Abstract
A key goal of model-based cognitive neuroscience is to estimate the trial-by-trial fluctuations of cognitive model parameters in order to link these fluctuations to brain signals. However, previously developed methods are limited by being difficult to implement, time-consuming, or model-specific. Here, we propose an easy, efficient and general approach to estimating trial-wise changes in parameters: Leave-One-Trial-Out (LOTO). The rationale behind LOTO is that the difference between parameter estimates for the complete dataset and for the dataset with one omitted trial reflects the parameter value in the omitted trial. We show that LOTO is superior to estimating parameter values from single trials and compare it to previously proposed approaches. Furthermore, the method makes it possible to distinguish true variability in a parameter from noise and from other sources of variability. In our view, the practicability and generality of LOTO will advance research on tracking fluctuations in latent cognitive variables and linking them to neural data.
Highlights
Model-based cognitive neuroscience attempts to link mathematical models of cognitive processes to neural data in order to advance our knowledge of the mind and brain (Forstmann et al, 2011)
The Linear Ballistic Accumulator (LBA) belongs to the class of sequential sampling models of decision making, which assume that decisions arise from an evidence-accumulation process that continues until a threshold has been reached, indicating that the required amount of evidence for a decision has been gathered
We propose a novel approach to capturing trial-by-trial variability in cognitive model parameters that tries to overcome the shortcomings of specificity, complexity, and inefficiency: Leave-One-Trial-Out (LOTO)
Summary
Model-based cognitive neuroscience attempts to link mathematical models of cognitive processes to neural data in order to advance our knowledge of the mind and brain (Forstmann et al, 2011). A promising but challenging approach in this regard is to derive trial-specific values for parameters of cognitive models and to relate these values to trial-specific brain data, which offers insights into cognitive and neural principles at a highly detailed level of analysis (Gluth and Rieskamp, 2017; van Maanen et al, 2011; Turner et al, 2017; Wiecki et al, 2013). Cognitive models specify the distribution from which this variability is assumed to originate (e.g., a normal distribution), but remain silent about its direction and extent in single trials. Previous attempts to capture parameter variability have often been specific to a single model. Van Maanen et al (2011) derived maximum likelihood estimates for two single-trial parameters of the Linear Ballistic Accumulator (LBA) model (Brown and Heathcote, 2008).
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