Learning through Reward or Punishment: Can Our Brains Perceive the Difference?

ChrisOne of the primary ways we learn a new skill is through our teachers providing feedback in the form encouragement or criticism. This feedback is very valuable to learning and is meant to incentivize the learner to improve their upcoming performance. But are there some types of feedback that can hinder to skill learning? Recently, researchers have focused on how reward and punishment affect skill learning and retention. These studies demonstrate that punishment feedback enables faster learning but diminishes task preservation. Whereas reward feedback, promotes both learning and retention (1). Additionally, reward and punishment feedback activate different areas of the brain (2). Collectively, these studies suggest reward and punishment have distinguished effects behavior and our brains. However one question still remains: How are these types of feedback actually changing our brain during the process of learning and retention? Currently, no study has looked at how reward and punishment affect the moment to moment processes of the brain after receiving the feedback.

In our lab we sought to answer this question by utilizing electroencephalography to monitor feedback event related potentials (ERPs), a distinct change in the neural activity associated with feedback, in human participants after they received a monetary reward, monetary punishment, or no feedback as they attempted to learn and retain a novel visuomotor rotation task. During the learning stage, we found that all groups learned the task at the same rate and there were no differences in the feedback ERP amplitude (strength of the electrical signal) across all groups. However, during retention testing, the punishment group failed retain the task and their feedback ERP amplitude decreased. Whereas reward and null maintained a similar performance and ERP amplitude throughout retention testing.

But what does this mean? Feedback ERPs have been associated with how our brains represent the motor task (3). We suggest that punishment interferes with the brain processes that are responsible for how our brain stores motor tasks by taking away resources used for learning and retention, to avoid the aversive outcomes. Thus punishment may not be suitable for creating long-term changes in learner’s performance.

So, if we ever find ourselves providing guidance for a new learner, let’s keep in mind how we provide direction not only affects how your pupil learns, but also affects how their brain prepares for the task in future.

1. Galea, J. M., Mallia, E., Rothwell, J., & Diedrichsen, J. (2015). The dissociable effects of punishment and reward on motor learning. Nature Neuroscience, 18, 597-602.

2. Steel, A., Silson, E. H., Stagg, C. J., & Baker, C. I. (2019). Reward and punishment differentially recruit cerebellum and medial temporal lobe to facilitate skill memory retention. Neuroimage, 189, 95-105.

3. Palidis, D. J., Cashaback, J. G., & Gribble, P. L. (2019). Neural signatures of reward and sensory error feedback processing in motor learning. Journal of Neurophysiology, 121, 1561-1574.

Christopher Hill

Health and Exercise Science

University of Mississippi


Unraveling the True Nature of Sex Differences in Spatial Memory: A Lesson from Birds

downloadThough we come to realize more and more how similar we are to one another, there remain certain slight differences between sexes across the animal kingdom. In humans and other mammals like mice and monkeys, many studies have revealed a sex difference in spatial memory. Spatial memory is how you know where things are in relation to yourself and other things, whether they be objects, places, or other people, and the ability to use this information to navigate yourself wherever you want to go. It appears that while overall spatial memory performance is similar between sexes, males have an initial upper hand due to the use of more efficient strategies to acquire a new spatial memory. The reason for this difference seems to be the simple fact that male mammals use their spatial memory more than females, most often for seeking out females across their territory. This reflects a property of the brain that many refer to as “use it or lose it” but that might be more accurately put as “don’t need it, don’t develop it,” though not quite as catchy. In birds, this feature of spatial memory can be seen even more clearly. For example, brown-headed cowbirds are brood parasites such that the female seeks out suitable host nests in which to lay her eggs and then remembers the location of the nest until she is ready to lay. In these birds, females perform better on spatial memory tests than males, who do not participate in the nest seeking (1). In two other studies on birds, there were some slight differences in how males and females learned a new spatial memory but overall performance was the same (2, 3). However, there were many outside factors involved in these tasks that could have affected the birds’ ability to learn.

In a new spatial task designed by my lab to eliminate all confounding factors (see Figure), we found that female and male zebra finches showed no differences in either learning of a spatial memory or overall ability to memorize the location of a goal. Since zebra finches live in huge flocks and do not have to seek out their mates, it makes sense to see no sex difference in spatial ability and adds strength to the idea that spatial memory ability is based on the spatial memory requirements of the animal.

  1. Guigueno, M. F., Snow, D. A., MacDougall-Shackleton, S. A., & Sherry, D. F. (2014). Female cowbirds have more accurate spatial memory than males. Biology Letters, 10(2). doi:10.1098/rsbl.2014.0026
  2. Kosarussavadi, S., Pennington, Z. T., Covell, J., Blaisdell, A. P., & Schlinger, B. A. (2017). Across sex and age: Learning and memory and patterns of avian hippocampal gene expression. Behavioral Neuroscience, 131, 483.
  3. Rensel, M. A., Ellis, J. M. S., Harvey, B., & Schlinger, B. A. (2015). Sex, estradiol, and spatial memory in a food-caching corvid. Hormones and Behavior, 75, 45.

