A01 A02 A03 A04 A05 A06 A07 A08 A09 A10 A11 A12 A13 A14 A15 A16 A18 F01 F02

F01 – Focus Group Learning Dynamics

Sen Cheng, Onur Güntürkün, Harald Lachnit

The trial-by-trial dynamics of behavioral changes can reveal the mechanisms underlying learning. Most experiments, however, quantify learning by comparing a post- to a pre-learning block and thus are blind to the dynamics during learning. Other studies obtain learning curves by averaging across subjects, but if learning systematically differs between individuals, the average learning curve can be misleading. As a service to other projects, this Focus Group will export support and training in the application of trial-by-trial and intra-trial analysis methods to these groups. Furthermore, the Focus Group will import data from the projects for the pursuit of a cross-cutting scientific agenda: to compare the learning dynamics across different individuals, learning stages, experimental paradigms, and species.

Guiding questions of F01:

  • Does the learning dynamics differ fundamentally between different learning paradigms or species? Or just in their parameters?
  • Are the dynamics of extinction learning and that of acquisition governed by different mechanisms?
  • Which factors, such as cues and contextual information, drive changes at a trial-by-trial resolution, and beyond, in individual subjects?
  • How do the dynamics of behavioral changes relate to that of neural activity and psychophysiological variables?
Sen Cheng

Sen Cheng

Projektleiter A14, F01

Ruhr-Universität Bochum

Jose Ramon Donoso

Jose Ramon Donoso

Postdoc F01

Ruhr-Universität Bochum

Onur Güntürkün

Onur Güntürkün

Projektleiter A01, F01, Z, Ö

Ruhr-Universität Bochum

Harald Lachnit

Harald Lachnit

Projektleiter A15, F01

Philipps-Universität Marburg

10 project-relevant publications

Cheng S, Frank LM (2008) New experiences enhance coordinated neural activity in the hippocampus. Neuron. 57(2): 303–313.

Cheng S, Sabes PN (2006) Modeling sensorimotor learning with linear dynamical systems. Neural Comput. 18(4): 760–793.

Cheng S, Sabes PN (2007) Calibration of visually guided reaching is driven by error-corrective learning and internal dynamics. J Neurophysiol. 97(4): 3057–3069.

Demic S, Cheng S (2014) Modeling the dynamics of disease states in depression. PLoS One. 9(10): e110358.

Koenig S, Lachnit H (2011) Curved saccade trajectories reveal conflicting predictions in associative learning. J Exp Psychol Learn Mem Cogn. 37(5): 1164–1177.

Lachnit H, Schultheis H, Konig S, Uengoer M, Melchers K (2008) Comparing elemental and configural associative theories in human causal learning: a case for attention. J Exp Psychol Anim Behav Process. 34(2): 303–313.

Lachnit H, Thorwart A, Schultheis H, Lotz A, Koenig S, Uengoer M (2013) Indicators of early and late processing reveal the importance of within-trial-time for theories of associative learning. PLoS One. 8(6): e66291.

Lengersdorf D, Pusch R, Güntürkün O, Stüttgen MC (2014) Neurons in the pigeon nidopallium caudolaterale signal the selection and execution of perceptual decisions. Eur J Neurosci. 40(9): 3316–3327.

Stüttgen MC, Kasties N, Lengersdorf D, Starosta S, Gunturkun O, Jakel F (2013) Suboptimal criterion setting in a perceptual choice task with asymmetric reinforcement. Behav Processes. 96: 59–70.

Thorwart A, Schultheis H, König S, Lachnit H (2009) ALTSim: a MATLAB simulator for current associative learning theories. Behav Res Methods. 41(1): 29–34.