learning
Causal learning: how different is it from normal learning?
5 years ago
I was browsing a write-up on Causal reasoning by Mixing Memory, and came across this article by Lagnado et al, regarding the Causal Structure underlying causal reasoning.
In brief , Causal reasoning refers to that ability of the humans by which they can classify some events as causes and some events as effects and also determine either deterministically or probabilistically as to which effects are caused by which causes. In simple words, the ability to assign causes to effects.
Historically, Causal reasoning has focused on the statistical methods of covariance or correlation between two events and used the strength of the correlation to calculate and predict the causal relation between the two events. This suffers from several drawbacks like inability to determine the direction of causation or the inability to rule out a third common cause of which the two observed events are the effects.
Langrado et al, in their paper, present a refreshing new perspective on causal reasoning by differentiating between the qualitative Causal Structure between two or more events and the quantitative Causal Strength of that relationship. For example, a causal structure may causally relate the presence of fever with bacterial infection thus identifying bacterial infection as a cause of fever; but the causal strength between bacterial infection and fever would determine what probability we assign to a particular case of fever to have been caused due to bacterial infection (diagnostic learning) or the probability that given bacterial infection a person would develop fever (predictive learning).
The authors contend that the issues involved in causal strength learning and causal structure learning are different and should be addressed differently. Further, they contend that most of the historical research has been limited to causal strength learning, ignoring the prior and more fundamental stage of causal structure learning; as in their theory, the causal strength of any relation can only be learned once one has some a priori qualitative assumptions about the underlying causal relationships. Their paper thus focuses what cues/mechanisms are involved in the formation of the causal structure.
Causal-model theory was a relatively early, qualitative attempt to capture the distinction between structure and strength. According to this proposal causal induction is guided by top-down assumptions about the structure of causal models. These hypothetical causal models guide the processing of the learning input. The basic idea behind this approach is that we rarely encounter a causal learning situation in which we do not have some intuitions about basic causal features, such as whether an event is a potential cause or effect. If, for example, the task is to press a button and observe a light, we may not know whether these events are causally related or not, but we assume that the button is a potential cause and the light is a potential effect. Once a hypothetical causal model is in place, we can start estimating causal strength by observing covariation information. The way covariation estimates are computed and interpreted is dependent on the assumed causal model.
They list the cues that humans use to form their Causal structures as
- Statistical relations
- Temporal order
- Intervention
- Prior knowledge
Before discussing, in depth, each of these cues and how they may affect causal reasoning, it is instructive to note that the concept of a Causal Structure underlying a given set of phenomena is quite close to the idea of a Cognitive Map underlying a given environment (say the maze or the mouse trap). While the latter is a spatial mental map of the objects in the surrounding 3-D space, the former may be conceived as a causal mental map of events in the temporal dimension. The reason I am using this analogy is to contrast the cues used in formulating a Causal structure with the different learning mechanisms used by mice to form a cognitive map of the mouse trap. The contention is that the same cognitive mechanisms are involved and also that these mechanisms are structured and unfold in a developmentally guided and staged manner.
The first cue to form a Causal structure or link two or more events is that of statistical relations. Here, correlation information between the events, as well as their conditional independences are used to arrive at a set of Markov equivalent causal models. Much of the learning is associative, probabilistic and maybe latent. It may not be accessible to consciousness and the learning of causal structure is more implicit, than explicit. For example, the regularities in the data may give rise to a fuzzy causal structure, where tentative causal relations are posited. Suppose from the data, it is determined that A and B are perfectly correlated. The person will have a strong sense of causation between A and B, but would be unable to determine the direction of causation. similarly if 3 events A,B and C are correlated, we would not be able to determine the directions of causation. This mechanism is very much similar to the latent learning mechanism exhibited by the mice in the mouse trap.
The second cue to form a causal structure that we consider here is that of Intervention. Here, human intervention takes place by affecting one of the events (potential cause) and by basis of that intervention or exercised choice, experiment to find out what effect that variable has on the outcome (effect). To more rigorously define Interventions, let me quote from the paper.
Informally, an intervention involves imposing a change on a variable in a causal system from outside the system. A strong intervention is one that sets the variable in question to a particular value, and thus overrides the effects of any other causes of that variable. It does this without directly changing anything else in the system, although of course other variables in the system can change indirectly as a result of changes to the intervened-on variable. What is important for the purposes of causal learning is that an intervention can act as a quasi-experiment, one that eliminates (or reduces) confounds and helps establish the existence of a causal relation between the intervened-on variable and its effects.
