Information processing speed (IPS) is a foundational cognitive skill that is vital for daily functioning. Changes in IPS are common in several medical conditions and in aging, supporting the need to understand the biological basis of these changes. Dynamic functional network connectivity (dFNC) is a relatively new functional magnetic resonance imaging method designed to estimate time-sensitive fluctuations in network-level connectivity patterns. Previous work investigating dFNC and IPS has suggested that dFNC may be a useful tool for investigating neurobiological models of IPS. However, there are considerable gaps in the literature, including the method of IPS estimation, sample representation, and size. Therefore, this study tested the relationship between dFNC measures and IPS. Neurotypical individuals were selected from the UK Biobank study (n=16831). Reaction time, symbol digit substitution, and trail-making tasks were used as estimates of IPS. IPS was quantified with raw, normed, and indexed scores. A sliding window approach with k-means clustering identified four dFNC states. Multiple linear regressions tested the relationship between IPS estimates and state-specific metrics. Significant associations were found between multiple state metrics and the three IPS measures. Findings generally align with prior work, including strong associations with time spent in highly connected visual and subcortical brain states. Findings also demonstrate consistency between raw and normed results. In sum, the findings further the literature by addressing questions about IPS estimation, sample size, and cohort biases. Future work addressing questions about scan and IPS acquisition methods are needed.