ArticlePDF Available

Abstract and Figures

The propositional or rationalist Bayesian approach to learning is contrasted with an interpretation of causal learning in associative terms. A review of the development of the use of rational causal models in the psychology of learning is discussed concluding with the presentation of three areas of research related to cause-effect learning. We explain how rational context choices, a selective association effect (i.e., blocking of inhibition) as well as causal structure can all emerge from processes that can be modeled using elements of standard associative theory. We present the auto-associator (e.g., Baetu & Baker, 2009) as one such simple account of causal structure.
Content may be subject to copyright.
International Journal of Comparative Psychology, 2011, 24, 365-388.
Copyright 2011 by the International Society for Comparative Psychology
This work was supported by a predoctoral fellowship awarded to Itxaso Barberia by the Generalitat
de Catalunya (with the support of the Comissionat per a Universitats i Recerca del Departament
d’Innovació, Universitats i Empresa de la Generalitat de Catalunya and the Fons Social Europeu), a
postgraduate fellowship awarded to Irina Baetu by the Natural Sciences and Engineering Research
Council of Canada (NSERC), a post-doctoral fellowship awarded to Irina Baetu by the Fonds
Québécois de la Recherche sur la Nature et les Technologies, an NSERC Discovery Grant awarded to
Andy Baker, and a grant from the Spanish Ministerio de Educación y Ciencia (SEJ2007 – 67409
C02 – 01). Correspondence should be addressed to A. G. Baker, Department of Psychology, McGill
University, 1205 Dr. Penfield Ave., Montreal, QC (
Do Associations Explain Mental Models of Cause?
Itxaso Barberia
University of Deusto, Spain
Irina Baetu
University of Adelaide, Australia
Robin A. Murphy
University of Oxford, United Kingdom
A. G. Baker
McGill University, Canada
The propositional or rationalist Bayesian approach to learning is contrasted with an interpretation of
causal learning in associative terms. A review of the development of the use of rational causal models
in the psychology of learning is discussed concluding with the presentation of three areas of research
related to cause-effect learning. We explain how rational context choices, a selective association
effect (i.e., blocking of inhibition) as well as causal structure can all emerge from processes that can
be modeled using elements of standard associative theory. We present the auto-associator (e.g., Baetu
& Baker, 2009) as one such simple account of causal structure.
Newton’s (1687/1934) laws of motion and gravitation describe a world
that is deterministic and rule-based. Apples fall with a regularity and obedience to
Newton’s law that would be the envy of any human law-maker. Post-Newtonian
physics is still rule-based, although the rules are now stochastic. Newton deduced
his laws but all living organisms have evolved mechanisms that internalize these
rules. Learning is a mechanism that allows internal adjustments to the physical
rules of the environment. Consequently an animal’s behavior can be described by a
series of rules that reflect laws of the external physical world or at least
transformations or approximations of them.
Rules by their nature have a structure and, as such, adaptations to the rules
in the world will appear on the surface to involve propositional or inferential
processes even when no such processes are involved. Take a simple classical
conditioning experiment from Pavlov (1927), where a metronome is regularly
followed by food powder. The rules in the world are: if metronome then food, if no
metronome then no food. The well-trained dog will come to salivate in the
presence of the metronome. In addition to the world’s rule about food, the dog has
additional rules and inferences it must make. It must represent some version of the
- 366 -
following rule: If I eat food then, in the interests of better digestion (homeostasis),
I had better salivate; however, because the food regularly follows the metronome, I
should prepare for the food by salivating during the metronome and refrain from
salivating in the absence of the metronome.
Ethologists might call this description of these rules a functional analysis
of the behavioral system but, for our purposes, one only need consider that it
describes an adapted sequence of physical rules in the world, although some rules
are outside and others are inside the animal. For psychology, there are three major
issues that need to be addressed. First, which rules in the world does the animal
internalize and how closely does its behavior map onto these rules? Second, how
and at what level of abstraction does the animal internalize these rules? Third, how
are these rules instantiated in the biology of the animal? These three questions also
reflect three levels of analysis of behavior. There has been a long friction in animal
learning between these levels of analysis, both concerning which is most important
and what should be the content of the levels (e.g., Murphy, Mondragon, &
Murphy, 2008). A similar friction exists in other areas of study. In vision, Marr
(1982) identified what he called the computational, algorithmic and
implementation levels of analysis. The computational level defines the rules that
describe the organism’s response to the events in the world. For example, it might
report a visual illusion and this might be a ruled based phenomenon. The
algorithmic level of analysis for instance might involve arguing that form vision is
a consequence of Fourier Transformations of the visual world (Cornsweet, 1970).
Finally, the implementation describes the neural basis for these abilities, for this
paper, we are only interested in the first two levels and will leave the physiological
implementation to one side.
Some learning psychologists have even asked if it is legitimate to go
beyond the first level of analysis. Radical behaviorists, led by Skinner, questioned
whether “theories” or algorithms were useful. In Skinner’s, often maligned, “black
box” approach, psychologists were entreated to only consider the inputs (stimuli)
and the outputs of the system (responses) and the mathematical rules that link
them. Both stimuli and responses could be very broadly defined. Although this
approach was often, perhaps unfairly, criticized (e.g., Chomsky, 1959; Fodor &
Piattelli-Palmarini, 2010), it has stood the test of time. Herrnstein’s (1961)
matching law is an example of a modern instantiation of this approach and it has
considerable generality (e.g., Koehler & James, 2009). An animal’s choice of two
(or more) alternatives is determined by the proportion or ratio of economic returns
connected to the alternatives. Clearly this is a description about rules or
contingencies in the world (see also Murphy et al., 2008). Two or more inputs
determine the output of the system. Like all “rules,” this can be written in
propositional and even inferential form, even if some consequences might not be
strictly rational. Radical behaviorists were interested in what the animal computed
and not in the internal mechanism or algorithm from which this computation might
- 367 -
Some Background: The North American Behaviorists
In contrast to radical behaviorism a number of other traditions in animal
learning did accept the second, algorithmic, level. They also foreshadow the
comparison between the associative and propositional classes of explanations of
learning that we will discuss further on. Hull, Spence, and Guthrie are
representative of one camp and Tolman the other. Hull was a stimulus-response (S-
R) psychologist. He and others believed that goal directed behavior could be
understood by claiming that animals formed associations between stimuli and
responses. These associations were fueled by the temporal order, timing and
motivational significance of the stimuli. S-R links could be strengthened by
rewards (reinforcement). In modern language, Hull believed that the goal-directed
nature of behavior, and hence expectations, were an emergent property of these
associative processes.
Goal directed behavior is interesting because it involves a quest for a thing
that is not present in the here and now. It is based on an expectation of the future.
And a materialist theory involving S-R associations and reinforcement has no
direct representation of expectations of future events (see Dickinson & Balleine,
1994). This begs the question: What causes the first step down a maze and fills the
gap between it and the expected reward? To solve this conundrum, Hull (1943)
called upon the processes of secondary reinforcement and S-R associations. An
animal learned to run down a maze to get food through the process of secondary
reinforcement strengthening chains of S-R associations between stimuli (including
those internal to the animal) along the maze and, rather molecular, responses,
presumably steps down the maze, that have been associated with them. When an
animal first learns to run down the maze, entering the goal box is rewarding
because food is immediately available and this reward both strengthens the
tendency to approach the reward but also is paired with stimuli in the maze that are
present when the reward is encountered, thus giving them value. These stimuli
themselves become rewarding so “acquiring” them rewards approach responses to
them. They have a dual role because they also elicit responses through S-R
associations. This process is mediated by Hull’s anticipatory goal responses and
goal stimuli (rg and sg). These are an amalgam of the initially consummatory
responses and their feedback. These intervening variables provide a formal
structure to mediate the expectations in the maze and help generalize initial goal
directed behavior throughout the maze and distinguish it from Skinner’s simple
chaining account. Thus an animal that has learned to run down a maze for food
takes its first step down the maze not because it is thinking about future food, but
because the first stimulus in the maze generates a step (through an S-R bond) that
takes it to the second stimulus that itself, through reinforcement, strengthens the
initial bond and so on. The rule in the world that describes goal directed behavior –
There is food at the end of the runway so I had better run down there and get it!
has emerged from a simple associative process. The Grice box experiment was
designed to test this hypothesis (Hughes, Davis, & Grice, 1960). Its rationale was
to determine if animals could tolerate a delay of reinforcement for choice behavior
when the chain of responses and stimuli was broken or made ambiguous. And, of
course, they could not. Goal directed choice behavior could only develop over very
- 368 -
brief delays. Longer delays were mediated by these immediate reinforcement
mechanisms rather than an appreciation or expectation of the delayed reward.
