Centroparietal activity mirrors the decision variable when tracking biased and time-varying sensory evidence

Abstract

Decision-making is a fundamental human activity requiring explanation at the neurocognitive level. Current theoretical frameworks assume that, during sensory-based decision-making, the stimulus is sampled sequentially. The resulting evidence is accumulated over time as a decision variable until a threshold is reached and a response is initiated. Several neural signals, including the centroparietal positivity (CPP) measured from the human electroencephalogram (EEG), appear to display the accumulation-to-bound profile associated with the decision variable. Here, we evaluate the putative computational role of the CPP as a model-derived accumulation-to-bound signal, focussing on point-by-point correspondence between model predictions and data in order to go beyond simple summary measures like average slope. In two experiments, we explored the CPP under two manipulations (namely non-stationary evidence and probabilistic decision biases) that complement one another by targeting the shape and amplitude of accumulation respectively.We fit sequential sampling models to the behavioural data, and used the resulting parameters to simulate the decision variable, before directly comparing the simulated profile to the CPP waveform. In both experiments, model predictions deviated from our naïve expectations, yet showed similarities with the neurodynamic data, illustrating the importance of a formal modelling approach. The CPP appears to arise from brain processes that implement a decision variable (as formalised in sequential-sampling models) and may therefore inform our understanding of decision-making at both the representational and implementational levels of analysis, but at this point it is uncertain whether a single model can explain how the CPP varies across different kinds of task manipulation.

Accumulation of continuously time-varying sensory evidence constrains neural and behavioral responses in human collision threat detection

“…3B and C show stimulus-locked and response-locked ERPs, per condition, averaged over five electrodes centred on Pz. The original paper on the CPP centred its analysis on the CPz electrode location [41], but many subsequent reports have shown more parietally located CPPs, consistent with what we observe here [42]- [44], [46], [60]. Again in line with previous observations, Fig.…”

“…Again in line with previous observations, Fig. 3C shows that this positive wave builds up before the overt response and peaks at the response itself, with a characteristic separation at this peak, with higher CPP amplitudes for the more salient looming conditions [41], [42], [44], [46]; see the dots along the bottom of the top panel in Fig. 3C.…”

“…As further described in the Supplementary Materials, none of these alternative models were found preferable over the variable-gain accumulator model (model AV). Fig 3A show a positivity at the overt response, in line with the CPP observed by many others [41]- [46]. Figs.…”

“…There is also strong neurophysiological support for the idea that the brain indeed implements something akin to evidence accumulation in these types of tasks [36], [38]- [40]. Notably, there is mounting support for the idea that signatures of neural evidence accumulation can be observed using human electroencephalography (EEG), in the form of a centroparietal positivity (CPP) that builds up during deliberation and peaks when the overt response is made [41]- [46]. However, computational modelling of evidence accumulation decision-making has so far focused on laboratory paradigms using stimuli that (i) have stationary or only intermittently and/or noisily changing saliency over time [46]- [49], and (ii) are abstract in nature, not mapping directly to any real-world task.…”

“…Notably, there is mounting support for the idea that signatures of neural evidence accumulation can be observed using human electroencephalography (EEG), in the form of a centroparietal positivity (CPP) that builds up during deliberation and peaks when the overt response is made [41]- [46]. However, computational modelling of evidence accumulation decision-making has so far focused on laboratory paradigms using stimuli that (i) have stationary or only intermittently and/or noisily changing saliency over time [46]- [49], and (ii) are abstract in nature, not mapping directly to any real-world task. We and others have begun exploring the applicability of evidence accumulation models in more naturalistic tasks, particularly in vehicle driving [50]- [53], but so far a conclusive test of these models in such contexts has been lacking: Model fitting is rendered more challenging both because real-world sensory evidence tends to be continuously time-varying, which precludes analytical expression of model likelihood functions, and because of difficulties in obtaining sufficient numbers of repeated trials in ecologically relevant experimental conditions.…”