mteb/BrightProPsychologyRetrieval huggingface dataset:
Pleasure centers  

 Pleasure  is a component of reward, but not all
rewards are pleasurable (e.g., money does not elicit pleasure unless this
response is conditioned).  [1]  Stimuli that are naturally pleasurable, and
therefore attractive, are known as _intrinsic rewards_ , whereas stimuli that
are attractive and motivate approach behavior, but are not inherently
pleasurable, are termed _extrinsic rewards_ .  [1]  Extrinsic rewards (e.g.,
money) are rewarding as a result of a  learned association
 with an intrinsic reward.
[1]  In other words, extrinsic rewards function as motivational magnets that
elicit "wanting", but not "liking" reactions once they have been acquired.
[1]

The reward system contains **pleasure centers** or **hedonic hotspots** –
i.e., brain structures that mediate pleasure or "liking" reactions from
intrinsic rewards. As of October 2017,  [ [update]
](https://en.wikipedia.org/w/index.php?title=Reward_system&action=edit)
hedonic hotspots have been identified in subcompartments within the  nucleus
accumbens shell  , 
ventral pallidum  ,  parabrachial
nucleus  ,  orbitofrontal
cortex  (OFC), and 
insular cortex  .  [2]  [17]  [39]
The hotspot within the nucleus accumbens shell is located in the rostrodorsal
quadrant of the medial shell, while the hedonic coldspot is located in a more
posterior region. The posterior ventral pallidum also contains a hedonic
hotspot, while the anterior ventral pallidum contains a hedonic coldspot. In
rats, microinjections of  opioids  , 
endocannabinoids  , and  orexin
 are capable of enhancing liking reactions in these
hotspots.  [2]  The hedonic hotspots located in the anterior OFC and posterior
insula have been demonstrated to respond to orexin and opioids in rats, as has
the overlapping hedonic coldspot in the anterior insula and posterior OFC.
[39]  On the other hand, the parabrachial nucleus hotspot has only been
demonstrated to respond to benzodiazepine receptor agonists.  [2]

Hedonic hotspots are functionally linked, in that activation of one hotspot
results in the recruitment of the others, as indexed by the  induced
expression  of  c-Fos  , an  immediate early gene  . Furthermore, inhibition of one hotspot results in the blunting
of the effects of activating another hotspot.  [2]  [39]  Therefore, the
simultaneous activation of every hedonic hotspot within the reward system is
believed to be necessary for generating the sensation of an intense  euphoria
 .  [40]
Definition  

In neuroscience, the reward system is a collection of brain structures and
neural pathways that are responsible for reward-related cognition, including 
associative learning 
(primarily  classical conditioning  and  operant reinforcement  ),  incentive salience  (i.e., motivation and "wanting", desire, or craving for
a reward), and  positively-valenced  "Valence
\(psychology\)")  emotions  , particularly emotions
that involve  pleasure  (i.e., hedonic "liking").
[3]  [2]

Terms that are commonly used to describe behavior related to the "wanting" or
desire component of reward include appetitive behavior, approach behavior,
preparatory behavior, instrumental behavior, anticipatory behavior, and
seeking.  [12]  Terms that are commonly used to describe behavior related to
the "liking" or pleasure component of reward include consummatory behavior and
taking behavior.  [12]

The three primary functions of rewards are their capacity to:

1. produce  associative learning  (i.e.,  classical conditioning  and  operant reinforcement  );  [3]
2. affect decision-making and induce approach behavior (via the assignment of  motivational salience  to rewarding stimuli);  [3]
3. elicit  positively-valenced  "Valence \(psychology\)") emotions, particularly pleasure.  [3]
the likelihood of survival and reproduction by causing associative learning,
eliciting approach and consummatory behavior, and triggering  positively-
valenced  "Valence \(psychology\)") emotions.
[3]  Thus, reward is a mechanism that evolved to help increase the adaptive
fitness of animals.  [8]  In drug  addiction  ,
certain substances over-activate the reward circuit, leading to compulsive
substance-seeking behavior resulting from synaptic plasticity in the circuit.
[9]

