auxiliary part C consisting of the width of the horizontal axis.

According to the method (1), since the brain activity information is used when constructing a prediction model for predicting human behavior in investment behavior, a prediction model that reflects unconscious behavior and emotional behavior. Can be built.

In addition, since the brain activity information is measured from the specific part called the auxiliary part B1, the auxiliary part B2, or the auxiliary part C, it is not necessary to measure the entire brain, and the brain activity measuring unit can be downsized. As a result, it is possible to construct a prediction model that predicts human behavior in investment behavior at relatively low introduction and maintenance costs. In addition, since no physical movement restriction is imposed on the user, physical and mental stress is not applied to the user.

In particular, since these specific parts are suitable for prediction of human behavior in investment behavior , a highly accurate prediction model can be constructed.

(2) A method for presenting auxiliary information on human behavior in investment behavior using the prediction model constructed by the method according to (1), the step of presenting specific information to a user, The step of measuring the brain activity information for determination of the specific part when the specific information is presented by the brain activity measuring unit, and predicting the action that the user can take from the brain activity information for determination and the prediction model And a step of determining whether or not the predicted behavior is appropriate, and a step of presenting auxiliary information on the condition that the behavior is determined to be inappropriate. how to.

  According to the method (2), the auxiliary information can be presented when the predicted user action is not appropriate, while the brain activity measuring unit is downsized, and can be applied to everyday society. It can be made practical.

  (3) The method according to (1) or (2), wherein the specific site is 10% below the nasal root portion Nz and the parietal portion Cz from the origin Fpz to the parietal portion Cz direction in the international 10-20 method. And the horizontal axis width of approximately 4% between the left pinna anterior point T3 and the right pinna anterior point T4 in the direction from the origin Fpz to the left pinna anterior point T3 and the right pinna anterior point T4, respectively. The main part A1 or the vertical axis width between the nose root Nz and the parietal portion Cz in the direction from the origin Fpz to the parietal portion Cz and from 10% to 20%, and from the origin Fpz to the left anterior pinna point T3 in front of the left pinna A method characterized in that one of the main parts A2 having a horizontal axis width of approximately 4% to 12.5% between the point T3 and the right anterior pinna point T4 is used.

According to the method (3), it is possible to improve the accuracy with respect to prediction (determination) of human behavior in investment behavior .

(4) A user terminal communicably connected to a server terminal capable of constructing a prediction model for predicting user behavior in investment behavior from brain activity information, and has no invasiveness to measure brain activity information of a specific part A brain activity measuring unit; and a communication unit that transmits the brain activity information measured by the brain activity measuring unit to the server terminal, and the specific part is moved from the origin Fpz to the parietal part Cz in the international 10-20 method. The vertical axis width from 10% to 20% between the nasal root portion Nz and the parietal portion Cz in the direction, and the approximate distance between the left anterior pinna point T3 and the right pinna point T4 in the direction from the origin Fpz to the left pinna point T3 A main part A2 having a horizontal axis width of 4% to 12.5%, and a vertical axis width of 10% to 20% below the nasal root Nz and the top Cz direction from the origin Fpz to the top Cz direction. And the left anterior pinna point T3 and the right from the origin Fpz An auxiliary site B1 having a horizontal axis width of approximately 4% between the left anterior pinna point T3 and the right anterior pinna point T4 in the direction of the anterior pinna point T4, and the nose root portion from the origin Fpz to the crown Cz direction. A vertical axis width of 10% below between Nz and the parietal portion Cz, and between the left anterior pinna point T3 and the right pinna point T4 in the direction from the origin Fpz to the left pinna point T3 and the right pinna point T4, respectively. Auxiliary part B2 having a horizontal axis width of 10%, or a vertical axis width of 20% between the nose root portion Nz and the parietal portion Cz from the origin Fpz to the parietal portion Cz, and from the origin Fpz to the left anterior pinna T3 direction A user terminal, characterized in that it is any one of auxiliary parts C having a horizontal axis width of 20% to approximately 28% between the left anterior pinna point T3 and the right pinna point T4.

(5) A prediction model in which the user's behavior in the investment behavior is predicted from the brain activity information of the specific region is stored, and the user's prediction behavior predicted from the user's brain activity information in the specific region using the prediction model When it is not appropriate, it is a user terminal communicably connected to a server terminal that transmits auxiliary information about the predicted behavior, and measures brain activity information for determination of the specific part when specific information is presented A non-invasive brain activity measurement unit, and a communication unit that transmits the determination brain activity information measured by the brain activity measurement unit to the server terminal, the communication unit receiving the auxiliary information from the server terminal And a presentation device that presents the received auxiliary information to the user, and the specific part is located in the nose from the origin Fpz in the international 10-20 method in the direction of the crown Cz. From the vertical axis width between 10% to 20% between the root Nz and the parietal portion Cz, and from approximately 4% between the left anterior pin point T3 and the right pin point T4 in the direction from the origin Fpz to the left pin point T3. The main part A2 consisting of a horizontal axis width of up to 12.5%, the vertical axis width from the lower 10% to the upper 20% between the nasal root Nz and the parietal part Cz with the head Fpz as the top of the head Cz direction, and the origin Auxiliary part B1 consisting of approximately 4% of the horizontal axis width between left anterior pinna point T3 and right anterior pinna point T4 in the direction from Fpz to left anterior pinna point T3 and right anterior pinna point T4, and origin Fpz The vertical axis width of 10% below the nasal root Nz and the parietal portion Cz from the top to the top Cz direction, and the left anterior pinna point from the origin Fpz in the direction of the left pinna point T3 and right pinna point T4, respectively. Auxiliary part B2 consisting of 10% of the horizontal axis width between T3 and right anterior pinna point T4, or from the origin Fpz to the crown Cz direction The vertical axis width of 20% between the root portion Nz and the parietal portion Cz, and from the origin Fpz to the left anterior pinna point T3 in the direction from the left pinna point T3 and the right pinna point T4 to approximately 28% A user terminal, characterized in that it is one of the auxiliary parts C having a horizontal axis width.