Tolman (1932, 1948) took another tack and argued that expectations were
not something that needed to be explained, rather they were fundamental
primitives of his explanations of goal directed behavior. That is to say, the rule in
the world called goal directed behavior was internalized within the animal and was
itself an explanation of behavior rather than a behavior that need be explained. To
demonstrate the failings of the S-R explanation of goal directed behavior, he
devised several experiments directly testing the notion that expectation was a
direct consequence of secondary reinforcement and immediate S-R associations.
His blocked path experiment illustrates his approach (Tolman & Honzik, 1930).
The experiment was done in a maze that had three separate paths to the goal each
longer than the other. A schematic diagram of this maze is shown in Figure 1.
Once animals were trained in the maze and had experience with all three arms, he
blocked either the shortest path to the goal (Block A in Fig. 1) or the two shortest
at a common point (Block B). The rationale rule system in the world for this maze
was If there are no blocks then I should choose the shortest path; If I discover
the shortest path is blocked then I should choose the medium length path; If I
discover the two shortest paths are blocked then I should switch immediately to the
longest path. Interestingly, the sg-rgmechanism has little trouble obeying or
generating the first two rules. Because the string of secondary reinforcers and S-R
associations grows longer as the path becomes longer, the strength of the
association that causes the animal to take its first step down an arm, and hence
choose an alternative, is stronger for the shorter arms. So the animal originally
(and rationally in the world) chooses the shortest arm. Once he abandons that arm,
presumably through extinction, he will be drawn down the second longest arm.
Thus the first two parts of the rules for goal directed behavior have emerged from
S-R theory without recourse to an expectations or thoughts about future events. At
this level the theory has provided an explanation of expectancy. However, S-R
theory also predicts that the animal should choose the second longest arm,
regardless of whether the shortest or the two shortest arms are blocked, because
only the sg-rgassociations in the chosen short path are extinguished. It is well
known that Tolman’s rats behaved as if they had an appreciation of the overall
structure of the maze because those for whom both shorter arms were blocked
were more likely to directly switch from the shortest to the longer, unblocked,
- 369 -
Figure 1. Diagram of the blocked path maze used by Tolman and Honzig (1930).
This experiment is relevant to our considerations because it reveals several
enduring features of the analysis of cognitive learning systems. First, it shows how
quite complex and “rational” behavior can emerge from a system that does not
have cognitive representations of goal directed behavior. S-R psychology does not
have expectations or an appreciation of the overall structure of the environment or
a particularly complex planning system built in. It is parsimonious and these
interesting features emerge from it. This very parsimonious system is “correct” on
two of its three choices. Second, the alternative position to simple associationism
is often couched in propositional or inferential terms. When this is done it is not
clear if it is being claimed that there is a propositional machine or actor inside the
organism that is doing this rule-based reasoning. That is, is this a propositional
algorithm? Or is it just a description of how the animal’s behavior maps onto
certain rules of the world? If so an algorithm for how this is done must be
developed. Tolman held the former position but it is not always so clear with
others. Third, it is hard to disconfirm these propositional theories, because, like
many such systems, they depend on the premises. If an animal fails to switch
immediately to the longest path this may be because S-R theory is correct, and it is
being dragged around the world by fractional anticipatory goal stimuli. Equally
this might happen because the rat has not developed an appropriate appreciation of
the maze (called a cognitive map by Tolman, 1948), and thus simply made a
mistake. Depending on the operator’s assumption, a propositional system can
either act rationally or not based on the premises, but this in no way disconfirms
the propositional mechanism, unless, of course, the theorist clearly specifies and
fixes the underlying premises (see also Mitchell, De Houwer, & Lovibond, 2009).
In these experiments this is difficult to do and thence often leads to circularity. The
behavior confirms the premises and the propositional account. If the animal fails,
then the premises may be different and thus the propositional account is still
confirmed. Following the work we have just described came the cognitive
revolution that rejected many of the tenets of traditional North American
behaviorism or associationism.
- 370 -
Brewer and the Cognitive Revolution
One consequence of the cognitive revolution was to focus conditioning
research on the representational questions that the algorithmic level analysis might
answer. At least partly, this approach was inspired by readers of Chomsky’s (1959)
review of Skinner’s Verbal Behavior (1957), which argued that the traditional
associative approach was impoverished and even circular (see also Fodor &
Piattelli-Palmarini, 2010). For instance, Brewer (1974) argued that conditioning is
not a mechanistic bottom up process driven directly by associations and
reinforcement; rather, in humans at least, it is always cognitively penetrable. While
it is easy to quibble with his characterization of associationism, the important point
is that there is a great deal of research supporting his position. There is a strong
prima fascia case for his position. In simple language, the notion of cognitive
penetrability is that all conditioning represents an internalization of the rules of the
conditioning experiment. For example, if a person learns an eyeblink response, she
does so because she learns the CS-US rule. And if asked an appropriate question
she can usually report the rule and behave as if she has internalized it as a
proposition and make inferences from it. There is a great deal of research on this
point, but the general theme can be understood with two examples. Brewer pointed
out that, in many experiments, only some of the people develop a conditioned
response. Interestingly, those people who have developed the response are much
more likely to be able to report the rule (e.g., tone is followed by shock and light
not) than those who have not. This is consistent with the claim that conditioning
occurred because they internalized the propositional rule and that that rule is
available to them. A second finding is also interesting. It seems that a well-
conditioned participant, just like a rat, can have her responding extinguished.
However, extinction can often be established by simply informing the person that
the shock will no longer be delivered. Disconnecting the electrodes is even more
effective. This again implies that the behavior is driven at the time of the test by a
propositional inference If tone then shock, so I should blink. However the
premise is changed by informing the participant of the absence of shock leading to
a new more cheerful inference.
There are of course many possible objections to the notion of cognitive
penetrability. Cognitive representations, including awareness, are supposed to be
an emergent property of associations, so it would not be surprising that only those
who behaved (i.e., formed associations), are also aware. Moreover, telling people
that the shock will no longer occur may engender generalization from other
experience and certainly changes in the context (Bouton, 2004), both of which can
immediately change behavior. But, crucially, there is a large corpus of behavior
supporting this propositional framework and, as we will discuss, the propositional
position has been very effective in generating empirical results that were not
obviously coming from within the associationist framework.
The enthusiasm for the rejection of associationism waned with the
development of connectionist and similar models, although these were rarely
identified with their behaviorist ancestors. Nonetheless, the idea of cognitive
penetrability has been kept alive both in animal research and human research (De
Houwer, 2009; De Houwer, Beckers, & Vandorpe, 2005; Lovibond & Shanks,
- 371 -
2002; Mitchell et al., 2009). Indeed, it has even been argued that, given existing
positive research in humans and rats, the most parsimonious position is to claim
that all conditioning in all organisms is propositional in nature (De Houwer, 2009;
Mitchell et al., 2009). Although it must be acknowledged that the authors wonder
at the plausibility of this claim. From our discussion above, this is not an issue if
they are talking at the computational level. If so, it is easy to accept the argument
that a planarian might compute inferences (perhaps in some associative way) with
its simple nervous system. This level of computation might be a little simpler than
a human’s. It is not so clear how this might be a meaningful algorithm.
In the subsequent sections of this paper we will discuss three lines of our
research that we believe will help to reconcile the propositional accounts of
learning with associative explanations. To foreshadow our eventual conclusions,
we will argue that propositional accounts can offer useful computational accounts
of behavior. Moreover they are a very powerful heuristics for generating new
experiments. However, they are rarely sufficiently well-specified to be considered
algorithms or explanations of behavior. Thus, they are difficult to disconfirm. The
potential for disconfirmation is the litmus test for a scientific theory or algorithm.