Suppose A and B have been found to be correlated. Further suppose that the happening of event A and B is under the control of the human subject. Then one can intervene to cause A and observe whether B occurred. If so the direction of causation is from A -> B. On the other hand if by intervening the human subject caused B to happen and did not observe A, then one could conclude that B does not cause A. To make the example concrete, consider event A as ‘Fire’ and event B as ‘Smoke’. We find that Fire and Smoke are correlated. By intervening and conducting experiments whereby we can control the occurrence of ‘fire’ or ‘smoke’ we can come up with correct causal relation that ‘fire’ -> ‘smoke’
Consider again, a 3 event situation whereby the relation between two causal events (A and B) and an outcome (C) has to be ascertained. Specifically, by intervening and causing A sometimes and B other times, and observing the happening of C we could ascertain the causal structure as to whether A->c or B-> C. The situation is not too different than the vicarious trail and error learning exhibited by a mouse when at a choice point. There, the mice has to, by trail-and error choosing of either right/left black /white turnings, learn which stimulus is associated with food (outcome). Thus, intervention mechanism is nothing but the refined vicarious trial-and-error learning.
The third, and perhaps the most important, mechanism that is used to form the Causal structure is Temporal ordering. This is a very simple mechanism whereby events that are occurring prior to some other event can be the cause of that event, but not vice versa.
The temporal order in which events occur provides a fundamental cue to causal structure. Causes occur before (or possibly simultaneously with) their effects, so if one knows that event A occurs after event B, one can be sure that A is not a cause of B. However, while the temporal order of events can be used to rule out potential causes, it does not provide a sufficient cue to rule them in. Just because events of type B reliably follow events of type A, it does not follow that A causes B. Their regular succession may be explained by a common cause C (e.g., heavy drinking first causes euphoria and only later causes sickness). Thus the temporal order of events is an imperfect cue to causal structure.
This mechanism is the same as the one used by mice in searching for stimulus. When two events follow each other than an active search mechanism is used to identify the salient stimulus which may have been the cause of the event. The concept of temporal ordering implying causation is inherent in this learning mechanism as are concepts of spatial and temporal contiguity and proximity. This is the normal avoidance learning mechanism in mice and in human causal structure learning may be more engaged in and relevant to identifying the causes of events that are undesirable.
The fourth cue used for identifying causal structure, that the authors do not touch on, but do hint in terms of highlighting the importance of causal mechanisms; but that I propose nonetheless, is that of causal chains construction and elaboration. This basically involves breaking the simple A-> B with intermediate and competing C, D, E etc and intervening and conducting experiments to come up with the correct causal chain. Thus, A->B may be refined as A->C->D->B or A-> E->B and experimentation done to narrow down on a particular causal chain.
This is similar to the hypothesis learning involved in mice and depends on a cognitive capacity to sequence events . Also this is normally exhibited in approach behavior and this elaboration of causal chain may be more relevant to the desirable outcomes that human subjects want to happen and all the small intermediate steps of they need to cause to make the final outcome happen.
The fifth, and for now final, cue that is used in the formation of causal structure is prior knowledge. The authors define it as follows:
Regardless of when we observe fever in a patient, our world knowledge tells us that fever is not a cause but rather an effect of an underlying disease. Prior knowledge may be very specific when we have already learned about a causal relation, but prior knowledge can also be abstract and hypothetical. We know that switches can turn on devices even when we do not know about the specific function of a switch in a novel device. Similarly we know that diseases can cause a wide range of symptoms prior to finding out which symptom is caused by which disease. In contrast, rarely do we consider symptoms as possible causes of a disease.
My take on prior knowledge is something close to that, but slightly different. The subject forms a general idea of which events are causes and which effects and also the general relationship between a primary cause and a desired/undesired later final outcome. Though, the intervening small steps of the causal chain may not be present, and thus no formal corroborating data based proof may be there, yet one can deduce the causal relationship between the primal cause and the later final outcome, ignoring the intermediate minor events down the line. A case in point would be food aversion learning, whereby one single vomit following consumption of say a spoiled food that was taken hours ago, may result in a strong automatic association and learning of that food as the cause of vomit and lead to avoidance of (or escape from) that food.
To me this mechanism is the same as that exhibited by the mice when they learn the spatial orientation in the mouse trap and are able to exhibit novel escape learning.
This summarizes the analogy between the causal learning and normal learning as of now. Will touch on the qualitatively different next 3 (causal) learning mechanisms later.
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How to maximise your bets : become a schizophrenic or damage your amygdala, the orbitofrontal cortex, or the right insular cortex!
5 years ago
A couple of recent news articles on neuroeconomics, lead to some surprising insights regarding how addictions like Gambling could be self-addictive and how some specific neurological malfunctioning may lead to people fairing better in games of chances and making more ‘rational’ gambles.
