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Prediction and control in a dynamic environment.

Osman M, Speekenbrink M - Front Psychol (2012)

Bottom Line: The present study compared the accuracy of cue-outcome knowledge gained during prediction-based and control-based learning in stable and unstable dynamic environments.Study 2 (N = 28) showed that Controllers showed equivalent task knowledge when to compared to Predictors.The cue-outcome knowledge acquired during learning was sufficiently flexible to enable successful transfer to tests of control and prediction.

View Article: PubMed Central - PubMed

Affiliation: Biological and Experimental Psychology Centre, School of Biological and Chemical Sciences, Queen Mary College, University of London London, UK.

ABSTRACT
The present study compared the accuracy of cue-outcome knowledge gained during prediction-based and control-based learning in stable and unstable dynamic environments. Participants either learnt to make cue-interventions in order to control an outcome, or learnt to predict the outcome from observing changes to the cue values. Study 1 (N = 60) revealed that in tests of control, after a short period of familiarization, performance of Predictors was equivalent to Controllers. Study 2 (N = 28) showed that Controllers showed equivalent task knowledge when to compared to Predictors. Though both Controllers and Predictors showed good performance at test, overall Controllers showed an advantage. The cue-outcome knowledge acquired during learning was sufficiently flexible to enable successful transfer to tests of control and prediction.

No MeSH data available.


Error scores (± SE) in the learning phase of Experiment 1. For Controllers, these are control error scores, and for Predictors, these are predictive error scores.
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Figure 2: Error scores (± SE) in the learning phase of Experiment 1. For Controllers, these are control error scores, and for Predictors, these are predictive error scores.

Mentions: The learning phase was divided into four blocks of 10 trials each. For the following analyses, prediction and control error scores were averaged across each block for each participant; these are presented in Figure 2. A 4 × 2 ANOVA was conducted on the control scores, with Block (learning block 1, 2, 3, 4) as within-subject factor and Noise (high, low) as a between subject factor. As indicated in Figure 2, there was a main effect of Block, F(3,84) = 28.89, p < 0.001, partial η2 = 0.508. To explore the possibility that accuracy of control-based decisions improved over blocks of trials, t-tests revealed that error scores were lower in Blocks 2, 3, and 4 as compare to Block 1 (t = 3.72, p = 0.036, t = 3.76, p = 0.013, t = 3.04, p = 0.013), no other differences reached significance. A main effect of Noise, F(1,28) = 9.48, p = 0.004, partial η2 = 0.253, suggests that control performance was poorer in the High compared to the Low Noise condition. Figure 2 also suggests more pronounced learning in the Low Noise compared to the High Noise condition, which was supported by a significant Noise × Block interaction, F(3,84) = 3.93, p = 0.011, partial η2 = 0.123.


Prediction and control in a dynamic environment.

Osman M, Speekenbrink M - Front Psychol (2012)

Error scores (± SE) in the learning phase of Experiment 1. For Controllers, these are control error scores, and for Predictors, these are predictive error scores.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3300109&req=5

Figure 2: Error scores (± SE) in the learning phase of Experiment 1. For Controllers, these are control error scores, and for Predictors, these are predictive error scores.
Mentions: The learning phase was divided into four blocks of 10 trials each. For the following analyses, prediction and control error scores were averaged across each block for each participant; these are presented in Figure 2. A 4 × 2 ANOVA was conducted on the control scores, with Block (learning block 1, 2, 3, 4) as within-subject factor and Noise (high, low) as a between subject factor. As indicated in Figure 2, there was a main effect of Block, F(3,84) = 28.89, p < 0.001, partial η2 = 0.508. To explore the possibility that accuracy of control-based decisions improved over blocks of trials, t-tests revealed that error scores were lower in Blocks 2, 3, and 4 as compare to Block 1 (t = 3.72, p = 0.036, t = 3.76, p = 0.013, t = 3.04, p = 0.013), no other differences reached significance. A main effect of Noise, F(1,28) = 9.48, p = 0.004, partial η2 = 0.253, suggests that control performance was poorer in the High compared to the Low Noise condition. Figure 2 also suggests more pronounced learning in the Low Noise compared to the High Noise condition, which was supported by a significant Noise × Block interaction, F(3,84) = 3.93, p = 0.011, partial η2 = 0.123.

Bottom Line: The present study compared the accuracy of cue-outcome knowledge gained during prediction-based and control-based learning in stable and unstable dynamic environments.Study 2 (N = 28) showed that Controllers showed equivalent task knowledge when to compared to Predictors.The cue-outcome knowledge acquired during learning was sufficiently flexible to enable successful transfer to tests of control and prediction.

View Article: PubMed Central - PubMed

Affiliation: Biological and Experimental Psychology Centre, School of Biological and Chemical Sciences, Queen Mary College, University of London London, UK.

ABSTRACT
The present study compared the accuracy of cue-outcome knowledge gained during prediction-based and control-based learning in stable and unstable dynamic environments. Participants either learnt to make cue-interventions in order to control an outcome, or learnt to predict the outcome from observing changes to the cue values. Study 1 (N = 60) revealed that in tests of control, after a short period of familiarization, performance of Predictors was equivalent to Controllers. Study 2 (N = 28) showed that Controllers showed equivalent task knowledge when to compared to Predictors. Though both Controllers and Predictors showed good performance at test, overall Controllers showed an advantage. The cue-outcome knowledge acquired during learning was sufficiently flexible to enable successful transfer to tests of control and prediction.

No MeSH data available.