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

Area of Science:

• Cognitive Neuroscience
• Computational Neuroscience
• Decision Science

Background:

• Decision-making is a core cognitive function explained by sequential sampling models where evidence accumulates to a threshold.
• The centroparietal positivity (CPP), a human electroencephalogram (EEG) signal, exhibits an accumulation-to-bound profile, suggesting a role in decision variables.

Purpose of the Study:

• To computationally evaluate the role of the CPP as an accumulation-to-bound signal in sensory-based decision-making.
• To compare model-simulated decision variables with the CPP waveform under manipulated evidence conditions.

Main Methods:

• Two experiments manipulated evidence non-stationarity and decision biases to probe CPP accumulation shape and amplitude.
• Sequential sampling models were fitted to behavioral data.
• Model-derived decision variable simulations were directly compared to the CPP EEG signal.

Main Results:

• Model predictions showed deviations from simple expectations but exhibited similarities to the neurodynamic CPP data.
• The CPP waveform generally aligned with simulated decision variables, supporting its role in evidence accumulation.
• Specific model predictions varied across task manipulations, indicating complexity in explaining CPP dynamics universally.

Conclusions:

• The CPP signal likely arises from neural processes implementing decision variables as formalized in sequential-sampling models.
• This research supports understanding decision-making at representational and implementational neural levels.
• Further research is needed to determine if a single model can account for CPP variations across diverse decision tasks.