FR Cnc revisited: photometry, polarimetry and spectroscopy

This paper is part of a multiwavelength study aimed at using complementary photometric, polarimetric and spectroscopic data to achieve an understanding of the activity process in late-type stars. Here, we present the study of FR Cnc, a young, active and spotted star. We performed analysis of All Sky Automated Survey 3 (ASAS-3) data for the years 2002–08 and amended the value of the rotational period to be 0.826518 d. The amplitude of photometric variations decreased abruptly in the year 2005, while the mean brightness remained the same, which was interpreted as a quick redistribution of spots. BVR_C and I_C broad-band photometric calibration was performed for 166 stars in FR Cnc vicinity. The photometry at Terskol Observatory shows two brightening episodes, one of which occurred at the same phase as the flare of 2006 November 23. Polarimetric BVR observations indicate the probable presence of a supplementary source of polarization. We monitored FR Cnc spectroscopically during the years 2004–08. We concluded that the radial velocity changes cannot be explained by the binary nature of FR Cnc. We determined the spectral type of FR Cnc as K7V. Calculated galactic space-velocity components (U, V, W) indicate that FR Cnc belongs to the young disc population and might also belong to the IC 2391 moving group. Based on Li Iλ6707.8 measurement, we estimated the age of FR Cnc to be between 10 and 120 Myr. Doppler tomography was applied to create a starspot image of FR Cnc. We optimized the goodness of fit to the deconvolved profiles for axial inclination, equivalent width and v sin i, finding v sin  i=46.2 km s^−1 and i= 55°. We also generated a syntheticV-band light curve based on Doppler imaging that makes simultaneous use of spectroscopic and photometric data. This synthetic light curve displays the same morphology and amplitude as the observed one. The starspot distribution of FR Cnc is also of interest since it is one of the latest spectral types to have been imaged. No polar spot was detected on FR Cnc.

Fixed vs. variable noise in 2AFC contrast discrimination: lessons from psychometric functions.

Recent discussion regarding whether the noise that limits 2AFC discrimination performance is fixed or variable has focused either on describing experimental methods that presumably dissociate the effects of response mean and variance or on reanalyzing a published data set with the aim of determining how to solve the question through goodness-of-fit statistics. This paper illustrates that the question cannot be solved by fitting models to data and assessing goodness-of-fit because data on detection and discrimination performance can be indistinguishably fitted by models that assume either type of noise when each is coupled with a convenient form for the transducer function. Thus, success or failure at fitting a transducer model merely illustrates the capability (or lack thereof) of some particular combination of transducer function and variance function to account for the data, but it cannot disclose the nature of the noise. We also comment on some of the issues that have been raised in recent exchange on the topic, namely, the existence of additional constraints for the models, the presence of asymmetric asymptotes, the likelihood of history-dependent noise, and the potential of certain experimental methods to dissociate the effects of response mean and variance.

The difference model with guessing explains interval bias in two-alternative forced- chocice detection procedures

The standard difference model of two-alternative forced-choice (2AFC) tasks implies that performance should be the same when the target is presented in the first or the second interval. Empirical data often show “interval bias” in that percentage correct differs significantly when the signal is presented in the first or the second interval. We present an extension of the standard difference model that accounts for interval bias by incorporating an indifference zone around the null value of the decision variable. Analytical predictions are derived which reveal how interval bias may occur when data generated by the guessing model are analyzed as prescribed by the standard difference model. Parameter estimation methods and goodness-of-fit testing approaches for the guessing model are also developed and presented. A simulation study is included whose results show that the parameters of the guessing model can be estimated accurately. Finally, the guessing model is tested empirically in a 2AFC detection procedure in which guesses were explicitly recorded. The results support the guessing model and indicate that interval bias is not observed when guesses are separated out.