## 1. What is Associationism?


Associationism is a theory that connects learning to thought based
on principles of the organism's causal history. Since its early
roots, associationists have sought to use the history of an
organism's experience as the main sculptor of cognitive
architecture. In its most basic form, associationism has claimed that
pairs of thoughts become associated based on the organism's past
experience. So, for example, a basic form of associationism (such as
Hume's) might claim that the frequency with which an organism
has come into contact with *X*s and *Y*s in one's
environment determines the frequency with which thoughts
about *X*s and thoughts about *Y*s will arise together
in the organism's future.


Associationism's popularity is in part due to how many
different masters it can serve. In particular, associationism can be
used as a theory of learning (e.g., as in behaviorist theorizing), a
theory of thinking (as in Jamesian "streams of thought"),
a theory of mental structures (e.g., as in concept pairs), and a theory
of the implementation of thought (e.g., as in connectionism). All these
theories are separable, but share a related, empiricist-friendly
core. As used here, a "pure associationist" will refer to
one who holds associationist theories of learning, thinking, mental
structure, and implementation. The "pure associationist"
is a somewhat idealized position, one that no particular theorist may
have ever held, but many have approximated to differing degrees (e.g.,
Locke 1690/1975; Hume 1738/1975; Thorndike 1911; Skinner 1953; Hull
1943; Churchland 1986, 1989; Churchland and Sejnowski 1990; Smolensky
1988; Elman 1991; Elman et al. 1996; McClelland et al. 2010; Rydell
and McConnell 2006; Fazio 2007).


Outside of these core uses of associationism the movement has also
been closely aligned with a number of different doctrines over the
years: empiricism, behaviorism, anti-representationalism (i.e.,
skepticism about the necessity of representational realism in
psychological explanation), gradual learning, and domain-general
learning. All of these theses are dissociable from core associationist
 thought (see section 7). While one
can be an associationist without holding those theses, some of those
theses imply associationism to differing degrees. These extra
theses' historical and sociological ties to associationism are
strong, and so will be intermittently discussed below.


## 2. Associationism as a Theory of Mental Processes: The Empiricist Connection


Empiricism is a general theoretical outlook, which tends to offer a
theory of learning to explain as much of our mental life as possible.
From the British empiricists through Skinner and the behaviorists
 (see the entry on behaviorism) the main focus
has been arguing for the acquisition of concepts (for the
empiricists' "Ideas", for the behaviorists
"responses") through learning. However, the mental
processes that underwrite such learning are almost never themselves
 posited to be learned.[1]
So winnowing down the amount of mental
processes one has to posit limits the amount of innate machinery with which the
theorist is saddled. Associationism, in its original form as in
Hume (1738/1975), was put forward as a theory of mental processes.
Associationists' attempt to answer the question of how many
mental processes there are by positing only a single mental
 process: the ability to associate 
ideas.[2]


Of course, thinkers execute many different types of cognitive acts,
so if there is only one mental process, the ability to associate, that
process must be flexible enough to accomplish a wide range of
cognitive work. In particular, it must be able to account for learning
and thinking. Accordingly, associationism has been utilized on both
fronts. We will first discuss the theory of learning and then, after
analyzing that theory and seeing what is putatively learned, we will
return to the associationist theory of thinking.


## 3. Associationism as a Theory of Learning


In one of its senses, "associationism" refers to a
theory of how organisms acquire concepts, associative structures,
response biases, and even propositional knowledge. It is commonly
acknowledged that associationism took hold after the publishing of
John Locke's *Essay Concerning Human Understanding*
 (1690/1975).[3]
However, Locke's comments on associationism were terse (though
fertile), and did not address learning to any great degree. The first
serious attempt to detail associationism as a theory of learning was
given by Hume in the *Treatise of Human Nature*
 (1738/1975).[4]
Hume's associationism was, first and foremost, a theory
connecting how perceptions ("Impressions") determined
trains of thought (successions of "Ideas"). Hume's
empiricism, as enshrined in the Copy
 Principle,[5]
demanded that there were no Ideas in the mind that were not first
given in experience. For Hume, the principles of association
constrained the functional role of Ideas once they were copied from
Impressions: if Impressions IM1 and IM2 were associated in
perception, then their corresponding Ideas, ID1 and ID2 would also
become associated. In other words, the ordering of Ideas was
determined by the ordering of the Impressions that caused the Ideas
to arise.


Hume's theory then needs to analyze what types of associative
relations between Impressions mattered for determining the ordering
of Ideas. Hume's analysis consisted of three types of
associative relations: cause and effect, contiguity, and
resemblance. If two Impressions instantiated one of these associative
relations, then their corresponding Ideas would mimic the same
 instantiation.[6]
For instance, if Impression IM1 was cotemporaneous with Impression
IM2, then (*ceteris paribus*) their corresponding Ideas, ID1
and ID2, would become associated.


As stated, Hume's associationism was mostly a way of
determining the functional profile of Ideas. But we have not yet said
what it is for two Ideas to be associated (for that
 see section 4). Instead, one can see
Hume's contribution as introducing a very influential type of
learning--associative learning--for Hume's theory
purports to explain how we learn to associate certain Ideas. We
can abstract away from Hume's framework of ideas and his
account of the specific relations that underlie associative learning,
and state the theory of associative learning more generally: if two
contents of experiences, *X* and *Y*, instantiate some
associative relation, *R*, then those contents will become
associated, so that future activations of *X* will tend to
bring about activations of *Y*. The associationist then has
to explain what relation *R* amounts to. The Humean form of
associative learning (where *R* is equated with cause and
effect, contiguity, or resemblance) has been hugely influential,
informing the accounts of those such as Jeremy Bentham, J.S. Mill,
 and Alexander Bain (see, e.g., the entries on
 John Stuart Mill and
 19th Century Scottish Philosophy).[7]


Associative learning didn't hit its stride until the work of
Ivan Pavlov, which spurred the subsequent rise of the behaviorist
movement in psychology. Pavlov introduced the concept of classical
conditioning as a modernized version of associative learning. For
Pavlov, classical conditioning was in part an experimental paradigm
for teaching animals to learn new associations between stimuli. The
general method of learning was to pair an unconditioned stimulus (US)
with a novel stimulus. An unconditioned stimulus is just a stimulus
that instinctively, without training, provokes a response in an
organism. Since this response is not itself learned, the response is
referred to as an "unconditioned response" (UR). In
Pavlov's canonical experiment, the US was a meat powder, as the
smell of meat automatically brought about salivation (UR) in his
canine subjects. The US is then paired with a neutral stimulus, such
as a bell. Over time, the contiguity between the US and the neutral
stimulus causes the neutral stimulus to provoke the same response as
the US. Once the bell starts to provoke salivation, the bell has
become a "conditioned stimulus" (CS) and the salivating,
when prompted by the bell alone, a "conditioned response"
(CR). The associative learning here is learning to form new
stimulus-response pairs between the bell and the
 salivation.[8]


