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Neural Correlates of Consumer Behavior in Online Marketplaces: An fMRI Study Leallyn Murtagh Department of Biomedical Engineering University of Southern.

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Presentation on theme: "Neural Correlates of Consumer Behavior in Online Marketplaces: An fMRI Study Leallyn Murtagh Department of Biomedical Engineering University of Southern."— Presentation transcript:

1 Neural Correlates of Consumer Behavior in Online Marketplaces: An fMRI Study Leallyn Murtagh Department of Biomedical Engineering University of Southern California, Los Angeles, CA 90007 Leallyn Murtagh Department of Biomedical Engineering University of Southern California, Los Angeles, CA 90007 Angelika Dimoka Department of Bioengineering University of California-Riverside, Riverside, CA 92521

2 Introduction  Neuroscience Information Systems Lab  Joint project between:  Department of Bioengineering at UCR  Department of Information Systems at UCR  Department of Biomedical Engineering at USC  Neuroscience Information Systems Lab  Joint project between:  Department of Bioengineering at UCR  Department of Information Systems at UCR  Department of Biomedical Engineering at USC

3 Background  Cognitive neuroscience  fMRI measures hemodynamics  Social sciences  Uncertainty  Trust  Purchase Intentions  Neuroeconomics  A branch of neuroscience that focuses on consumer decision-making and interaction  Cognitive neuroscience  fMRI measures hemodynamics  Social sciences  Uncertainty  Trust  Purchase Intentions  Neuroeconomics  A branch of neuroscience that focuses on consumer decision-making and interaction

4 Goals  Develop a methodology for localizing neural correlates of constructs  Further our understanding of the brain  Enhance online transactions to benefit society  Develop a methodology for localizing neural correlates of constructs  Further our understanding of the brain  Enhance online transactions to benefit society

5 The Experiment

6 Amazon

7 Matrix

8 The Protocol  Amazon vs. Matrix Likert-type questionnaire  80 questions total  Amazon vs. Matrix Likert-type questionnaire  80 questions total

9

10 The Protocol  Amazon vs. Matrix Likert-type questionnaire  80 questions total  10 s intervals  Followed by paper survey  Amazon vs. Matrix Likert-type questionnaire  80 questions total  10 s intervals  Followed by paper survey

11 Analysis  Preprocessing  Statistical Analysis with SPM2  Neuroscience evaluation  Preprocessing  Statistical Analysis with SPM2  Neuroscience evaluation

12 Results for Uncertainty  Amazon: Inferior Parietal Cortex (IPC)  Low levels of uncertainty  Anticipation of reward  Matrix: Orbitofrontal Cortex  High levels of uncertainty  Dorsolateral prefrontal cortex (DLPFC)  Working memory  Medial frontal gyrus (MFG)  Voluntary movement  Amazon: Inferior Parietal Cortex (IPC)  Low levels of uncertainty  Anticipation of reward  Matrix: Orbitofrontal Cortex  High levels of uncertainty  Dorsolateral prefrontal cortex (DLPFC)  Working memory  Medial frontal gyrus (MFG)  Voluntary movement

13 Results for Trust  Amazon: Lateral prefrontal cortex (LPFC)  Delayed rewards  Amazon: Right putamen  Positive reward prediction  Matrix: Orbitofrontal Cortex  Lack of trust  Amazon: Lateral prefrontal cortex (LPFC)  Delayed rewards  Amazon: Right putamen  Positive reward prediction  Matrix: Orbitofrontal Cortex  Lack of trust

14 Results for Intention  Amazon: Ventrolateral prefrontal cortex (VLPFC)  Higher intentions  Amazon: Bilateral amygdala  Positive reward prediction  Amazon: Insular Cortex  Negative emotions  Matrix: Left putamen  Error in prediction  Amazon: Ventrolateral prefrontal cortex (VLPFC)  Higher intentions  Amazon: Bilateral amygdala  Positive reward prediction  Amazon: Insular Cortex  Negative emotions  Matrix: Left putamen  Error in prediction

15 Conclusions  Preliminary results show the method’s potential in localizing neural correlates of constructs  Challenges previous linear assumptions  Enhances our knowledge of how brain activity corresponds to social phenomena  Characterizes the ideal website  Preliminary results show the method’s potential in localizing neural correlates of constructs  Challenges previous linear assumptions  Enhances our knowledge of how brain activity corresponds to social phenomena  Characterizes the ideal website

16 Thank You Acknowledgements: Dr. Angelika Dimoka Bryan Yeh Farouk Bruce Gabrielle Goodman Dr. Victor Rodgers And the BRITE REU Program Acknowledgements: Dr. Angelika Dimoka Bryan Yeh Farouk Bruce Gabrielle Goodman Dr. Victor Rodgers And the BRITE REU Program

17 Questions?


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