Article in Journal of Experimental Psychology Learning Memory and Cognition · March 2005:
This paper explores the phenomenon where students overestimate their understanding of material, leading to an illusion of competence, particularly when studying paired-associates tasks (tasks where a cue is paired with a target). During study, learners often judge their knowledge (Judgments of Learning, JOLs) with the answer present, which creates a bias; they find it difficult to predict their performance on tests where the answer is not provided.
This discrepancy between study and test conditions can lead to overconfidence in their recall abilities, which often proves inaccurate during testing. The authors highlight that while overall judgments of learning are generally well-calibrated, item-by-item assessments tend to overestimate performance more than aggregate judgments.
This paper explores the phenomenon where students overestimate their understanding of material, leading to an illusion of competence, particularly when studying paired-associates tasks (tasks where a cue is paired with a target). During study, learners often judge their knowledge (Judgments of Learning, JOLs) with the answer present, which creates a bias; they find it difficult to predict their performance on tests where the answer is not provided.
This discrepancy between study and test conditions can lead to overconfidence in their recall abilities, which often proves inaccurate during testing. The authors highlight that while overall judgments of learning are generally well-calibrated, item-by-item assessments tend to overestimate performance more than aggregate judgments.
Peter and Ted will touch on the differences in curated and aggregated knowledge
P1. Loss function. In any evolving system, there exists a loss function of time-dependent variables that is minimized during evolution.
P2. Hierarchy of scales. Evolving systems encompass multiple dynamical variables that change on different temporal scales (with different characteristic frequencies).
P3. Frequency gaps. Dynamical variables are split among distinct levels of organization separated by sufficiently wide frequency gaps.
P4. Renormalizability. Across the entire range of organization of evolving systems, a statistical description of faster-changing (higher-frequency) variables is feasible through the slower-changing (lower-frequency) variables.
P5. Extension. Evolving systems have the capacity to recruit additional variables that can be utilized to sustain the system and the ability to exclude variables that could destabilize the system.
P6. Replication. In evolving systems, replication and elimination of the corresponding information-processing units (IPUs) can take place on every level of organization.
P7. Information flow. In evolving systems, slower-changing levels absorb information from faster-changing levels during learning and pass information down to the faster levels for prediction of the state of the environment and the system itself.
P2. Hierarchy of scales. Evolving systems encompass multiple dynamical variables that change on different temporal scales (with different characteristic frequencies).
P3. Frequency gaps. Dynamical variables are split among distinct levels of organization separated by sufficiently wide frequency gaps.
P4. Renormalizability. Across the entire range of organization of evolving systems, a statistical description of faster-changing (higher-frequency) variables is feasible through the slower-changing (lower-frequency) variables.
P5. Extension. Evolving systems have the capacity to recruit additional variables that can be utilized to sustain the system and the ability to exclude variables that could destabilize the system.
P6. Replication. In evolving systems, replication and elimination of the corresponding information-processing units (IPUs) can take place on every level of organization.
P7. Information flow. In evolving systems, slower-changing levels absorb information from faster-changing levels during learning and pass information down to the faster levels for prediction of the state of the environment and the system itself.