  (6) The user terminal according to (4) or (5), wherein the specific portion is a lower 10 between the nose root portion Nz and the parietal portion Cz from the origin Fpz to the parietal portion Cz direction in the international 10-20 method. And the horizontal axis width of approximately 4% between the left anterior pinna point T3 and the right pinna point T4 in the direction from the origin Fpz to the left anterior pinna point T3 and the right pinna point T4, respectively. Or a vertical axis width from 10% to 20% between the nasal root Nz and the parietal portion Cz from the origin Fpz to the parietal portion Cz, and the left auricle from the origin Fpz to the left anterior anterior point T3. A user terminal, characterized in that it is any one of a main part A2 having a horizontal axis width of approximately 4% to 12.5% between the front point T3 and the right auricle anterior point T4.

(7) A prediction model construction system for predicting human behavior in investment behavior , the non-invasive brain activity measurement unit measuring brain activity information of a specific part, and the brain measured by the brain activity measurement unit An analysis unit that builds a prediction model from the activity information, and the specific part is 10% to 20% between the nose root Nz and the parietal part Cz in the direction of the parietal part Cz from the origin Fpz in the international 10-20 method. A main part A2 having a vertical axis width and a horizontal axis width of approximately 4% to 12.5% between the left anterior pinna point T3 and the right pinna point T4 in the direction from the origin Fpz to the left pinna point T3. The vertical axis width from the bottom 10% to the top 20% between the nose root Nz and the top Cz from the origin Fpz to the top Cz direction, and the left auricular anterior point T3 and the right auricular anterior point T4 from the origin Fpz. In the direction between the left anterior pinna point T3 and the right pinna point T4 Auxiliary part B1 having a horizontal axis width of 4%, 10% vertical axis width between nasal root Nz and parietal Cz from top Fpz to top of head Cz, and left auricular anterior point from origin Fpz An auxiliary site B2 having a horizontal axis width of 10% between the left anterior pin point T3 and the right pin point T4 in the direction of T3 and the right pin point T4 or the nose root portion from the origin Fpz to the crown Cz direction 20% vertical axis width between Nz and parietal portion Cz, and from 20% to approximately 28% between the left anterior pinna point T3 and the right anterior pin point T4 in the direction from the origin Fpz to the left anterior pinna point T3 A prediction model construction system, characterized in that the system is any one of auxiliary parts C having an axial width.

(8) An auxiliary information presentation system that presents auxiliary information about human behavior in investment behavior using the prediction model constructed by the prediction model construction system according to (7), wherein the brain activity measurement unit includes: A behavior prediction unit that measures the brain activity information for determination of the specific part when specific information is presented, and predicts a user's possible behavior from the brain activity information for determination and the prediction model; and the behavior prediction An appropriate determination unit that determines whether or not the behavior predicted by the unit is appropriate, and a presentation device that presents auxiliary information on condition that the appropriate determination unit determines that the action is not appropriate An auxiliary information presentation system characterized by

  (9) The specific portion is defined as the vertical 10% vertical width between the nose root Nz and the parietal portion Cz with the top portion Cz direction from the origin Fpz in the international 10-20 method, and the left anterior pinna point from the origin Fpz. A main part A1 having a horizontal axis width of approximately 4% between the left anterior pinna point T3 and the right anterior pinna point T4 in the direction of T3 and right anterior pinna point T4, or from the origin Fpz to the crown Cz direction The vertical axis width from 10% to 20% between the nasal root portion Nz and the parietal portion Cz, and approximately 4% between the left anterior pinpoint T3 and the right pinna point T4 in the direction from the origin Fpz to the left pinna point T3. The prediction model construction system according to (7) or the auxiliary information presentation system according to (8), characterized in that the prediction part is a main part A2 having a horizontal axis width from 1 to 12.5%.

  According to the user terminal described in (4) or the prediction model construction system described in (7), the same effect as the method described in (1) can be obtained.

  According to the user terminal described in (5) or the auxiliary information presentation system described in (8), it is possible to achieve the same effect as the method described in (2).

  According to the user terminal described in (6) or the system described in (9), the same effect as the method described in (3) can be obtained.

  According to the present invention, it is possible to provide a method and an apparatus that perform prediction of a user's preference relationship unconsciously while suppressing introduction costs and maintenance costs, and that do not apply physical and mental stress to a subject.

It is a block diagram which shows the prediction model construction system of this invention. It is a figure which shows a NIRS mounting position. It is a figure which shows the international 10-20 law. It is a figure which shows the international 10-20 law. It is a figure which shows the relationship between a stock investment ratio and cerebral blood oxyhemoglobin density | concentration. It is a figure which shows the relationship between a stock investment ratio and cerebral blood oxyhemoglobin density | concentration. It is a figure which shows investment action prediction. It is a block diagram which shows an auxiliary information presentation system. It is a figure which shows the flowchart for showing auxiliary information. It is a figure which shows an example of a brain activity measurement part. It is a figure which shows the main site | part regarding prediction of investment behavior. It is a figure which shows the auxiliary | assistant part regarding prediction of investment behavior. It is a figure which shows the auxiliary | assistant part regarding prediction of investment behavior.

  Embodiments of the present invention will be described below with reference to the drawings.

[Experiment Overview]
In order to realize the decision-making under the risk (for example, investment behavior) without imposing a body movement restriction on the user, the following experiment was conducted.