Primary rewards are a class of rewarding stimuli which facilitate the 
survival of one's self and offspring  , and they
include homeostatic (e.g.,  palatable food  ) and reproductive (e.g.,  sexual contact
 and  parental investment
 ) rewards.  [1]  [10]
Intrinsic rewards are unconditioned rewards that are attractive and motivate
behavior because they are inherently pleasurable.  [1]  Extrinsic rewards
(e.g., money or seeing one's favorite sports team winning a game) are
conditioned rewards that are attractive and motivate behavior but are not
inherently pleasurable.  [1]  [11]  Extrinsic rewards derive their
motivational value as a result of a  learned association
 (i.e., conditioning) with
intrinsic rewards.  [1]  Extrinsic rewards may also elicit pleasure (e.g.,
euphoria from winning a lot of money in a lottery) after being  classically
conditioned  with
intrinsic rewards.  [1]
Historically, IQ was a score obtained by dividing a person's  mental age
 score, obtained by administering an
intelligence test, by the person's chronological age, both expressed in terms
of years and months. The resulting fraction (  quotient  ) was multiplied by 100 to obtain the IQ score.  [3]  For modern
IQ tests  , the  raw score  is  transformed
 "Data transformation
\(statistics\)") to a  normal distribution  with mean 100 and  standard deviation
 15\.  [4]  This results in
approximately two-thirds of the population scoring between IQ 85 and IQ 115
and about 2 percent each above 130 and below 70.  [5]  [6]

Scores from intelligence tests are estimates of intelligence. Unlike, for
example, distance and mass, a concrete measure of intelligence cannot be
achieved given the abstract nature of the concept of "intelligence".  [7]  IQ
scores have been shown to be associated with such factors as  nutrition
 ,  [8]  [9]  [10]  parental 
socioeconomic status  ,
[11]  [12]  [ morbidity ](/wiki/Disease
General framework  

 Conceptual
Schematic of Predictive Coding with 2 levels

Most of the research literature in the field has been about  sensory
perception  , particularly
vision, which is more easily conceptualized. However, the predictive coding
framework could also be applied to different neural systems. Taking the
sensory system as an example, the brain solves the seemingly intractable
problem of modelling distal causes of sensory input through a version of 
Bayesian inference  . It does
this by modelling predictions of lower-level sensory inputs via backward
connections from relatively higher levels in a cortical hierarchy.  [9]
Constrained by the statistical regularities of the outside world (and certain
 evolutionarily 
prepared predictions), the brain encodes top-down generative models at various
temporal and spatial scales in order to predict and effectively suppress
sensory inputs rising up from lower levels. A comparison between predictions
(priors) and sensory input (likelihood) yields a difference measure (e.g.
prediction error, free energy, or surprise) which, if it is sufficiently large
beyond the levels of expected statistical noise, will cause the generative
model to update so that it better predicts sensory input in the future.

In general, it can be more easily stated that it minimizes the amount of
surprise (the measure of difference). This is also the reason for what is
Neural theory in predictive coding  

Evaluating the empirical evidence that suggests a neurologically plausible
basis for predictive coding is a broad and varied task. For one thing, and
according to the model, predictive coding occurs at every iterative step in
the perceptual and cognitive processes; accordingly, manifestations of
predictive coding in the brain include genetics, specific cytoarchitecture of
cells, systemic networks of neurons, and whole brain analyses. Due to this
range of specificity, different methods of investigating the neural mechanisms
of predictive coding have been applied, where available; more generally,
however, and at least as it relates to humans, there are significant
methodological limitations to investigating the potential evidence and much of
the work is based on computational modeling of microcircuits in the brain.
Notwithstanding, there has been substantial (theoretical) work that has been
applied to understanding predictive coding mechanisms in the brain. This
section will focus on specific evidence as it relates to the predictive coding
phenomenon, rather than analogues, such as homeostasis (which are,
nonetheless, integral to our overall understanding of Bayesian inference but
already supported heavily; see  Clark  for a
review  [9]  ).