Finally, we will argue that simpler mechanistic algorithms can be developed to
explain this propositional behavior. In the first section, that describes interventions
to use the most informative context in causal discovery, we will show how a
propositional analysis motivated these experiments, but also how some wrinkles in
the data illustrate the weaknesses of underspecified propositional accounts. In the
second section, investigating blocking of conditioned inhibition in causal
reasoning, we will test some predictions of a popular, inferential, propositional
theory and find them wanting. Again we will illustrate the difficulty disconfirming
various versions of this approach. In the third section, we will discuss some
experiments investigating novel predictions about how participants construct
causal chains from negative and positive individual causal links. We will then
show how these predictions that came from a logical propositional analysis emerge
from a simple associative model. The model also generates important
philosophical features of causal reasoning such as temporal precedence and timing
of cause and effect.
Choosing the Most Informative Context
One of the advantages of having a mental model of the mechanism of a
cause, over having a simple representation of its effect in a specific context, is that
it will allow the observer to plan informative interventions. Indeed, the use of such
strategies, sometimes called causal graph surgery, has been argued to be one of the
compelling arguments in favor of mental models of cause and the “Bayesian”
movement in causal reasoning (e.g., Steyvers, Tenenbaum, Wagenmakers, &
Blum, 2003). That is, if people assume that causal power exists within the cause,
this implies that they will be aware of the situations in which this power will be
more effective. The differential informative value of a set of interventions is
especially obvious in the simplest case in which the target cause and its effect are
binary, and the influence of the target cause and the sum of the effects of the
unknown alternative causes (i.e., the causal context) are independent. In this
- 372 -
situation clear predictions can be made about the expected rational preference for
some interventions over others. And this expected preference is different for
generative and preventive causes.
A generative cause is analogous to an excitatory stimulus in conditioning;
it is one whose presence signals an increase in the likelihood of an outcome. The
actions of a generative cause will be masked in the presence of other alternative
causes of the same outcome. Consequently, the best situation to observe a target
cause’s effectiveness is one with no effective, or at least weakly effective
alternative generative causes. Thus, the best interventions would involve choosing
to introduce a generative cause in contexts in which the influence of other potential
generative causes is weak. For example, if we want to study if a new public service
announcement effectively promotes the use of helmets by bicycle riders, the best
option would be to test it on a group of people who normally do not wear helmets.
At the other extreme, it might be quite uninformative to test its effect on people
who already use helmets.
Preventive causes reduce the likelihood of an outcome and thus are
analogous to inhibitors in conditioning. For them to show their efficacy there must
be some outcomes to prevent. Thus, the best contexts to observe their actions will
be ones in which the effect is frequently generated by the context or alternative
generative causes, because there will be more instances in which the target cause
may show its preventive influence. For example, if we want to study if a drug
effectively prevents headaches, then it would be better to give it to people who
regularly have headaches. Testing the drug in people who never have headaches
would give no information about its effectiveness. In fact, Wu and Cheng (1999)
have shown that people tend to report that causal conclusions are not possible in
these extreme situations with effect ceilings or floors. We carried out a series of
experiments designed to test if people actively choose more informative and avoid
less informative interventions when they are allowed to do so (Barberia, Baetu,
Sansa, & Baker, 2010).
We studied the way people would intervene in order to choose the most
informative context to discover a potential causal relationship. In order to show
people the effectiveness of the contexts or alternative causes, we exposed
participants to contexts with different outcome base rates. The base rate is simply
the probability or likelihood of the outcome in the absence of the target cause.
Subsequently, we asked participants to assess the influence of a potential target
cause on the effect. To do so, on each trial, the participants could introduce the
cause in one of the previously trained contexts and observe whether or not the
effect happened. This strategy differs from the traditional causal discovery task in
which participants simply observe contingencies, usually with no context switch
(e.g., Vallée-Tourangeau, Murphy, Drew, & Baker, 1998). The scenario involved
evaluating whether some unknown folk medicines brought back from the Amazon
by a group of scientific explorers could provoke, prevent, or have no influence in
the probability of strokes. The “medicines” could be generative causes, that is, they
could generate strokes, they could be preventive causes, that is, they could prevent
strokes, or they could be ineffective. The substances could be tested in several
populations of patients that differed in their genetic predisposition to, and hence
their base rate of, strokes. Therefore, the folk medicines were the target causes,
- 373 -
having a stroke was the effect and the different populations were the different
contexts in which the causes could be introduced.
The participants initially learned about the likelihood that patients of each
genetic type would have strokes by observing records of individual patients who
had not been exposed to the folk remedies. To maintain their interest, participants
predicted if each patient would have a stroke or not. They were then given
feedback. In the subsequent phases participants were “given” 60 doses of each
substance. They were then permitted to choose to administer each dose to a patient
from one of the previously trained genetic types. On each trial, one dose of the
substance appeared on the computer screen, together with the picture of a patient
from each genetic type. Participants decided which patient would receive the
substance by clicking on the patient’s picture. They then predicted if the patient
would have a stroke after receiving the substance. Once the prediction was made
and feedback received, the next trial appeared on the computer screen.
Participants’ estimates of the causal status of the contexts and the target causes
were recorded, but the critical data here were the proportion of observations they
chose to make on each population.
The predictions from the causal model or Bayesian perspective were
straightforward. Participants should preferentially choose those genetic types in
which the empirical medicine-stroke contingency will more closely reflect the
influence or power of the medicine. This implies choosing, for a generative
harmful medicine, the genetic type showing the lowest base rate of strokes and, in
the case of a preventive medicine, the genetic type with the highest probability of
suffering strokes.
In one experiment participants were pre-trained with three contexts with
different base rates (BR) of the effect: a population of a genetic type that never
had strokes (BR = 0), a population that had strokes half of the time (BR = 0.5) and
a population always having strokes (BR = 1). After learning about the three base
rates, participants were presented with three substances that could potentially cause
or prevent the strokes. They were instructed to find out about their real influence.
There were three folk medicines. There was a generative medicine that, in the
absence of alternative causes, produced strokes half of the time. This medicine had
a causal power (Cheng, 1997) of p= 0.5. The, second, preventive medicine
prevented the strokes half of the time in a context in which strokes always occurred
(p= -0.5. Although Cheng represents all powers as positive, we use a minus sign
to identify preventative powers). Finally, there was a neutral medicine that did not
influence the probability of the strokes (p= 0). This third substance acted as a
control. Figure 2 shows the results of this experiment (Barberia et al., 2010,
Experiment 2 - Group Deterministic). As can be observed in Figure 2, for the
neutral control substance, there was a preference for the medium base rate context,
BR = 0.5, maybe because only in this population the potential increase or decrease
in the probability of the effect could be simultaneously observed. Most
importantly, and as expected, participants showed a preference for the low base
rate (BR = 0) population when testing a generative substance, and a preference for
the high base rate (BR = 1) population when testing a preventive substance.
- 374 -
Figure 2. Proportion of choices of the low, medium, and high base rate contexts for the Generative,
Neutral, and Preventive target causes, respectively (data from Barberia et al., 2010, Experiment 2 -
Group Deterministic).
The results of this experiment seem consistent with the expectations of
propositional reasoning. Mental models or “Bayesian” accounts do suggest that
people will intervene to choose the most informative context. However, there is a
problem. It will be observed that, while the participants preferred the more
informative context, they still continued to choose the least informative context a
substantial proportion of the time. Indeed, they chose the two less informative
contexts about 50% of the time. The propositional account has no simple
explanation of this result. However, it might be possible to argue that the
participants were using “higher” level strategies such as a belief that the context
might interact with the outcomes. They might also believe there is some pattern of
context choices that might “explain” the occurrence of all the outcomes. The latter
argument is weakened greatly by the fact that elsewhere we have reported other
experiments in which we used deterministic causes (Barberia et al., 2010,
Experiment 1). With deterministic causes (i.e., p= 1 or -1), the probability of
outcomes was either 0 or 100% so there was no pattern, yet people still chose the
less, and sometimes the entirely uninformative context, a substantial proportion of
the time as they have done in all of our experiments.
However, the point here is to demonstrate both a strength and a weakness
of the propositional explanations of behavior. They provide a very useful heuristic
for guiding research. It is not clear that we would have chosen to study context
choices without such a framework. Nevertheless, they are very difficult to
disconfirm. It is the nature of propositional logic that, if one set of premises is
untrue, there are many others that are true. Unless serious work is done to constrain
the number and class of premises, the propositional accounts approach tautology.