  The subject was given 1 million yen as an initial asset, and the subject was allowed to determine the stock investment ratio to invest in the stock from this asset. The stock investment ratio can be changed at any time during the experiment, the amount corresponding to the determined stock investment ratio is invested in stock, and the remaining assets are sent to deposits The interest rate on assets is 0%). The investment amount in the stock is changed according to the fluctuation of the stock price, and the stock price is automatically updated every 750 msec. Then, for each stock price time series, an experiment of 10 tasks per subject was performed by changing the stock price data, assuming that the total number of updates of the stock price was 75 times. Also, a rest period of 20 seconds was taken between tasks.

[Overview of prediction model construction system for investment behavior]
It is a block diagram which shows the prediction model construction system 1 about investment action.

  The prediction model construction system 1 includes a user terminal 1a including a brain activity information measurement unit 14 that measures brain activity information (for example, cerebral blood oxyhemoglobin concentration) of the user X, and a brain measured by the brain activity information measurement unit 14. The server terminal 1b which builds the prediction model which estimates a user's investment behavior based on activity information is provided. Note that the user terminal 1a and the server terminal 1b are connected so as to be able to communicate with each other.

  The user terminal 1 a includes a presentation device 12, a decision making unit 13, and a brain activity measurement unit 14. The server terminal 1b includes a financial information database 10, a presentation information determination unit 11, a brain information preprocessing unit 15, a subject information database 16, and an analysis unit 17. The user terminal 1a and the server terminal 1b are each provided with a communication unit capable of transmitting and receiving, but are not shown in order to clarify the data communication source and communication destination.

  The financial information database 10 stores stock price data used for experiments. The stock price data may be actual or may be simulated. The presentation information determination unit 11 reads the stock price data stored in the financial information database in a timely manner and transmits it to the presentation device 12. The stock price data transmitted to the presentation device 12 is also transmitted to the subject information database.

  The presentation device 12 presents the received stock price data to the user X. When the stock price is presented, the user X uses the decision making unit 13 to determine the stock investment ratio. The simulation stock price may be updated according to the determined stock investment ratio. When the user X determines the stock investment ratio, information on the stock investment ratio is transmitted to the subject information database 16.

  On the other hand, the brain activity measuring unit 14 measures the brain activity information of the user X when the stock price is presented by the presentation device 12. Here, for the construction of the prediction model, a change in the concentration of oxyhemoglobin in the cerebral blood is used. Moreover, in order to measure such brain activity information, NIRS (Near InfraRed Spectroscopy) which has a low spatial resolution and can only observe a low depth is used.

  NIRS is a non-invasive device that measures the amount of change in oxygenated hemoglobin and deoxygenated hemoglobin contained in blood using highly transmissive near-infrared light (wavelength 700 to 1000 nm). The measurement is performed by a pair of light transmitting points and light receiving points installed on the scalp at intervals of 3 cm, and light is absorbed while near infrared light irradiated from the light transmitting points is detected by the light receiving points. The amount of hemoglobin change in the blood is calculated backward from the amount of light, that is, the amount of attenuation of light reaching the light receiving point. NIRS is an apparatus suitable for experiments involving electronic devices because it is not affected by electronic noise in principle.

  Here, in this experiment, in order to pay attention to the prefrontal cortex, NIRS was attached to the frontal part, and 32 channels were simultaneously measured by arranging light transmission points and light reception points in a grid pattern (see FIG. 2). In FIG. 2, a black character portion indicates a light transmitting point in a white circle, a white character portion indicates a light receiving point in a black circle, and a black character portion indicates a channel in a white square. The NIRS was mounted using the International 10-20 method (see FIGS. 3 and 4), and the sampling period was 110 ms.

  Referring to FIG. 1 again, the brain information preprocessing unit 15 performs preprocessing such as smoothing to remove periodic fluctuations peculiar to the living body from the brain activity information measured by the brain activity measurement unit 14. . The brain activity information smoothed by the brain information preprocessing unit 15 is transmitted to the subject information database 16.

  The subject information database 16 includes the stock price data read by the presentation information determination unit 11, the stock investment ratio determined by the user X when the stock price is presented, and brain activity information when the stock price is presented (smoothed). , The stock price data, stock investment ratio, and brain activity information are transmitted to the analysis unit 17. The analysis unit 17 constructs a prediction model using the measured brain activity information as a prediction factor, and transmits the prediction model to the subject information database 16.

  In this way, the server terminal 1b constructs a prediction model that predicts the investment behavior of the user X based on the brain activity information measured by the user terminal 1a. Therefore, it can be said that the user terminal 1a is communicably connected to the server terminal 1b that builds a prediction model for predicting the investment behavior of the user X based on the brain activity information.

[Experimental result]
5 and 6 are sample paths for experimental data. FIG. 5 and FIG. 6A show the stock price presented to the subject in time series, and FIG. 5D and FIG. 6D show the stock investment ratio of the subject. 5 (b), (c), (e), and (f) in FIG. 5 and FIG. 6 show changes in cerebral blood oxyhemoglobin concentration in each part of the brain. 5 and 6 (b) show changes in cerebral blood oxyhemoglobin concentration in channel 11 (11ch), and FIGS. 5 and 6 (c) show changes in cerebral blood oxyhemoglobin concentration in channel 13 (13ch). Indicates. FIGS. 5 and 6 (e) show changes in oxyhemoglobin concentration in channel 29 (29ch), and FIGS. 5 (f) and 6 (f) show changes in oxyhemoglobin concentration in channel 30 (30ch). Indicates.

  Here, a positive correlation can be observed by comparing the change in cerebral blood oxyhemoglobin concentration between 11ch and 13ch and the stock investment ratio of the subject. Such parallel fluctuations in blood flow rate and investment rate were also observed fairly robustly in other subjects.

