Back

The motivational control of instrumental performance by nutrient-specific appetites depends on incentive learning

Roy, D. J.; Burton, T. J.; Balleine, B.

2026-06-18 animal behavior and cognition
10.64898/2026.06.14.732213 bioRxiv
Show abstract

Considerable evidence suggests that the motivational control of instrumental action depends on incentive learning; i.e., on the opportunity to learn how the value of the consequences or outcome of an action, (e.g., a specific food) varies under different motivational conditions (e.g., under different degrees of hunger). The current study investigated whether learning the values of high-protein and high-carbohydrate rewards under different degrees of protein and carbohydrate appetite is also necessary for these nutrient-specific appetites to exert control over instrumental performance. Experiment 1 gave differing consummatory experience to whey protein and polycose carbohydrate outcomes under protein and carbohydrate appetite and found that, without the opportunity for incentive learning, the performance of actions earning these outcomes was insensitive to a shift in appetite. However, once the opportunity for incentive learning was provided, the rats increased their instrumental performance on a lever that earned the whey outcome relative to the polycose lever when protein hungry and on the polycose lever relative to the whey lever when carbohydrate hungry. Experiment 2 assessed how these nutrient-specific states exerted this control; whether, once learned, nutrient values were immediately controlled by nutrient appetite or whether this was based on conditional control acquired during experience with the outcomes under different nutrient appetites. We found that exposure to an outcome under a single nutrient-specific state was not sufficient to establish state-specific control. Instead, establishing the conditional control of outcome value required exposure to both the whey and polycose outcomes under both protein and carbohydrate appetites.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
Appetite
18 papers in training set
Top 0.1%
11.8%
2
Physiology & Behavior
31 papers in training set
Top 0.1%
11.8%
3
PLOS ONE
5266 papers in training set
Top 19%
9.7%
4
Behavioral Neuroscience
25 papers in training set
Top 0.1%
7.8%
5
Scientific Reports
3612 papers in training set
Top 11%
6.7%
6
eLife
5828 papers in training set
Top 19%
6.2%
50% of probability mass above
7
Animal Cognition
23 papers in training set
Top 0.1%
5.4%
8
Proceedings of the National Academy of Sciences
2444 papers in training set
Top 19%
2.8%
9
Behavioural Brain Research
77 papers in training set
Top 0.5%
2.8%
10
Behavioural Processes
18 papers in training set
Top 0.2%
2.4%
11
Experimental Brain Research
53 papers in training set
Top 0.3%
2.1%
12
Neurobiology of Learning and Memory
40 papers in training set
Top 0.2%
1.9%
13
Proceedings of the Royal Society B: Biological Sciences
393 papers in training set
Top 4%
1.7%
14
The Journal of Neuroscience
1025 papers in training set
Top 8%
1.5%
15
Journal of Neurophysiology
302 papers in training set
Top 2%
1.3%
16
PeerJ
308 papers in training set
Top 8%
1.1%
17
iScience
1154 papers in training set
Top 26%
1.1%
18
Frontiers in Neuroscience
256 papers in training set
Top 5%
1.0%
19
European Journal of Neuroscience
189 papers in training set
Top 3%
1.0%
20
Developmental Cognitive Neuroscience
96 papers in training set
Top 1%
1.0%
21
Nature Communications
5641 papers in training set
Top 54%
1.0%
22
Learning & Memory
23 papers in training set
Top 0.2%
0.9%
23
Brain and Behavior
43 papers in training set
Top 2%
0.8%
24
Journal of Experimental Biology
259 papers in training set
Top 2%
0.8%
25
Hormones and Behavior
45 papers in training set
Top 0.4%
0.8%
26
Royal Society Open Science
214 papers in training set
Top 6%
0.8%
27
Journal of Cognitive Neuroscience
135 papers in training set
Top 2%
0.6%
28
Frontiers in Behavioral Neuroscience
49 papers in training set
Top 1%
0.6%
29
Animal Behaviour
73 papers in training set
Top 1.0%
0.6%
30
PLOS Computational Biology
1863 papers in training set
Top 22%
0.6%