Much of the early work that applied a predictive coding framework to neural
mechanisms came from sensory neurons, particularly in the visual cortex.  [6]
[16]

More generally, however, what seems to be required by the theory are (at
least) two types of neurons (at every level of the perceptual hierarchy): one
set of neurons that encode incoming sensory input, so called feed-forward
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In  neuroscience  , **predictive coding**
(also known as **predictive processing** ) is a theory of  brain
 function which postulates that the brain is constantly
generating and updating a "  mental model  " of the environment. According to the theory, such a mental model is
used to predict input signals from the  senses  that
are then compared with the actual input signals from those senses.  [1]  With
the rising popularity of  representation learning  , the theory is being actively pursued and applied in 
machine learning  and related
fields.  [2]  [3]

The phrase 'predictive coding' is also used in several other disciplines such
as signal-processing technologies and law in loosely-related or unrelated
senses.
nowadays called  confirmation bias  or what might historically be  prejudice  (although the latter has more negative connotations) since it
better fits one's individual experience accumulated so far and supports
consistency. Therefore, this turns out to be rather a disadvantage in today's
world.  [10]

If, instead, the model accurately predicts driving sensory signals, activity
at higher levels cancels out activity at lower levels, and the posterior
probability of the model is increased. Thus, predictive coding inverts the
conventional view of perception as a mostly bottom-up process, suggesting that
it is largely constrained by prior predictions, where signals from the
external world only shape perception to the extent that they are propagated up
the cortical hierarchy in the form of prediction error.

In predictive coding, errors are neither good nor bad, but simply signal the
difference between the expected and actual input. The exception is in reward
processing, where a better than expected reward produces a positive prediction
error and a disappointing result produces a negative prediction error.  [11]
Layers "Cerebral cortex") (aka laminae) may
facilitate the integration of feedforward and feed-backward projections across
hierarchies. These cortical layers, divided into granular, agranular, and
dysgranular, which house the subpopulations of neurons mentioned above, are
divided into 6 main layers. The cytoarchitecture within these layers are the
same, but they differ across layers. For example, layer 4 of the granular
cortex contain granule cells which are excitatory and distribute
thalamocortical inputs to the rest of the cortex. According to one model:

> “...prediction neurons... in deep layers of agranular cortex drive active
> inference by sending sensory predictions via projections ...to supragranular
> layers of dysgranular and granular sensory cortices. Prediction-error
> neurons ….in the supragranular layers of granular cortex compute the
> difference between the predicted and received sensory signal, and send
> prediction-error signals via projections...back to the deep layers of
> agranular cortical regions. Precision cells … tune the gain on predictions
> and prediction error dynamically, thereby giving these signals reduced (or,
> in some cases, greater) weight depending on the relative confidence in the
> descending predictions or the reliability of incoming sensory signals.”
> [19]

The theory that the unit of prediction is the  cortical column
 [20]  is based on the remarkable
correspondence between the microcircuitry of the cortical column and the
connectivity implied by predictive coding.  [21]
Precision weighting  

Expectations about the precision (or inverse variance) of incoming sensory
input are crucial for effectively minimizing prediction error in that the
expected precision of a given prediction error can inform confidence in that
error, which influences the extent to which the error is weighted in updating
predictions.  [12]  Given that the world we live in is loaded with 
statistical noise  , precision
expectations must be represented as part of the brain's generative models, and
they should be able to flexibly adapt to changing contexts. For instance, the
expected precision of visual prediction errors likely varies between dawn and
dusk, such that greater conditional confidence is assigned to errors in broad
daylight than errors in prediction at nightfall.  [13]  It has recently been
proposed that such weighting of prediction errors in proportion to their
estimated precision is, in essence,  attention 
,  [14]  and that the process of devoting attention may be neurobiologically
accomplished by  ascending reticular activating systems
 (ARAS)
optimizing the “gain” of prediction error units.
In  statistics  and related fields, a
**similarity measure** or **similarity function** or **similarity metric** is
a  real-valued function 
that quantifies the similarity between two objects. Although no single
definition of a similarity exists, usually such measures are in some sense the
inverse of  distance metrics  "Metric
\(mathematics\)") : they take on large values for similar objects and either
zero or a negative value for very dissimilar objects. Though, in more broad
terms, a similarity function may also satisfy metric axioms.

