Unit 3 Essay Questions Flashcards
Research has implicated the important role of sleep in learning and memory, especially the slow wave sleep (SWS) and REM phases. Explain a) how each phase contributes to memory consolidation b) how each phase contributes to neuronal changes that is thought to underlie the behavioral performance on learning/memory tasks (mention a brain region). Make sure to describe a study that showed how one of these sleep phases impacted task performance.
In Stickgold et al., 2001, study of sleep and implicit skill learning task, individuals were presented with either a letter T or L in the periphery, and were asked to identify the letter. This study revealed that overnight interval between training and testing (i.e., one night’s sleep) led to significant improvement on the implicit skill learning task, and this skill improvement remained for many days. In this study, it was also revealed that lots of slow wave sleep (SWS) at the beginning of the night = more learning, and lots of REM sleep at the end of the night = more learning. I.e., SWS at beginning of night and REM at end is most impactful for learning.
To be specific, the direct neuronal role of these two phases in memory consolidation can be explained via the memory engram model and the idea of systems consolidation. According to the memory engram model, during slow wave sleep (SWS), the hippocampus recruits areas of the cortex to help replay recently encoded memories for consolidation purposes. In this situation, there is a recently encoded hippocampal part of a representation AND a recently encoded neocortical part of a representation. Think of this as “training” the cortex to encode memory on its own. This is known as “systems consolidation.”
This model of memory explains the aforementioned task performance findings. That is, we want SWS early in the night so that the hippocampus can “train” the cortex, and we want REM later in the night so that the cortex itself can develop strengthened connections that truly fortify the memory.
As we move into REM sleep, however, after memories are fully consolidated, the hippocampus is no longer involved and memories are just encoded by the cortex. There are new synaptic connections between nodes in the cortex, and connections within nodes are strengthened. Think of this as the cortex (now independent from hippocampus) driving other areas of the cortex to permanentize the memory engram.
Describe what we have learned about developmental patterns of sleep cycles and the importance of sleep for learning. Then, argue for the optimal time of day for high school to begin. Make sure to include in your argument the evidence for altered adolescent sleep cycles, and the evidence for the impact of sleep on learning, achievement and motivation.
Mednick, 2013, analyzed how much sleep we really need, exhibiting the idea that sleep patterns change greatly as we age, with younger individuals tending to need more sleep. This includes an obvious sleep phase shift in teens (elaborated on more below). Further studies implicate the importance of sleep in learning, showing that memory is actively consolidated in the night, with the hippocampus “training” cortical areas in the SWS stage (active system consolidation), and synaptic consolidation occuring between cortical areas during REM sleep.
Based on what we have learned about developmental patterns of sleep cycles and the importance of sleep for learning, I argue that high school should be moved back at least one (1) hour, which equates to at least 8:30 AM at most schools.
Perkinson-Gloor et al., 2013, analyzed the effect of later Switzerland school start times on academic achievement, daytime tiredness, behavioral persistence, and attitude toward life. It was found that school starting an hour later, meaning 1+ extra hour of sleep for students, improved metrics in both Math and German, as well as correlated to decreased daytime tiredness, increased behavioral persistence, and increased positive attitudes toward life. That said, even just one extra hour of sleep possesses the ability to completely revamp the life trajectory of these developing students, enhancing motivation and improving grades.
This should come as no surprise, as adolescents have a biologically distinct sleep cycle. Specifically, a multitude of studies have demonstrated a sleep-phase delay among this demographic, corresponding to a 1-3 hour shift in sleep-wake cycle. It is a common myth that teens are just “lazy” and that this phenomenon is controllable, but Hagenauer et al., 2009, demonstrates that sleep delay shortly after puberty begins in at least 6 mammalian species, giving merit to the idea that this sleep delay is biological.
Therefore, moving start times back supports the very biological basis of high school students. This fact, paired with the aforementioned discussion of the effects of sleep on learning, achievement, and motivation, paves the way for the deployment of at least 1 hour delays (meaning ~8:30 AM) to current school start times.
What is the relationship between socio-economic status (SES) and cognitive function, what types of cognitive function correlate with SES (make sure to describe the behavioral task in question)? Describe the neuroscientific evidence from one study that supports the idea of a functional difference in the brain in low vs high SES children, making sure to describe the task, the scientific methods, and what was found.