Classical conditioning is a fairly circumscribed process. It is a
"stimulus substitution" paradigm where one stimulus can
be swapped for another to provoke a
 response.[9]
However, the responses that are provoked are supposed to remain unchanged; all that
changes is the stimulus that gets associated with the response. Thus,
classical conditioning seemed to some to be too restrictive to
explain the panoply of novel behavior organisms appear to
 execute.[10]


Edward Thorndike's research with cats in puzzle boxes
broadened the theory of associative learning by introducing the notion
of consequences to associative learning. Thorndike expanded the notion
of associative learning beyond instinctual behaviors and sensory
substitution to genuinely novel behaviors. Thorndike's
experiments initially probed, e.g., how cats learned to lift a lever
to escape the "puzzle boxes" (the forbearer to
"Skinner boxes") that they were trapped in. The
cats' behaviors, such as attempting to lift a lever, were not
themselves instinctual behaviors like the URs of Pavlov's
experiments. Additionally, the cats' behaviors were shaped by
the consequences that they brought on. For Thorndike it was because
lifting the lever caused the door to open that the cats learned the
connection between the lever and the door. This new view of learning,
operant conditioning (for the organism is "operating" on
its environment), was not merely the passive learning of Pavlov, but a
species-nonspecific, general, active theory of learning.


This research culminated in Thorndike's famous "Law of
Effect" (1911), the first canonical psychological law of
associationist learning. It asserted that responses that are
accompanied by the organism feeling satisfied will, *ceteris
paribus*, be more likely to be associated with the situation in
which the behavior was executed, whereas responses that are
accompanied with a feeling of discomfort to the animal
will, *ceteris paribus*, make the response less likely to
occur when the organism encounters the same
 situation.[11]
The greater the positive or negative feelings produced, the greater
the likelihood that the behavior will be evinced. To this Thorndike
added the "Law of Exercise", that responses to situations
will, *ceteris paribus*, be more connected to those situations
in proportion to the frequency of past pairings between situation and
response. Thorndike's paradigm was popularized and extended by
B.F. Skinner (see, e.g., Skinner 1953) who stressed the notion not
just of consequences but of *reinforcement* as the basis of
forming associations. For Skinner, a behavior would get associated
with a situation according to the frequency and strength of
reinforcement that would arise as a consequence of the behavior.


Since the days of Skinner, associative learning has come in many
different variations. But what all varieties should share with their
historical predecessors is that associative learning is supposed to
mirror the contingencies in the world without adding additional
structure to them (see section 9 for some examples of when supposedly associative theories smuggle in extra structure). The question of what contingencies associative
learning detects (that is, one's preferred analysis of what the
associative relation *R* is), is up for debate and changes
between theorists.


The final widely shared, though less central, property of
associative learning concerns the domain generality of associative
learning. Domain generality's prevalence among associationists
is due in large part to their traditional empiricist allegiances:
excising domain-specific learning mechanisms constrains the amount of
innate mental processes one has to posit. Thus it is no surprise to
find that both Hume and Pavlov assumed that associative learning could
be used to acquire associations between any contents, regardless of
the types of contents they were. For example, Pavlov writes,



> Any natural phenomenon chosen at will may be converted
> into a conditioned stimulus. Any ocular stimulus, any desired sound,
> any odor, and the stimulation of any portion of the skin, whether by
> mechanical means or by the application of heat or cold never failed to
> stimulate the salivary glands. (Pavlov 1906: 615)
> 
> 


For Pavlov the content of the CS
doesn't matter. Any content will do, as long as it bears the
right functional relationship in the organism's learning
history. In that sense, the learning is domain general--it
matters not what the content is, just the role it plays (for more on
this topic, see
 section 9.4).[12]


## 4. Associationism as a Theory of Mental Structure


Associative learning amounts to a constellation of related views
that interprets learning as associating stimuli with responses (in
operant conditioning), or stimuli with other stimuli (in classical
conditioning), or stimuli with valences (in evaluative
conditioning).[13] Associative learning accounts raise the question: when
one learns to associate contents *X* and *Y* because,
e.g., previous experiences with *X*s and *Y*s
instantiated *R*, how does one store the information
that *X* and *Y* are associated?[14] A highly contrived
sample answer to this question would be that a thinker learns an
explicitly represented unconscious conditional rule that states
"when a token of *x* is activated, then also activate a
token of *y*". Instead of such a highly intellectualized
response, associationists have found a natural (though by no means
 necessary, see section 4.2) complementary
view that the information is stored in an *associative
structure*.


An associative structure describes the type of bond that connects
 two distinct mental states.[15]
 An example of such a structure is the
 associative pair 
salt/pepper.[16]
 The associative structure is defined, in
the first instance, functionally: if *X* and *Y* form
an associative structure, then, *ceteris paribus*, activations
of mental state *X* bring about mental state *Y*
and *vice versa* without the mediation of any other
psychological states (such as an explicitly represented rule telling
the system to activate a concept because its associate has been
 activated).[17]
In other words, saying that two concepts are associated amounts to
saying that there is a reliable, psychologically basic causal
relation that holds between them--the activation of one of
the concepts causes the activation of the other. So, saying that
someone harbors the structure
 salt/pepper amounts to saying that
activations of salt will cause activations of
 pepper (and *vice
versa*) without the aid of any other cognitive states.


Associative structures are most naturally contrasted with
propositional structures. A pure associationist is opposed to
propositional structures--strings of mental representations that
express a proposition--because propositionally structured mental
representations have structure over and above the mere associative
bond between two concepts. Take, for example, the associative
structure
 green/toucan. This structure does not predicate green onto
toucan. If we know that a mind has an associative bond between
 green
and
 toucan, then we know that activating one of those concepts leads
to the activation of the other. A pure associative theory rules out
predication, for propositional structures aren't just strings of
associations. "Association" (in associative structures)
just denotes a causal relation among mental representations, whereas
predication (roughly) expresses a relation between things in the world
(or intentional contents that specify external relations). Saying that
someone has an associative thought
 green/toucan tells you something
about the causal and temporal sequences of the activation of concepts
in one's mind; saying that someone has the thought

 there is a green toucan
 tells you that a person is predicating greenness of a
particular toucan (see Fodor 2003: 91-94, for an expansion of
this point).