  Next, it can be seen that the change in cerebral blood oxyhemoglobin concentration between 29ch and 30ch is reversed and is roughly correlated with the stock investment ratio. In particular, when taking a risk-avoidance action by sharply reducing the stock investment ratio, it can be seen that the blood flow in this portion is increasing. In addition, there were some subjects who could and could not observe this part because it was difficult to observe.

[Building a prediction model]
In order to verify the effectiveness of such observation data, we constructed an investment behavior prediction model using the observed blood flow as a factor. For each subject, experimental data for eight stock prices (for eight tasks) was used to learn the parameters of the prediction model, and the investment rate for the remaining two stock prices (for two tasks) was predicted. The prediction model uses a three-layer perceptron, and parameters and hyperparameters are Bayesian estimated by the Markov chain Monte Carlo method (MCMC method). Hyperparameter marginal likelihood was used to select the intermediate layer and prediction factors.

  Effective sites were selected according to the Akaike Bayesian Information Criterion (ABIC) in Bayesian estimation using each region of the prefrontal cortex represented by each channel in FIG. 2 as a candidate for a prediction factor. As a result, 11ch, 13ch, 29ch, and 30ch cerebral blood oxyhemoglobin concentrations are adopted as a significant factor in the prediction model, and compared with the primary autoregressive AR (1) prediction model, the hyperparameter marginal likelihood There was an improvement of about 3% to 24% in degree and about 0% to 35% in the mean squared predictions (MSD). In other words, it was confirmed that the explanatory power of investment behavior is greatly increased by the concentration of oxyhemoglobin in the brain blood.

  By the way, a large noise is included in individual investment behavior, and this becomes a problem when predicting investment behavior. That is, even if there is a statistical tendency in investment behavior, the presence of such noise makes it difficult to observe the tendency. One convenient and effective way to remove individual noise is to average between individuals. FIG. 7 is a prediction using the averaged investment rate and cerebral blood oxyhemoglobin concentration. However, the cerebral blood oxyhemoglobin concentration of fNIRS represents the relative changes of individuals, and caution is required for averaging. In the present embodiment, the data of each individual is normalized and added and averaged after processing to remove the magnitude of fluctuation between individuals and the difference in average trend. In addition, the vertical axis | shaft in FIG. 7 has shown the investment ratio, and the horizontal axis has shown time.

  The solid line 51 in FIG. 7A represents the investment rate predicted by the model, and the broken line 53 represents the actual investment rate by the subject. When both are compared, it can be confirmed that the prediction can be made with relatively high accuracy. As a result of selecting the model, the blood flow rate of 11ch and 29ch was adopted as the middle layer and the prediction factor in addition to the investment rate immediately before the decision making. By adding these blood flow data, the prediction error can be improved by about 30% compared to the case where there is no blood flow.

  The solid line 51 in FIG. 7B represents the investment rate predicted by the model, and the broken lines 55a and 55b represent the 50% interval for investment behavior prediction. Referring to FIG. 7B, it can be confirmed that the actual investment rate is included in this section in most periods.

  Thus, it was found that even when a low-resolution and low-depth light measurement device (NIRS) is used, investment decision making by the user can be predicted to some extent by specifying a measurement site.

  That is, conventional knowledge about user decision-making has been obtained by measuring a relatively deep part of the brain using MRI with high spatial resolution (for example, reward system or emotional system). In the case of using a low-depth and easy-to-use measurement device, there are many uncertainties as to how much knowledge about user decision-making can be obtained. About this point, it became clear by this experiment that even if it is a case where NIRS which is a convenient measuring device with low spatial resolution and low depth is used, it is possible to predict a user's decision making.

  As a result, it is possible to predict the investment decision of the user at a relatively low introduction cost and maintenance cost without using a large-scale facility such as MRI. Moreover, since the site | part to measure is specified, a user's investment decision-making can be estimated, relieving a user's physical and mental stress, without imposing a body movement restriction | limiting on a user.

[Overall view of auxiliary information presentation system]
The auxiliary information presentation system 2 using the prediction model constructed as described above will be described with reference to FIG.

  The auxiliary information presentation system 2 is predicted based on the user terminal 2a including the brain activity information measuring unit 14 that measures the brain activity information (for example, cerebral blood oxyhemoglobin concentration) of the user X, and the measured brain activity information. A server terminal 2b for determining whether or not the investment behavior of the user X is appropriate. The user terminal 2a and the server terminal 2b are connected so as to be able to communicate with each other.

  The user terminal 2 a includes at least a presentation device 12 and a brain activity measurement unit 14. The server terminal 2b includes at least a brain information preprocessing unit 15, a subject information database 16, an investment behavior prediction unit 20, a market analysis database 21, an appropriateness determination unit 22, and an auxiliary information creation unit 23. Here, the prediction model is stored in the subject information database 16. In addition, although the user terminal 2a and the server terminal 2b are each provided with the communication part which can be transmitted / received, in order to clarify the communication origin and communication destination of data, illustration is abbreviate | omitted.

  The presentation device 12 presents information such as a stock price received from the financial market Y to the user X. The brain activity measuring unit 14 continuously measures the brain activity information of the user X (cerebral blood oxyhemoglobin) while the financial market information is presented. The measured brain activity information is transmitted to the brain information preprocessing unit 15.

  The brain information preprocessing unit 15 performs preprocessing such as smoothing on the measured value. Then, the measured value smoothed by the brain information preprocessing unit 15 is transmitted to the investment behavior prediction unit 20.

  The investment behavior prediction unit 20 takes out the prediction model stored in the subject information database 16 and predicts the investment behavior of the user X from the prediction model and the measured value. The investment behavior is predicted based on, for example, whether or not the measured value exceeds the threshold of the prediction model.

