Neural correlates for price involvement in purchase decisions with regards to fast-moving-consumer-goods

Some customers are loyal to their favorite brands, others easily switch between them. A new technique is available to assess differences in brand related behavior. We assume that price and brand-conscious participants show nearly the same activations in emotionally associated brain areas. Price-conscious participants also show an activation of cognitive associated regions. We employed functional magnet resonance imaging during a preference judgment task for fast mov-ing consumer goods. We discuss the results with differences in product and price specific in-volvement and advance that involvement of price-conscious participants is higher because of a higher price interest.internet Neuro market research, Involvement, Price Interest, Reward Circuitry

Neural correlates for price involvement in purchase decisions with regards to fast-moving-consumer-goods

Some customers are loyal to their favorite brands, others easily switch between them. A new technique is available to assess differences in brand related behavior. We assume that price and brand-conscious participants show nearly the same activations in emotionally associated brain areas. Price-conscious participants also show an activation of cognitive associated regions. We employed functional magnet resonance imaging during a preference judgment task for fast mov-ing consumer goods. We discuss the results with differences in product and price specific in-volvement and advance that involvement of price-conscious participants is higher because of a higher price interest

Link-INVENT: generative linker design with reinforcement learning

In this work, we present Link-INVENT as an extension to the existing de novo molecular design platform REINVENT. We provide illustrative examples on how Link-INVENT can be applied to fragment linking, scaffold hopping, and PROTAC design case studies where the desirable molecules should satisfy a combination of different criteria. With the help of reinforcement learning, the agent used by Link-INVENT learns to generate favourable linkers connecting molecular subunits that satisfy diverse objectives, facilitating practical application of the model for real-world drug discovery projects. We also introduce a range of linker-specific objectives in the Scoring Function of REINVENT. The code is freely available at https://github.com/MolecularAI/Reinvent

Semi-automatic tumor segmentation of rectal cancer based on functional magnetic resonance imaging

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