Alternatively, they are folk psychology. We now go on to, very briefly, describe
some experiments we have done that analyzed a popular propositional description
of cue competition or blocking in causal reasoning.
- 375 -
Blocking of Conditioned Inhibition
A number of recent studies asked if people make rational inferences when
they observe multiple cues that predict the same outcome. When there are multiple
possible causes of an outcome or, in conditioning, multiple predictors (conditional
stimuli) of an outcome (unconditional stimulus), cue competition occurs. A
common example of this competition is blocking (Kamin, 1969), in which the
presence of a stronger or a previously trained predictor reduces judgments or
responding to a moderate or new predictor. Propositional inferential accounts
argue that this blocking occurs because of the inherent redundancy of the blocking
design. Because the blocking stimulus already predicts, or is a cause, of the
outcome, then the other stimulus must not be. One can easily see that there are a
number of implicit premises about the nature of cause within this framework, but
our goal is not to question them here.
The inferential account is much richer and more complex than the simple
frame just outlined. Inferential theories argue that people’s causal judgments, and
their decisions about the possible redundancy of the target cause, are influenced by
constraints on the maximum or minimum magnitude of the outcome observed.
According to these theories, an observer can most efficiently analyze the influence
of an excitatory, or generative, cause only if there is room for the target to actually,
and observably, influence, usually increase, the outcome magnitude. For instance,
in a blocking design in which a cause, or cue, is followed by an outcome both
when presented alone (A - outcome) and when presented with another potential
cause, B (AB - outcome; Kamin, 1969; Shanks, 1985) the status of B is ambiguous
because it always occurs in the presence of A which has already been established
as a cause of the outcome. If this outcome is of maximum observed strength, it
would mask the ability of the target to show its effectiveness as a cause because
the outcome already has a known effective cause. The effect of B might be
disambiguated, however, if the participant could reasonably assume that a
compound of two effective causes might generate a stronger effect than either
presented alone. Thus, if B is an effective cause, then a larger outcome should be
expected when causes A and B are present, if it is assumed that causes have
additive effects. Since in a normal blocking procedure the same outcome follows
both A and AB, this provides stronger evidence that B is not an effective cause.
This strong inference, however, should not be made if a stronger effect than that
which occurred on A trials is not possible. Although, as mentioned above, the
weaker inferential structure based on simple redundancy of predictors might still
operate. Nonetheless, if A alone is followed by the maximum possible outcome,
then the effectiveness of B cannot be determined because of a ceiling on the
magnitude of the outcome. Consistent with this idea, a number of studies reported
stronger blocking effects (i.e., weaker ratings for B) if A and AB were followed by
an outcome smaller than the maximum possible outcome, than if the maximum
possible outcome occurred on A and AB trials (Beckers, De Houwer, Pineno, &
Miller, 2005; De Houwer, Beckers, & Glautier, 2002; Vandorpe, De Houwer, &
Beckers, 2005; and, in rats, Beckers, Miller, De Houwer, & Urushihara, 2006).
Simpler versions of associative theories, on the other hand, anticipate no
such influence of outcome magnitude and thus do not account for this result. The
- 376 -
simple delta rule used by many associative theories, such as Rescorla and
Wagner’s (1972) model, changes associations if there is a discrepancy between the
actual and the expected outcome, so it operates in the same way regardless of
whether there is the possibility of a larger outcome. This learning rule predicts that
blocking should occur to the same extent regardless of whether there is a
possibility for the outcome to be further increased. Hence, the reported outcome
magnitude effects on blocking have been taken as evidence that causal discovery
relies on inferential rather than associative processes. Disconfirming this
associative prediction has been a major impetus for the inferential blocking
experiments we have described.
No one would question whether people actually make inferences or test
logical syllogisms when instructed to do so. However, the important question is
whether this is the fundamental cognitive structure of all conditioning and of
causal reasoning. And it has been argued that it is (De Houwer, 2009; Mitchell et
al., 2009). However, many, but not all, of the experiments testing the inferential
reasoning account have used very simple experiment designs and possibly leading
instructions. When we used very simple instructions and more complex designs,
we found a different pattern of results. With minimal instructions and more
complex tasks (our participants learned about many cues simultaneously) we found
similar, strong, blocking effects regardless of outcome magnitude (Baetu, 2009), as
anticipated by most associative theories but not by inferential theories. According
to some instantiations of inferential theories, learning that a cue is followed by an
outcome requires fewer cognitive resources than making a blocking inference,
hence, if the complexity of the task prevents participants from making a blocking
inference, then ratings for the target cue B should be high (De Houwer, 2009;
Mitchell et al., 2009). Thus, these inferential theories predict that if the task is too
complex, blocking should not occur regardless of a ceiling in the outcome level.
Our finding of a blocking effect regardless of outcome magnitude was clearly
inconsistent with this prediction.
More interestingly, we have extended our findings concerning blocking
with generative causes to preventative, or inhibitory, relationships (Baetu & Baker,
2010). We used a blocking of inhibition design analogous to the generative one
described previously. A cue was followed by the outcome on its own (A -
outcome), but not in the presence of various potentially inhibitory cues (AB - no
outcome, ABC - no outcome, ADE - no outcome). B is a traditional conditioned
inhibitor because it prevents the outcome caused by A. It is an unambiguous
preventive cause in the AB compound. C, on the other hand, is potentially a
blocked inhibitory cue because it always co-occurs with B and B already predicts
the outcome’s omission. D and E are control cues for blocking of C because
neither has been trained separately with A as B was, but they would also
demonstrate overshadowing of conditioned inhibition, because together they
predict the outcome’s omission. They are the appropriate control cues to determine
whether learning about C is blocked by B: D and E are always trained in
compound with another inhibitory cue like C, but, unlike C, neither D nor E is
paired with a cue that predicts the outcome’s omission on its own.
According to inferential theories, one can evaluate the influence of a
preventive cause most efficiently if there is room for it to decrease the outcome
- 377 -
magnitude (Melchers, Wolff, & Lachnit, 2006). Thus, if B already reduces the
outcome level to its minimum in the AB compound, there is no further room for C
to decrease the outcome magnitude. In this case, C’s possible inhibitory strength
might be masked by a floor in the outcome level. Conversely, if there is a
possibility for the outcome magnitude to decrease below the level of the outcome
on AB trials, then one should be certain that C has no causal power or
effectiveness when the same outcome level occurs on ABC trials. Thus, C should
be more likely to be blocked when there is no floor on the outcome magnitude.
Associative theories, on the other hand, predict blocking of cue C regardless of
whether there is a possibility for the outcome level to be further decreased. In this
experiment we directly compared the level of blocking in a group in which AB
brought the outcome level to the “floor” with one in which there was room for C to
act in the ABC compound. We used a scenario from Melchers et al. (2006) in
which participants discovered whether various food cues influenced a hypothetical
hormone level (the outcome) in a patient. As in Melchers et al. (2006), we
manipulated the possibility of a lower outcome magnitude by presenting
participants in one group only with foods that increased or caused no change in the
hormone level (Group Floor), while a second group also observed foods that
decreased the hormone level (Group No Floor).
Following training with the above cues and compounds as well as other
control cues (Baetu & Baker, 2010), we assessed the inhibitory strengths of the
cues of interest using two tests. In the first test we followed the tradition in causal
reasoning and asked the participants to directly rate the strengths of the inhibitors.
Negative ratings represent preventive causes. This method of assessment is not
available in experiments with rats but the second, summation, test is. In the
summation test we paired the causes of interest with a novel excitatory cause and
observed if the causal strength of the compound was weaker than the strength of
the excitor. Would the inhibitory cues inhibit the excitatory strength of the novel
excitor? This test is interesting not only because it is directly comparable to tests
used with rats, but also because one of the characteristics of causal power is that it
is a property of the cause and should transfer to novel situations or contexts. It is
this generality of causal power that is the main justification for theories that
represent causes as mental models (Waldmann, Hagmayer, & Blaisdell, 2006).
