Psychophysical Data on Choice-Induced Biases in Evidence Accumulation

Comprising behavioral data from two psychophysical experiments investigating how categorical choices alter the accumulation of subsequent evidence. The study examines biases in processing abstract numerical and low-level perceptual information, with computational modeling revealing a reduction in sensitivity via multiplicative gain modulation. The author is Zohar Z. Bronfman.

Use Cases

• Analyze choice-induced biases in the accumulation of abstract numerical evidence from experiment 1
• Model the reduction of sensitivity to subsequent evidence via multiplicative gain modulation as revealed by computational modeling
• Examine the likelihood to revise decisions based on low-level perceptual evidence from experiment 2
• Study the computational mechanisms underlying choice-dependent biases using sequential-sampling model frameworks

Strengths

• Data originates from two novel psychophysical protocols designed to dissect evidence accumulation quantitatively
• Findings are based on robust choice-induced biases observed in both abstract numerical and low-level perceptual evidence experiments
• The dataset is associated with a peer-reviewed study published in 2020

Limitations

• The specific number of participants, trials, rows, and columns is unknown
• The raw data files and their formats are not described
• The sample data is unavailable for inspection prior to download

Provenance

Source

dryad

Collection Method

Collected via novel psychophysical protocols in two controlled experiments

Freshness

Last updated on 2020-06-24.