Associative structures needn't just hold between simple
concepts. One might have reason to posit associative structures
 between propositional elements (see section 5)
 or between concepts and valences (see section 8). 
But none of the proceeding is meant to imply that all
structures are associative or propositional--there are other
representational formats that the mind might harbor (e.g., analog
magnitudes or iconic structures; see Camp 2007; Quilty-Dunn forthcoming). For instance, not all semantically
related concepts are harbored in associative structures. Semantically
related concepts may in fact also be directly associated (as in
doctor/nurse) or they may not
 (as in
 horse/zebra; see Perea and Rosa
2002). The
difference in structure is not just a theoretical possibility, as these
different structures have different functional profiles: for example,
conditioned associations appear to last longer than semantic
associations do in subjects with dementia (Glosser and Friedman
1991).


### 4.1 Associative Symmetry


The analysis of associative structures implies that, *ceteris
paribus*, associations are symmetric in their causal effects: if a
thinker has a bond between
 salt/pepper,
 then
 salt should bring about
pepper just as well as

 pepper brings about
 salt (for extensive discussion of the symmetry point see Quilty-Dunn and Mandelbaum 2019). But all else is
rarely equal. For example, behaviorists such as Thorndike, Hull, and
Skinner knew that the order of learning affected the causal sequence
of recall: if one is always hearing "salt and pepper" then
salt will be more poised to
 activate
 pepper than
 pepper to activate
salt. So, included in the *ceteris paribus* clause in the
analysis of associative structures is the idealization that the
learning of the associative elements was equally well randomized in
order.


Similarly, associative symmetry is violated when there are
differing amounts of associative connections between the individual
associated elements. For example, in the
 green/toucan case, most
thinkers will have many more associations stemming from
 green than
stemming from
 toucan. Suppose we have a thinker that only associates
toucan with
 green,
 but associates
 green with a large host of other
concepts (e.g.,
 grass,

 vegetables,
 tea,

 kermit,
 seasickness,

 moss,
mold,
 lantern,

 ireland, etc). In this case one can expect that

 toucan
will more quickly activate
 green than

 green will activate
 toucan, for
the former bond will have its activation strength less weakened
amongst other associates than the latter will.


### 4.2 Activation Maps of Associative Structure


An associative activation map (sometimes called a "spreading
activation" map, Collins and Luftus 1975) is a mapping for a
single thinker of all the associative connections between
 concepts.[18]
There are many ways of operationalizing associative connections. In
the abstract, a psychologist will attempt to probe which concepts (or
other mental elements) activate which other concepts (or
elements). Imagine a subject who is asked to say whether a string of
letters constitutes a word or not, which is the typical goal given to
subjects in a "lexical decision task". If a subject has
just seen the word "mouse", we assume that the concept mouse was activated. If the subject is then quicker to say that, e.g.,
"cursor" is a word than the subject is to say that
"toaster" is, then we can infer that
 cursor was primed,
and is thus associatively related to 
 mouse, in this thinker. Likewise,
if we find that "rodent" is also responded to
quicker, then we know that
 rodent is associatively related to
mouse. Using this procedure, one can generate an
associative mapping of a thinker's mind. Such a mapping would
constitute a mapping of the associative structures one harbors.
However, to be a true activation map--a true mapping of what
concepts facilitate what--the mapping would also need to include
information about the violations of symmetry between concepts.


### 4.3 Relation Between Associative Learning and Associative Structures


The British Empiricists desired to have a thoroughgoing pure
associationist theory, for it allowed them to lessen the load of
innate machinery they needed to posit. Likewise, the behaviorists also
tended to want a pure associationist theory (sometimes out of a
similar empiricist tendency, other times because they were radical
behaviorists like Skinner, who banned all discussion of mental
representations). Pure associationists tend to be partial to a
connection that Fodor (2003) refers to as "Bare-Boned
Association". The idea is that the current strength of an
association connection between *X* and *Y* is
determined, *ceteris paribus*, by the frequency of the past
associations of *X* and *Y*. As stated, Bare-Boned
Association assumes that associative structures encode, at least
implicitly, the frequency of past associations of *X*
and *Y*, and the strength of that associative bond is
determined by the organism's previous history of
experiencing *X*s and
 *Y*s.[19]
 In other words, the learning history of
past associations determines the current functional profile of the
 corresponding associative 
structures.[20]


Although the picture sketched
above, where associative learning eventuates in associative
structure, is appealing for many, it is not forced upon
one, as there is no a priori reason to bar any type of
structure to arise from a particular type of learning. One may, for
example, gain propositional structures from associative learning (see
Mitchell et al. 2009 and Mandelbaum 2016 for arguments that
this is more than a mere logical possibility). This can happen in two
ways. In the first, one may gain an associative structure that has a
proposition as one of its associates. Assume that every time
one's father came home he immediately made dinner. In such a
case one might associate the proposition
 daddy is home with the
concept
 dinner (that is one might acquire: 
daddy is home/dinner). However,
 one might also just have a propositional
structure result from associative learning. If every time one's
father came home he made dinner, then one might just end up learning
if daddy is home then dinner will come soon, which is a propositional
structure.


### 4.4 Extinction and Counterconditioning


There is a different, tighter relationship between associative
learning and associative structures concerning how to modulate an
association. Associative theorists, especially from Pavlov onward,
have been clear on the functional characteristics necessary to
modulate an already created association. There have been two generally
agreed upon routes: *extinction*
and *counterconditioning*. Suppose that, through associative
learning, you have learned to associate a CS with a US. How do we
break that association? Associationists have posited that one breaks
an associative structure via two different types of
associative learning (/unlearning). *Extinction* is the name
for one such process. During extinction one decouples the external
presentation of the CS and the US by presenting the CS without the US
(and sometimes the US without the CS). Over time, the organism will
learn to disconnect the CS and US.


