Individual differences in cortical processing speed predict cognitive abilities: a model-based cognitive neuroscience account

Abstract

Previous research has shown that individuals with greater cognitive abilities display a greater speed of higher-order cognitive processing. These results suggest that speeded neural information-processing may facilitate evidence accumulation during decision making and memory updating and thus yield advantages in general cognitive abilities. We used a hierarchical Bayesian cognitive modeling approach to test the hypothesis that individual differences in the velocity of evidence accumulation mediate the relationship between neural processing speed and cognitive abilities. We found that a higher neural speed predicted both the velocity of evidence accumulation across behavioral tasks as well as cognitive ability test scores. However, only a small part of the association between neural processing speed and cognitive abilities was mediated by individual differences in the velocity of evidence accumulation. The model demonstrated impressive forecasting abilities by predicting 36% of individual variation in cognitive ability test scores in an entirely new sample solely based on their electrophysiological and behavioral data. Our results suggest that individual differences in neural processing speed might affect a plethora of higher-order cognitive processes, that only in concert explain the large association between neural processing speed and cognitive abilities, instead of the effect being entirely explained by differences in evidence accumulation speeds.

The latency of a visual evoked potential tracks the onset of decision making

“…Furthermore we found some individual differences in N200 peak-latencies across participants, which matches expectations that individual differences in cognitive processing exists that explains individual differences in behavior (e.g. see Schubert et al, 2017Schubert et al, , 2018.…”

Processing Speed, Working Memory, and Executive Functions: Independent or inter-related predictors of general intelligence

“…cognitive models; Frischkorn , with biological indicators of neural processing related to these two processes (c.f. Schubert, Nunez, Hagemann, & Vandekerckhove, 2018).…”

An Investigation of the Slope Parameters of Reaction Times and P3 Latencies in the Sternberg Memory Scanning Task – A Fixed-Links Model Approach

“…In the SMST in particular, P3 latencies seem to be sensitive to the experimental manipulation of the set size as they increase with a larger memory set (e.g., Ergen et al 2012;Houlihan 1998;Pelosi, McHayward, and Blumhardt 1995;Schubert et al 2015). Furthermore, especially latencies of ERP components associated with stimulus evaluation, memory updating, and response selection that occur later in the stream of information-processing have been shown to be strongly associated with intelligence (Bazana and Stelmack 2002;Kapanci et al 2019;McGarry-Roberts, Stelmack, and Campbell 1992;Saville et al 2015;Schubert et al 2015;Schubert, Hagemann, and Frischkorn 2017;Schubert et al 2018;Schubert et al 2019;Troche et al 2015;Troche and Rammsayer 2009). In particular, some studies have reported negative correlations between intelligence and P3 latencies measured in the SMST (McGarry-Roberts, Stelmack, and Campbell 1992;Schubert et al 2015;Schubert, Hagemann, and Frischkorn 2017).…”