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Assessing model-based inferences in decision making with single-trial response time decomposition

Dr. Gabriel Weindel
University of Groningen, The Netherlands ~ Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence
Royce Anders
Aix Marseille Univ., CNRS, LPC UMR 7290, Marseille, France; Univ Lumière Lyon 2, EMC, Lyon, France
F.-Xavier Alario
Aix Marseille Univ., CNRS, LPC UMR 7290, Marseille, France; Department of Neurological Surgery, University of Pittsburgh, PA 15213, USA
Boris Burle
Aix-Marseille Univ., CNRS, LNC UMR 7291, Marseille, France

Decision-making models based on evidence accumulation processes (the most prolific one being the drift-diffusion model – DDM) are widely used to draw inferences about latent psychological processes from chronometric data. While the observed goodness of fit in a wide range of tasks supports the model’s validity, the derived interpretations have yet to be sufficiently cross-validated with other measures that also reflect cognitive processing. To do so, we recorded electromyographic (EMG) activity along with response times (RT), and used it to decompose every RT into two components: pre-motor (PMT) and motor time (MT). These measures were mapped to the DDM’s parameters, thus allowing a test, beyond quality of fit, of the validity of the model’s assumptions and some of their usual interpretation. In two perceptual decision tasks, performed within a canonical task setting, we manipulated stimulus contrast, speed-accuracy trade-off, and response force, and assessed their effects on PMT, MT, and RT. Contrary to common assumptions, the three factors consistently affected MT. DDM parameter estimates of non-decision processes are thought to include motor execution processes, and they were globally linked to the recorded response execution MT. However, when the assumption of independence between decision and non-decision processes was not met, in the fastest trials, the link was weaker. Overall, the results show a fair concordance between model-based and EMG-based decompositions of RTs, but also establish some limits on the interpretability of decision model parameters linked to response execution.



Decision Making Models
Selective Influence Test
Parameter Interpretability
Physiological Chronometry


Model Analysis and Comparison
Accumulator/Diffusion models
Cognitive Neuromodeling
individual differences? Last updated 3 years ago

Awesome work! I was wondering whether you have any ideas why for some people the EMG is more correlated with the decision process than for other participants, as you mentioned in your talk. Thanks!

Dr. Marieke Van Vugt 1 comment
... on the other hand ... Last updated 3 years ago

Great talk!! And sorry for the bad pun in my question title. I wonder lots of things (also related to Aline’s work). To help me think more about this, I wonder if you can tell me about what the emg signal looks like in the non-responding hand? I bet there is still some signal there, and it’s relationship with the signal in the responding hand ...

Dr. Scott Brown 1 comment

very nice talk, thanks! I had several questions / comments but cannot dare starting 3 threads all by myself, I'll try to keep them tidy 1) in light of your data, do you still buy all the assumptions that form the 1st part of your talk? is MT really fully included into non-decision time if it is directly affected by strategic adjustment and task...

Dr. Aline Bompas 1 comment

Thank you for your talk Gabriel! It was very clear. Do your results suggest that some trials are not from a sequential sampling process or do they just suggest that the motor system may be performing evidence accumulation (possibly in parallel to sensory accumulation)? There seems to be growing evidence for the latter interpretation. I had thought...

Dr. Michael D. Nunez 4 comments
Cite this as:

Weindel, G., Anders, R., Alario, F.-X., & Burle, B. (2020, July). Assessing model-based inferences in decision making with single-trial response time decomposition. Paper presented at Virtual MathPsych/ICCM 2020. Via