Effects of prototype abstraction on pattern completion and inference in concept space

Poster No:

MT716 

Submission Type:

Abstract Submission 

Authors:

Theo Schäfer1, Eric Schulz2, Stephanie Theves1, Christian Doeller1,3

Institutions:

1Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, 2Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 3Kavli Institute for Systems Neuroscience, NTNU, Trondheim, Norway

First Author:

Theo Schäfer  
Max Planck Institute for Human Cognitive and Brain Sciences
Leipzig, Germany

Co-Author(s):

Eric Schulz  
Max Planck Institute for Biological Cybernetics
Tübingen, Germany
Stephanie Theves  
Max Planck Institute for Human Cognitive and Brain Sciences
Leipzig, Germany
Christian Doeller  
Max Planck Institute for Human Cognitive and Brain Sciences|Kavli Institute for Systems Neuroscience, NTNU
Leipzig, Germany|Trondheim, Norway

Introduction:

The processing of complex environments is greatly facilitated by the formation and use of concepts. Concepts represent combinations of features shared by similar entities and allow generalisation from limited experience to novel situations. Recent research suggests that map-like codes in the hippocampal-entorhinal system can support concept learning by representing the relations between experiences along relevant feature dimensions (Constantinescu, 2016; Park et al., 2020; Theves et al., 2019; Theves et al., 2020). In a behavioral and functional magnetic resonance imaging (fMRI) experiment, we investigate if this map-like representation of concepts supports the retrieval of abstracted information to guide inference.

Methods:

40 participants performed a novel concept learning paradigm inside the fMRI scanner (3T MRI, TR: 1500 ms, TE: 22 ms, voxel size: 2.5 mm). Participants were trained to categorize a set of visual exemplars based on the ratio of their two continuous features (see figure below). Subsequently, they encountered exemplars that exhibited only one of the features and were instructed to complete the missing feature in a continuous fashion according to their category label. This concept learning and inference test phase was preceded and followed by stimulus viewing blocks. We examined whether feature inferences were more attracted by the previously experienced exemplar locations as suggested by an exemplar learning account (Medin & Schaffer, 1978; Nosofsky, 1984) or by the category prototypes as suggested by an abstract representation account (Posner & Keele, 1968; Reed, 1972). To this end, we trained a support vector regression on multivariate fMRI responses from the stimulus viewing blocks to decode completion responses during the feature inference task.
Supporting Image: ohbm2022_theoschaefer_abstract.png
 

Results:

Participants improved their categorization performance well above chance during training, suggesting that they were able to learn the category structure. Both categories were classified equally well. Subjective prototypes that were generated by the participants at the end of the study lied within their respective category regions in feature space. fMRI-based feature decoding revealed moderate to high correlations with true feature dimension values in the visual cortex. We found that feature completions were attracted more towards the prototype location than to the nearest experienced exemplars, suggesting the retrieval of an abstract representation during feature inference.

Conclusions:

Our findings are consistent with the hypothesis that abstract representations are formed during concept learning. Furthermore, our results indicate that abstracted representations are employed to infer the missing information of an incomplete pattern. This study provides a first investigation of the relation between abstract category representations and cognitive maps.

Higher Cognitive Functions:

Higher Cognitive Functions Other

Learning and Memory:

Long-Term Memory (Episodic and Semantic) 2
Learning and Memory Other 1

Modeling and Analysis Methods:

Activation (eg. BOLD task-fMRI)
Multivariate Approaches

Keywords:

Cognition
Computational Neuroscience
FUNCTIONAL MRI
Learning
Memory
MRI
Multivariate
Perception
Other - Concept Learning; Inference

1|2Indicates the priority used for review

My abstract is being submitted as a Software Demonstration.

No

Please indicate below if your study was a "resting state" or "task-activation” study.

Task-activation

Healthy subjects only or patients (note that patient studies may also involve healthy subjects):

Healthy subjects

Was any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.

Yes

Was any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.

Not applicable

Please indicate which methods were used in your research:

Functional MRI
Structural MRI
Behavior
Computational modeling

For human MRI, what field strength scanner do you use?

3.0T

Which processing packages did you use for your study?

FSL
Free Surfer
Other, Please list  -   nilearn; fmriprep

Provide references using author date format

Constantinescu, A. O., OReilly, J. X., & Behrens, T. E. J. (2016). Organizing conceptual knowledge in humans with a gridlike code. Science, 352(6292), 1464– 1468.

Medin, D. L., & Schaffer, M. M. (1978). Context Theory of Classification Learning. Psychological Review, 32.

Nosofsky, R. M. (1984). Choice, similarity, and the context theory of classification. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10(1), 104.

Park, S. A., Miller, D. S., Nili, H., Ranganath, C., & Boorman, E. D. (2020). Map making: constructing, combining, and inferring on abstract cognitive maps. Neuron, 107(6), 1226-1238.

Posner, M. I., & Keele, S. W. (1968). On the genesis of abstract ideas. Journal of experimental psychology, 77(3p1), 353.

Reed, S. K. (1972). Pattern recognition and categorization. Cognitive Psychology, 3(3), 382-407.

Theves, S., Fernandez, G., & Doeller, C. F. (2019). The Hippocampus Encodes Distances in Multidimensional Feature Space. Current Biology, 29, 1226-1231.

Theves, S., Fernández, G., Doeller, C.F. (2020). The hippocampus maps concept space, not feature space. J Neurosci, 40, 7318–7325.