The results of our experiment are clearly consistent with the predictions of
associative models. The top panel of Figure 3 shows the first, direct, test for
inhibition. This panel shows the ratings given to B (Inhibitory), D and E (Oversh.),
C (Blocked), and a neutral, control, cue (Neutral) by two groups of participants,
one which experienced a lower outcome level than the one shown on AB and ABC
trials (Group No Floor), and one that did not (Group Floor). Both groups showed a
similar blocking effect: Ratings of the blocked cue are closer to zero than those of
the overshadowed cues. It also shows the predicted overshadowing effect whereby
the overshadowed cues were less inhibitory than B. And, again as the simple
associative model predicts, the floor manipulation had little effect. It seems the
potential ambiguity that arises when the AB compound was on the floor did not
interfere with blocking or overshadowing. The summation tests in which the cues
were tested in compound with a cue trained to predict the outcome on its own are
shown in the lower panel of Figure 3. The results of these tests are similar to the
- 378 -
single-cue ratings: The blocked cue reduced the expected outcome level to a lesser
extent than the overshadowed cues in both groups. Again there was evidence of the
predicted overshadowing effect. There were, however, overall differences in the
ratings given by the two groups since they experienced different outcome levels,
which might render between-group comparisons difficult. Nevertheless, there was
no reliable difference between the blocked and the neutral cues, demonstrating that
C was maximally blocked in the two groups.
Figure 3. Ratings of the target cues tested individually (upper panel) and in summation tests (lower
panel) in Groups Floor and No Floor of Experiment 2 of Baetu and Baker (2010).
Overall, our finding that, in complex learning tasks, blocking of excitatory
and inhibitory cues occurs regardless of a ceiling or floor in the outcome
magnitude undermines the statement that associative mechanisms play no role in
causal learning and that learning effects such as blocking only occur to the extent
that inferential reasoning takes place (De Houwer, 2009; Mitchell et al., 2009).
Associative-like processes do seem to play a part in causal discovery. What we
take from this experiment is that, at the very least, the generality of the predictions
of the inferential account is in question. We should also mention that the initial
inferential model we described which only makes inferences about the redundancy
of the cues and not the absolute level of the outcome could handle our results.
However, this demonstrates once more that these accounts are, at the very least,
quite difficult to disconfirm. Again propositional models are a good heuristic for
- 379 -
generating research but at their present level of development do not provide good
formal models of behavior. We will now discuss some research on learning causal
chains (i.e., A lead to B leads to C) and an associative model that predicts this
Forming Causal Chains Form the Links
One of the critical features of propositional theories of cause is causal
precedence. Causes must logically come before effects. Thus, an appreciation of
the ordering and timing of events is critical to an understanding of causes. It should
not go un-noted that, likewise, it has been known since the days of Pavlov that
timing and ordering of events is crucial to conditioning. Similar to causal
reasoning, when the Conditional Stimulus (CS) comes after the Unconditional
Stimulus (US), little anticipatory excitatory conditioning is observed. We have
been developing an associative model that can model the timing and ordering of
stimuli. In addition, we have carried out experiments that ask whether participants
can form “causal” chains from links that have only been observed in isolation.
The design of our experiments could be summarized with the following
syllogism: Participants first learned If A then B and If B then C; they were then
asked if they subsequently inferred: If A then C. Instead of being asked to reason
about propositions, however, our participants discovered the relationships between
A and B and between B and C in a trial-by-trial manner similar to what would be
done in a conditioning experiment. That is, they observed instances of the A-B
link, intermixed with other instances of the B-C link. Rather than reasoning about
chains in which the links were deterministic, the participants were asked to reason
about probabilistic links. In these links the two events could occur together or
apart. With such an arrangement we could program positive or generative links in
which the first event predicted the presence of the second event or we could
program inhibitory or preventive links in which the second event was less likely to
occur when the first was present. This arrangement was instantiated by a display of
three virtual lights on a computer screen. On any trial only two lights were visible
and the third was occluded so the participants could not know its state. We did this
to maintain the fiction that there was a three light chain and that the participants
were only observing two of the three lights on any trial. Following this training,
participants evaluated whether A would be followed by C or whether it would
prevent C.
The syllogism described above involves a simple chain in which there are
positive relationships between A and B and between B and C. We were
particularly interested, however, in the way people would reason about chains that
include one or two negative links. If A was often followed by B, but B prevented C
from happening, would people still expect C to follow A? More interestingly, what
would they infer about the A-C relationship if A prevented B from occurring and B
prevented C from occurring? From a rational perspective, people should infer that
A prevents C in the first case because it enables B to prevent C, whereas they
should infer that A causes C to occur in the second case because it prevents B from
preventing C. The second event in each link could occur on its own in the absence
of the first event, and this implies that some, perhaps hidden, alterative cause of the
- 380 -
second event exists. So in this case, inhibitory B would inhibit C events that were
caused by these alternatives, so its elimination would increase the probability of C.
In fact, we have shown that this kind of reasoning can emerge from Bayesian
principles if one assumes that A acts upon C only through the influence of B
(Baetu & Baker, 2009). Cases in which the chain is not made up of only positive
links are interesting because they rule out certain confounding explanations. For
example, if all the relations are positive then participants may report a positive A-C
relationship after having observed positive A-B and B-C links because they have
been exposed exclusively to positive relationships. Alternatively, they may simply
base their evaluation of the A-C relationship on an average of the two links. Or
they may generalize their judgment from any single link in the chain.
Through the mechanism described above (i.e., A prevents B which would
otherwise prevent C, so presenting A increases the likelihood of C), reporting a
positive A-C relationship after having observed negative A-B and B-C
relationships would be rational and it would rule out all of these alternative
explanations. Furthermore, participants should report a relationship between A and
C only if they perceive nonzero causal links between A and B and between B and
C. It only takes one link to break a chain. Participants should be able to detect
cases in which one or both links in the chain are “broken,” i.e., chains in which, for
example, A might influence B, but B would have no influence on C (C would be
equally likely in the presence and in the absence of B). In that case, one should
rationally infer that A should have no influence on C. We investigated this
possibility by having links in which the two events of a link occurred independent
of one another, that is to say when they were uncorrelated.
It turns out that people behave rationally in all these cases. In our
experiments the A-B and B-C contingencies were positive, negative, or zero, with
the constraint that each of the lights turned on on 50% of the trials. For the positive
links the conditional probability of the second event in the presence of the first
[i.e., P(Event2| Event1)] was 0.8 and the probability in its absence was 0.2 so that
the overall contingency (i.e., the difference between these) is p= 0.6 [where p=
(P(Event2| Event1) P(Event2 | noEvent1). For the preventive links these
probabilities were reversed. Following 40 randomly intermixed A-B trials and B-C
trials, participants were asked to evaluate the effect of A on C: whether it would
turn C on, prevent C from turning on, or whether it would have no effect on C.
Figure 4 shows the mean ratings of the influence of A on C reported on a scale
ranging from -10 to +10 (Baetu & Baker, 2009, Experiment 2). When participants
observed positive A-B and B-C contingencies (Treatment PP in the figure) or when
they observed negative A-B and B-C contingencies (Treatment NN), they inferred
that A would turn C on. Conversely, when one of the observed contingencies was
positive and the other negative (Treatments PN and NP), they concluded that A
would prevent C from turning on. They also inferred that A would have little effect
on C if one or both experienced contingencies were zero (Baetu & Baker, 2009,
Experiments 1A and 1B; data not shown in the figure).
- 381 -
Figure 4. Ratings of the influence of A on C in Experiment 2 of Baetu and Baker (2009) and
simulations with the auto-associator.
Our participants behaved rationally and this is consistent with a Bayesian
analysis. We were more interested, however, in demonstrating that this kind of
rational behavior could emerge as a result of processes that are not constrained by
rational premises, but rather from simple associative processes. To do this, we
asked whether a simple connectionist model, an auto-associator network
implementing a prediction-error learning rule (McClelland & Rumelhart, 1988),
would behave in a way similar to our participants and generate a representation of
the complete chain from experience with the two individual links of the chain. The
network consisted of a single layer of units that might become connected to each
other if the stimuli represented by these units co-occur in close temporal proximity.
Associations between units allow activation in one unit to spread to others. The
appendix briefly describes the way temporal information is represented in the
model; the complete model specifications can be found in Baetu and Baker (2009)
and McClelland and Rumelhart (1988).