*Counterconditioning* names a similar process to extinction,
though one which proceeds via a slightly different method.
Counterconditioning can only occur when an organism has an
association between a mental representation and a valence, as
acquired in an evaluative conditioning paradigm. Suppose that one
associates
 ducks with a positive valence. To break this association
via counterconditioning one introduces ducks not with a lack of
positive valence (as would happen in extinction) but with the
opposite valence, a negative valence. Over multiple
exposures, the initial representation/valence association weakens,
 and is perhaps completely 
broken.[21]


How successful extinction and counterconditioning are, and how they
work, is the source of some controversy, and some reason to see both methods as highly ineffectual (Bouton 2004). Although the traditional
view is that extinction breaks associative bonds, it is an open
empirical question whether extinction proceeds by breaking the
previously created associative bonds, or whether it proceeds by
leaving that bond alone but creating new, more salient (and perhaps
context-specific) associations between the CS and other mental states (Bouton 2002, Bendana and Mandelbaum forthcoming). Additionally, reinstatement, the spontaneous
reappearance of an associative bond after seemingly successful
extinction, has been observed in many contexts (see, e.g., Dirikx et
al. 2004 for
 reinstatement of fear in 
humans).[22]


One fixed point in this debate is that one reverses associative
structures via these two types of associative learning/unlearning, and
only via these two pathways. What one does *not* do is try to
break an associative structure by using practical or theoretical
reasoning. If you associate
 salt with
 pepper, then telling you that
salt has nothing to do with pepper or giving you very good reasons
not to associate the two (say, someone will give you $50,000 for not
associating them) won't affect the association. This much has at
least been clear since Locke. In the *Essay concerning Human
Understanding*, in his chapter "On the Association of
Ideas" (chapter XXIII) he writes,



> 
> When this combination is settled, and while it lasts, it is not in
> the power of reason to help us, and relieve us from the effects of it.
> Ideas in our minds, when they are there, will operate according to
> their natures and circumstances. And here we see the cause why time
> cures certain affections, which reason, though in the right, and
> allowed to be so, has not power over, nor is able against them to
> prevail with those who are apt to hearken to it in other
> cases. (2.23.13)
> 
> 
> 


Likewise, say one has just eaten lutefisk and then vomited. The
smell and taste of lutefisk will then be associated with feeling
nauseated, and no amount of telling one that they shouldn't be
nauseated will be very effective. Say the lutefisk that made one
vomit was covered in poison, so that we know that the lutefisk
wasn't the root cause of the
 sickness.[23]
Having this knowledge won't dislodge the association. In
essence, associative structures are functionally defined as being
fungible based on counterconditioning, extinction, and nothing
else. Thus, assuming one sees counterconditioning and extinction as
types of associative learning, we can say that associative learning
does not necessarily eventuate in associative structures, but
associative structures can only be modified by associative
learning.


## 5. Associative Transitions


So far we've discussed learning and mental structures, but
have yet to discuss *thinking*. The pure associationist will
want a theory that covers not just acquisition and cognitive
structure, but also the transition between thoughts. Associative
transitions are a particular type of thinking, akin to what William
James called "The Stream of Thought" (James
1890). Associative transitions are movements between thoughts that are
not predicated on a prior logical relationship between the elements of
the thoughts that one connects. In this sense, associative transitions
are contrasted with computational transitions as analyzed by the
Computational Theory of Mind (Fodor 2001; Quilty-Dunn and Mandelbaum 2018,2019; see
 the entry on Computational Theory of Mind). 
CTM understands *inferences* as truth preserving
movements in thought that are underwritten by the formal/syntactic
properties of thoughts. For example inferring the conclusion
in *modus ponens* from the premises is possible just based on
the form of the major and minor premise, and not on the content of the
premises. Associative transitions are transitions in thought that are
not based on the logico-syntactic properties of thoughts. Rather, they
are transitions in thought that occur based on the associative
relations among the separate thoughts.


Imagine an impure associationist model of the mind, one that
contains both propositional and associative structures. A
computational inference might be one such as inferring 
you are a *g* from the thoughts 
if you are an *f*, then you are a *g*, and 
you are an *f*. However, an associative
transition is just a stream of ideas that needn't have any
formal, or even rational, relation between them, such as the
transition from 
this coffee shop is cold to russia should annex idaho, 
without there being any intervening thoughts. This transition
could be subserved merely by one's association of
 idaho and
cold, or it could happen because the two thoughts have tended to
co-occur in the past, and their close temporal proximity caused an
association between the two thoughts to arise (or for many other
reasons). Regardless of the etiology, the transition doesn't
occur on the basis of the formal properties of the
 thoughts.[24]


According to this taxonomy, talk of an "associative
inference" (e.g., Anderson et al. 1994; Armstrong et al. 2012)
is a borderline oxymoron. The easiest way to give sense to the idea of
an associative inference is for it to involve transitions in thought
that began because they were purely inferential (as understood by the
computational theory of mind) but then became associated over
time. For example, at first one might make the *modus ponens*
inference because a particular series of thoughts instantiates
the *modus ponens* form. Over time the premises and conclusion of that
particular token of a *modus ponens* argument become associated
with each other through their continued use in that inference and now
the thinker merely associates the premises with the conclusion. That
is, the constant contiguity between the premises and the conclusion
occurred because the inference was made so frequently, but the
inference was originally made so frequently not because of the
associative relations between the premises and conclusion, but because
the form of the thoughts (and the particular motivations of the
thinker). This constant contiguity then formed the basis for an
associative linkage between the premises and the conclusion. [25]


As was the case for associative structures, associative transitions
in thought are not just a logical possibility. There are particular
empirical differences associated with associative transitions versus
inferential transitions. Associative transitions tend to move across
different content domains, whereas inferential transitions tend to
stay on a more focused set of contents. These differences have been
seen to result in measurable differences in mood: associative thinking
across topics bolsters mood when compared to logical thinking on a
single topic (Mason and Bar 2012).


## 6. Associative Instantiation


The associationist position so far has been neutral on how
associations are to be implemented. Implementation can be seen at a
representational (that is psychological) level of explanation, or at
the neural level. A pure associationist picture would posit an
associative implementation base at one, or both, of these
 levels.[26]


The most well-known associative instantiation base is a class of
networks called Connectionist networks (see
 the entry on connectionism). 
Connectionist networks are sometimes pitched at the
psychological level (see, e.g., Elman 1991; Elman et al. 1996;
Smolensky 1988). This amounts to the claim that models of algorithms
embedded in the networks capture the essence of certain mental
processes, such as associative learning. Other times connectionist
networks are said to be models of neural activity ("neural
networks"). Connectionist networks consist in sets of nodes,
generally input nodes, hidden nodes, and output nodes. Input nodes
are taken to be analogs of sensory neurons (or sub-symbolic sensory
representations), output nodes the analog of motor neurons (or
sub-symbolic behavioral representations), and hidden nodes are
 stand-ins for all other
 neurons.[27]
 The network consists in these nodes
being connected to each other with varying strengths. The topology of
the connections gives one an associative mapping of the system, with
the associative weights understood as the differing strengths of
connections. On the psychological reading, these associations are
functionally defined; on the neurological reading, they are generally
understood to be representing synaptic conductance (and are the
analogs of dendrites).[28] *Prima facie*, these networks are
purely associative and do not contain propositional elements, and the
nodes themselves are not to be equated with single representational
states (such as concepts; see, e.g., Gallistel and King 2009).