Chyna-Rae Dearman

Department of Biology

University of Mississippi

Orientation Perception in Real and Virtual Environments

downloadWhen we look into the world, we see geometry that tells us about the layout of our surroundings.  People have spent a lot of time and effort researching how people perceive distance and scale.  An important tool that is often used to this perception is virtual reality (VR).  However, VR has its own limitations that affect people’s perceptions and actions.  A common problem is distance compression.  This problem causes people to perceive their surroundings to be closer to them than they actually are, essentially making the world look smaller.  Most space perception research in VR has focused on this topic.  However, we see more than just distances.  We also see orientations or angular relationships between objects.  Very little work has examined whether or not orientation perception is similarly affected by VR.  The work that we talked about in our presentation was the first study to investigate this area.

We found that people are very inaccurate when judging orientations in both VR and the real-world (1).  Interestingly, the compression seen in VR distance perception does not seem to affect orientation perception.  This finding has implications for how people do tasks that need accurate judgment of positions, like air-traffic control or ground-traffic coordination.  This also may affect how we design tools and computer interfaces for reading or displaying orientations.  Fortunately, the orientation errors that people make are very consistent which may let us model and correct them in real-time.

  1. Jones, J. Adam, Jonathan E. Hopper, Mark T. Bolas, and David M. Krum (2019). “Orientation Perception in Real and Virtual Environments.” IEEE transactions on visualization and computer graphics 25, 2050-2060.

J. Adam Jones

Department of Computer and Information Science

University of Mississippi

Cathinone Associated Memory Deficit and Neurodegeneration

downloadSynthetic derivatives of cathinones often referred to as “bath salts” are obtained from psychoactive alkaloid Khat (Catha edulis). The prevalence of the consumption of cathinones has been growing alarmingly in the United States in the last decade with new structural  analogs emerging globally each of which differs from the other.

Generally cathinones resemble amphetamine and thus are hypothesized to produce cytotoxicity by acting as substrates and/or inhibitors to the monoamine transporters like dopamine transporter (DAT) and norepinephrine transporter (NET). These compounds are synthetically modified to boost their potency by 10 to 100 fold compared to their natural psychostimulant counterpart and enhanced affinity to the DAT, indicative of high compulsive and rewarding behavior. A growing body of evidence points towards abuse liability, dependence, neurocognitive deficits and cardiovascular toxicities associated with synthetic cathinones. Recently synthetic forms of cathinones like 3,4-MDPV (Methylenedioxypyrovalerone), Methylone (3,4-methylenedioxy-N-methylcathinone) and Mephedrone (4-methylmethcathinone) were investigated for their abuse liability, cognitive deficits and neurodegenerative ability (1-3).

The addictive potential of cathinones were recapitulated by intracranial self-administration paradigm in rodents. Interestingly synthetic cathinones displayed dose dependent increase in drug seeking behavior (1-3). Furthermore neurocognitive deficits were observed in spatial and object recognition memory tasks in different studies (1-3). Finally, histological assays indicated prominence of neurodegeneration confined to the frontal cortex part of the brain as quantified by FlouroJade C (FJC) fluorescent marker and accumulation of malondialdehyde (MDA) as a surrogate marker of oxidative stress (1-3). Taken together, use of synthetic cathinones may pose risks for addiction, inducing cognitive deficits and neurodegeneration targeted to the frontal cortex. Additional studies are needed to investigate the neuroimmune and neurochemical mechanism underlying these effects and potential therapeutics to ameliorate associated neurotoxicity.

  1. Sewalia K, Watterson LR, Hryciw A, Belloc A, Ortiz JB and Olive MF (2018) Neurocognitive dysfunction following repeated binge-like self-administration of the synthetic cathinone 3,4 methylenedioxypyrovalerone (MDPV). Neuropharmacology 134, 36-45.
  2. R. Lopez-Arnau, J. Martinez-Clemente, T. Rodrigo et al (2015) Neuronal changes and oxidative stress in adolescent rats after repeated exposure to mephedrone Toxicol. Appl. Pharmacol. 286, 27-35.
  3. Motbey CP, Karanges E, Li KM et al (2012) Mephedrone in adolescent rats: residual memory impairment and acute but not lasting 5-HT depletion. PLoS ONE 7,e45473.