The structure of the network was inspired by the traditional analysis of
conditioning experiments. It consisted of six interconnected units. There was one
unit representing each of the three events (A, B, and C). There was a unit
representing the general context. Finally there were two units representing the
context of the A-B trials and B-C trials (Fig. 5). The different trial types were
discriminably different because of the presence of the object that would occlude
the state of either light A or C depending on whether it was an A-B trial or a B-C
trial. Thus, it makes sense to have different trial type contexts. The presence of
context units is crucial because, just as has been shown in conditioning inhibition,
with negative CS-US correlations (Baker, 1977) the context is critical for
modulating contingency learning (Murphy & Baker, 2004; Vallée-Tourangeau et
al., 1998).
- 382 -
Figure 5. This figure illustrates the structure of the auto-associator composed of six units: three units
representing the three lights (A, B and C), a general context unit, a unit representing contextual cues
present only on A-B trials, and a unit representing contextual cues present on only B-C trials. The
arrows represent all the possible unidirectional connections that might develop between the six units.
Figure adapted from Baetu and Baker (2009).
Once this network had been “trained” we asked it about the A-C chain by
activating the A unit and monitoring C activation. Figure 4 also shows the results
of these “queries” and it can be readily seen that they are consistent with the
participants’ ratings of the A-C relationships in all treatments. The networks also
behaved appropriately when trained with one or two zero links: Unit A failed to
activate unit C, which is analogous to our participants reporting that there was no
relationship between lights A and C in Experiments 1A and 1B of Baetu and Baker
(2009). In addition to carrying out these tests, we investigated the strengths and
polarities of the associations in the net. What we found was that the network had
“discovered” the causal structure of the events. There were strong and appropriate
excitatory and inhibitory connection strengths or associations in the correct
direction between the events A, B, and C. The associations involving the context
units and the associations in the incorrect direction between A, B and C were much
weaker (Fig. 6 shows an example of a trained network). What this means is that in
the case of the simple syllogism between positive events described at the beginning
of this section when asked “If A?” the network answers “then C!” but if asked “If
C?” it does not answer. And this is just what would be expected by propositional
theories that posit mental models of cause; but it is done by a simple associative
net using standard conditioning assumptions with no formal propositional
- 383 -
Figure 6. Network trained with the negative or inhibitory A-B and B-C contingencies that were
presented in Treatment NN. The arrows represent connection weights that developed during training.
Full arrows represent positive connections, and dotted arrows represent negative connections. The
relative strength of each connection is indicated by the width of the arrow. In addition to the
connection weights, the activation level of every unit during a test in which only unit A is turned on
is shown. The radial lines around unit A indicate that only unit A was turned on (i.e., received
external input). The nuance of each unit indicates how strongly it was activated during this test, with
darker shades indicating stronger activation. Unit B was inhibited, as indicated by its dotted contour.
Some Conclusions
Yesterday, upon the stair,
I met a man who wasn’t there,
He wasn’t there again today,
I wish, I wish he’d go away...
(Antigonish, Hugh Mearns circa 1899; McCord, 1955)
One of us (AGB) is frequently reminded of the poem Antigonish when
reading the various instantiations of propositional/ inferential theories. Inferences,
beliefs and goals are things that we believe psychology should explain and are not
things that should be used to explain behaviors. We acknowledge that this is an
article of faith. The level of analysis for research is a meaningful epistemological
question for all sciences and certainly is for psychology. The question is: What are
the fundamental primitives of an appropriate explanation in psychology? Clearly,
the early associationists believed the fundamental primitives were at the level of
associations. Inferential and propositional theorists seem to believe that they are at
the level of propositions or inferences, although, as we have mentioned, they are
rarely clear about the exact form and constraints on these propositions. Our
impressions are that, as a theory, they arise deux ex machina to explain findings
that are difficult to account for with more reductionist explanations. It seems that
these explanations involve a homunculus that has many of the properties of the
cognitive processes that we wish to explain.
It would seem from this opening statement that we are unsympathetic to
the various propositional theories, but we are not. These theories have generated a
great deal of interesting empirical work and have discovered phenomena that
might never have been investigated from a purely associationist perspective.
- 384 -
Moreover, many of these findings are at the moment very difficult to explain with
moderately parsimonious associative networks. But it is the nature of science that
many original mysteries defy reductionist explanations.
So how do we reconcile propositional accounts and associative accounts?
We have argued that the levels of analysis outlined by Marr (1982) produce a
useful perspective on this problem. He argued that a primary level of analysis is
computation. Research asks just what the organism “computes” in the world. That
is to say, what aspects of the physical world is the organism sensitive to and what
is the nature of the response to these aspects? The world of physics includes rules
about the behavior of objects and these can be written in mathematical or
propositional from. No one would argue that, because a falling body follows the
propositions of physics, it has instantiated them in its nature. Likewise, the world
of cognition implies a series of rules and propositions. As we have mentioned
before, these rules include descriptions of the rules of causal inference but also
include the rules relating the events in conditioning (see also, Baker, Murphy, &
Vallée-Tourangeau, 1996).
Thus, when a researcher shows that an organism’s behavior maps onto a
syllogism or other propositional structure, there are two possibilities of what this
means. First, and we believe noncontroversially, it shows that the organism is
sensitive to these rules about the world and presumably this sensitivity is an
adaptation to accommodate them in behavior. But second, the organism may have
an internal representation or algorithm that directly represents the rules of the
world and this is what generates the behavior. Alternatively, some other
mechanism, possibly associative, generates the computation. While it is obviously
our position that the best and most parsimonious algorithm involves an associative
approach, this does not mean we are correct. It is possible that the fundamental
primitives necessary to explain causal reasoning and conditioning involve
propositions. But, if so, it is crucial that proponents of this position generate
theories with constraints that are potentially falsifiable. However, even if this is not
done, the propositional approach has historically been, and still is, a useful
heuristic for generating research.
The three research sections we have presented here illustrate this position.
We initiated the research on choosing the most informative context from principles
derived from causal model and power theory (Cheng, 1997; Lagnado, Waldmann,
Hagmayer, & Sloman, 2007). The results were at least partially consistent with the
theory but the finding that participants did not abandon the less informative
contexts was not – unless new and unexpected propositions were generated to
explain them. In the experiments on blocking of conditioned inhibition, we tested
the argument that blocking would be more effective if the blocked stimulus could
be unambiguously shown to have no effect or causal power. We found that this
prediction of one version of inferential learning theory, in our hands at least, was
not confirmed. And all of our results were broadly consistent with Rescorla and
Wagner’s (1972) associative model. But again we showed that an unconstrained
inferential approach could accommodate the findings. In the final experiments on
building causal chains from their links, we generated the predictions concerning
the polarity and strength of the chains from formal logic and then verified them
computationally with probability or contingency theory using the notions of causal
- 385 -
power and its derivative p(Cheng, 1997). We found that people’s behavior
mapped onto this analysis very well. We then showed how a simple associative net
could account for this “propositional” behavior including the critical elements of
timing and causal precedence.
We would be remiss in not pointing out that we have used the notion of
parsimony rather cavalierly throughout. As Mitchell et al. (2009) have pointed out,
associative theories are not necessarily parsimonious if for every new problem a
new theory is generated. And, although our auto-associator has only six units in it,
it does have many links. However, it should be emphasized that it computes
predictions about timing and event sequencing. We are also trying to extend its use
to a wider range of phenomenon but leaving it largely unchanged. Nonetheless, it
should be emphasized that we are not immune to the parsimony argument we have
used against the propositional accounts as algorithms. Nevertheless, it is our
position that at least in terms of face validity the associative models are more
plausible candidates for implementing in the physiology of the organism.
In conclusion we have argued that propositional accounts of cognition are
very useful for generating research. They provide a useful framework for
formalizing the rules of the world and asking what behavioral adaptations an
animal might have that map onto them. However, for them to provide a useful
theory or algorithm of behavior they must be more formally specified and be
clearly falsifiable. Until this is done we still are concerned with the “… man who
wasn’t there.”
Aitken, M. R., & Dickinson, A. (2005). Simulations of a modified SOP model applied to
retrospective revaluation of human causal learning. Learning & Behavior, 33,
Baetu, I. (2009). Associative and inferential accounts of extinction and blocking in causal
learning. Ph.D. dissertation, McGill University, Montreal, Quebec, Canada.
Dissertation Abstracts International (Publication No. AAT NR66377).
