In this course we discuss in detail some issues in psychophysics. Topics include the Fechnerian representation, affine representations, integration of just noticeable differences to recover the sensory scales, threshold models and signal detection models, applications of adaptive procedures for detection/discrimination, Markov models, etc. We also discuss some of the empirical laws such as the near-miss to Weber's law, power function, shift invariance... etc, and show how such empirical laws put constraints on psychophysical representations. We also introduce the basic principle behind the hierarchical Bayesian modeling.