However, a connectionist network can implement a classical Turing
machine architecture (see, e.g., Fodor and McLaughlin
1990; Chalmers 1993). Many, if not most, of the
adherents of classical computation, for example proponents of CTM,
think that the brain is an associative network, one which implements a
classical computational program. Some adherents of CTM do deny that
the brain runs an associative network (see, e.g., Gallistel and King
2009, who appear to deny that there is any scientific level of
explanation that association is intimately involved in), but they do
so on separate empirical grounds and not because of any logical
inconsistency with an associative brain implementing a classical
mind.


When discussing an associative implementation base it is important
to distinguish questions of associationist structure from questions of
representational reality. Connectionists have often been followers of
the Skinnerian anti-representationalist tradition (Skinner 1938).
Because of the distributed nature of the nodes in connectionist
networks, the networks have tended to be analyzed as associative
stimulus/response chains of subsymbolic elements. However, the
question of whether connectionist networks have representations which
are distributed in patterns of activity throughout different nodes of
the network, or whether connectionist networks are best understood as
containing no representational structures at all, is orthogonal to
both the question of whether the networks are purely associative or
computational, and whether the networks can implement classical
architectures.


## 7. Relation between the Varieties of Association and Related Positions


These four types of associationism share a certain empiricist
spiritual similarity, but are logically, and empirically, separable.
The pure associationist who wants to posit the smallest number of
domain-general mental processes will theorize that the mind consists
of associative structures acquired by associative learning which enter
into associative transitions and are implemented in an associative
instantiation base. However, many hybrid views are available and
frequently different associationist positions become mixed and
matched, especially once issues of empiricism, domain-specificity, and
gradual learning arise. Below is a partial taxonomy of where some
well-known theorists lie in terms of associationism and these other,
often related doctrines.


Prinz (2002) and Karmiloff-Smith
(1995) are examples of
empiricist non-associationists. It is rare to find an associationist
who is a nativist, but plenty of nativists have aspects of
associationism in their own work. For example, even the arch-nativist
Jerry Fodor maintains that intramodular lexicons contain associative
structures (Fodor 1983). Similarly, there are many non-behaviorist
(at least non-radical, analytic, or methodological behaviorist)
associationists, such as Elman (1991), Smolensky (1988), Baeyens (De
Houwer and Baeyens 2001), and modern day dual process theorists such as
Evans and Stanovich (2013). It is quite difficult to find a
non-associationist behaviorist, though Tolman approximates one
(Tolman 1948). Elman and Smolensky also qualify as
representationalist associationists, and Van Gelder (1995) as an
anti-representationalist non-associationist. Karmiloff-Smith
(1995) can be
interpreted as, for some areas of learning, a proponent of gradual
learning without being associationist (some might also read
contemporary Bayesian theorists, e.g., Tenenbaum et al. 2011 and
Chater et al. 2006 as holding a similar position for some areas of
learning). Rescorla (1988) and Heyes (2012) claim to be
associationists who are pro step-wise, one shot learning (though
Rescorla sees his project as a continuation of the classical
conditioning program, others see his data as grist for the
anti-associationist, pro-computationalist mill, see Gallistel and
King 2009; Quilty-Dunn and Mandelbaum 2019). Lastly, Tenenbaum and his contemporary Bayesians
colleagues sometimes qualify as holding a domain-general learning
position without it being
 associationist.[29]


## 8. Associationism in Social Psychology


Since the cognitive revolution, associationism's influence
has mostly died out in cognitive psychology and
psycholinguistics. This is not to say that all aspects of associative
theorizing are dead in these areas; rather, they have just taken on
much smaller, more peripheral roles (for example, it has often been suggested that
mental lexicons are structured, in part, associatively, which is why
lexical decision tasks are taken to be facilitation maps of
one's lexicon). In other areas of cognitive psychology (for
example, the study of causal cognition), associationism is no longer
the dominant theoretical paradigm, but vestiges of associationism still persist (see Shanks 2010 for an overview of associationism
in causal cognition). Associationism is also still alive in the
connectionist literature, as well as in the animal cognition
tradition.


But the biggest contemporary stronghold of associationist
theorizing resides in social psychology, an area which has
traditionally been hostile to associationism (see, e.g., Asch 1962,
1969). The ascendance of associationism in social psychology has been
a fairly modern development, and has caused a revival of
associationist theories in philosophy (e.g., Madva and Brownstein 2019). The two
areas of social psychology that have seen the greatest renaissance of
associationism are the implicit attitude and dual-process theory
literature. However, in the late 2010s social psychology has begun to take a critical look at associationist theories (e.g., Mann et al. 2019).


### 8.1 Implicit Attitudes


Implicit attitudes are generally operationally defined as the attitudes tested on implicit tests such as the Implicit Association Test
(Greenwald et al. 1998), the Affect Misattribution Procedure (Payne et
al. 2005), the Sorted Paired Feature Task (Bar-Annan et al. 2009) and
the Go/No-Go Association Task (Nosek and Banaji 2001). Implicit attitudes are contrasted with explicit attitudes, attitudes operationalized as the one's being probed when one gives an explicit response like a marking on a Likert scale, feeling thermometer, or in free report. Such operationalizations leave open the question of whether there are any natural kinds to which explicit and implicit attitudes refer. In general implicit attitudes are characterized as being mental
representations that are unavailable for explicit report and inaccessible to consciousness (cf. Hahn et al. 2014; Berger 2020).