Baetu, I., & Baker, A. G. (2009). Human judgments of positive and negative causal chains.
Journal of Experimental Psychology: Animal Behavior Processes, 35, 153-168.
Baetu, I., & Baker, A. G. (2010). Extinction and blocking of conditioned inhibition in
human causal learning. Learning & Behavior, 38, 394-407.
Baker, A. G. (1977). Conditioned inhibition arising from a between-sessions negative
correlation. Journal of Experimental Psychology: Animal Behaviour Processes, 3,
Baker, A. G., Murphy, R. A., & Vallée-Tourangeau, F. (1996). Associative and normative
models of causal induction: Reacting to versus understanding cause. In D. R.
Shanks, K. J. Holyoak, & D. L. Medin (Eds.), The psychology of learning and
motivation, Vol. 34, (pp. 1-45). San Diego, CA: Academic Press.
Barberia, I., Baetu, I., Sansa, J., & Baker, A. G. (2010). Choosing optimal causal
backgrounds for causal discovery. Quarterly Journal of Experimental Psychology,
63, 2413-2431.
Beckers, T., De Houwer, J., Pineño, O., & Miller, R. R. (2005). Outcome additivity and
outcome maximality influence cue competition in human causal learning. Journal
of Experimental Psychology: Learning, Memory, & Cognition, 31, 238-249.
Beckers, T., Miller, R. R., De Houwer, J., & Urushihara, K. (2006). Reasoning rats:
Forward blocking in Pavlovian animal conditioning is sensitive to constraints of
- 386 -
causal inference. Journal of Experimental Psychology: General, 135, 92-102.
Bouton, M. E. (2004). Context and behavioral processes in extinction. Learning &
Memory, 11, 485-494.
Brewer, W. F. (1974). There is no convincing evidence for operant or classical
conditioning in adult humans. In W. B. Weiner & D. S. Palermo (Eds.), Cognition
and the symbolic processes (pp. 1-42). Hillsdale, NJ.: Erlbaum.
Cheng, P. W. (1997). From covariation to causation: A causal power theory. Psychological
Review, 104, 367-405.
Chomsky, N. (1959). A review of B. F. Skinner's verbal behavior. Language, 35, 26-58.
Cornsweet, T. N. (1970). Visual perception. New York: Academic Press.
De Houwer, J. (2009). The propositional approach to associative learning as an alternative
for association formation models. Learning & Behavior, 37, 1-20.
De Houwer, J., Beckers, T., & Glautier, S. (2002). Outcome and cue properties modulate
blocking. Quarterly Journal of Experimental Psychology, 55A, 965-985.
De Houwer, J., Beckers, T., & Vandorpe, S. (2005). Evidence for the role of higher order
reasoning processes in cue competition and other learning phenomena. Learning
& Behavior, 33, 239-249.
Dickinson, A., & Balleine, B.W. (1994). Motivational control of goal-directed action.
Animal Learning and Behavior, 22, 1-18.
Fodor, J., & Piattelli-Palmarini, M. (2010). What Darwin got wrong. New York: Farrar,
Straus, & Giroux.
Herrnstein, R. J. (1961). Relative and absolute strength of response as a function of
frequency of reinforcement. Journal of the Experimental Analysis of Behavior, 4,
Hull, C. L. (1943). Principles of behavior: An introduction to behavior theory. New York:
Hughes, D., Davis, J. D., & Grice, G. R. (1960). Goal box and alley similarity as a factor in
latent extinction. Journal of Comparative and Physiological Psychology, 53, 612-
Kamin, L. J. (1969). Selective association and conditioning. In W. K. Honig & N. J.
Mackintosh (Eds.), Fundamental issues in associative learning (pp. 42-64).
Halifax, Nova Scotia, CA: Dalhousie University Press.
Koehler, D. J., & James, G. (2009). Probability matching in choice under uncertainty:
Intuition versus deliberation. Cognition, 113, 123-127.
Lagnado, D. A., Waldmann, M. R., Hagmayer, Y., & Sloman, S. A. (2007). Beyond
covariation: Cues to causal structure. In A. Gopnik & L. Schultz (Eds.), Causal
learning: Psychology, philosophy, and computation (pp. 154-172). New York:
Oxford University Press.
Lovibond, P. F., & Shanks, D. R. (2002). The role of awareness in Pavlovian conditioning:
Empirical evidence and theoretical implications. Journal of Experimental
Psychology: Animal Behavior Processes, 28, 3-26.
Marr, D. (1982). Vision: A computational investigation into the human representation and
processing of visual information. New York: Freeman.
Matzel, L. D., Held, F. P., & Miller, R. R. (1998). Information and expression of
simultaneous and backward associations: Implications for contiguity theory.
Learning and Motivation, 19, 317-344.
McClelland, J. L., & Rumelhart, D. E. (1988). Explorations in parallel distributed
processing: A handbook of models, programs, and exercises. Cambridge, MA:
MIT Press.
McCord, D. T. W. (1955). What cheer: An anthology of American and British humorous
and witty verse. New York: The Modern Library.
Melchers, K. G., Wolff, S., & Lachnit, H. (2006). Extinction of conditioned inhibition
- 387 -
through nonreinforced presentation of the inhibitor. Psychonomic Bulletin &
Review, 13, 662-667.
Mitchell, C. J., De Houwer, J., & Lovibond, P. F. (2009). The propositional nature of
human associative learning. Behavioral and Brain Sciences, 32, 183–246.
Murphy, R. A., & Baker, A. G. (2004). A role for CS-US contingency in Pavlovian
conditioning. Journal of Experimental Psychology: Animal Behavior Processes,
30, 229-239.
Murphy, R. A., Mondragon, E., & Murphy, V. A. (2008). Rule learning by rats. Science,
319, 1849-1851.
Newton, I. (1934). Sir Isaac Newton's mathematical principles of natural philosophy and
his system of the world. Berkeley, CA: University of California Press.
Pavlov, I. (1927). Conditioned reflexes. London: Oxford University Press.
Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: Variations
in the effectiveness of reinforcement and non-reinforcement. In A. H. Black & W.
F. Prokasy (Eds.), Classical conditioning II: Current theory and research (pp. 64-
99). New York: Appleton-Century-Crofts.
Skinner, B. F. (1957). Verbal learning. New York: Appleton-Century-Crofts.
Shanks, D. R. (1985). Forward and backward blocking in human contingency judgment.
Quarterly Journal of Experimental Psychology, 37B, 1-21.
Steyvers, M., Tenenbaum, J. B., Wagenmakers, E. J., & Blum, B. (2003). Inferring causal
networks from observations and interventions. Cognitive Science, 27, 453-489.
Tolman, E. C. (1932). Purposive behavior in animals and men. New York: Appleton-
Tolman, E. C. (1948). Cognitive maps in rats and men. The Psychological Review, 55, 189-
Tolman, E. C., & Honzik, C. H. (1930). "Insight" in rats. University of California
Publications in Psychology, 4, 215-232.
Vallée-Tourangeau, F., Murphy, R. A., Drew, S., & Baker, A. G. (1998). Judging the
importance of constant and variable candidate causes: A test of the power PC
theory. Quarterly Journal of Experimental Psychology, 51A, 65-84.
Vandorpe, S., De Houwer, J., & Beckers, T. (2005). Further evidence for the role of
inferential reasoning in forward blocking. Memory & Cognition, 33, 1047-1056.
Wagner, A. R. (1981). SOP: A model of automatic memory processing in animal behavior.
In N. E. Spear & R. R. Miller (Eds.), Information processing in animals: Memory
mechanisms (pp. 5-47). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.
Waldmann, M. R., Hagmayer, Y, & Blaisdell, A. P. (2006). Beyond the information given:
Causal models in learning and reasoning. Current Directions in Psychological
Science, 15, 307-311.
Wu, M., & Cheng, P. W. (1999). Why causation need not follow from statistical
association: Boundary conditions for the evaluation of generative and preventive
causal powers. Psychological Science, 10, 92-97.
- 388 -
Like a few other associative models (e.g., Aitken & Dickinson, 2005; Wagner,
1981), this model represents events in real time. When a stimulus is physically present, the
unit or units representing it receive some external input that causes their activation level to
gradually increase. When the external stimulation ceases, the activation level of the units
will gradually decay back to a resting level of zero. An active unit may spread its activation
to other units via its connections. Depending on whether these associations are excitatory
or inhibitory, the active unit will excite or inhibit the units connected to it.