  When the investment behavior is predicted, the appropriateness determination unit 22 refers to the state of the financial market stored in the market analysis database and determines whether or not the predicted investment behavior is appropriate. The market analysis database stores the status of the financial market obtained by using statistical analysis technology or stochastic data mining technology.

  When the investment behavior of the user X predicted from the measured brain activity information is not appropriate (for example, when there is a significant discrepancy with the statistical analysis result), the auxiliary information creation unit 23 An error is pointed out and auxiliary information (for example, auxiliary information for taking appropriate actions such as statistical analysis results and past similar situation data) is transmitted to the presentation device 12. The presentation device 12 presents the received auxiliary information to the user X.

  Thus, the server terminal 2b determines the preference of the user X based on the brain activity information measured by the user terminal 2a. Therefore, it can be said that the user terminal 2a is communicably connected to the server terminal 2b that determines whether or not the investment behavior of the user X predicted based on the brain activity information is appropriate.

  In the auxiliary information presentation system 2, auxiliary information regarding the predicted investment behavior of the user X is presented, but a configuration for constructing a prediction model may be provided along with this. That is, as shown in FIG. 8, the decision making part 13 and the analysis part 17 are provided, and it is good also as building a prediction model as mentioned above. As a result, the prediction model can be updated as appropriate.

[Flowchart for Presenting Auxiliary Information]
With reference to FIG. 9, the flowchart for presenting auxiliary information by the auxiliary information presentation system 2 will be described.

  First, a stimulus such as a stock price is presented to the user via the presentation device 12 (S101). Subsequently, a change in cerebral blood oxyhemoglobin concentration at a specific site is measured via the brain activity measuring unit 14 (S102).

  Subsequently, the measured value is smoothed through the brain information preprocessing unit 15 (S103). Subsequently, the smoothed measurement value is input to the prediction model via the investment behavior prediction unit 20 (S104), and it is determined whether or not the measurement value exceeds the threshold value of the prediction model (S105).

  If this determination exceeds a threshold, it is predicted that the user will make an investment (increase the stock investment ratio) (S106), and if the determination does not exceed the threshold, the user will not make an investment (lower the stock investment ratio). (S107). Subsequently, the state of the financial market is acquired from the market analysis database 21 (S108), and whether or not the predicted investment behavior is appropriate is determined via the appropriateness determination unit 22 (S109).

  If this determination is appropriate, the process is terminated. If it is not appropriate, auxiliary information is created via the auxiliary information creation unit 23, and this auxiliary information is presented to the presentation device 12 (S110). Thereafter, the process ends.

  As described above, in the auxiliary information presentation system 2, when the behavior predicted from the brain activity information of the user is not appropriate, the user is alerted and the auxiliary information for taking a more appropriate behavior is presented. be able to. As a result, it is possible to provide interactive information using brain activity information.

  In addition, since it is possible to determine whether or not the emotional user's behavior is appropriate using objective information that is a statistical analysis result, for example, financial risk such as depression can be avoided.

  Moreover, by comparing with the brain activity information of stock investment professionals, it is possible to encourage investment beginners to make appropriate decisions, and it can also be applied to investment education.

  In the present embodiment, the behavior prediction of stock investment and the determination of its suitability are performed, but the present invention is not limited to this. In other words, the present invention is not limited to stock investment, and it is possible to predict and determine the suitability of human behavior in general under uncertainty.

[Brain Activity Measurement Unit]
According to this embodiment, since the site | part which measures brain information is specified, size reduction of the brain activity measurement part 14 can be expected. An example of a suitable brain activity measurement unit 14 according to this embodiment will be described with reference to FIG.

  The brain activity measuring unit 14 includes a band unit 200, a measuring unit 201, and a brain information transmitting unit (not shown). The measuring unit 201 includes a light transmitting unit 201a and a light receiving unit 201b, and measures user brain information. The light transmitting unit 201a and the light receiving unit 201b measure brain information in a site (for example, 11ch) suitable for predicting investment behavior. The band unit 200 is used to fix the measurement unit 201 at a suitable position for measuring brain information.

  As described above, according to the present embodiment, the apparatus can be reduced in size, the body movement restriction is not imposed on the user, and the investment behavior of the user is predicted (determined) at a relatively low introduction cost and maintenance cost. can do. In particular, since the measurement unit 201 identifies the part to be measured, it is possible to accurately predict (determine) the investment behavior of the user.

[Identification of measurement site]
Below, the suitable site | part for predicting (determining) investment behavior is demonstrated with reference to FIGS.

  In addition, in FIGS. 11-13, a measurement site | part is identified using the description (refer FIG.3 and FIG.4) in the international 10-20 method. In addition, in FIGS. 11 to 13, the length of the vertical axis from the nasal root Nz to the parietal portion Cz is 20 cm, and the length of the horizontal axis from the left anterior pinna point T3 to the right pinna point T4 is 30 cm. Assuming that the measurement site is specified. Further, the direction from the origin Fpz toward the crown Cz is “up”, the direction from the origin Fpz to the nasal root Nz is “down”, the direction from the origin Fpz to the left auricular anterior point T3 is “left”, and the origin Fpz The direction from the right to the right anterior point T4 is expressed as “right”.

  With reference to FIG. 11, the most preferable main parts A1 and A2 regarding prediction (determination) of investment behavior will be described.

  The main part A1 has a width of 2 cm with the vertical axis 2 cm below the origin Fpz. This is a range from 10% to 20% from the nasal root Nz (0%) to the crown Cz (100%), that is, a range of 10% below the origin Fpz. The horizontal axis is 2.5 cm wide from the origin Fpz to the left and right by 1.25 cm. This is a range from about 46% to about 54% from the left anterior pinna point T3 (0%) to the right pinna point T4 (100%), that is, the left pinna point from the origin Fpz to the left and right respectively. The range is approximately 4% between T3 and the right anterior pinna point T4.