The default position among social psychologists is to treat implicit attitudes as
if they are associations among mental representations (Fazio 2007), or
among pairs of mental representations and valences. In particular,
they treat implicit attitudes as associative structures which enter
into associative transitions. Recently this issue has come under much
debate. In an ever expanding series of studies De Houwer and his collaborators have taken to show that associative learning is, at base, relational, propositional contingency learning; i.e., that all putatively associative learning is in fact a nonautomatic learning process that generates and evaluates propositional hypotheses (Mitchell et al. 2009; De Houwer 2009, 2011, 2014 2019; Hughes et al. 2019). Other researchers have approached the question also using learning as the entrance point to the debate, demonstrating effects that non-associative acquisition creates stronger attitudes than associative acquisition (Hughes et al. 2019). For example, one might demonstrate that learning through merely reading an evaluative statement creates a stronger implicit attitude than repeated associative exposures (Kurdi and Banaji 2017, 2019; Mann et al. 2019). Other researchers have championed propositional models not based on learning, but instead based on how implicit attitudes change regardless of how they are acquired. For instance, Mandelbaum (2016) argued that logical/evidential interventions modulate implicit attitudes in predictable ways (e.g., using double negation to cancel each other out), while others have used diagnosticity to show that implicit attitudes update in a non-associationistic, propositional way (e.g., after reading a story about a man who broke into a building and appeared to ransack it you learn that we jumped into save people from a fire and immediately change your opinion of the man from negative to positive; Mann and Ferguson 2015; Mann et al. 2017; Van Dessel et al. 2019). (For more on implicit attitudes see the entry
on
 implicit bias).


### 8.2 Dual Process Theories


Associative structures and transitions are widely implicated in a
particular type of influential dual-process theory. Though there are
many dual-process theories in social psychology (see, e.g., the papers
in Chaiken and Trope 1999, or the discussion in Evans and Stanovich
2013), the one most germane to associationism is also the most
popular. It originates from work in the psychology of reasoning and
is often also invoked in the heuristics and biases tradition (see,
e.g., Kahneman 2011). It has been developed by many different
psychological theorists (Sloman 1996; Smith and Decoster 2000; Wilson
et al. 2000;
Evans and Stanovich 2013) and, in parts, taken up by
philosophers too (see, e.g., Gendler 2008; Frankish 2009; see also
some of the essays in Evans and Frankish 2009).


The dual-process strain most relevant to the current discussion
posits two systems, one evolutionarily ancient intuitive system
underlying unconscious, automatic, fast, parallel and associative
processing, the other an evolutionarily recent reflective system
characterized by conscious, controlled, slow,
"rule-governed" serial processes (see, e.g., Evans and
Stanovich 2013). The
ancient system, sometimes called "System 1", is often
understood to include a collection of autonomous, distinct
subsystems, each of which is recruited to deal with distinct types of
problems (see Stanovich 2011 for a discussion of
"TASS--the autonomous set of systems"). Although
theories differ on how System 1 interacts with System
 2,[30] the
theoretical core of System 1 is arguing that its processing is
essentially associative. As in the implicit attitude debate, dual
systems models have recently come under fire (see Kruglanski 2013;
Osman 2013; Mandelbaum 2016; De Houwer 2019), though they remain very
popular.


## 9. Criticisms of Associationism


Associationism has been a dominant theme in mental theorizing for
centuries. As such, it has garnered an appreciable amount of
criticism.


### 9.1 Learning Curves


The basic associative learning theories imply, either explicitly or
implicitly, slow, gradual learning of associations (Baeyens et al.
1995). The learning process can be summarized in a learning curve
which plots the frequency (or magnitude) of the conditioned response
as a function of the number of reinforcements (Gallistel et al. 2004:
13124). Mappings between CRs and USs are gradually built up over
numerous trials (in the lab) or experiences (in the world). Gradual,
slow learning has come under fire from a variety of areas (see sections 9.3 and 9.4.1). However, here we just
focus on the behavioral data. In a series of works re-analyzing animal
behavior, Gallistel (Gallistel et al. 2004; Gallistel and King 2009)
has argued that although *group-level* learning curves do
display the properties of being negatively accelerated and gradually
developing, these curves are misleading because
no *individual's* learning curve has these
properties. Gallistel has argued that learning for individuals is
generally step-like, rapid, and abrupt. An individual's learning
from a low-level of responding to asymptotic responding is very quick.
Sometimes, the learning is so quick that it is literally one-shot
learning. For example, after analyzing multiple experiments of animal
learning of spatial location Gallistel writes



> The learning of a spatial location generally
> requires but a single experience. Several trials may, however, be
> required to convince the subject that the location is predictable from
> trial to trial. (Gallistel et al. 2004: 13130)
> 
> 


Gallistel argues that the reason the group learning curves look to
be smooth and gradual is that there are large individual differences
between subjects in terms of when the onset latency of the step-wise
curves begin (Gallistel et al. 2004: 13125); in other words, different
animals take different amounts of time for the learning to
commence. The differences between individual subject's learning
curves are predicated on when the steps begin and not by the speed of
the individual animal's learning process. All individuals appear
to show rapid rises in learning, but since each begins their learning
at a different time, when we average over the group, the rapid step-wise
learning appears to look like slow, gradual learning (Gallistel et
al. 2004: 13124).


### 9.2 The Problem of Predication


The problem of predication is, at its core, a problem of how an
associative mechanism can result in the acquisition of
subject/predicate structures, structures which many theorists believe
appear in language, thought, and judgment. The first major discussion
of the problem appears in Kant (1781/1787), but variants of the basic
Kantian criticism can be seen across the contemporary literature (see,
e.g., Chomsky 1959; Fodor and Pylyshyn 1988; Fodor 2003; Mandelbaum
2013a; for the details of the Kantian argument see the entry
 on Kant's Transcendental Argument).


For a pure associationist, association is "semantically
transparent" (see Fodor 2003), in that it purports to add no
additional structure to thoughts. When a simple concept, *X*
and a simple concept *Y*, become associated one acquires the
associative structure *X*/*Y*. But *X*/*Y*
has no additional structure on top of their contents. Knowing
that *X* and *Y* are associated amounts to knowing a
causal fact: that activating *X*s will bring about the
activation of *Y*s and *vice versa*. However, so the
argument goes, some of our thoughts appear to have more structure than
this: the thought
 birds fly predicates the property of flying onto
birds. The task for the associationist is to explain how associative
structures can distinguish a thinker who has a single (complex)
thought
 birds fly from a thinker who conjoins two simple thoughts in
an associative structure where one thought,
 birds, is immediately
followed by another,
 fly. As long as the two simple thoughts are
reliably causally correlated so that, for a thinker, activations of
birds regularly brings about
 fly, then that thinker has the
associative structure
 birds/fly. Yet it appears that thinker
hasn't yet had the thought
 birds fly. The problem of predication
is explaining how a purely associative mechanism could eventuate in
complex thoughts. In Fodor's terms the problem boils down to how
association, a causal relation among mental representations, can
affect predication, a relation among intentional contents (Fodor
2003).


