Second Guessing in Perceptual Decision-Making.

Area of Science:

• Cognitive Neuroscience
• Decision Science
• Visual Perception

Background:

• Perceptual decisions involve converting sensory input into discrete choices.
• Computational models often use evidence accumulation to a threshold to explain choice distributions and response times.
• Confidence reports reflect decision-makers' awareness of their choice certainty.

Purpose of the Study:

• To investigate if decision-makers have access to evidence supporting unchosen options during multi-alternative perceptual decisions.
• To test if a computational model can explain primary choices, response times, and second guesses based on accumulated sensory evidence.

Main Methods:

• Human participants performed a visual motion perceptual decision task with multiple alternatives.
• Subjects reported a primary choice and a secondary guess, indicating their preferred alternative if the primary choice was incorrect.
• A computational integration-to-threshold model was applied to analyze choice distributions, response times, and second guesses.

Main Results:

• Second guesses were significantly informed by the amount of sensory evidence available for different motion directions.
• A single computational model accurately predicted primary choices, response times, and the distribution of second guesses.
• The model's success suggests decision-makers can access the evidential support for unchosen options.

Conclusions:

• Decision-makers possess knowledge about the evidential support for unchosen options, extending beyond their primary choice and confidence.
• This finding implies a more nuanced understanding of the information available to the decision-maker during perceptual tasks.
• The integration-to-threshold framework can account for complex decision readouts, including second guesses informed by sensory evidence.