  The main part A2 has a width of 2 cm from 2 cm to 4 cm above the origin Fpz on the vertical axis. This is a range from 30% to 40% from the nasal root Nz (0%) to the crown Cz (100%), that is, a range from 10% to 20% above the origin Fpz. The horizontal axis is a width of 2.5 cm from 1.25 cm to 3.75 cm to the left from the origin Fpz. This is a range from 37.5% to about 46% from the left anterior pinna point T3 (0%) to the right anterior pin point T4 (100%), that is, from the origin Fpz to the left pinna point T3 direction. In the range from about 4% to 12.5% between the left anterior pinna point T3 and the right pinna point T4.

  As described above, the main part A1 is a part having a range of 10% below the origin Fpz and a range of approximately 4% between the left anterior pinna point T3 and the right pinna point T4 to the left and right from the origin Fpz. is there. In addition, the main part A2 is from a range from 10% to 20% above the origin Fpz and from approximately 4% between the left anterior point T3 and the right anterior point T4 in the direction from the origin Fpz to the left anterior point T3. This is a region consisting of 12.5% of the range. The main part A1 is a part including the 29ch and 30ch described above, and the main part A2 is a part including the 11ch described above.

  As described above, the most suitable part regarding the prediction (determination) of the investment behavior has been described. However, there are auxiliary parts other than these parts that can achieve a certain accuracy regarding the prediction (determination) of the investment action. Hereinafter, with reference to FIG. 12 and FIG. 13, an explanation will be given on a suitable assisting part for prediction (determination) of investment behavior.

  FIG. 12 shows auxiliary parts B1 and B2 that can achieve a certain degree of accuracy regarding the prediction (determination) of investment behavior.

  The auxiliary site B1 has a vertical axis of 6 cm in width from 2 cm below the origin Fpz to 4 cm above. This is a range from 10% to 40% from the nasal root Nz (0%) to the crown Cz (100%), that is, a range from 10% below the origin Fpz to 20% above. The horizontal axis is 2.5 cm wide by 1.25 cm from side to side from the origin Fpz. This is a range from about 46% to about 54% from the left anterior pinna point T3 (0%) to the right pinna point T4 (100%), that is, the left pinna point from the origin Fpz to the left and right respectively. The range is approximately 4% between T3 and the right anterior pinna point T4.

  The auxiliary part B2 has a width of 2 cm with the vertical axis being 2 cm below the origin Fpz. This is a range from 10% to 20% from the nasal root Nz (0%) to the crown Cz (100%), that is, a range of 10% below the origin Fpz. The horizontal axis is a width of 6 cm, 3 cm left and right from the origin Fpz. This is a range from about 40% to about 60% from the left anterior pinna point T3 (0%) to the right pinna point T4 (100%), that is, the left pinna point from the origin Fpz to the left and right respectively. It is a range of about 10% between T3 and right anterior pinna point T4.

  As described above, the auxiliary part B1 has a range from the lower 10% to the upper 20% of the origin Fpz and a range of approximately 4% between the left anterior pinnae point T3 and the right anterior pinna point T4 on the left and right from the origin Fpz. It is a part consisting of. The auxiliary part B2 is a part having a range of 10% below the origin Fpz and a range of approximately 10% between the left anterior pinna point T3 and the right pinna point T4 on the left and right sides from the origin Fpz. Even in such an auxiliary part B1 or B2, a certain degree of accuracy can be realized in the prediction (determination) of investment behavior.

  FIG. 13 shows an auxiliary part C capable of realizing a certain accuracy with respect to prediction (determination) of investment behavior.

  The auxiliary part C has a width of 4 cm with the vertical axis 4 cm above the origin Fpz. This is a range from 20% to 40% from the nasal root Nz (0%) to the top of the head Cz (100%), that is, a range of 20% above the origin Fpz. The horizontal axis is a width of 2.5 cm from 6 cm to 8.5 cm to the left from the origin Fpz. This is a range from about 22% to 30% from the left anterior pinna point T3 (0%) to the right anterior pin point T4 (100%), that is, from the origin Fpz to the left anterior pinna point T3. The range from 20% to approximately 28% between the preauricular point T3 and the right auricular point T4.

  As described above, the auxiliary part C has a range of 20% above the origin Fpz and approximately 20% to 20% between the left anterior point T3 and the right anterior point T4 in the direction from the origin Fpz to the left anterior point T3. This is a region consisting of Even in such an auxiliary part C, it is possible to achieve a certain accuracy in the prediction and determination of investment behavior. The auxiliary part C is a part including the 13ch described above.

[effect]
Thus, according to the present embodiment, since the user's investment behavior is predicted using the brain activity information, unconscious or emotional investment behavior can also be quantitatively measured. In addition, since it is determined whether such unconscious or emotional investment behavior is appropriate using objective information such as statistical analysis results, for example, financial risk avoidance such as depression is avoided. become able to.

  Moreover, since the site | part to measure is specified in the prediction (determination) using brain activity information, size reduction (refer FIG. 10) of a measuring apparatus can be achieved, and a body motion restriction may be imposed on a user. The investment behavior of the user can be predicted (determined) at a relatively low introduction cost and maintenance cost.

  Further, the identified part is a part suitable for prediction (determination) of investment behavior, and the investment behavior can be predicted (determination) with high accuracy.

  In addition, for example, it is possible to encourage investment beginners to make appropriate judgments by comparing with professional brain activity information of stock investment, and it can be applied to investment education. In this case, since the size of the measuring device can be reduced, it can be made practical.