Socio-economic status has a profound relationship with several prominent brain regions, as demonstrated by the Noble, McCandliss, and Farah, 2007 behavioral battery. In this study, researchers investigated the effects of SES on 6 broad anatomical/cognitive domains among 150 1st graders. These regions include the left perisylvian/language system (tested using the PPVT and CTOPP blending words tasks), parietal/spatial cognition system (tested using NEPSY arrows and mental rotation), lateral prefrontal/working memory system (tested using spatial working memory task and delayed non-match to sample), medial temporal/declarative memory system (tested using NEPSY delayed memory for faces and incidental picture pair learning), anterior cingulate/cognitive control system (tested using go/no-go task and NEPSY auditory attention set), and the orbitofrontal/reward processing system (tested using reversal learning and delayed gratification). It was ultimately discovered that there is a correlation between SES and each of these domains EXCEPT for orbitofrontal/reward processing. Moreover, language skills mediated SES effects on cognitive conflict, and supportive school and home environments offset the effects of low maternal education and low family income, respectively.
Another study that directly supports the idea of a functional difference in the brain in low vs. high SES children is that conducted by Hackman & Farah, 2008, which analyzed attention using EEG. Researches played two distinct stories in the ears of subjects and provided a cue that dictated which stream was to be attended. ERP results showed a clear difference in the ERP peaks in those with high maternal education, with the attended side exhibiting a larger amplitude peak. However, in low SES individuals, there was nearly no difference in peak amplitudes between the attended side and nonattended side, indicating clear attention deficits in the brain.
What does the case of Phineas Gage (which we discussed earlier in the course as well) teach us about reward circuitry in the brain? Summarize his story, the modern neuroscience understanding of the brain regions impacted, and the functional implications specifically for reward sensitivity.
Why is it thought that adolescents engage in increased risk-taking behavior, especially when in the presence of peers? Use evidence from experiments discussed in class or in the readings, making sure to mention multiple brain regions. How might these behaviors be adaptive or helpful, and what are some negative consequences?
It is thought that adolescents engage in increased risk-taking behavior as the result of functional differences in the brain. Several studies demonstrate this in a multitude of settings. Steinberg et al., 2007, shows that psychosocial maturity (which corresponds to hot CC) isn’t completely developed in adolescence. This aligns with direct neuronal findings, as Casey et al., 2011, shows that prefrontal regions aren’t fully developed in adolescence. Hot CC is represented by the ventromedial prefrontal cortex (VMPFC), so these findings should make sense.
The two most prominent drivers of increased risk-taking behavior in teens, however, are hypothesized to be altered reward sensitivity and self-regulation manners (e.g., peer influence). Specifically, a new, 2023 study by Maza et al. shows starkly increased reward sensitivity due to elevated ventral striatum activity in adolescents relative to adults. This can be seen in social media checking behaviors. As for peer influence, a study involving fMRI monitoring during use of a driving simulator found that adolescents make more risky decisions in the presence of their peers, also correlating to an increase in ventral striatum activity. These studies clearly implicate differences in reward circuitry, which likely causes risky decision making due to a supercharged pursuit of fulfillment.
These behaviors are clearly biological, as they are also seen in other animals, and may be adaptive for facilitating greater peer affiliation, exploratory range, or to even facilitate separation from family (especially in other mammals). Negative consequences can clearly be tied in as well, for making dumb decisions could be dangerous and lead to injury, trauma, trouble with the law, etc. One example of this is driving too fast.
Studies have shown that the arrival of autistic symptoms between 9 months and 2 years corresponds to atypical brain development during that time. Describe both the atypical global and regional brain development in autistic children, making sure to mention at least one brain region. How do these differences in brain development link to the differences in behaviors and cognitive processing that kids with autism often demonstrate?
Between 9 months and 2 years of age, the arrival of autistic symptoms is correlated with clear atypical brain development. One prominent finding is the idea of global brain overgrowth, or the idea that gray matter is significantly elevated in autistic children, corresponding to a much larger head. It is hypothesized that this is due to a lack of synaptic pruning in those with ASD.
