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This Is How Your Brain Responds to Social Influence

social influence neuroscience brain

I’m a doormat when it comes to peer pressure. Jump off a 32-foot (10 meter) diving board without any experience? Sure! Propel off a cliff my first time outdoor climbing? I’ll try! Those were obviously terrible decisions for someone afraid of heights, and each ended with “I really should’ve known better.” But it illustrates a […]

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I’m a doormat when it comes to peer pressure. Jump off a 32-foot (10 meter) diving board without any experience? Sure! Propel off a cliff my first time outdoor climbing? I’ll try!

Those were obviously terrible decisions for someone afraid of heights, and each ended with “I really should’ve known better.” But it illustrates a point: it’s obvious that our decisions don’t solely come from our own experiences. From what career you choose to what sandwich you want for lunch, we care about what our friends, families, and complete strangers think—otherwise, Yelp wouldn’t exist.

In academic speak, observing and learning from other people is called “social influence,” a term that’s obviously crossed into pop culture lexicon. Yet neuroscientists have struggled to understand why this happens. How do our brains process others’ decisions? And how does it weigh those decisions against our (potentially saner) judgment?

This month, Drs. Lei Zhang and Jan Gläscher from Germany and Austria teased out the neural networks that allow us to evaluate social influence. They then figured out how those networks link to our internal, or “direct” learning networks—that is, should I listen to my fear of heights or to social pressure?

By scanning the brains of 39 people playing a multiplayer betting game, the team synthesized a “social prediction error” from brain activity in the reward circuit, which measures the difference between how we expect people to behave versus how they actually do. They then used neurocomputational tools to model these brain activity data, and found a link between the brain’s reward circuit and social ones.

It’s not all academic curiosity. One of the most prominent AI models today, deep reinforcement learning, stemmed from research on how humans learn from their mistakes—formally known as “reward prediction error.” (Sound familiar?) As momentum gains for a more cognitive and social approach for training AI, neuroscience studies that help us understand how we learn from one another may also benefit learning algorithms that teach AI to learn by observing us.

Our work shows that we need to constantly balance our own expectations with those we observe in others, and whether the two match up, the authors concluded. Thanks to our brain’s social error signal, we can flexibly adapt our choices to social influence, maybe for the better.

Error! Bail!

Let’s talk direct learning first—the type of learning we gain from our own experiences. At its heart is the reward prediction error, and even if you haven’t heard of it, you’ve experienced it.

Take this scenario: you’re meeting your spouse’s coworkers for the first time. You have an expectation based on what you’ve heard, and you adjust your mannerisms appropriately…or so you think. When you meet them, you realized they’re nothing like what you expected.

This is where the reward prediction error comes in. We often hear about the brain’s “reward circuits,” but that’s not quite accurate. They don’t shell out pleasant reward rushes all the time. What these brain circuits actually do is calculate an estimated reward, based on your knowledge, expectations, and decisions, versus what actually happens.

If they match up, the reward error is very low, meaning your brain says that you don’t need to adjust your behavior. You also get a nice dose of happy-feeling neurochemicals to reinforce those behaviors; hence, reinforcement learning. If expectation and reality don’t match up, then the error is high—and that’s when you figure out you probably need to change your strategy.

In other words, you learn. At its root, reinforcement learning is learning from your mistakes in judgment.

Neuroanatomy studies have traced these learning circuits to two main regions: part of the prefrontal cortex (vmPFC), at the very forefront of your brain, which encodes your own valuation or judgment, and parts of the “reward” learning circuit, which encodes the error signal.

Influencer in the Lab

Social influence gets messier. Previous studies suggest that vmPFC is also involved in decision-making that incorporates social influence, but so far few people have traced how one mind can change another’s decision.

The team began answering these questions with 185 people, randomly assigned into groups of 5, in a social influence test. It starts with direct learning: people choose between two abstract fractal images—a yellow swirl and a blue snowflake—on a computer screen, with one choice getting a higher score than the other. They also laid down bets on how confident they were in their choice. The person was then able to see others’ choices in their groups sequentially, and was then provided with a second chance to choose.

The study “required participants to learn and continuously relearn,” the authors explained, so that they begin to naturally incorporate other peoples’ choices into their own decisions—even when they know that there is no bonus for participation or competition. Perhaps unsurprisingly, people were more eager to change their choice when it didn’t fit with the group’s, while sticking to their guns when it matched with the general flow. What’s more, if the person’s choice matched the group’s, they were also willing to bet higher on its chance of success in the next round.

