How the Brain Accumulates Evidence to Make a Decision

Post by Natalia Ladyka-Wojcik

The takeaway

Intracranial electroencephalography was used in a large group of pre-surgical epilepsy patients to identify where in the brain perceptual decision-making begins and to show how brain activity might accumulate to support the strength and accuracy of decisions.

What's the science?

Perceptual decision-making involves selecting one choice from a set of alternatives based on incoming information from our senses. For example, a football player is engaged in perceptual decision-making when judging which player to pass the ball to during a game. When we make these decisions, our brain is also planning the corresponding motor actions, like the player gripping the football behind his head to throw it to his teammate. Recent research has identified parts of the brain involved in making these decisions, even in complex situations when the appropriate motor action might not be known in advance, but it remains unclear where exactly in the brain this process actually starts. This week in Nature Human Behavior, Gherman and colleagues pinpoint the signals in the brain responsible for these abstract decision-making processes.

How did they do it?

Previous research has found that brain activity linked to decision-making builds gradually over time, proportionally to the strength of incoming sensory information. As this evidence accumulates, it reaches a fixed threshold just before the animal makes a response and this threshold predicts both the animal’s choice and response time. In human neuroimaging research where resolution at the single neuron level is often not feasible, past studies have instead relied largely on functional magnetic resonance imaging (fMRI) to investigate brain regions involved in perceptual decision-making. However, fMRI has low temporal resolution, meaning that it doesn't show changes in brain activity quickly enough to demonstrate if there is a signal related to evidence accumulation. To overcome this limitation, the authors measured brain activity directly using intracranial EEG in a group of pre-surgical epilepsy patients who were asked to perform different perceptual decision-making tasks. This technique allows for measurement of a larger portion of the brain compared to single-neuron recordings and with better temporal resolution than fMRI.

First, the authors showed patients two simultaneous random-dot patches and asked patients to report the direction of the moving dots. The researchers measured the patients’ responses and the time it took to press a button to make their decision while recording their brain activity using intracranial EEG. Some patients also had to do the same task but with verbal responses after a delay, helping the researchers find brain activity related to perceptual decision-making, not just motor responses.

What did they find?

The authors found a widely distributed network of brain regions associated with perceptual decision-making, including the prefrontal cortex, parietal cortex, as well as inferior temporal and insular regions. Importantly, this network of brain regions showed high-frequency activity consistent with evidence accumulation in decision-making (whereas lower-frequency activity tends to be associated with motor preparation signals). This work suggests that activity in these regions gradually builds up until enough evidence is accumulated to make a decision (here, in response to the direction of dots) even before the patient presses a button or verbalizes their decision. The authors observed a gradual buildup of activity following the onset of sensory evidence at a rate that scaled with the strength of that sensory evidence. This activity reflected both the choice accuracy and response times of patients.

What's the impact?

This study found that activity in a distributed network of brain regions accumulates in response to sensory information to enable perceptual decision-making. It is the first to use intracranial EEG to directly measure high-frequency activity in these regions in humans, enabling an investigation into brain responses for decision-making even before motor response planning. 

Access the original scientific publication here.

Neuroscience-Backed Strategies to Help You Learn More Effectively

Post by Rachel Sharp

What is learning?

Whether we are in school, with our friends, or pursuing hobbies, we are always learning. But what exactly is learning? And why is it that we can pick up our favorite musician's new song after just a few listens, yet struggle to recall the contents of the Bill of Rights? Biologically, learning is the process by which neurons form and strengthen connections between each other. Every thought and decision we make requires communication between neurons across the brain. Picture groups of neurons working together, firing in unison to produce a thought, idea, or reaction. As these neurons fire together, their connections strengthen, making it more likely they'll fire together in the future. Forming new neural connections, strengthening existing ones, and pruning away unnecessary ones are the building blocks of learning, and understanding how to engage these processes can help us become more effective learners.

Learning occurs in three stages: encoding, storage, and retrieval. Let's explore what happens during each stage, both biologically and practically, and identify strategies to make these processes faster, more accurate, and longer lasting.

Effective encoding, storage and retrieval

Encoding refers to the initial process of encountering and interpreting new information. Our brains are constantly bombarded with information, so when it comes to learning, encoding is highly selective. Most of this filtering happens automatically based on the perceived importance of the information. Here are some strategies to enhance encoding:

1)    Direct Attention: Directing attention to relevant information and minimizing distractions helps strengthen encoding. This might involve highlighting key points or using different colors for important information.

2)    Attach Meaning: Attaching meaning to new information, especially by connecting it to what we already know, helps with encoding. Providing examples or relating topics to personal experiences makes it easier for the brain to process and retain information.

3)     Multiple Modalities: Absorbing information visually, verbally, and actionably recruits multiple different parts of the brain during the encoding process. Effectively, this creates multiple pathways by which the information can be retrieved later.

Storage involves the processing and maintenance of encoded information in the hippocampus. As you learn, neural connections will be formed, rearranged, and lost. So how information is stored, and how strongly it’s stored and maintained, will impact whether learned information persists over time. Varied repetition is key to improving storage, as it strengthens neural connections associated with the information. When you encounter information again and again, the associated neural connections will activate and become stronger. By diversifying repetition through different modalities, contexts, or examples, we ensure robust storage and easier retrieval.

Retrieval is the process of accessing and recalling stored information. It strengthens existing connections and is crucial for long-term memory retention. Now, let's explore some neuroscience-backed learning strategies that enhance encoding, storage, and retrieval.