A family of related objections to associationism can be interpreted
as variations on this theme. For example, problems of productivity,
compositionality, and systematicity for associationist theorizing
appear to be variants of the problem of predication (for more on these
 specific issues see the entries on the
 Language of Thought Hypothesis and
 on compositionality). If
association doesn't add any additional structure to the mental
representations that get associated, then it is hard to see how it can
explain the compositionality of thought, which relies on structures
that specify relations among intentional contents. Compositionality
requires that the meaning of a complex thought is determined by the
meanings of its simple constituents along with their syntactic
arrangements. The challenge to associationism is to explain how an
associative mechanism can give rise to the syntactic structures
necessary to distinguish a complex thought like

 birds fly from the
temporal succession of two simple thoughts

 birds and
 fly. Since the
compositionality of thought is posited to undergird the productivity
of thought (thinkers' abilities to think novel sentences of
arbitrary lengths, e.g.,
 green birds fly,
 giant green birds fly,
cuddly giant green birds fly, etc.), associationism has problems
explaining productivity.


Systematicity is the thesis that there are predictable patterns
among which thoughts a thinker is capable of entertaining. Thinkers
that can entertain thoughts of certain structures can always entertain
distinct thoughts that have related structure. For instance, any
thinker who can think a complex thought of the form "*X*
transitive verb *Y*" can think "*Y*
transitive verb *X*".[31] Systematicity entails that we
won't find any thinker that can only think one of those
two thoughts, in which case we could not find a person who could think
audrey wronged max, but not

 max wronged audrey. Of course, these two
thoughts have very different effects in one's cognitive
economy. The challenge for the associationist is to explain how the
associative structure
 audrey/wronged/max can be distinguished from the
structure
 max/wronged/audrey, while capturing the differences in those
thoughts' effects.


Associationists have had different responses to the problem. Some
have denied that human thought is actually compositional, productive,
and systematic, and other non-associationists have agreed with this
critique. For example, Prinz and Clark claim "concepts do not
compose most of the time" (2002: 62), and Johnson (2004) argues
that the systematicity criterion is wrongheaded (see Aydede 1997 for
extended discussion of these issues). Rumelhart et al. offer a
connectionist interpretation of "schemata", one which is
intended to cover some of the phenomenon mentioned in this section
(Rumelhart et al. 1986). Others have worked to show that classical
conditioning can indeed give rise to complex associative structures
(Rescorla 1988). In defense of the associationist construal of complex
associations Rescorla writes,



> Clearly, the animals had not simply coded the RH
> [complex] compound in terms of parallel associations with its
> elements. Rather they had engaged in some more hierarchical
> structuring of the situation, forming a representation of the compound
> and using it as an associate. (Rescorla 1988: 156)
> 
> 




Whether or not associationism has the theoretical
tools to explain such complex compounds by itself is still debated
(see, e.g., Fodor 2003; Mitchell 2009; Gallistel and King 2009; Quilty-Dunn and Mandelbaum 2019).


### 9.3 Word Learning


Multiple issues in the acquisition of the lexicon appear to cause
problems for associationism. Some of the most well known examples are
reviewed below (for further discussion of word learning and
associationism see Bloom 2000).


#### 9.3.1 Fast Mapping


Children learn words at an incredible rate, acquiring around 6,000
words by age 6 (Carey 2010: 184). If gradual learning is the rule,
then words too should be learned gradually across this time. However,
this does not appear to be the case. Susan Carey
discovered the phenomenon of "fast mapping", which is
one-shot learning of a word (Carey 1978a, 1978b; Carey and Bartlett
1978). Her most influential example investigated children's
acquisition of "chromium" (a color word referring to olive
green). Children were shown one of two otherwise identical objects,
which only differed in color and asked, "Can you get me the
chromium tray, not the red one, the chromium one" (recited in
Carey 2010: 2). All of the children handed over the correct tray at
that time. When the children were later tested in differing contexts,
more than half remembered the referent of
"chromium". These findings have been extended--for
example, Markson and Bloom (1997) showed that they are not specific to
the remembering of novel words, but also hold for novel facts.


Fast mapping poses two problems for associationism. The first is
that the learning of a new word did not develop slowly, as would be
predicted by proponents of gradual learning. The second is that in
order for the word learning to proceed, the mind must have been aided
by additional principles not given by the environment. Some of these
principles such as Markman's (1989) taxonomic, whole object, and
mutual exclusivity constraints, and Gleitman's syntactic
bootstrapping (Gleitman et al. 2005), imply that the mind does add structure to what is
learned. Consequently, the associationist claim that learning is just
mapping external contingencies without adding structure is
imperiled.


#### 9.3.2 Syntactic Category Learning


"Motherese", the name of the type of language that
infants generally hear, consists of simple sentences such as
"Nora want a bottle?" and "Are you tired?".
These sentences almost always contain a noun and a verb. Yet, the
infant's vocabulary massively over-represents nouns in the first
100 words or so, while massively under-representing the verbs (never
mind adjectives or adverbs, which almost never appear in the first 100
words infants produce; see, e.g., Goldin-Meadow, Seligman, and Gelman
1976). Even more surprising is that the over-representation of
nouns to verbs holds even though



> the incidence of each word (that is, the token
> frequency) is higher for the verbs than for the nouns in the common
> set used by mothers. (Snedeker and Gleitman 2004: 259, citing data
> from Sandhoffer, Smith, and Luo 2000)
> 
> 
> 


Moreover, children hear a preponderance of
determiners ("the" and "a") but don't
produce them (Bloom 2000). These facts are not specific to English,
but hold cross-culturally (see, e.g., Caselli et al. 1995). The
disparity between the variation of the syntactic categories infants
receive as input and produce as output is troublesome to
associationism, insofar as associationism is committed to the learned
structures (and the behaviors that follow from them) merely
patterning what is given in experience.


### 9.4 Against the Contiguity Analysis of Associationism


Contiguity has been a central part of associationist analyses since
the British Empiricists. In the experimental literature, the problem
of figuring out the parameters needed for acquiring an association due to the contiguity of its relata has sometimes
been termed the problem of the "Window of Association"
(e.g., Gallistel and King 2009). Every associationist theory has to specify what temporal window two properties must instantiate in order for those properties to be associated.[32]
A related problem for contiguity theorists is that if the domain
generality of associative learning is desired, then the window needs
to be homogenous across content domains. The late 1960s saw
persuasive attacks on domain generality, as well as the necessity and
sufficiency of the contiguity criterion in general.


