How do animals weigh conflicting information about reward sources over time? Comparing dynamic averaging models.
Anim Cogn
; 27(1): 11, 2024 Mar 02.
Article
in En
| MEDLINE
| ID: mdl-38429608
ABSTRACT
Optimal foraging theory suggests that animals make decisions which maximize their food intake per unit time when foraging, but the mechanisms animals use to track the value of behavioral alternatives and choose between them remain unclear. Several models for how animals integrate past experience have been suggested. However, these models make differential predictions for the occurrence of spontaneous recovery of choice a behavioral phenomenon in which a hiatus from the experimental environment results in animals reverting to a behavioral allocation consistent with a reward distribution from the more distant past, rather than one consistent with their most recently experienced distribution. To explore this phenomenon and compare these models, three free-operant experiments with rats were conducted using a serial reversal design. In Phase 1, two responses (A and B) were baited with pellets on concurrent variable interval schedules, favoring option A. In Phase 2, lever baiting was reversed to favor option B. Rats then entered a delay period, where they were maintained at weight in their home cages and no experimental sessions took place. Following this delay, preference was assessed using initial responding in test sessions where levers were presented, but not baited. Models were compared in performance, including an exponentially weighted moving average, the Temporal Weighting Rule, and variants of these models. While the data provided strong evidence of spontaneous recovery of choice, the form and extent of recovery was inconsistent with the models under investigation. Potential interpretations are discussed in relation to both the decision rule and valuation functions employed.
Key words
Full text:
1
Database:
MEDLINE
Main subject:
Choice Behavior
/
Conditioning, Operant
Limits:
Animals
Language:
En
Journal:
Anim Cogn
Journal subject:
MEDICINA VETERINARIA
Year:
2024
Type:
Article
Affiliation country:
United States