 Cosine similarity  is a
commonly used similarity measure for real-valued vectors, used in (among other
fields)  information retrieval  to score the similarity of documents in the  vector space model
 . In  machine learning
 , common  kernel functions
 such as the  RBF kernel
 can be
viewed as similarity functions.  [1]
Definition  

The "theory of mind" is described as a  theory  ,
because the behavior of the other person, such as their statements and
expressions, is the only thing being directly observed; no one has direct
access to the mind of another, and the existence and nature of the mind must
be inferred.  [12]  It is typically assumed others have minds analogous to
one's own; this assumption is based on three reciprocal social interactions,
as observed in  joint attention  ,
[2]  the functional use of language,  [13]  and the understanding of others'
emotions and actions.  [14]  Theory of mind allows one to attribute thoughts,
desires, and intentions to others, to predict or explain their actions, and to
posit their intentions. It enables one to understand that mental states can be
the cause of—and can be used to explain and predict—the behavior of others.
[12]  Being able to attribute mental states to others and understanding them
as causes of behavior implies, in part, one must be able to conceive of the
mind as a "generator of representations".  [15]  If a person does not have a
mature theory of mind, it may be a sign of cognitive or developmental
impairment.  [16]

Theory of mind appears to be an innate potential ability in humans that
requires social and other experience over many years for its full development.
Different people may develop more or less effective theories of mind.  Neo-
Piagetian theories of cognitive development  maintain that theory of mind is a byproduct of a
broader  hypercognitive  ability of
the human mind to register, monitor, and represent its own functioning.  [17]
In  psychology  , **theory of mind** refers
to the capacity to understand other people by ascribing  mental states
 to them. A theory of mind includes the
knowledge that others'  beliefs  ,  desires
 ,  intentions  , 
emotions  , and  thoughts  may be different from one's own.  [1]  Possessing a functional
theory of mind is crucial for success in everyday human  social interactions
 . People utilize a theory of
mind when  analyzing  ,  judging
 , and  inferring  others' behaviors. The discovery and development of theory of
mind primarily came from studies done with animals and infants.  [2]  Factors
including drug and alcohol consumption, language development, cognitive
delays, age, and culture can affect a person's capacity to display theory of
mind. Having a theory of mind is similar to but not identical with having the
capacity for  empathy  [3]  or  sympathy
 .

It has been proposed that deficits in theory of mind can occur in people with
 autism  ,  [5]   anorexia nervosa
 ,  [6]   schizophrenia
 ,  dysphoria  ,  attention deficit hyperactivity disorder
 ,  [7]   cocaine addiction  ,  [8]  and  brain damage  caused by  alcohol's neurotoxicity  .  [9]  [10]   Neuroimaging  shows that the  medial prefrontal cortex
 (mPFC), the posterior 
superior temporal sulcus  (pSTS), the  precuneus  , and the 
amygdala  are associated with theory of mind
Form of cognitive bias

The **horn effect** , closely related to the  halo effect  , is a form of  cognitive bias  that causes one's perception of another to be unduly
influenced by a single negative trait.  [1]  [2]  [3]  An example of the horn
effect may be that an observer is more likely to assume a physically
unattractive person is morally inferior to an attractive person, despite the
lack of relationship between morality and physical appearance.  [4]  [5]

The term is derived from the word "horn" and refers to the  devil's horns
 . This is in contrast to the
word halo and the  halo effect  , based on
the concept of a  saint's halo  "Halo
\(religious iconography\)") .  [6]

In a 1920 study published by Thorndike  [7]  that focused on the halo effect,
it was noted that "ratings were apparently affected by a marked tendency to
think of the person in general as rather good or rather interior [ _ sic
 _ ]  [a]  and to color the judgments of the qualities by
this general feeling".  [b]

It is sometimes called the _horns effect_ ,  [2]  _reverse-halo effect_ , or
_devil effect_ .  [8]

The horn effect occurs when "individuals believe that negative traits are
connected to each other."  [3]  It is a phenomenon in which an observer's
judgment of a person is adversely affected by the presence of (for the
observer) an unfavorable aspect of this person.
Definition  

 Paul Tholey  laid the  epistemological
 basis for the research of lucid
dreams, proposing seven different conditions of clarity that a dream must
fulfill in order to be defined as a lucid dream:  [9]  [10]  [11]