What are some examples of neuroscience-backed learning strategies?

1.     Orchestrated Immersion: This involves fully immersing learners in their own learning experience. Activities like brainstorming sessions, relating new information to personal experiences, and engaging with thought-provoking questions enhance focused attention and the perceived value of the information, thus improving encoding.

2.     Relaxed Alertness: Creating a fun yet challenging learning environment can aid memory retention. Teaching through songs, dances, or competitive activities triggers positive emotions, diversifies neural connections, and promotes recall.

3.  Active Processing: Encouraging independent learning through group discussions and seeking out additional information strengthens knowledge retention. Engaging in contextual discussions with diverse perspectives deepens understanding and enhances memory consolidation.

Understanding how our brains learn can significantly improve our learning outcomes. By employing effective encoding, storage, and retrieval strategies rooted in neuroscience, we can improve our ability to acquire and retain information. From directed attention, meaningful engagement, and varied repetition, to immersive learning experiences and active processing, incorporating these strategies into our learning routines can make the journey of acquiring knowledge more efficient and enjoyable.

References +

Abdullah, Z., Istiqomah, T., & Sari, R. (2022). NEUROSCIENCE-BASED BIOLOGY SCIENCE LEARNING STRATEGIES AT THE ELEMENTARY SCHOOL LEVEL. Proceeding International Conference on Islam and Education (ICONIE), 2(1), Article 1.

BrainWare. (2020, October 10). Neuroscience of Learning Brain-Based Learning Strategies. Cognitive Literacy Solutions. https://mybrainware.com/blog/brain-based-learning-strategies/

Colvin, R. (2016). Optimising, generalising and integrating educational practice using neuroscience. Npj Science of Learning, 1(1), Article 1. https://doi.org/10.1038/npjscilearn.2016.12

Jamaludin, A., Henik, A., & Hale, J. B. (2019). Educational neuroscience: Bridging theory and practice. Learning: Research and Practice, 5(2), 93–98. https://doi.org/10.1080/23735082.2019.1685027

Memory (Encoding, Storage, Retrieval). (n.d.). Noba. Retrieved February 25, 2024, from https://nobaproject.com/modules/memory-encoding-storage-retrieval

A Neural Prosthetic to Improve Memory

Post by Trisha Vaidyanathan

The takeaway

A neural prosthetic in the hippocampus has the potential to improve memory. In Alzheimer’s disease patients, using a personalized prosthetic to stimulate neurons in specific patterns improved memory performance in some individuals.        

What's the science?

The ability to selectively preserve important or urgent memories can have major therapeutic benefits for patients who suffer from memory loss in diseases like Alzheimer’s Disease. Neural prosthetics that can stimulate the hippocampus, the brain area most associated with memory, have exciting potential. However, existing approaches have relied on stimulation to try to boost general memory function and the underlying biological mechanism is unclear. This week in Frontiers in Computational Neuroscience, Roeder, a research fellow in the department of translational neuroscience at Wake Forest University School of Medicine, and colleagues at Wake Forest and the University of Southern California tested the effectiveness of a new neural prosthetic approach to improving memory.

How did they do it?

Fourteen adult subjects underwent surgery for hippocampus implants that allowed for continuous recording and stimulation of single-neuron activity. After surgery, all subjects performed a “Delayed Match to Sample Task” in which they were shown a “sample image”, followed by a 3-5 second delay, and then asked to identify the sample image amongst a choice of four images. The sample images fell under one of 5 categories: animal, building, plant, tool, or vehicle.

Next, the authors used the neural recordings obtained during the Delayed Match to Sample Task to identify the spatiotemporal pattern of neuronal activity elicited by each category of sample image. For each individual patient, a computer model was used to calculate a stimulation pattern for each image category that was derived from their neural response to all images within that category. The result was five fixed patterns of stimulation, uniquely designed for each patient, corresponding to each of the five image categories (animal, building, plant, tool, vehicle).

Finally, the subjects repeated the Delayed Match to Sample Task but during the sample image presentation, they were exposed to either (1) no stimulation, (2) “match stimulation”, in which the implanted prosthetic stimulated neurons in the pattern intended to match the category of the sample image, (3) “non-match stimulation” in which the stimulation corresponded to a category that did not match the sample image, or (4) random stimulation. After 15-20 minutes, the effect of stimulation on memory was tested by administering a Delayed Recognition Task to the subjects, in which they were asked to rank the familiarity of a collection of images that included the previous sample image. A correct trial was one in which the sample image was ranked as greater than a 2 out of 5 in familiarity, and the ranking was equal to or higher than the other image options.

What did they find?

When performance was examined for each image category for “match stimulation,” instances of increased performance occurred about twice as frequently as instances of decreased performance. Further, enhanced memory performance was primarily observed when the patient received bilateral stimulation, rather than unilateral stimulation, and had pre-existing memory impairments. This suggests that bilateral stimulation in select patients with pre-existing impairments might have the most therapeutic potential.

In trials where subjects received a “non-match stimulation,” the authors observed more instances of decreased performance relative to trials with no stimulation or match stimulation. This supported the authors’ prediction that “non-match stimulation” would interfere with natural memory processes. Overall, however, the result on memory was variable across patients and image categories and suggested that the stimulation patterns created by the computer model may not have been as specific to the image category as intended.

What's the impact?

This study provides exciting evidence to support the use of neural prosthetics in the hippocampus for enhancing memory and identifies several areas for further development. Therapeutic approaches like these may have dramatic impacts on patients who suffer from memory loss, such as those with Alzheimer’s disease.