Salahuddin Mohammed

Department of Biomolecular Sciences

University of Mississippi


Keeping Time – The Stars of the Brain

Of all the different types of cells that make up the brain, neurons are invariably the stars of the central nervous system. But what about astrocytes? They’re literally named for their star-like shape! Though many people consider astrocytes to play a supporting role in brain function, new evidence suggests their part may be front-and-center…

Astrocyte Clock (small)A previous Brain Storm blog post discussed the importance of circadian rhythms to general physiology, and how changes in our ‘biological clock’ may influence our ability to learn and remember as we get older. However, a critical question that remains to be answered is: if each of the neurons in the suprachiasmatic nuclei (SCN – the ‘master clock’) have their own independent rhythms, how are they all coordinated? A recent publication from Marco Brancaccio and colleagues provides one possible answer (1). In this work, the authors set out to determine what role astrocytes play in the collective rhythm of the SCN. They began by creating a model which allowed them visualize each cell’s circadian rhythm. To do this, they used specialized viruses which can selectively target either neurons or astrocytes. Using these viruses, they were able to install a fluorescent marker which lights up when the cells’ clock genes are activated. By constantly recording images of these cells, they were able to see that the rhythms of astrocytes and neurons are slightly out of phase!

One of the most fascinating findings from this report involved mice that are functionally arrhythmic. These mice have a mutation in two vital clock genes – Cry1 and Cry2 – which prevents them from exhibiting normal circadian (~24 hour) patterns of activity. Rather, these mice have bouts of activity and rest with no observable pattern. In this experiment, the authors used those same cell type-specific viruses to restore the Cry1 gene to either astrocytes or neurons in the SCN of these mutant mice. Surprisingly, restoration of Cry1 in astrocytes alone was sufficient to reinstate locomotor rhythms in these previously arrhythmic mice!

The relationship of astrocytes and neurons in the SCN is complex, and many more experiments are required to further reveal the influence of astrocytes on circadian rhythms. Nevertheless, this report supports what we’ve known for ages – using only the stars you can tell the time!

  1. Brancaccio, M., Edwards MD. et al. (2019) Cell-autonomous clock of astrocytes drives circadian behavior in mammals. Science 363, 187-192.

Erik Hodges

Department of Biomolecular Sciences

University of Mississippi

How Coincidence Detectors and G-Proteins Help Us Learn and Remember

Understanding and identifying the molecular mechanisms responsible for Pavlovian or classical conditioning remains a significant goal in neurobiology. Drosophila melanogaster, or fruit flies, have been used as a model organism to investigate learning and memory on a functional and behavioral level. They learn to associate an odor, or the paired of a conditioned stimulus (CS+) to an electric shock (unconditioned stimulus or US). This type of associative learning is important for approach and avoidance behaviors from flies to humans.

Coincidence detection is the process by which a neuron or neural circuit can encode information by detecting the occurrence of temporally close but spatially distributed input signals. The canonical view of how Pavlovian conditioning occurs is through what is called the CS+/US temporal coincidence detection (see Figure). Adenylyl cyclase (AC) has been implicated in memory formation as a coincidence detector and is the best-characterized effector regulated by G-proteins. AC catalyzes the formation of cAMP, a secondary cellular messenger, from ATP. There is an increase in cAMP production that occurs through integration of information from the CS+ and information from the US. cAMP deficient flies like rutabaga mutants have been shown to have a deficit in learning while dunce mutants, defective in cAMP degradation, are also impaired in learning and memory.

Maria NJbC

G-proteins, such as G(o), are involved in associative learning (1) and have also been shown to play a role in neurodegenerative diseases such as Alzheimer’s disease. G(o) proteins are activated by numerous G protein-coupled receptors (GPCRs) and by amyloid precursor protein (2). Through work done in the Roman lab, a new signaling pathway required for the formation of associative memory has been attributed to G(o) proteins where G(o) is required to generate a differential conditioned stimulus salience during discriminative learning (3). Understanding coincidence detectors and the function of G-proteins in the regulation of cellular effectors will help reveal the mechanisms involved in how we learn and remember.

  1. Ferris, J., G. Hong, L. Liu, & G. Roman. (2006). G(o) signaling is required for Drosophila associative learning. Nature Neuroscience, 9, 1036-40.
  2. Sola Vigo, F., Kedikian, G., Heredia, L., Heredia, F., Anel, A. D., Rosa, A. L., & Lorenzo, A. (2009). Amyloid-beta precursor protein mediates neuronal toxicity of amyloid beta through Go protein activation. Neurobiol Aging, 30, 1379-1392.
  3. Zhang, S. & Roman, G. (2013). Presynaptic inhibition of gamma lobe neurons is required for olfactory learning in Drosophila. Current Biology, 23, 2519-2527.

Maria Pena

Department of Biology

University of Mississippi