What We’ve Learned About Mirror Neurons

Post by Shireen Parimoo

Think back to a time when you picked up the cup in front of you and took a sip of hot coffee. Now, think about a time when you watched somebody do the same thing. The same set of neurons in the brain – dubbed mirror neurons (MNs) – were likely activated in both of those instances. Mirror neurons were initially discovered in the monkey brain in 1992 when the same set of neurons in the ventral premotor cortex would fire when the monkey performed an action and when the monkey watched somebody else perform the same action. This finding was followed by a slew of research studies that sought to identify the role of mirror neurons in cognitive abilities like empathy or decision-making. When mirror neurons were initially discovered, there was considerable debate regarding their function and origin. In the last 30 years, substantial progress has been made in better understanding the role of mirror neurons, yet some questions still remain.   

What are mirror neurons, exactly?

Early studies using single-cell recordings initially identified what are known as strictly congruent MNs that fire when an animal performs an action and observes someone else performing exactly the same action. Though they were initially found in premotor regions of the frontal cortex, MNs have since been located in other frontal areas as well as parietal and medial temporal areas in the brain, including in humans. Subsequent studies have also identified a broadly congruent type of MN that would fire when executing an action or observing a similar enough action, as well as mirror-like neurons that are active while observing but not performing an action. This finding is interesting because as the name suggests, motor areas in the brain are predominantly involved in preparing for and performing actions, but mirror-like neurons do not appear to be associated with any motor output. In addition to actions, these cells are also responsive to information about emotions, space, decisions, and beliefs.

Population-level activity in different regions of the MN system has also been shown to represent action sequences during observation and execution. Thus, there are multiple types of neurons distributed across the brain that exhibit MN or mirror-like properties, both at a single-cell level and as a neuronal population.

What is the function of mirror neurons? 

The existence of both MNs and mirror-like neurons, especially in motor regions of the brain, begs the question: what are they useful for? Mirror neurons were initially thought to serve a range of functions, including action understanding, speech perception, and imitation. In fact, they were even implicated in autism as researchers speculated that dysfunction of the MN system might explain many of the social and emotional symptoms associated with autism. Although initial studies hinted at atypical recruitment of regions containing MNs in individuals with autism during action observation, this hypothesis was not confirmed by subsequent experiments. Similarly, there is no clear evidence that MNs are involved in understanding others’ actions, that is, in inferring the intentions behind actions performed by others. This is because most of the evidence supporting this idea cannot distinguish between the role of MNs in low-level cognitive processes (e.g., recognizing an action) and higher-level processes (e.g., inferring others’ goals from their actions). Nonetheless, many neuroimaging and patient studies in humans do provide support for the notion that regions containing MNs are important for speech perception and imitation – both of which are important for social cognition and learning.

Do mirror neurons have an evolutionary origin?

Humans are social creatures. Much of what we learn – as children and as adults – is through observation and imitation. So, it’s not surprising to consider the possibility that our brains evolved in tandem to optimize learning from others, in turn guiding our behavior in social situations. Two major perspectives on the origins of MNs emphasize the role of (i) genetics (we come with MNs hardwired!), or (ii) associative learning. Initial evidence for the idea that MN development is genetically determined came from studies with newborn babies and monkeys. Unlike young infants and toddlers, newborn monkeys have not had as much opportunity and experience to learn from their surroundings. Yet, they were able to spontaneously imitate the facial expressions of their caregivers. However, the genetic explanation has been questioned because some of the neonatal imitation findings have not been reliably replicated or suffer from limitations in the research methods used.

Instead, some speculate that MNs develop from sensorimotor associative learning. Essentially, this means that as we learn to associate actions (e.g., reaching for a cup of coffee) with visual input (watching our hands reach for the cup), motor neurons form stronger connections with the visual neurons. Over time, MNs develop as the motor neurons begin selectively responding to both performing the action and to watching someone else perform the action. The engagement of the MN system in response to both biological (e.g., grasping) and artificial or unnatural (e.g., robotic) stimuli lends further support to the associative over the genetic account. Nevertheless, some researchers have provided alternative explanations for these findings and the debate regarding the origin of MNs has not yet been fully resolved.

