Understanding the nature of decision-related signals in sensory neurons promises to give insights into their role in perceptual decision-making. Those signals, traditionally quantified as choice probabilities (CP), are well-understood in a feedforward framework assuming zero-signal trials with no choice bias. Here, we extend this understanding by analytically solving models of choice-related signals that account for informative stimuli, choice bias, and importantly, feedback signals reflecting either internal states, such as attention or belief, or the outcome of the decision process. First, we relate CPs to Choice Triggered Averages (CTAs), which quantify choice-related average changes in neural responses, and show that both have general expressions valid for activity-choice covariations of both feedforward or feedback origin. These expressions allow a meaningful calculation of CPs across all trials, including non-zero signal trials. Second, we derive how CPs and CTAs depend on feedforward and feedback weights and on noise correlations under several plausible model architectures. Third, we examine different types of feedback signals, related to predictive coding, probabilistic inference, and attention, and we predict how CPs and CTAs depend in each case on the stimulus signal level and on the neural tuning properties. Finally, we show that measuring both CPs and CTAs offers complementary information about the origin of choice-related signals, especially when studying temporal changes of activity-choice covariations across the trial time. Overall, our work provides new analytical tools to better understand the link between sensory representations and perceptual decisions.