Social information altered people’s choice and their confidence in the choice, which helped them readjust their choice next time around—an obvious sign of learning, the authors said.

The Brain’s Influencer Nodes

During the betting game, one of five participants laid inside an MRI machine and had their brains scanned. Altogether, the team watched the brain activity of 39 people as they grappled with sticking with their choice or succumbing to peer pressure.

Using their model to analyze MRI data, the team honed in on a brain region called the anterior cingulate cortex (ACC), which was previously found to track a sense of “good will” that we allocate to other humans. The ACC was particularly responsible for learning from others. Digging deeper, the team found the link between the two nodes: vmPFC, for direct learning, and ACC, for social learning.

learning brain experiences map regions
Distinct but interactive brain networks control direct and social learning. Credit: Dr. Lei Zhang

The bridge is the putamen (Latin for “nutshell”), a round nugget of brain tissue that forms the broader reward circuit, which extends to a surface region of the brain (rTPJ, or right temporoparietal junction) that seem to be involved in immediate social influence. As for “should I stay or should I go?” in picking a choice, the ACC hooks up with another part of the prefrontal cortex, the dlPFC, to finally decide.

If your eyes are glazing over these acronyms, yeah, me too. Here it is in a nutshell (lol): the brain region that governs how we learn from our own experience is connected with ones that help us learn from the experiences of others. When the two circuits strongly sync up, we’re likely to change our behavior due to social influencers. But the final decision is still up to us.

Similar to learning from our own experiences, this social learning circuit generates a “social prediction error”—one that heavily guides how we learn from others, but surprisingly, also how we learn from ourselves. Both errors silently drive our next decision: what sandwich to get (listen to my own tastes or depend on Yelp?), what advice to take, and yeah—whether to jump off a diving board while freaking out from peer pressure.

“Direct learning is efficient in stable situations,” explains Gläscher, “and when situations are changing and uncertain, social learning may play an important role together with direct learning to adapt to novel situations.”

Now that we better understand brain networks for social learning, the next step is “disrupt them using non-invasive brain stimulation techniques,” said Gläscher, and see how our decisions change in response. “And in light of the ongoing Covid-19 pandemic, there is no way individuals and governments learn from mistakes all by themselves, and instead, a global and collective human society is needed to address all these challenges.”

Image Credit: Free-Photos from Pixabay

Source: https://singularityhub.com/2020/08/25/this-is-how-your-brain-responds-to-social-influence/

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Graph Convolutional Networks (GCN)

In this post, we’re gonna take a close look at one of the well-known graph neural networks named Graph Convolutional Network (GCN). First, we’ll get the intuition to see how it works, then we’ll go deeper into the maths behind it. Why Graphs? Many problems are graphs in true nature. In our world, we see many data are graphs, […]

The post Graph Convolutional Networks (GCN) appeared first on TOPBOTS.

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graph convolutional networks

In this post, we’re gonna take a close look at one of the well-known graph neural networks named Graph Convolutional Network (GCN). First, we’ll get the intuition to see how it works, then we’ll go deeper into the maths behind it.

Why Graphs?

Many problems are graphs in true nature. In our world, we see many data are graphs, such as molecules, social networks, and paper citations networks.

Tasks on Graphs

  • Node classification: Predict a type of a given node
  • Link prediction: Predict whether two nodes are linked
  • Community detection: Identify densely linked clusters of nodes
  • Network similarity: How similar are two (sub)networks

Machine Learning Lifecycle

In the graph, we have node features (the data of nodes) and the structure of the graph (how nodes are connected).

For the former, we can easily get the data from each node. But when it comes to the structure, it is not trivial to extract useful information from it. For example, if 2 nodes are close to one another, should we treat them differently to other pairs? How about high and low degree nodes? In fact, each specific task can consume a lot of time and effort just for Feature Engineering, i.e., to distill the structure into our features.

graph convolutional network
Feature engineering on graphs. (Picture from [1])

It would be much better to somehow get both the node features and the structure as the input, and let the machine to figure out what information is useful by itself.

That’s why we need Graph Representation Learning.

graph convolutional network
We want the graph can learn the “feature engineering” by itself. (Picture from [1])

If this in-depth educational content on convolutional neural networks is useful for you, you can subscribe to our AI research mailing list to be alerted when we release new material. 

