Journal of Mathematical Psychology 76 (2017) 142–155 Contents lists available at ScienceDirect Journal of Mathematical Psychology journal homepage: www.elsevier.com/locate/jmp On the efficiency of neurally-informed cognitive models to identify latent cognitive states ✩ Guy E. Hawkins a,* , Matthias Mittner b , Birte U. Forstmann a , Andrew Heathcote c a Amsterdam Brain and Cognition Center, University of Amsterdam, Amsterdam, The Netherlands b Department of Psychology, University of Tromsø, Tromsø, Norway c School of Medicine – Division of Psychology, University of Tasmania, Hobart, Tasmania, Australia highlights • Explores the recovery of cognitive models that are informed with neural data. • Contrasts two frameworks for using neural data to identify latent cognitive states. • Neural data have more power to recover discrete versus continuous latent states. • Reliably identifying latent cognitive states depends on effect size in neural data. article info Article history: Available online 25 July 2016 Keywords: Cognitive model Behavioral data Neural data Model recovery Simulation abstract Psychological theory is advanced through empirical tests of predictions derived from quantitative cog- nitive models. As cognitive models are developed and extended, they tend to increase in complexity – leading to more precise predictions – which places concomitant demands on the behavioral data used to discriminate between candidate theories. To aid discrimination between cognitive models and, more recently, to constrain parameter estimation, neural data have been used as an adjunct to behavioral data, or as a central stream of information, in the evaluation of cognitive models. Such a model-based neuro- science approach entails many advantages, including precise tests of hypotheses about brain–behavior relationships. There have, however, been few systematic investigations of the capacity for neural data to constrain the recovery of cognitive models. Through the lens of cognitive models of speeded decision- making, we investigated the efficiency of neural data to aid identification of latent cognitive states in models fit to behavioral data. We studied two theoretical frameworks that differed in their assumptions about the composition of the latent generating state. The first assumed that observed performance was generated from a mixture of discrete latent states. The second conceived of the latent state as dynami- cally varying along a continuous dimension. We used a simulation-based approach to compare recovery of latent data-generating states in neurally-informed versus neurally-uninformed cognitive models. We found that neurally-informed cognitive models were more reliably recovered under a discrete state rep- resentation than a continuous dimension representation for medium effect sizes, although recovery was difficult for small sample sizes and moderate noise in neural data. Recovery improved for both represen- tations when a larger effect size differentiated the latent states. We conclude that neural data aids the identification of latent states in cognitive models, but different frameworks for quantitatively inform- ing cognitive models with neural information have different model recovery efficiencies. We provide full worked examples and freely-available code to implement the two theoretical frameworks. © 2016 Elsevier Inc. All rights reserved. ✩ This research was supported by a Netherlands Organisation for Scientific Research (NWO) Vidi (452-11-008) grant to Birte Forstmann and an Australian Research Council (ARC) (DP110100234) Professorial Fellowship to Andrew Heathcote. The authors declare no competing financial interests. * Correspondence to: Amsterdam Brain and Cognition Center, University of Amsterdam, Nieuwe Achtergracht 129, Amsterdam 1018 WS, The Netherlands. 1. Introduction Quantitative models that explicate the cognitive processes driving observed behavior are becoming increasingly complex, E-mail address: guy.e.hawkins@gmail.com (G.E. Hawkins). http://dx.doi.org/10.1016/j.jmp.2016.06.007 0022-2496/© 2016 Elsevier Inc. All rights reserved.