A note on choice and detect probabilities in the presence of choice bias

TL;DR

This paper extends analytical models of choice and detect probabilities to biased decision scenarios, providing insights into how biases affect these probabilities and offering correction methods for empirical data analysis.

Contribution

It generalizes previous models to include choice bias, applicable to detection tasks, and offers correction techniques for empirical measurements.

Findings

CPs and DPs increase with choice bias p

Bias impacts choice and detect probabilities monotonically

Provides a method to correct for bias in empirical data

Abstract

Recently we have presented the analytical relationship between choice probabilities, noise correlations and read-out weights in the classical feedforward decision-making framework (Haefner et al. 2013). The derivation assumed that behavioral reports are distributed evenly between the two possible choices. This assumption is often violated in empirical data - especially when computing so-called grand CPs combining data across stimulus conditions. Here, we extend our analytical results to situations when subjects show clear biases towards one choice over the other, e.g. in non-zero signal conditions. Importantly, this also extends our results from discrimination tasks to detection tasks and detect probabilities for which much empirical data is available. We find that CPs and DPs depend monotonously on the fraction, p, of choices assigned to the more likely option: CPs and DPs are smallest for p equal to 0.5 and increase as p increases, i.e. as the data deviates from the ideal, zero-signal, unbiased scenario. While this deviation is small, our results suggest a) an empirical test for the feedforward framework and b) a way in which to correct choice probability and detect probability measurements before combining different stimulus conditions to increase signal/noise.