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Measuring Cognitive Judgement Bias in Rats Using the Ambiguous-Cue Interpretation Test
Justyna Papciak1,2, Rafal Rygula1
1Institute of Pharmacology, Polish Academy of Sciences, Department of Behavioral Neuroscience and Drug Development, Affective Cognitive Neuroscience Lab, Krakow, Poland.
Current Protocols in Neuroscience
|January 4, 2017
Summary
This study introduces a new rat behavioral test to measure cognitive judgment bias. The ambiguous-cue interpretation (ACI) paradigm assesses
Area of Science:
- Behavioral neuroscience
- Animal cognition
- Operant conditioning
Background:
- Cognitive judgment bias is a key aspect of animal emotional states.
- Measuring this bias in rodents requires validated behavioral paradigms.
- Previous methods may lack the nuance to capture subtle shifts in expectation.
Purpose of the Study:
- To describe and validate a novel active-choice, operant, ambiguous-cue interpretation (ACI) paradigm.
- To establish ACI as a reliable tool for assessing cognitive judgment bias in rats.
- To provide a framework for understanding 'optimism' and 'pessimism' in rodent decision-making.
Main Methods:
- Rats were trained in an operant chamber using distinct tones signaling food reward or foot-shock punishment.
- Ambiguous tones, intermediate in frequency, were introduced after stable discrimination was achieved.
- Lever-press responses to ambiguous cues were recorded to infer expectation bias.
Main Results:
- The ACI paradigm successfully trained rats to discriminate between positive and negative cues.
- Response patterns to ambiguous cues varied, indicating differential expectations of reward or punishment.
- This differential responding serves as a quantifiable measure of cognitive judgment bias.
Conclusions:
- The active-choice, operant, ambiguous-cue interpretation (ACI) paradigm is a viable method for measuring cognitive judgment bias in rats.
- This paradigm offers a sensitive tool to investigate the neurobiological underpinnings of optimism and pessimism.
- ACI advances the study of affective states and decision-making in preclinical models.