What’s next?

Since 1992, the diversity of neuron types in the MN system, as well as the discovery of MNs in emotion-processing areas (outside of motor areas), has led to the idea that by integrating sensorimotor representations about the self with sensorimotor representations about others, the MN system facilitates behavior during social interactions. The diversity of neuronal types within the MN system may each contribute to a different aspect of social learning. For instance, unlike the strictly congruent MNs found in the premotor cortex, mirror-like neurons in the monkey motor cortex that regulate motor output are sometimes inhibited during action observation. These findings illustrate how the MN system enables us to learn through observation without imitation or necessarily producing actions of our own. Moreover, the extended MN system has been linked to both self-related emotional regulation as well as emotional perception in others. This may be unsurprising since emotional expression is closely tied to actions – including facial expressions, gestures, and body language – that MNs are responsive to. However, more research is required to understand the role that MNs and mirror-like neurons play in emotional processing and traits like empathy and prosocial behavior. Together, these findings highlight the need to further investigate the extended MN system at both the neuronal and network level to understand its role in social behavior.

References +

Bonini et al (2022). Mirror neurons 30 years later: implications and applications. Trends in Cognitive Sciences, in press.

Heyes & Catmur (2022). What happened to mirror neurons? Perspectives on Psychological Science, 17(1).

Gallese et al (1996). Action recognition in the premotor cortex. Brain, 119(2).

Jerjian et al (2020). Movement initiation and grasp representation in premotor and primary motor cortex mirror neurons. eLife, 9, e54139.

Kraskov et al (2009). Corticospinal neurons in macaque ventral premotor cortex with mirror properties: a potential mechanism for action suppression? Neuron, 64.

Mazurek et al (2018). Mirror neuron populations represent sequences of behavioral epochs during both execution and observation. Journal of Neuroscience, 38(18).

Papadourakis & Raos (2018). Neurons in the macaque dorsal premotor cortex respond to execution and observation of actions. Cerebral Cortex, 29(10).

Di Pellegrino et al (1992). Understanding motor events: a neurophysiological study. Experimental Brain Research, 91.

Ubaldi et al (2015). Bottom-up and top-down visuomotor responses to action observation. Cerebral Cortex, 24(4).

Vigneswaran et al (2013). M1 corticospinal mirror neurons and their role in movement suppression during action observation. Current Biology, 23(3).

Ripples Facilitate Memory Recall via Widespread Synchronization of Brain Activity

Post by Leanna Kalinowski

The takeaway

The encoding, consolidation, and retrieval of memory requires the integration of multiple regions across the cortex. High-frequency brain oscillations, called “ripples”, may help to organize activity across these regions into a coherent representation.

What's the science?

Different elements of a memory are stored in different regions across the brain. Therefore a coordination of brain activity is required whenever a memory is consolidated, encoded, or retrieved. While this coordination is not yet fully understood, it is believed that these regions become synchronized via high-frequency brain oscillations called “ripples”. This week in PNAS, Dickey and colleagues used intracranial recordings in humans to measure ripple patterns during sleep, waking, and memory recall.

How did they do it?

The researchers used data from 25 participants who already had electrodes surgically implanted into their brains for an unrelated medical issue (i.e., treatment-resistant epilepsy). This procedure, called stereoelectroencephalography (SEEG), is used to measure activity in regions deeper than what regular electroencephalography (EEG) can reach. In order to study healthy brain activity, they excluded data that was suspected to be related to underlying epilepsy. They measured ripple patterns from the cortex and hippocampus during three key periods: (1) during non-rapid eye movement (NREM) sleep, which is when the brain replays and sorts through memories for consolidation into long-term storage, (2) during waking, which is when information is transferred from the cortex to the hippocampus for memory encoding, and (3) during memory recall.