The connections in the network do not represent temporal relationships (as in the
temporal coding hypothesis; Matzel, Held, & Miller, 1998). Instead, the model learns
temporal relationships merely as a result of units being active at various points in time. For
instance, if a stimulus (A) is presented for a brief period of time, the activation level of the
unit or units that represent it gradually increases and then decays back to zero when the
stimulus ceases. If a second stimulus (B) is presented before the activation level of A has
decayed, then there is an opportunity for an association from A to B to form. If B is
presented at a later period of time when the activation level of A is very low, then the
opportunity for an association to form is lost. Thus, the model explains the effect of delays
between a potential cause and an effect simply by allowing a stimulus representation to
decay from memory once the stimulus is no longer perceived.
Connections might form between any two units in the network. The model uses
the delta learning rule (also used in the model developed by Rescorla and Wagner, 1972) to
change the strength of the associations. According to this rule, the change in the connection
from unit A to unit B (∆WA-B) is computed as follows:
WA-B = (external activation of B – internal activation of B) x total activation of A
(Equation 1)
Each unit has two sources of input that contribute to its activation level: an external source
and an internal source. The external activation of B refers to the input that the unit receives
from perception while stimulus B is presented. The internal activation of B refers to the
input that the unit receives when other units activate it if they have become associated with
B. Thus, when B is presented for the first time, its external input is positive, but its internal
input is zero because its presentation was unexpected (i.e., no other unit predicted that B
would occur). Since the difference between the external and internal input to unit B is
positive, this allows A to develop an association with B, but only if the activation of A is
not zero. This is because the change in the association from A to B depends not only on
how surprising the occurrence of B is [represented by the term (external activation of B –
internal activation of B)], but also on the activation level of A.
Of direct relevance to learning directed chains of events, the model also predicts
that the association from A to B (A→B) becomes stronger than the association from B to A
(B→A) if A precedes B in training. The B→A association is weaker because by the time B
occurs, unit A no longer receives external input. Thus, after training, A might be able to
activate the representation of B through the A→B association, but B will not be able to
activate the representation of A since the B→A association is weak.
... Current research on causal reasoning in animals is hotly contested between three approaches: those who maintain learning can be explained in terms of associative principles (i.e., classical and instrumental conditioning) (Barberia et al. 2011), Bayesian logic (Gopnik & Tenenbaum 2007), or domain-specific prior knowledge (Penn & Povinelli 2007). Regardless of the species, humans and all animals learn relationships between events through observation and intervention. ...
Full-text available
The dominant paradigm of comparative psychology is that nonhuman primatecognitive abilities differ from humans in degree, not kind. In broadly reviewing thehuman-nonhuman research landscape, including two areas still well under-represented-volitional control and innovation, it was found that while nonhuman primates (and apesin particular) demonstrate flexible physical adeptness, this flexibility does not extend tothe realm of imagination and ideas. Specifically, apes were found to have "first orderawareness of the unseen world", that is, the ability to infer some physical unseencausation, but lack "second order awareness of the unseen world"-the ability to inferproperties of things based purely on observational knowledge of internal structurederived from primary experience of simple elements. It is argued that second orderunseen awareness emerges from self-reflective consciousness. Access to unseen worldsof imagination and ideas allows humans to create cumulative culture through a process ofrenewed hybridization of form and function. Nonhuman primate culture by comparisonlacks hybridization and therefore displays a general paucity of cumulative traditions.
Associative and connectionist models can be used to understand learning processes underlying the perception of causal relationships between observed events. We discuss how contingency and temporal information influence judgments of causality and illustrate how such effects might be captured within an associative framework that relies on prediction error (the mismatch between experienced and expected events) to alter connections. This computational approach has generated an interest in discovering the neural substrates that might contribute to different components of associative models, such as learning-dependent expectations and prediction error signaling. We review selectively evidence from neuroscience that is consistent with associative accounts, namely studies showing brain activity that correlates with learning from prediction errors. Our final discussion focuses on how this computational approach can be used to study and characterize individual differences in learning, focusing on individual differences linked to polymorphisms in dopaminergic genes.
Full-text available
Five conditioned lick-suppression experiments with water-deprived rats examined the possibility that simultaneous and backward associations are learned, but are not expressed as anticipatory responses in common indexes of associative strength. Experiments 1–4 used a sensory preconditioning procedure in which clicks preceded the onset of a tone. Subsequently, the tone was paired with footshock in either a forward, simultaneous, or backward arrangement. In no case did the tone trained in the simultaneous or backward manner elicit a conditioned response. However, Experiments 1, 2, and 3 determined that the clicks, which predicted the tone, evoked equally strong conditioned responses regardless of whether the tone was paired with the shock in a forward, simultaneous, or backward manner. Experiment 4 found that responding to the clicks was degraded following postconditioning extinction of the tone, regardless of whether the tone had been paired with the shock in a forward or simultaneous manner. Experiment 5 determined that if the click and tone were paired simultaneously, the click failed a test for excitation following tone-shock simultaneous pairings but passed a test for excitation following tone-shock forward pairings. Collectively, these findings suggest that predictive information (i.e., a forward relationship between stimuli) is not necessary for the acquisition of an association, but may promote the expression of the association in an anticipatory response system. Moreover, these results suggest that associations are not simple linkages, but contain information regarding the temporal relationship of the associates.
Dickinson and Burke (1996) proposed a modified version of Wagner's (1981) SOP associative theory to explain retrospective revaluation of human causal judgments. In this modified SOP (MSOP), excitatory learning occurs when cue and outcome representations are either both directly activated or both associatively activated. By contrast, inhibitory learning occurs when one representation is directly activated while the other is associatively activated. Finite node simulations of MSOP yielded simple acquisition, overshadowing, blocking, and inhibitory learning under forward contingencies. Importantly, retrospective revaluation was predicted in the form of unovershadowing and backward inhibitory learning. However, MSOP did not yield backward blocking. These predictions are evaluated against the relevant empirical evidence anti contrasted with the predictions of other associative theories that have been applied to retrospective revaluation of human causal and predictive learning.
The philosopher David Hume's conclusion that causal induction is solely based on observed associations still presents a puzzle to psychology. If we only acquired knowledge about statistical covariations between observed events without accessing deeper information about causality, we would be unable to understand the differences between causal and spurious relations, between prediction and diagnosis, and between observational and interventional inferences. All these distinctions require a deep understanding of causality that goes beyond the information given. We report a number of recent studies that demonstrate that people and rats do not stick to the superficial level of event covariations but reason and learn on the basis of deeper causal representations. Causal-model theory provides a unified account of this remarkable competence.
In experimental design, a tacit principle is that to test whether a candidate cause c (i.e., a manipulation) prevents an effect e, e must occur at least some of the time without the introduction of c. This principle is the preventive analogue of the explicit principle of avoiding a ceiling effect in tests of whether c produces e. Psychological models of causal inference that adopt either the covariation approach or the power approach, among their other problems, fail to explain these principles. The present article reports an experiment that demonstrates the operation of these principles in untutored reasoning. The results support an explanation of these principles according to the power PC theory, a theory that integrates the previous approaches to overcome the problems that cripple each.
This chapter discusses two views of causal judgment that are roughly analogous to a distinction between being able to react appropriately to causes and being able to understand them. The associationist view is identified with the British Empiricists. It claims that the judgments of cause come from certain empirical cues to causality, which includes: (1) regular succession, (2) temporal contiguity, and (3) spatial contiguity. Associations between events are strengthened when the events are contiguous and are weakened when an event occurs by itself. These models have the advantage that they are computationally simple and they impose a low memory load on the organism because experience is stored as a small number of associative strengths. They have the disadvantage that information about past events is lost in the computation. Also, these models do not have episodic memory. The second classes of models are referred to as normative models. They claim that humans and other animals compute the covariation between cause and effect and then use this information as part of a causal model or schema. The chapter reviews that a retrospective normative model makes the choice of domain in which to do the normative calculation. Associative models and those that involve causal models or schema are appropriate to overlapping but not identical domains of information processing. Simple associative ideas can be used in many situations in which contiguity is important, but in which mental models are unavailable. These can be used in situations in which associative networks are difficult to apply.