Win-shift learning is sensitive to food type
in Noisy Miners(Manorina melanocephala)
Danielle Sulikowski
Darren Burke
Win-shift learning is sensitive to food type. Sulikowski & Burke
Understanding the evolution of cognition…
it follows that variation in such cognitive abilities should have adaptive significance.
One example of such an adaptive specialisation is the win-shift bias of various nectarivores.
If we presume that cognitive abilities are subject to natural selection…
Win-shift learning is sensitive to food type. Sulikowski & Burke
Win-shift learning is sensitive to food type. Sulikowski & Burke
The Noisy Miner Bird (Aves: Meliphagidae, Manorina melanocephala)
Win-shift learning is sensitive to food type. Sulikowski & Burke
Baited feeder
Unbaited feeder
Exploration Phase
Test Phase
Stay Shift
Win-shift learning is sensitive to food type. Sulikowski & Burke
P = 0.051*
*
*
Win-shift learning is sensitive to food type. Sulikowski & Burke
P = 0.007*
*
0.06
Win-shift learning is sensitive to food type. Sulikowski & Burke
Where to from here?
What properties of nectar trigger the shift algorithm?
- taste, nutritional content, distribution?
Why was performance in invertebrates groups so poor?
- other algorithms, other cues?
Is the shift-bias an evolved adaptation or purely the result of experience?
- evidence against a purely experiential explanation