The mirror neuron system (MNS) also displays differences, with two studies demonstrating distinctions from neurotypical in these regions. One study, implementing EEG, found that in neurotypical individuals, rest phase synchronous EEG activity goes down during both movement and while watching movement. In ASD, however, rest phase synchronous EEG activity goes down only during own movement and not while watching movement. This contributes to the “broken mirror hypothesis,” which is the idea that MNS deficits underlie ASD. This is evident in several cognitive deficits associated with ASD, specifically involving facial expression observation and mimicry.
It has also been hypothesized tha early amygdala dysfunction underlies ASD. We know that ASD has an important role in “training” the specialization of other areas, and this widely replicated idea that facial responsiveness is reduced in ASD supports this. In this regard, differences are clearly present in early stages of face processing, involving the FFA (which is trained by the amygdala).
Using one specific example of plasticity from the book and/or class, outline the relationship between lifelong brain plasticity and experience (e.g., violin players, cab drivers). Be detailed in describing the study, the task, and the brain regions implicated as well as the behavioral and neural mechanisms involved.
One study investigating lifelong plasticity involves that of string instrument players. From Elbert et al., 1995, Subjects were surveyed on when they began playing the instrument, which happened to be a violin in this case, and MEG was deployed, measuring magnetic dipole strength in the somatosensory cortex (D5 dipole), specifically on the right side of the brain). A correlation was found between magnetic dipole strength and the age at conception of musical practice. Those who began playing earlier in life had greater magnetic field strength than those who began playing later in life, and everyone who plays a string instrument had greater dipole strength than controls. This shows that plasticity not only occurs in childhood as expected, where we see the greatest strength, but also in adulthood, as evident by the distint field strength even in those who began playing between 15-20 years of age. Moreover, a stark correlation is evident between experience and plasticity in this same regard.
This aligns with findings from a taxi driver study of plasticity, which shows that increased time as taxi driver is correlated with increased gray matter volume in the posterior hippocampus, suggesting adult plasticity. This was fit with a linear curve, and a clear positive correlation is evident. This demonstrates that experience is directly implicated in lifelong plasticity.
In the Willingham and Lloyd article, we saw four different ways that researchers may use brain data to inform psychological theories, and in turn, educational practice. Discuss two of these approaches, taking care to describe the neuroscientific basis of each an example that includes details about brain regions, neuroscience methods, and the educational implications.
- Direct Observation of Internal Representations
- Investigations of the lateral prefrontal cortex in humans and Macaques using single-unit physiology found that both have cells that preferentially respond to a particular number = representation of number in the brain. E.g., some neurons respond best to the number 4, while a different set will respond best to the number 1. This is quite literally a direct observation of internal representation, and other imaging studies can be, and have been, deployed with similar success in other regions of the brain. This allows us to directly verify our very actions from a neural perspective, visualizing the way by which we think and act. - The Reality of Cognitive Constructs
- Separate Systems for Apparently Unitary Functions
- Reliable Knowledge of Brain Guides Cognitive Theory
- Before brain imaging, psychologists couldn’t agree if mental imagery was based on images or built upon linguistic representations! Studies show that visual areas (occipital lobe/primary visual cortex) are active during mental imagery, and this correlates with mental imagery size as well, with a larger part of occipital cortex active for larger objects (i.e., we use reliable knowledge of the brain to guide this idea that mental imagery is true imagery in the brain)…also, language regions seem uninvolved. Moreover, mental imagery has category selectivity, with mental imagery of faces and places corresponding to FFA and PPA activation, respectively.
This has educational implications because knowing how neuroscience and its findings can influence cognitive theories will be a step closer to taking neuroscience findings and using it to create effective brain-based products for education and other educational teaching methods that allow for the most effective ways for students to learn. In regard to the examples above, being able to literally observe the neurons that fire in response to a given number allows us to tailor products accordingly, and is overall incredibly fascinating to directly observe brain happenings. As for reliable knowledge of brain guiding cognitive theory, it is important from an educational perspective because it literally solved a raging debate of what brain region was responsible for a specific process. Now that it’s solved, we have a grounded theory.