They then evaluated memory recall by having participants undergo the paired-associated memory task. In this task, participants first encoded eight pairs of words (e.g., “moon” + “mouth”) and were subsequently cued with one word from each pair (e.g., “moon”) to immediately recall the second word in the pair (e.g., “mouth”). After a 60-second delay with distraction, they were once again asked to recall the second word in the pair. This process was repeated until each participant attempted to recall about 100-160 unique word pairs in total.

What did they find?

The researchers found that ripples couple (i.e., occur within 500 ms of each other) and co-occur (i.e., have > 25 ms of activity overlap between these ~70 ms long events) across multiple lobes and both hemispheres during both NREM sleep and waking. They also found an increase in ripple co-occurrences between cortical sites and between the cortex and hippocampus during the memory recall task. These ripples phase lock with neuron firing (i.e., synchronize individual neuron activity) and phase synchronize with each other (i.e., synchronize activity across brain regions) across short and long distances.

What's the impact?

Taken together, these results demonstrate a mechanism by which the brain synchronizes activity across multiple cortical regions, which may help to integrate different components of a memory. This synchronization is not impacted by distance, including instances where both hemispheres of the brain are activated. Further work is needed to assess how ripples may help with the integration of brain activity for neural events beyond memory.

Statistical Learning From Speech

Post by Anastasia Sares

The takeaway

Spontaneous speech synchronizers are those who unconsciously align their own speech production with an external stimulus. People who do this are also better at identifying patterns in artificial babble speech, and it may be because they are using additional brain areas when processing speech in general.

What's the science?

Language learning is a complex and challenging process. When first learning to speak, or when learning a second language, we often hear strings of sounds with no obvious break between them. But through repeated listening, our brains can pick up on patterns, determining which groups of sounds occur together more often—these are more likely to be words. The process is called “statistical learning,” and it can be tested in the lab using artificial speech. Individuals vary in their statistical learning ability. In a recent study, it was discovered that some people spontaneously synchronize their own speech with external speech, and these people are also better at statistical learning.

But why would spontaneous speech synchrony make a person better at statistical learning? This week in PLoS Biology, Orpella, Assaneo and colleagues tried to see whether brain activity during statistical learning could distinguish between spontaneous synchronizers and non-synchronizers and whether this difference in brain activity could account for their enhanced statistical learning ability.

How did they do it?

The authors recruited two groups of people: one to confirm the effect of spontaneous speech synchrony on statistical learning, and another group to perform this task while undergoing magnetic resonance imaging (MRI) to understand the brain regions involved.

The first measure was spontaneous speech synchrony—participants heard a string of spoken syllables and were instructed to whisper “tah” repeatedly while listening. Synchronizers will align their speech to the syllables they hear, while non-synchronizers will not align their speech, so this task separated the participants into two groups.

In the statistical learning task, participants heard strings of nonstop syllables containing embedded “words” (three syllables that repeatedly occurred together in the same order, like “rakuso”). Afterward, they were tested by being shown two “words” and choosing which one they had heard. There were two conditions in this task—in one, participants listened to the stream of syllables normally, while in the other, they were required to say “tah” repeatedly (this is supposed to interfere with the statistical learning).

The second group of participants completed the statistical learning task while in an MRI, and afterward their brain activity was split up into networks using a technique called Independent Component Analysis (ICA). The networks were tested individually to see which ones had activity related to the task.

What did they find?

Synchronizers did better at the statistical learning task, but only if they were allowed to listen passively. When they had to say “tah” while listening, their performance decreased to the level of the non-synchronizers. Non-synchronizers did equally well whether they were saying “tah” or passively listening.

MRI revealed that synchronizers were recruiting an additional brain network to process speech during statistical learning. While non-synchronizers only used the auditory network, synchronizers also used a fronto-parietal network, which is composed of some regions in the frontal lobe and others in the parietal lobe. Previous research identifies the fronto-parietal network as being involved in salience, attention, or monitoring.

What's the impact?

This study highlights some important individual differences in language processing. Research into spontaneous synchronization and the fronto-parietal network’s role in speech might shed some light on normal and abnormal language development.

Access the original scientific publication here.