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

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