## Perception

• Goldstein, E. Sensation and perception. Cengage Learning, 2013.

## Psychophysics

• 2013 Lu Dosher. Visual Psychophysics.

• 2012 Knoblauch Maloney. Modeling psyhophysical data in R.

• 2016 Kingdom Prins. Psychophysics: A practical introduction.

• 2004 Macmillan Creelman. Detection theory.

• 2002 Wickens, T. D. Elementary signal detection theory. Oxford: Oxford University Press.
• Fig 1.1. I think that False Alarms cannot not be retrieved using the proposed method under SDT framework given that LRT doesn’t result in a decision rule X>C for unequal variance normal distributions.

• Section 9.1. Many different procedures have been developed for hypothesis testing. In much scientific research, null-hypothesis testing is common, in which a specific null hypothesis (there is no effect) is contrasted with a general alternative hypothesis (there is some effect). The asymmetry of these hypotheses potheses introduces corresponding asymmetries in the conclusions (one can reject the null hypothesis but not the alternative). Signal-detection theory is closer to another hypothesis testing procedure, known as likelihood-ratio testing. In it, the hypotheses being tested are equally specific, and the test lets one make a decision in favor of one of them. The conclusions from such tests are symmetrical. I think that it might be imprecise to classify hypothesis testing in null-hypothesis testing and likelihood ratio testing. Likelihood ratio is a general method to find a test to decide between hypotheses independently on whether the hypotheses are symmetric or not. If the size $$\alpha$$ happens to be fixed, then the hypothesis testing is called null-hypothesis testing.

• 1997 Gescheider. Psychophysics: the fundamentals.

• 1966 Green Swets. Signal detection theory and psychophysics.

## Statistics

• 2014 Crawley. Statistics: An introduction using R.

• 2014 Pewsey Neuhauser. Circular statitistics in R.

• 2013 Chang. R Graphics Cookbook.

• 2012 Field Miles Field. Discovering statistics using R.

• 2008 Dalgaard. Introductory statistics with R.

• 2005 Young, G. A., & Smith, R. L. Essentials of statistical inference (Vol. 16). Cambridge University Press.

• 2004 Wasserman, L. All of statistics. Springer Science & Business Media.

• 2004 Motulsky Christopoulos. Fitting Models to Biological Data Using Linear and Nonlinear Regression: A Practical Guide to Curve Fitting.

• 2002 Casella, G., & Berger, R. L. Statistical inference (Vol. 2). Pacific Grove, CA: Duxbury.

• 2002 Burnham Anderson. Model Selection and Multi-Model Inference.

• 1995 Schervish, M.J. Theory of statistics.

## Writing style, publishing

• 2014 Pinker. The sense of style.

• 2012 Sword. Stylish academic writing.

• 2011 Lisberger. From science to citation.

• 2005 Kirkman. Good style. Writing for science and technology.

• 1999 Strunk White. The elements of style.

• 1998 Alley. The craft of scientific writing.

## Computational neuroscience

• 2011 Trommershauser Kording. Sensory cue integration.

• 2010 Gabbiani Cox. Mathematics for neuroscientists.

• 2001 Abbott Dayan. Theoretical Neuroscience. Computational and Mathematical Modeling of Neural Systems.

## Neuroscience (public)

• 2014 Dehaene. Consciousness and the brain.

• 2012 Koch. Consciousness: Confessions of a Romantic Reductionist

• 2012 Buonomano. Brain bugs.

• 2008 Kandel. In search of memory.