[Others]
In the present embodiment, NIRS is used for measuring brain activity information, but the present invention is not limited to this. For example, a general apparatus such as EEG (Electroencephalogram) that is not invasive and does not limit body movement or experimental environment may be used. That is, the present invention is characterized in that it measures brain activity information of a specific part in predicting (determining) investment behavior. If the apparatus is not invasive and has little body movement limitation, the measuring apparatus is appropriately used. It can be changed.

DESCRIPTION OF SYMBOLS 1 Prediction model construction system 2 Auxiliary information presentation system 10 Financial information database 11 Presentation information determination part 12 Presentation apparatus 13 Decision-making part 14 Brain activity measurement part 15 Brain information pre-processing part 16 Subject information database 17 Analysis part 20 Investment behavior prediction part 21 Market analysis database 22 Appropriateness judgment unit 23 Auxiliary information creation unit

Claims (9)

A method of building a prediction model for predicting user behavior in investment behavior ,
A step of measuring brain activity information of a specific part by a non-invasive brain activity measurement unit;
Building the prediction model from the measured brain activity information,
The specific site,
In the international 10-20 method, the vertical axis width between the nasal root Nz and the parietal portion Cz in the direction from the origin Fpz to the parietal portion Cz is 10% to 20%, and the left anterior pinnae in the direction from the origin Fpz to the left anterior pin point T3. A main part A2 having a horizontal axis width of approximately 4% to 12.5% between the point T3 and the right anterior point T4;
The vertical axis width from the lower part 10% to the upper part 20% between the nose root part Nz and the parietal part Cz from the origin Fpz to the parietal part Cz direction, and the left anterior pinna point T3 and the right anterior pinna point T4 direction from the origin Fpz Auxiliary regions B1 each having a lateral axis width of approximately 4% between the left anterior pinna point T3 and the right pinna point T4,
The vertical axis width of 10% below the nose root portion Nz and the parietal portion Cz from the origin Fpz to the parietal portion Cz direction, and the left auricle from the origin Fpz in the direction of the left auricular anterior point T3 and the right auricular anterior point T4, respectively. Auxiliary region B2 consisting of a 10% horizontal axis width between the anterior point T3 and the right auricular anterior point T4,
Alternatively, the vertical axis width of 20% between the nasal root Nz and the parietal portion Cz in the direction from the origin Fpz to the parietal portion Cz, and the left auricular front point T3 and the right auricular front point T4 in the direction from the origin Fpz to the left auricular point T3. Auxiliary part C having a horizontal axis width of 20% to about 28% between,
Any one of the methods described above. A method of presenting auxiliary information of user behavior in investment behavior using a prediction model constructed by the method according to claim 1,
Presenting specific information to the user;
Measuring the brain activity information for determining the specific part when the specific information is presented by the brain activity measuring unit;
Predicting a user's possible actions from the determination brain activity information and the prediction model;
Determining whether the predicted behavior is appropriate;
Presenting auxiliary information on the condition that the action is determined to be inappropriate. The method according to claim 1 or 2, wherein
The specific site,
In the international 10-20 method, the vertical axis width of the bottom 10% between the nose root Nz and the parietal portion Cz with the top of the parietal Cz direction from the origin Fpz, and the left anterior point T3 and the right anterior point T4 from the origin Fpz A main part A1 having a horizontal axis width of approximately 4% between the left anterior pinna point T3 and the right pinna point T4 in the direction;
Alternatively, the vertical axis width from 10% to 20% between the nasal root Nz and the parietal portion Cz in the direction from the origin Fpz to the parietal portion Cz, and the left anterior pinna point T3 and the right ear in the direction from the origin Fpz to the left anterior pinpoint T3. A main portion A2 having a horizontal axis width of approximately 4% to 12.5% between the intervention points T4,
Any one of the methods described above. A user terminal communicably connected to a server terminal capable of constructing a prediction model for predicting user behavior in investment behavior from brain activity information,
A non-invasive brain activity measurement unit that measures brain activity information of a specific site;
A communication unit that transmits the brain activity information measured by the brain activity measurement unit to the server terminal,
The specific site,
In the international 10-20 method, the vertical axis width between the nasal root Nz and the parietal portion Cz in the direction from the origin Fpz to the parietal portion Cz is 10% to 20%, and the left anterior pinnae in the direction from the origin Fpz to the left anterior pin point T3. A main part A2 having a horizontal axis width of approximately 4% to 12.5% between the point T3 and the right anterior point T4;
The vertical axis width from the lower part 10% to the upper part 20% between the nose root part Nz and the parietal part Cz from the origin Fpz to the parietal part Cz direction, and the left anterior pinna point T3 and the right anterior pinna point T4 direction from the origin Fpz Auxiliary regions B1 each having a lateral axis width of approximately 4% between the left anterior pinna point T3 and the right pinna point T4,
The vertical axis width of 10% below the nose root portion Nz and the parietal portion Cz from the origin Fpz to the parietal portion Cz direction, and the left auricle from the origin Fpz in the direction of the left auricular anterior point T3 and the right auricular anterior point T4, respectively. Auxiliary region B2 consisting of a 10% horizontal axis width between the anterior point T3 and the right auricular anterior point T4,
Alternatively, the vertical axis width of 20% between the nasal root Nz and the parietal portion Cz in the direction from the origin Fpz to the parietal portion Cz, and the left auricular front point T3 and the right auricular front point T4 in the direction from the origin Fpz to the left auricular point T3. Auxiliary part C having a horizontal axis width of 20% to about 28% between,
Any one of the above-mentioned user terminals. When a prediction model in which a user's behavior in investment behavior is predicted from brain activity information of a specific part is stored, and the predicted behavior of the user predicted from the brain activity information of the user in the specific part using the prediction model is not appropriate A user terminal that is communicably connected to a server terminal that transmits auxiliary information about the predicted behavior,
