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Can Serial Dependencies in Choices and Neural Activity Explain Choice Probabilities?
Jan-Matthis Lueckmann1, Jakob H Macke1,2, Hendrikje Nienborg3
1Research Center Caesar, An associate of the Max-Planck Society, 53175 Bonn, Germany.
Summary
Choice probabilities in sensory neurons and serial dependencies in choices are separate processes. Temporal correlations in neural activity and behavior do not explain choice probabilities, constraining computational models.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Systems Neuroscience
Background:
- Sensory neuron activity covaries with perceptual choice (choice probability).
- Previous choices influence current decisions (serial dependence).
- Neuronal activity exhibits long-timescale temporal correlations.
Purpose of the Study:
- Investigate the link between choice probabilities, serial dependence, and temporal correlations in neural activity.
- Determine if temporal correlations explain choice probabilities.
Main Methods:
- Generalized linear models to analyze simultaneous behavioral and V2 neural activity in macaques.
- Semipartial correlation analysis to remove history effects.
Main Results:
- Both decisions and neural activity showed temporal correlations.
- Choice probabilities remained largely unchanged after removing history effects.
- Serial dependencies in choices and neural activity appear to be separate processes.
Conclusions:
- Serial dependencies in choices and neural activity do not explain choice probabilities.
- These processes are predominantly separate but parallel, coupled by trial-by-trial covariations.
- Findings constrain computational models of perceptual decision-making with feedback signals.
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