Graph Convolutional Networks (GCNs)

Paper: Semi-supervised Classification with Graph Convolutional Networks (2017) [3]

GCN is a type of convolutional neural network that can work directly on graphs and take advantage of their structural information.

it solves the problem of classifying nodes (such as documents) in a graph (such as a citation network), where labels are only available for a small subset of nodes (semi-supervised learning).

graph convolutional network
Example of Semi-supervised learning on Graphs. Some nodes dont have labels (unknown nodes).

Main Ideas

As the name “Convolutional” suggests, the idea was from Images and then brought to Graphs. However, when Images have a fixed structure, Graphs are much more complex.

graph convolutional network
Convolution idea from images to graphs. (Picture from [1])

The general idea of GCN: For each node, we get the feature information from all its neighbors and of course, the feature of itself. Assume we use the average() function. We will do the same for all the nodes. Finally, we feed these average values into a neural network.

In the following figure, we have a simple example with a citation network. Each node represents a research paper, while edges are the citations. We have a pre-process step here. Instead of using the raw papers as features, we convert the papers into vectors (by using NLP embedding, e.g., tf–idf).

Let’s consider the green node. First off, we get all the feature values of its neighbors, including itself, then take the average. The result will be passed through a neural network to return a resulting vector.

graph convolutional network
The main idea of GCN. Consider the green node. First, we take the average of all its neighbors, including itself. After that, the average value is passed through a neural network. Note that, in GCN, we simply use a fully connected layer. In this example, we get 2-dimension vectors as the output (2 nodes at the fully connected layer).

In practice, we can use more sophisticated aggregate functions rather than the average function. We can also stack more layers on top of each other to get a deeper GCN. The output of a layer will be treated as the input for the next layer.

graph convolutional network
Example of 2-layer GCN: The output of the first layer is the input of the second layer. Again, note that the neural network in GCN is simply a fully connected layer (Picture from [2])

Let’s take a closer look at the maths to see how it really works.

Intuition and the Maths behind

First, we need some notations

Let’s consider a graph G as below.

graph convolutional network
From the graph G, we have an adjacency matrix A and a Degree matrix D. We also have feature matrix X.

How can we get all the feature values from neighbors for each node? The solution lies in the multiplication of A and X.

Take a look at the first row of the adjacency matrix, we see that node A has a connection to E. The first row of the resulting matrix is the feature vector of E, which A connects to (Figure below). Similarly, the second row of the resulting matrix is the sum of feature vectors of D and E. By doing this, we can get the sum of all neighbors’ vectors.

graph convolutional network
Calculate the first row of the “sum vector matrix” AX
  • There are still some things that need to improve here.
  1. We miss the feature of the node itself. For example, the first row of the result matrix should contain features of node A too.
  2. Instead of sum() function, we need to take the average, or even better, the weighted average of neighbors’ feature vectors. Why don’t we use the sum() function? The reason is that when using the sum() function, high-degree nodes are likely to have huge v vectors, while low-degree nodes tend to get small aggregate vectors, which may later cause exploding or vanishing gradients (e.g., when using sigmoid). Besides, Neural networks seem to be sensitive to the scale of input data. Thus, we need to normalize these vectors to get rid of the potential issues.

In Problem (1), we can fix by adding an Identity matrix I to A to get a new adjacency matrix Ã.

Pick lambda = 1 (the feature of the node itself is just important as its neighbors), we have Ã = A + I. Note that we can treat lambda as a trainable parameter, but for now, just assign the lambda to 1, and even in the paper, lambda is just simply assigned to 1.

By adding a self-loop to each node, we have the new adjacency matrix

Problem (2)For matrix scaling, we usually multiply the matrix by a diagonal matrix. In this case, we want to take the average of the sum feature, or mathematically, to scale the sum vector matrix ÃX according to the node degrees. The gut feeling tells us that our diagonal matrix used to scale here is something related to the Degree matrix D̃ (Why , not D? Because we’re considering Degree matrix  of new adjacency matrix Ã, not A anymore).

The problem now becomes how we want to scale/normalize the sum vectors? In other words:

How we pass the information from neighbors to a specific node?

We would start with our old friend average. In this case, D̃ inverse (i.e., D̃^{-1}) comes into play. Basically, each element in D̃ inverse is the reciprocal of its corresponding term of the diagonal matrix D.