#### 9.4.1 Against the Necessity of Contiguity


Research on "taste aversions" and
"bait-shyness" provided a variety of problems with
contiguity in the associative learning tradition of classical
conditioning. Garcia observed that a gustatory stimulus (e.g.,
drinking water or eating a hot dog) but not an audiovisual stimulus (a
light and a sound) would naturally become associated with feeling
nauseated. For instance, Garcia and Koelling (1966) paired an
audiovisual stimulus (a light and a sound) with a gustatory stimulus
(flavored water). The two stimuli were then paired with the rats
receiving radiation, which made the rats feel nauseated. The rats
associated the feeling of nausea with the water and not with the
sound, even though the sound was contiguous with the water. Moreover,
the delay between ingesting the gustatory stimulus and feeling
nauseated could be quite long, with the feeling not coming on until 12
hours later (Roll and Smith 1972), and the organism needn't even
be conscious when the negative feeling arises. (For a review, see
Seligman 1970; Garcia et al. 1974). The temporal delay shows that the
CS (the flavored water) needn't be contiguous with the US (the
feeling of nausea) in order for learning to occur, thus showing that
contiguity isn't necessary for associative learning.


Garcia's work also laid bare the problems with the domain
general aspect of associationism. In the above study the rat was
prepared to associate the nausea with the gustatory stimulus, but
would not associate it with the audiovisual stimulus. However, if one
changes the US from feeling nauseated to receiving shocks in perfect
contiguity with the audiovisual and gustatory stimuli, then the rats
will associate the shocks with the audiovisual stimulus but not with
the gustatory stimulus. That is, rats are prepared to associate
audiovisual stimuli with the shock but are contraprepared to associate
the shocks with the gustatory stimulus. Thus, learning does not seem
to be entirely domain general (for similar content specificity
effects in humans, see Baeyens et al.
 1990).[33]


Lastly, "The Garcia effect" has also been used to show
 problems in the learning curve (see section 9.1). 
"Taste aversions" are the phenomena whereby an
organism gets sick from ingesting the stimulus and the taste (or odor,
Garcia et al. 1974) of that stimulus gets associated with the feeling
of sickness. As anyone who has had food poisoning can attest, this
learning can proceed in a one-shot fashion, and needn't have a
gradual rise over many trials (taste aversions have also been observed
in humans, see, e.g., Bernstein and Webster 1980; Bernsetin 1985;
Logue et al. 1981; Rozin 1986).


#### 9.4.2. Against the Sufficiency of Contiguity


Kamin's famous blocking experiments (1969) showed that not
all contiguous structures lead to classical conditioning. A rat that
has already learned that CS1 predicts a US, will not learn that a
subsequent CS2 predicts the US, if the CS2 is always paired with the
CS1. Suppose that a rat has learned that a light predicts a shock
because of the constant contiguity of the light and shock. After
learning this, the rat has a sound introduced which only arises in
conjunction with the light and the shock. As long as the rat had
previously learned that the light predicts the shock, it will not
learn that the sound does (as can be seen on later trials that have
the sound alone). In sum, having learned that the CS1 predicts the US
blocks the organism from learning that the CS2 predicts the
 US.[34] So even
though CS2 is perfectly contiguous with the US, the association
between CS2 and the US remains unlearned, thus serving as a
counterexample to sufficiency of
 contiguity.[35]


Similarly Rescorla (1968) demonstrated that a CS can appear only
when the US appears and yet still have the association between them be
unlearnable. If a tone is arranged to bellow only when there are
shocks, but there are still shocks when there are no tones (that is,
the CS only appears with the US, but the US sometimes appears without
the CS), no associative learning between the CS and the US will occur.
Instead, subjects (in Rescorla 1968, rats) will only learn a
connection between the shock and the experimental
situation--e.g., the room in which the experiment is carried
out.


In large part because of the problems discussed in 9.4, many
classical conditioning theorists gave up the traditional
program. Some, like Garcia, appeared to give up the classical
theoretical framework altogether (Garcia et al. 1974), others, such
as Rescorla and Wagner, tried to usher the framework into the modern
era (see, Rescorla and Wagner 1972; Rescorla 1988), where
conditioning is seen as sensitive to base rates and driven by
informational 
pick-up.[36] 
Whether this movement is interpreted as
a substantive revision of classical conditioning (Rescorla 1988;
Heyes 2012) or a wholesale abandoning of it (Gallistel and King 2009)
is debatable.


### 9.5 Coextensionality


The Rescorla experiment also demonstrates another problem in
associative theorizing: the question of why some property is singled
out as a CS as opposed to different, equally contemporaneously
instantiated properties. Put a different way, one needs a principle
to say what the "same situation" amounts to in
generalizations such as Thorndike's laws. For instance, if a CS
and a US, say a tone and a shock, are perfectly paired so that they
are either both present or both absent, the organism won't
associate the location it received shocks (e.g., the experimental
setting) with getting shocked, it will just associate the tone with
the shocks. But in the condition where the US occurs without the CS,
but the CS does not occur without the US, the organism will gain an
association between the shocks and the location. However, in both
cases the location is present on every trial.[37] In contrast to shocks,
x-ray radiation, when used as a US, never appears to become
associated with location, even if they are always perfectly paired
 (Garcia et al. 
1972).[38]


The problem of saying which properties become associated when
multiple properties are coinstantiated sometimes goes by the name the
"Credit Assignment Problem" (see, e.g., Gallistel and
 King 2009).[39]
Some would argue that this problem is a symptom of a larger issue:
trying to use extensional criteria to specify intentional content
(see, e.g., Fodor 2003). Associationists need a criterion to specify which
of the coextensive properties will in fact be learned, and which
not.


An additional worry stems from the observation that sometimes the
lack of a property being instantiated is an integral component of
what is learned. To deal with the problem of missing properties,
contemporary associationists have introduced an important element to
the theory: inhibition. For example, if a US and a CS only appear
when the other is absent, the organism will learn a negative
relationship holds between them; that is, the organism will learn
that the absence of the CS predicts the
 US.[40] Here
the CS becomes a "conditioned inhibitor" of the US.
Inhibition, using associations as modulators and not just activators,
is a central part of current associationist thinking. For example, in
connectionist networks, inhibition is implemented by the activation
of certain nodes inhibiting the activation of other nodes. Connection
weights can be positive or negative, with the negative weight
standing in for the inhibitory strength of the association.