The Student’s Guide to Social
Neuroscience
Inleiding
Dit document is een samenvatting van het boek “The student’s guide to social neuroscience”
van Jamie Ward (2e editie, 2016; New York: Psychology Press). De schuingedrukte,
onderstreepte titels komen ook overeen met de subtitel van het stukje tekst in het boek in
dat hoofdstuk. Mocht je het boek hebben, dan kan je zo het onderwerp iets makkelijker
terugvinden. In deze samenvatting worden hoofdstuk 1,2,3 en 11 behandeld .
Chapter 1: Introduction to social neuroscience
Introduction:
Cognition in an individual brain is characterized by a network of flowing signal between
different regions of the brain. However, social interaction between individuals can be
characterized by the same principle. Then there is some kind of “mega-brain” in which
different regions in different brains can have mutual influence over each other. This
interaction is facilitated by our ability to perceive, interpret, and act on the social behaviour
of others.
The emerge of social neuroscience:
Social neuroscience is an attempt to understand and explain, using neural mechanisms, how
the thoughts, feelings and behaviours of individuals are influenced by the actual, imagined
or implied presence of others. At first sight social neuroscience was linked to social
psychology which is an attempt to understand and explain how the thoughts, feelings and
behaviours of individuals are influenced by the actual, imagined or implied presence of
others. However, researchers in neuroscience are mostly drawn to cognitive psychology.
Cognitive psychology is the study of mental processes such as thinking, perceiving, speaking,
acting and planning. It aims to decompose complex social behaviours into simpler
mechanisms. In a survey of Stanley and Adolphs researcher reported that right now they are
mainly working on topics such as emotion, self-regulation and decision making. The
researchers also think that social neuroscience needs more interdisciplinary integration,
more statistical and methodological rigor (=strengheid) and needs to be more ecologically
valid (= measure is meaningful outside laboratory context). The future of social neuroscience
lies in the real-world applications and in terms of an additional level of sophistication
afforded by computational approaches to brain networks.
The social brain:
One issue is whether the “social brain” can be considered distinct from all the other
functions the brain carries out. Is the social brain special in any way? There are some
arguments in favour of this thought:
, 1. Particular neural substrates in the brain are involved in social cognition but not in
other types of cognitive processing, this relates to modularity and domain specifity.
An example is that a module responds to the sight of faces but not to the sight of
bodies or the sound of voices.
* modularity = the notion that certain cognitive processes are restricted in in the type
of information they process and the type of processing carried out. So
it is a computational routine that responds to particular inputs and
performs a particular computation to them, also known as a routine
that is highly specialized in terms of what it does to what
* domain specificity = the idea that a cognitive process is specialized for processing
only one particular kind of information.
The very opposite approach to the social brain is that the social brain is not specialised
uniquely for social behaviour, but is also involved in non-social aspects of cognition. The
evolutionary need to be socially smarter could lead to general cognitive advances in other
domains
With regards to this discussion Barrett and Saptute offer an overview concerning the
nature of the social and emotional brain. They consider 3 broad ways in which the social
brain may be implemented:
1. Domain specific view: brain regions are specialized for processing particular kinds of
social information few researchers endorse such a view
2. Postulation of networks: each region in a network has a high degree of specializartion
3. Neither brain regions nor individual brain networks are functionally specialized or
segregated into social and non-social functions
Is neuroscience an appropriate level of explanation for studying social behaviour?:
The most general criticism for investigating social neuroscience is that the brain is not the
most appropriate level of explanation for
understanding social processes. Because what can
we learn from brain-based measures without
situating them in social, economic and historical
context. Only looking at brain based results creates a
distorted view of what social neuroscience is all
about. Reductionism is when one type of
explanation will become replaced with another,
more basic type of explanation over time.
Researchers try to avoid this approach and rather try
to create bridges between the different types and
levels of explanation (see picture on the right). The
reverse inference approach is another way in which neuroscience data is used to create
bridges between levels of explanation. Reverse inference is an attempt to infer the nature of
cognitive processes from neuroscience data such as neuro-imaging. Beware that inference is
not always correct. For example if someone is frightened, their amygdala is activated.
, However, that does not mean that when someone’s amygdala is activated that they are
frightened. There are multiple reasons why a certain brain region can be activated. There is
one scenario where brain-based data has no significant impact on our understanding of
social processes: the blank slate scenario. Blank slate is the idea that the brain learns
environmental contingencies without imposing biases, constraints or pre-existing knowledge
on that learning. Important to keep in mind is that social processes are all in the brain, but
some are created by environmental constraints and historical accidents whereas others may
be caused by inherent organization, biases and limitations of the brain itself.
Gene-culture co-evolution:
Cultural neuroscience is an interdisciplinary field bridging cultural psychology, neurosciences
and neurogenetics that explain how neurobiological processes give rise to cultural values,
practices and beliefs as well as how culture shapes neurobiological processes. Gene-culture
co-evolution states that culture can influence gene frequencies in a population, and genes
have an impact on cultural evolution via psychological predispositions. In other words,
certain genotypes may predispose people to create particular features in their environment
(thus influencing cultural selection) and at the same time aspects of a give culture may tend
to favour individuals of a certain genotype (thus influencing genetic selection).
Chapter 2: The methods of social neuroscience
intro:
Social neuroscience is such a new category in neuroscience that it does not have its own
methodology, so it borrows it from psychology and cognitive neuroscience. The main methods of
cognitive neuroscience can be placed on a number of dimensions:
Temporal resolution = the accuracy with which one can measure when an event is occurring
Spatial resolution = the accuracy to which one can measure where an event is occurring
Invasiveness = refers to whether or not the equipment is located internally or externally
Measuring behaviour and cognition: psychological methods
There are 3 ways of measuring behaviour and cognition that will be discussed below. The 3 methods
are: performance-based measures, observation-based measures and first-person-based measures.
Performance based measures: response times and accuracy rates
Mental chronometry can be defined as the study of the time-course of information processing in the
human nervous system. The basic idea is that changes in the nature or efficiency of information
processing will manifest themselves in the time it takes to complete a task. An example is that
participants are faster at verifying 2 + 4 = 6 then at 4 + 3 = 7. Aside from response times, the other
performance measure is accuracy. This can be measured in terms of error rates, percentage correct
or percentile performance in which individual scores are recalculated relative to the population
mean. Accuracy is crucially related to whether certain knowledge is absent or present rather than
processing efficiency which is more related to response time. In some cases accuracy and efficiency
are related, for example in the so-called speed-accuracy trade-off. The advantages of performance
measures are that they reflect actual behaviour and are simple to analyse/interpret. The
disadvantages are that they are hard to link directly to neural substrates and that there is not always
a clear relationship between laboratory tasks and real-world behaviour.
Observational measures:
Performance-based measures focused on “how well or how fast”, but observational measures focus
