psychophysics lab database

table of contents

michael silver laboratory

• Visual neuroscience lab, Berkeley School of Optometry

• Graduate students and post-docs work with several undergraduate students to conduct psychophysical experiments

eer diagram

relations schema

1. Lab(Lab_ID, Name, Department, Building, Room)2. Test_Subject(Sub_ID, LName, FName, MI, Screening_Results, Payment_Info, Age,

Gender, Affiliation, Glasses, Email)3. Experiment(Exp_ID, Project, Name, Start_date , PI)4. Lab_Personnel(LP_ID, FName, LName, MI, Email)

a. Undergrad(LP_ID4, Dept, Year, Major, Grade, Supervisor_ID4)b. Faculty(LP_ID4,Position, Dept, Affiliation, Focus, Salary)

5. Equipment (EQ_ID, Name, Cost)a. Display(EQ_ID5, Resolution, Brand)b. Computer(EQ_ID5, RAM, HD)c. Other(EQ_ID5)

6. Event(EV_ID, Description, Date, Time, Location)7. Transaction(Tran_ID, Amount, Date, Description)

a. Funding(Tran_ID7, RecipientID4)b. Experiment_Cost(Tran_ID7, Exp_ID3, Sub_ID2, Type)c. Other(Tran_ID7)

relations schema (cont.)

8. Publication(Pub_ID, Publisher, Date, Page_No)9. Publication_Author(Pub_ID8, LP_ID4)10. Publication_Lab(Pub_ID8, Lab_ID1)11. Lab_Employment(Lab_ID1, LP_ID4)12. Event_Participant(EV_ID6, LP_ID4)13. Event_Host(EV_ID6, Lab_ID1)14. Exp_Conductor(Exp_ID3, LP_ID4)15. Exp_Analysis(Exp_ID3, LP_ID4)16. Exp_Equipment(Exp_ID3, EQ_ID5)17. Exp_Publication(Exp_ID3, Pub_ID8)18. Test(Exp_ID3, Sub_ID2)a. Motion_Learning_Var_Test(Exp_ID3, Sub_ID2, Var_Type, Room_No23)19. Session(Exp_ID3, Sub_ID2, Block_ID)a. Motion_Learning_Var_Session(Exp_ID3, Sub_ID2, Block_ID19, Direction, Var_Type,

Date, Time, Result)

relations schema (cont.)

20. Time_Slot(TS_ID, Month, Day, Year, Hour, Quarter, Room_no 23, Exp_ID3, Sub_ID2,

Block_ID19)21. Map(Exp_ID3, Sub_ID2, Map_ID)22. Set(Exp_ID3, Sub_ID2, Block_ID19,Set_ID)

a. Motion_Learning_Var_Set(Exp_ID3, Sub_ID2, Block_ID19,Set_ID22, Dir1, Dir2, Threshold, Std, Answer, Result)

23. Room(Room_No, Location)

queries: cost effectiveness

Find the cost effectiveness of each experiment (which are a group of tests under a certain category).

queries: forecastingForecast the number of tests needed to be run in a year using Winter’s seasonal forecasting method. This takes into account past data, seasonal fluctuations, and trends.

queries: forecasting (cont.)

queries: test results and gender

Find the average and variance of test results to determine factors. This one is for gender trends; past observations have shown that women usually perform better than men on tests.

queries: test results and time of day

Find how time of day correlates with experiment data. Past experience has shown that people perform better in the day than in the night.

queries: scheduling

Find the percentage a certain hour of the day is busy. This will help decide when to schedule tests.

Can then use a query to find a free time slot

normalization

Undergrad(LP_ID4, Dept, Year, Major, Grade, Supervisor_ID4)

• 2NF: major can give department• can solve this by either eliminating the department attribute or making two tables.

R1(LP_ID4, Year, Major, Grade, Supervisor_ID4)R2(Major, Department)

Experiment(Exp_ID, Project, Name, Start_date , PI)

• 2NF: knowing project and name gives primary investigator• can be normalized by splitting into two relations, but is more convenient as is

R1(Exp_ID, Project, Name, Start_date)R2(Exp_ID, PI)

normalization

Motion_Learning_Var_Session(Exp_ID3, Sub_ID2, Block_ID19, Direction, Var_Type, Date, Time, Result) 

• 3NF: Var_Type and Direction defines Exp_ID but not the other parts of the key• can be normalized by splitting the attributes• keeping all the data together might be better

R1(Exp_ID3, Sub_ID2, Block_ID19, Date, Time, Result)R2(Exp_ID3, Direction, Var_Type) 

Event(EV_ID, Description, Date, Time, Location)

In BCNF

Publication(Pub_ID, Publisher, Date, Page_No)

In BCNF

access: relationship view

access: tables

access: tables

access: queries

access: forms

access: reports

thank you and questions