Characterisation of a touchscreen-based task for assessing cognitive judgement bias in mice: a new translational tool for affective state disorder drug screening
Bibliographic record
Abstract
RATIONALE: A major obstacle in the pre-clinical study of mood-related disorders and novel affective state therapeutic evaluation is the lack of animal models that fully recapitulate human symptomatology. OBJECTIVE: In this study, we developed a touchscreen-based cognitive judgement bias (CJB) task for mice. METHODS: In the CJB task, animals first learned to discriminate between two visual stimuli displayed on the touchscreen: one associated with a reward (S+) and one associated with a time-out and flashing house light (S-). Once mice learned to respond to the S + and to withhold responding to the S- consistently, a set of four ambiguous stimuli ranging in visual similarity to the S + and S- stimuli were randomly interspersed in the stimulus presentation sequence. Responses to these ambiguous stimuli were interpreted as a greater expectation of positive ('optimistic bias') or negative ('pessimistic bias') outcomes as a function of their similarity to the S + or S- stimuli. RESULTS: The acute administration of the SSRIs fluoxetine and citalopram, and the 5HT-2 C receptor antagonist SB 242084, did not produce any effects on CJB task performance. However, the noradrenaline/dopamine reuptake inhibitor, bupropion, increased responses to the ambiguous stimuli consistent with the induction of an 'optimistic bias', and the pro-depressant tetrabenazine yielded the opposite effect. CONCLUSION: This study underscores the capacity of mice to respond to visually ambiguous stimuli in an ambiguity-dependent manner, a phenomenon observed across various species, including humans. Furthermore, it establishes and validates an operant behavioural task to assess CJB in mice delivered using the touchscreen platform, which has significant cross-species translational potential.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".