Data from: Cognitive resonance: when information carry-over constrains cognitive plasticity
Bibliographic record
Abstract
1. When faced with a changing environment, some species appear to adapt quickly, while others seem unable to update the value of environmental cues on which they base their decisions, leading them to display seemingly maladaptive responses. 2. While behavioural and cognitive plasticity are two traits that should predict the ability of species to update the value of environmental cues, we argue that this flexibility may be constrained by ontogeny. While sensitive periods have been shown to exist for establishing an individual’s food, habitat and mate preference, no studies have established the existence of a cognitive sensitive period for predation-related information. 3. In this study, we used wood frogs, Lithobates sylvaticus, to demonstrate the existence of a sensitive period for predation-related information, with risk information learned as embryos maintained for more than 5 weeks, while the same information learned as tadpoles was unused after just 10 days. Next we demonstrated that tadpoles that had learned a cue as safe as embryos were unable to update the cue as risky after three fear conditioning attempts, while tadpoles that learned the cue as safe a few days prior did successfully update the cue as risky after three conditionings. 4. We coined the term ‘cognitive resonance’ to describe how information learned early in life can have marked cognitive consequence later in life, affecting not only the duration for which information learned is actively used in decision-making, but how this information can interfere with the acquisition of up-to-date information about the environment. 5. Cognitive resonance might be beneficial in stable environments where the change in the value of a cue is relatively small through time, but it can quickly become costly in environments where the identity of potential threats changes quickly, as in the case of introduced species.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.089 | 0.020 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".