1/f noise and effort on implicit measures of bias

Phenomena that vary over time can often be represented as a complex waveform. Fourier analysis decomposes this complex wave into a set of sinusoidal component waves. In some phenomena, the amplitude of these waves varies in inverse relation to frequency. This pattern has been called 1/f noise and, unlike white noise, it reflects nonrandom variation. Latencies in simple computer tasks typically reveal 1/f noise, but the magnitude of the noise decreases as tasks become more challenging. The current work hypothesizes a correspondence between 1/f noise and effort, leading to the prediction that increasing effort will reduce 1/f noise. In 2 studies, the author examined the relationship between an individual's attempts to avoid bias (measured in Study 1, manipulated in Study 2) and 1/f noise in implicit measures of stereotyping and prejudice. In each study, participants who made an effort to modulate the use of racial information showed less 1/f noise than did participants who made less effort. The potential value of this analytic approach to social psychology is discussed.