A non-invasive brain activity measurement unit that measures the brain activity information for determination of the specific part when the specific information is presented;
A communication unit that transmits the determination brain activity information measured by the brain activity measurement unit to the server terminal, the communication unit receiving the auxiliary information from the server terminal;
A presentation device for presenting the received auxiliary information to a user,
The specific site,
In the international 10-20 method, the vertical axis width between the nasal root Nz and the parietal portion Cz in the direction from the origin Fpz to the parietal portion Cz is 10% to 20%, and the left anterior pinnae in the direction from the origin Fpz to the left anterior pin point T3. A main part A2 having a horizontal axis width of approximately 4% to 12.5% between the point T3 and the right anterior point T4;
The vertical axis width from the lower part 10% to the upper part 20% between the nose root part Nz and the parietal part Cz from the origin Fpz to the parietal part Cz direction, and the left anterior pinna point T3 and the right anterior pinna point T4 direction from the origin Fpz Auxiliary regions B1 each having a lateral axis width of approximately 4% between the left anterior pinna point T3 and the right pinna point T4,
The vertical axis width of 10% below the nose root portion Nz and the parietal portion Cz from the origin Fpz to the parietal portion Cz direction, and the left auricle from the origin Fpz in the direction of the left auricular anterior point T3 and the right auricular anterior point T4, respectively. Auxiliary region B2 consisting of a 10% horizontal axis width between the anterior point T3 and the right auricular anterior point T4,
Alternatively, the vertical axis width of 20% between the nasal root Nz and the parietal portion Cz in the direction from the origin Fpz to the parietal portion Cz, and the left auricular front point T3 and the right auricular front point T4 in the direction from the origin Fpz to the left auricular point T3. Auxiliary part C having a horizontal axis width of 20% to about 28% between,
Any one of the above-mentioned user terminals. The user terminal according to claim 4 or 5, wherein
The specific site,
In the international 10-20 method, the vertical axis width of the bottom 10% between the nose root Nz and the parietal portion Cz with the top of the parietal Cz direction from the origin Fpz, and the left anterior point T3 and the right anterior point T4 from the origin Fpz A main part A1 having a horizontal axis width of approximately 4% between the left anterior pinna point T3 and the right pinna point T4 in the direction;
Alternatively, the vertical axis width from 10% to 20% between the nasal root Nz and the parietal portion Cz in the direction from the origin Fpz to the parietal portion Cz, and the left anterior pinna point T3 and the right ear in the direction from the origin Fpz to the left anterior pinpoint T3. A main portion A2 having a horizontal axis width of approximately 4% to 12.5% between the intervention points T4,
Any one of the above-mentioned user terminals. A prediction model construction system for predicting user behavior in investment behavior ,
A non-invasive brain activity measurement unit that measures brain activity information of a specific site;
An analysis unit that builds a prediction model from the brain activity information measured by the brain activity measurement unit,
The specific site,
In the international 10-20 method, the vertical axis width between the nasal root Nz and the parietal portion Cz in the direction from the origin Fpz to the parietal portion Cz is 10% to 20%, and the left anterior pinnae in the direction from the origin Fpz to the left anterior pin point T3. A main part A2 having a horizontal axis width of approximately 4% to 12.5% between the point T3 and the right anterior point T4;
The vertical axis width from the lower part 10% to the upper part 20% between the nose root part Nz and the parietal part Cz from the origin Fpz to the parietal part Cz direction, and the left anterior pinna point T3 and the right anterior pinna point T4 direction from the origin Fpz Auxiliary regions B1 each having a lateral axis width of approximately 4% between the left anterior pinna point T3 and the right pinna point T4,
The vertical axis width of 10% below the nose root portion Nz and the parietal portion Cz from the origin Fpz to the parietal portion Cz direction, and the left auricle from the origin Fpz in the direction of the left auricular anterior point T3 and the right auricular anterior point T4, respectively. Auxiliary region B2 consisting of a 10% horizontal axis width between the anterior point T3 and the right auricular anterior point T4,
Alternatively, the vertical axis width of 20% between the nasal root Nz and the parietal portion Cz in the direction from the origin Fpz to the parietal portion Cz, and the left auricular front point T3 and the right auricular front point T4 in the direction from the origin Fpz to the left auricular point T3. Auxiliary part C having a horizontal axis width of 20% to about 28% between,
A prediction model construction system characterized by being one of the following. An auxiliary information presentation system that presents auxiliary information about user behavior in investment behavior using the prediction model constructed by the prediction model construction system according to claim 7,
The brain activity measurement unit measures the brain activity information for determination of the specific part when specific information is presented,
A behavior prediction unit that predicts a user's possible behavior from the determination brain activity information and the prediction model;
An appropriateness determination unit that determines whether or not the behavior predicted by the behavior prediction unit is appropriate;
A supplementary information presentation system comprising: a presentation device that presents supplementary information on the condition that the appropriateness determination unit determines that the information is not appropriate. The specific site,
In the international 10-20 method, the vertical axis width of the bottom 10% between the nose root Nz and the parietal portion Cz with the top of the parietal Cz direction from the origin Fpz, and the left anterior point T3 and the right anterior point T4 from the origin Fpz A main part A1 having a horizontal axis width of approximately 4% between the left anterior pinna point T3 and the right pinna point T4 in the direction;
Alternatively, the vertical axis width from 10% to 20% between the nasal root Nz and the parietal portion Cz in the direction from the origin Fpz to the parietal portion Cz, and the left anterior pinna point T3 and the right ear in the direction from the origin Fpz to the left anterior pinpoint T3. A main portion A2 having a horizontal axis width of approximately 4% to 12.5% between the intervention points T4,
The prediction model construction system according to claim 7 or the auxiliary information presentation system according to claim 8, wherein the prediction model construction system according to claim 7 is used.

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