A Bayesian and efficient observer model explains concurrent attractive and repulsive history biases in visual perception
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Netherlands
Figures
Figure 1
Task of Experiment 1.
Observers saw a Gabor stimulus followed by a noise mask and subsequently reproduced the orientation of the stimulus by adjusting a response bar. Stimulus presentation in the left or right visual …
Figure 2
Results of Experiment 1: Estimation responses are attracted towards short-term, but repelled from long-term stimulus history.
(A) Serial dependence of current response errors on the previous stimulus orientation. We expressed the response errors (y-axis) as a function of the difference between previous and current stimulus …
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Figure 2—source data 1
Results of Experiment 1: Estimation responses are attracted towards short-term, but repelled from long-term stimulus history.
The source data file contains a csv file with the 1- to 40-back DoG model parameter estimates and a csv file with p-values for testing the model’s amplitude parameters against zero. It also contains csv files with moving averages of response errors conditioned on the 1- and 4-back trial, respectively (participants x orientation). Furthermore, it contains a mat file with all 1- to 40-back moving averages of each participant.
- https://cdn.elifesciences.org/articles/55389/elife-55389-fig2-data1-v2.zip
Figure 3
Results of Experiment 2: Long-term repulsive biases are spatially specific.
(A) Attraction and repulsion biases exerted by the 10 preceding stimuli, regardless of changes in spatial locations. The current response is attracted towards the previous stimulus, but repelled …
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Figure 3—source data 1
Results of Experiment 2: Long-term repulsive biases are spatially specific.
The source data file contains a csv file with the 1- to 10-back DoG model parameter estimates and a csv file with p-values for testing the model’s amplitude parameters against zero. It also contains two csv files with model parameters fit to 1-back or 4- to 9-back conditioned response errors, split by location change. Furthermore, it contains a mat file with all 1- to 10-back moving averages of each participant (same and different locations).
- https://cdn.elifesciences.org/articles/55389/elife-55389-fig3-data1-v2.zip
Figure 4
Results of Experiment 3: Long-term repulsive biases are not strongly modulated by working memory delay.
(A) Attraction and repulsion biases exerted by the 10 preceding stimuli, pooled across response delay conditions. The current response is attracted towards the previous stimulus, but repelled from …
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Figure 4—source data 1
Results of Experiment 3: Long-term repulsive biases are not strongly modulated by working memory delay.
The source data file contains a csv file with the 1- to 10-back DoG model parameter estimates and a csv file with p-values for testing the model’s amplitude parameters against zero. It also contains two csv files with model parameters fit to 1-back or 2- to 6-back conditioned response errors, split by memory delay duration. Furthermore, it contains a mat file with all 1- to 10-back moving averages of each participant (short and long memory delay).
- https://cdn.elifesciences.org/articles/55389/elife-55389-fig4-data1-v2.zip
Figure 5
Task and results of Experiment 4: The long-term stimulus history directly biases the perceived orientation of current stimuli.
(A) Observers were cued to reproduce one of two Gabor stimuli by adjusting a response bar (adjustment response). Subsequently, two new Gabor stimuli appeared at priorly cued locations in the left …
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Figure 5—source data 1
Task and results of Experiment 4: The long-term stimulus history directly biases the perceived orientation of current stimuli.
The source data file contains two csv files with estimated biases exerted by the previous 10 inducer stimuli, when the inducer was presented at the same or different spatial location, respectively (participants x n-back inducer).
- https://cdn.elifesciences.org/articles/55389/elife-55389-fig5-data1-v2.zip
Figure 6
Bayesian decoding and efficient encoding of orientation information in a stable environment.
(A) Bayesian decoding. Orange box: The observer encodes a grating stimulus with orientation θ into a noisy measurement m. Since the noisy measurement is uncertain, it is consistent with a range of …
Figure 7 with 5 supplements
Empirical biases and ideal observer predictions.
Left column: An observer with efficient encoding and history-dependent Bayesian decoding (green) accurately captures the empirical magnitudes of short-term attractive and long-term repulsive biases …
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Figure 7—source data 1
Empirical biases and ideal observer predictions.
The source data file contains csv files with the empirical bias amplitudes of 1- to 20-back stimuli, as well as the predicted bias amplitudes of the ideal observer with efficient encoding and Bayesian decoding and the observer with Bayesian decoding alone. Three separate files are provided for Experiment 1–3, respectively. Furthermore, for each experiment we provide two mat files with the model fitting results of the two ideal observer models.
- https://cdn.elifesciences.org/articles/55389/elife-55389-fig7-data1-v2.zip
Figure 7—figure supplement 1
Serial dependence biases of an ideal observer with efficient encoding and history-dependent Bayesian decoding (green).
The model can accurately capture the tuning profile of short-term attraction and long-term repulsion biases across Experiments 1–3. Model biases were computed by simulating the ideal observer model …
Figure 7—figure supplement 2
Serial dependence biases of an ideal observer, in which sensory history influenced only Bayesian decoding (orange) or only efficient encoding (blue).
Neither of the models can account for both attractive and repulsive biases. While the observer with Bayesian decoding can only produce attraction biases, the observer with efficient encoding only …
Figure 7—figure supplement 3
Cross-validated prediction accuracies of the four different observer models.
Across all three experiments, an ideal observer model with efficient encoding and Bayesian decoding optimized according to distinct priors (distinct transition distributions and integration time …
Figure 7—figure supplement 4
Normalized variability of estimation response errors as a function of the orientation difference between current and n-back trial.
Variability of estimation response errors was quantified by computing each participant’s standard deviation of estimation response errors in a 30° sliding window over relative orientation …
Figure 7—figure supplement 5
Best fitting parameters of the observer model with efficient encoding and history-dependent Bayesian decoding (Full efficient-encoding-Bayesian-decoding model).
Parameters of the encoding and decoding stages are presented in the blue and red shaded cells, respectively. Note that exponential integration time constants are fitted in units of trials, and can …
