Individual Differences in Learning Social and Nonsocial Network Structures

Journal Article #1

Journal of Experimental Psychology: Learning, Memory, and

Cognition

Individual Differences in Learning Social and Nonsocial Network Structures

Steven H. Tompson, Ari E. Kahn, Emily B. Falk, Jean M. Vettel, and Danielle

S. Bassett

Genesis Garcia

Professor Paul Barbato

PSYCH343 Learning

20 September 2020

 

 

 

Supporting Information Study looked at how people learn about which individuals belong to

different cliques or communities. Participants were able to learn community structure of both social and nonsocial networks. Their performance in social network learning was uncorrelated with their performance in nonsocial network learning. Social traits, including social orientation and perspective taking, uniquely predicted the learning of social community structure but not of nonsocial community structure. The results suggest that the process of learning higher order community structure in social networks is partially distinct from the process.

5 Studies Study 1

In the first study, a cross-subject approach was used to test tacit network-learning signatures on social and non-social networks. The primary objective of Study 1 was to ensure that participants were able to understand the dynamics of the group. Intuitively, slower RT on post-transition trials and better accuracy on the odd-man test would indicate that individuals have been successfully mastered the network structure. Methods

For the first study, they recruited 76 participants (37 non-social, 39 social) using Amazon Mechanical Turk. They omitted two participants whose precision was less than predicted by chance. Total pay for participants who completed all phases of each analysis ranged from $6.25 to $9.00.

Procedures In Study 1, participants viewed a sequence of fractal images that were

created using the Qbist filter. Each image was unique, and for each participant, each image was randomly assigned to a network node. Images were presented for 1,500 ms and participants were asked to rotate the images by pressing a key to reveal the rotated or non-rotated version. During the task, participants often got auditory input to help them understand how to rotate pictures and heard a high audio tone when they did an incorrect response and a low audio tone when they responded too slowly. The network structure consisted of three clusters each composed of five nodes and each cluster was made of three nodes.

After performing the image rotation judgment task, participants completed an odd man out test. They picked sets of images such that none of the images were boundary nodes. Each set of three images was then presented in all permuted orders giving six trials per set and 54 trials total. The probability of each image being presented with other images was equivalent.

Results First, they investigated whether participants were able to learn the

network architecture implicit in the temporal contingencies between

 

 

stimuli. They fit a linear mixed effects model with node type, condition, and trial number as predictor variables. There was a significant main effect of node type (pretransition vs. post transition), such that participants were slower at responding to the post Transition trial than the pretransition trial for both social and nonsocial networks.

Study 2 The second study is the same as the first study, except that

participants learned a different number of communities. Study 3

In the third study, they directly examined whether individuals with better performance on the nonsocial network learning task also displayed better performance. To the extent that these skills are independent, they would expect minimal relationship between performance on one task and performance on another task. Study 4

Study 4 utilized the same technique as Study 3, except that the participants were given a rock formation cover story for a non-social situation. Study 5

Study 5: Understanding the group structure of social and non-social networks is affected by social characteristics. Study 4: Participants completed individual questionnaires intended to assess social characteristics, including social preference and perspective-taking. Study 5: In order to accommodate longer study length, they have chosen to recruit participants from the Philadelphia area.

Method They hired 33 participants from the University of Pennsylvania who

conducted the on-site laboratory research. Total Research 5 incentives varied from $20 to $30 based on the success incentive.

Procedure Study 5 measured individual differences in social orientation and

perspective-taking. The procedure for Study 5 was identical to Study 4 but included extra instructions to encourage participants to differentiate between the instructions for the two conditions.

Result Study 5 replicated the main results of the first four studies using an

in-lab experiment. They showed that participants were capable of indirectly learning about the dynamic, higher order nature of social networks. Social characteristics, including social orientation and perspective-taking, uniquely predicted social group learning structure.

Conclusion

 

 

In this article, the statistical analysis of social and non-social network systems was discussed. Statistical learning is expected to play a central role in social networking activity, such as beginning a new job or forming a new social group. These findings advance understanding of how humans are constructing theoretical representations of the social and non-social aspects of the natural world. Future studies could investigate whether human variations in these abilities are related to psychological change and post- movement wellbeing or social transfer. Research has significant consequences for how easily people understand and respond to new social environments.

 

 

Work Cited: Tompson, S. H., Kahn, A. E., Falk, E. B., Vettel, J. M., & Bassett, D. S. (2019). Individual differences in learning social and nonsocial network

structures. Journal of Experimental Psychology: Learning, Memory, and Cognition, 45(2), 253