For example, node A has a degree of 2, so we multiple the sum vectors of node A by 1/2, while node E has a degree of 5, we should multiple the sum vector of E by 1/5, and so on.

Thus, by taking the multiplication of D̃ inverse and X, we can take the average of all neighbors’ feature vectors (including itself).

So far so good. But you may ask How about the weighted average()?. Intuitively, it should be better if we treat high and low degree nodes differently.

We’re just scaling by rows, but ignoring their corresponding columns (dash boxes)
Add a new scaler for columns.

The new scaler gives us the “weighted” average. What are we doing here is to put more weights on the nodes that have low-degree and reduce the impact of high-degree nodes. The idea of this weighted average is that we assume low-degree nodes would have bigger impacts on their neighbors, whereas high-degree nodes generate lower impacts as they scatter their influence at too many neighbors.

graph convolutional network
When aggregating feature at node B, we assign the biggest weight for node B itself (degree of 3), and the lowest weight for node E (degree of 5)
Because we normalize twice, we change “-1” to “-1/2”

For example, we have a multi-classification problem with 10 classes, F will be set to 10. After having the 10-dimension vectors at layer 2, we pass these vectors through a softmax function for the prediction.

The Loss function is simply calculated by the cross-entropy error over all labeled examples, where Y_{l} is the set of node indices that have labels.

The number of layers

The meaning of #layers

The number of layers is the farthest distance that node features can travel. For example, with 1 layer GCN, each node can only get the information from its neighbors. The gathering information process takes place independentlyat the same time for all the nodes.

When stacking another layer on top of the first one, we repeat the gathering info process, but this time, the neighbors already have information about their own neighbors (from the previous step). It makes the number of layers as the maximum number of hops that each node can travel. So, depends on how far we think a node should get information from the networks, we can config a proper number for #layers. But again, in the graph, normally we don’t want to go too far. With 6–7 hops, we almost get the entire graph which makes the aggregation less meaningful.

graph convolutional network
Example: Gathering info process with 2 layers of target node i

How many layers should we stack the GCN?

In the paper, the authors also conducted some experiments with shallow and deep GCNs. From the figure below, we see that the best results are obtained with a 2- or 3-layer model. Besides, with a deep GCN (more than 7 layers), it tends to get bad performances (dashed blue line). One solution is to use the residual connections between hidden layers (purple line).

graph convolutional network
Performance over #layers. Picture from the paper [3]

Take home notes

  • GCNs are used for semi-supervised learning on the graph.
  • GCNs use both node features and the structure for the training
  • The main idea of the GCN is to take the weighted average of all neighbors’ node features (including itself): Lower-degree nodes get larger weights. Then, we pass the resulting feature vectors through a neural network for training.
  • We can stack more layers to make GCNs deeper. Consider residual connections for deep GCNs. Normally, we go for 2 or 3-layer GCN.
  • Maths Note: When seeing a diagonal matrix, think of matrix scaling.
  • A demo for GCN with StellarGraph library here [5]. The library also provides many other algorithms for GNNs.

Note from the authors of the paper: The framework is currently limited to undirected graphs (weighted or unweighted). However, it is possible to handle both directed edges and edge features by representing the original directed graph as an undirected bipartite graph with additional nodes that represent edges in the original graph.

What’s next?

With GCNs, it seems we can make use of both the node features and the structure of the graph. However, what if the edges have different types? Should we treat each relationship differently? How to aggregate neighbors in this case? What are the advanced approaches recently?

In the next post of the graph topic, we will look into some more sophisticated methods.

graph convolutional network
How to deal with different relationships on the edges (brother, friend,….)?

REFERENCES

[1] Excellent slides on Graph Representation Learning by Jure Leskovec (Stanford):  https://drive.google.com/file/d/1By3udbOt10moIcSEgUQ0TR9twQX9Aq0G/view?usp=sharing

[2] Video Graph Convolutional Networks (GCNs) made simple: https://www.youtube.com/watch?v=2KRAOZIULzw

[3] Paper Semi-supervised Classification with Graph Convolutional Networks (2017): https://arxiv.org/pdf/1609.02907.pdf

[4] GCN source code: https://github.com/tkipf/gcn

[5] Demo with StellarGraph library: https://stellargraph.readthedocs.io/en/stable/demos/node-classification/gcn-node-classification.html

This article was originally published on Medium and re-published to TOPBOTS with permission from the author.