1. Awareness of the dream state (orientation)
2. Awareness of the capacity to make decisions
3. Awareness of memory functions
4. Awareness of self
5. Awareness of the dream environment
6. Awareness of the meaning of the dream
7. Awareness of concentration and focus (the subjective clarity of that state)

Later, in 1992, a study by  Deirdre Barrett  examined whether lucid dreams contained four "  corollaries
 " of lucidity:

1. The dreamer is aware that they are dreaming
2. They are aware actions will not carry over after waking
3. Physical laws need not apply in the dream
4. The dreamer has a clear memory of the waking world

Barrett found less than a quarter of lucidity accounts exhibited all four.
[12]

Subsequently,  Stephen LaBerge 
studied the prevalence of being able to control the dream scenario among lucid
dreams, and found that while dream control and dream awareness are correlated,
neither requires the other. LaBerge found dreams that exhibit one clearly
without the capacity for the other; also, in some dreams where the dreamer is
lucid and aware they could exercise control, they choose simply to observe.
[2]
From Wikipedia, the free encyclopedia

Dream where one is aware that one is dreaming

For other uses, see  Lucid Dream (disambiguation)
 "Lucid Dream \(disambiguation\)") .

In the  psychology  subfield of  oneirology
 , a **lucid dream** is a type of  dream
 in which the dreamer realizes that they are dreaming
whilst dreaming. The capacity to have lucid dreams is a trainable  cognitive
skill  .  [1]  During a lucid dream,
the dreamer may gain some amount of  volitional control
 "Volition \(psychology\)") over the dream
characters, narrative, or environment, although this control of dream content
is not the salient feature of lucid dreaming.  [2]  [3]  [4]  [5]  An
important distinction is that lucid dreaming is a distinct type of dream from
other types of dreams such as prelucid dreams and vivid dreams, although
prelucid dreams are a precursor to lucid dreams, and lucid dreams are often
accompanied with enhanced dream vividness. Lucid dreams are also distinct
state from other lucid boundary sleep states such as lucid  hypnagogia
 or lucid  hypnopompia  .

In formal psychology, lucid dreaming has been studied and reported for many
years. Prominent figures from ancient to modern times have been fascinated by
lucid dreams and have sought ways to better understand their causes and
purpose. Many different theories have emerged as a result of scientific
research on the subject.  [6]  [7]  Further developments in psychological
research have pointed to ways in which this form of dreaming may be utilized
as a form of  sleep therapy 
.  [8]

The term _lucid dream_ was coined by  Dutch  author and  psychiatrist 
 Frederik van Eeden  in his
1913 article _A Study of Dreams_ ,  [5]  though descriptions of dreamers being
aware that they are dreaming predate the article. Van Eeden studied his own
dreams between January 20, 1898, and December 26, 1912, recording the ones he
deemed most important in a  dream diary  .
352 of these dreams are categorized as lucid.  [5]
**Psychological pricing** (also **price ending** , **charm pricing** ) is a 
pricing and marketing strategy  based on the theory that certain prices have a psychological
impact. In this pricing method,  retail prices  are often expressed as just-below numbers: numbers that are just a
little less than a round number, e.g. $19.99 or £2.98.  [1]  There is evidence
that  consumers  tend to perceive just-below
prices (also referred to as "odd prices") as being lower than they actually
are, tending to round to the next lowest monetary unit.  [2]  [3]  Thus,
prices such as $1.99 may to some degree be associated with spending $1 rather
than $2. The theory that drives this is that pricing practices such as this
cause greater demand than if consumers were perfectly rational. Psychological
pricing is one cause of  price points  .
Evolutionary significance  Damasio has posited that the ability of humans to perform abstract thinking
quickly and efficiently coincides with both the development of the vmPFC and
with the use of somatic markers to guide human behavior during evolution.  [6]
Patients with damage to the vmPFC are more likely to engage in behaviors that
negatively impact personal relationships in the distant future, but they never
engage in actions that would lead to immediate harm to themselves or others.
[1]  The evolution of the prefrontal cortex was associated with the ability to
represent events that may occur in the future.  [6]