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Microsoft BOT Framework — Loops

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Loops is one of the basic programming structure in any programming language. In this article, I would demonstrate Loops within Microsoft BOT framework.

To follow this article clearly, please have a quick read on the basics of the Microsoft BOT framework. I wrote a couple of articles sometime back and the links are below:

Let’s Get Started.

I would be using the example of a TaxiBot described in one of my previous article. The BOT asks some general questions and books a Taxi for the user. In this article, I would be providing an option to the user to choose there preferred cars for the ride. The flow will look like below:

Create a new Dialog Class for Loops

We would need 2 Dialog classes to be able to achieve this task:

  1. SuperTaxiBotDialog.cs: This would be the main dialog class. The waterfall will contains all the steps as defined in the previous article.
  2. ChooseCarDialog.cs: A new dialog class will be created which would allow the user to pick preferred cars. The loop will be defined in this class.

The water fall steps for both the classes could be visualized as:

The complete code base is present on the Github page.

Important Technical Aspects

  • Link between the Dialogs: In the constructor initialization of SuperTaxiBotDialog, add a dialog for ChooseCarDialog by adding the line:
AddDialog(new ChooseCarDialog());

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  • Call ChooseCarDialog from SuperTaxiBotDialog: SuperTaxiBotDialog calls ChooseCarDialog from the step SetPreferredCars, hence the return statement of the step should be like:
await stepContext.BeginDialogAsync(nameof(ChooseCarDialog), null, cancellationToken);
  • Return the flow back from ChooseCarDialog to SuperTaxiBotDialog: Once the user has selected 2 cars, the flow has to be sent back to SuperTaxiBotDialog from the step LoopCarAsync. This should be achieved by ending the ChooseCarDialog in the step LoopCarAsync.
return await stepContext.EndDialogAsync(carsSelected, cancellationToken);

The complete code base is present on the Github page.

Once the project is executed using BOT Framework Emulator, the output would look like:

Hopefully, this article will help the readers in implementing a loop with Microsoft BOT framework. For questions: Hit me.

Regards

Tarun

Source: https://chatbotslife.com/microsoft-bot-framework-loops-fe415f0e7ca1?source=rss—-a49517e4c30b—4

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The Bleeding Edge of Voice

This fall, a little known event is starting to make waves. As COVID dominates the headlines, an event called “Voice Launch” is pulling…

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Tapaan Chauhan

This fall, a little known event is starting to make waves. As COVID dominates the headlines, an event called “Voice Launch” is pulling together an impressive roster of start-ups and voice tech companies intending to uncover the next big ideas and start-ups in voice.

While voice tech has been around for a while, as the accuracy of speech recognition improves, it moves into its prime. “As speech recognition moves from 85% to 95% accuracy, who will use a keyboard anymore?” says Voice Launch organizer Eric Sauve. “And that new, more natural way to interact with our devices will usher in a series of technological advances,” he added.

Voice technology is something that has been dreamt of and worked on for decades all over the world. Why? Well, the answer is very straightforward. Voice recognition allows consumers to multitask by merely speaking to their Google Home, Amazon Alexa, Siri, etc. Digital voice recording works by recording a voice sample of a person’s speech and quickly converting it into written texts using machine language and sophisticated algorithms. Voice input is just the more efficient form of computing, says Mary Meeker in her ‘Annual Internet Trends Report.’ As a matter of fact, according to ComScore, 50% of all searches will be done by voice by 2020, and 30% of searches will be done without even a screen, according to Gartner. As voice becomes a part of things we use every day like our cars, phones, etc. it will become the new “norm.”

The event includes a number of inspiration sessions meant to help start-ups and founders pick the best strategies. Companies presenting here include industry leaders like Google and Amazon and less known hyper-growth voice tech companies like Deepgram and Balto and VCs like OMERS Ventures and Techstars.

But the focus of the event is the voice tech start-ups themselves, and this year’s event has some interesting participants. Start-ups will pitch their ideas, and the audience will vote to select the winners. The event is a cross between a standard pitchfest and Britain’s Got Talent.

Source: https://chatbotslife.com/the-bleeding-edge-of-voice-67538bd859a9?source=rss—-a49517e4c30b—4

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