The first article in the New Scientist refers to a recent research by Chris Frith et al at University College London, UK in which the authors found that people who had been given dopamine agonists (like L-DOPA) were able to determine the winning strategy involved in a gambling game early then those who were given placebo. The study contained choosing symbols – some of whom were associated with large chances of winning, while others were associated with average chances and still others were associated with financial penalties and should ideally be learned as avoidable symbols.
What they found was that dopamine facilitated the early learning of the symbols that were associated with (monetary) winning outcomes or rewards as compared to controls, but had no effect on the learning of the avoiding or punishment symbols. This, they hypothesize is due to the fact that people get a Dopamine surge whenever ‘rewarded’ and when base dopamine levels are high (it has already been administered prior to the betting game) this leads to greater strength of dopamine reward signal , thus leading to faster learning of the winning strategy. The fact that dopamine does not affect the learning of negative outcomes, confirms that the effect selective and due to the ‘rewarding’ nature of dopamine as opposed to a general improvement in learning due to dopamine administration.
The participants played a computer game in which they were repeatedly shown pairs of unmatched symbols, and had to choose one or the other without being told anything about them beforehand.
Unknown to the participants, one symbol gave them an 80% higher chance of winning £1, whereas another symbol gave them only a 20% higher chance of winning. Other symbols incurred financial penalties.
The volunteers on dopamine prospered because they identified the winning symbols faster than the haloperidol treated patients. And the winning effect was more pronounced if they actually received money in the study.
The dopamine recipients only noticed winning symbols, however. The chemical did not appear to alert recipients to “losing” symbols.
Learning from losing is controlled by other chemicals in the brain, the most dominant probably being serotonin, a chemical linked with depression, Frith concludes.
This brings up some interesting scenarios. If one has started gambling somehow, then as one keeps gambling further, the successive wins would generate more and more dopamine surges (as baseline dopamine increases after a few wins), the gambler would start identifying the winning patterns, and the strength of winning patterns and rewards associated with them would continue to get stronger in the gambler’s mind; there would be no corresponding effect on the learning of negative or losing strategies by him and consequently his learning would be skewed in such a way that winning outcomes would be disproportionately perceived as being rewarding as compared to the losing outcomes – thus in the gamblers mind loses are processed in a ‘normal’ way ; but wins or winning strategies are perceived differently in the sense that they would be learned more strongly, earlier and more persistently – as each win would result in more and more dopamine surge and thus skew the learning in favor of the winning strategy more and more. this is a vicious circle- the gambler is getting more and more dopamine surge and is also becoming better and better at identifying the winning strategies- thus its difficult to convince him otherwise that he is gambling in vain- what he doesn’t realize that he is not attaching a corresponding increased negative outcome to losses or is learning the losing strategies also at the same rate.
The other article is a good review of the field of neuroeconomics in the New Yorker. It touches on many current issues in neuroeconomics, but what is most relevant to us here is the concept of loss aversion, whereby people perceive losses of what they already have as more aversive than a wasted chance of making an equivalent or more gain. To paraphrase from the article:
If you present people with an even chance of winning a hundred and fifty dollars or losing a hundred dollars, most refuse the gamble, even though it is to their advantage to accept it: if you multiply the odds of winning—fifty per cent—times a hundred and fifty dollars, minus the odds of losing—also fifty per cent—times a hundred dollars, you end up with a gain of twenty-five dollars. If you accepted this bet ten times in a row, you could expect to gain two hundred and fifty dollars. But, when people are presented with it once, a prospective return of a hundred and fifty dollars isn’t enough to compensate them for a possible loss of a hundred dollars. In fact, most people won’t accept the gamble unless the winning stake is raised to two hundred dollars.
Further, the article notes that this loss aversion is due to the fact that under ambiguous situations (or situations that involve probabilistic estimates in face of incomplete information to make the probabilistic judgments), our ‘emotional’ brain takes precedence over the ‘rational’ brain and prevents us from making ‘rational’ decisions.
In one study, Camerer and several colleagues performed brain scans on a group of volunteers while they placed bets on whether the next card drawn from a deck would be red or black. In an initial set of trials, the players were told how many red cards and black cards were in the deck, so that they could calculate the probability of the next card’s being a certain color. Then a second set of trials was held, in which the participants were told only the total number of cards in the deck.
The first scenario corresponds to the theoretical ideal: investors facing a set of known risks. The second setup was more like the real world: the players knew something about what might happen, but not very much. As the researchers expected, the players’ brains reacted to the two scenarios differently. With less information to go on, the players exhibited substantially more activity in the amygdala and in the orbitofrontal cortex, which is believed to modulate activity in the amygdala. “The brain doesn’t like ambiguous situations,” Camerer said to me. “When it can’t figure out what is happening, the amygdala transmits fear to the orbitofrontal cortex.”
The results of the experiment suggested that when people are confronted with ambiguity their emotions can overpower their reasoning, leading them to reject risky propositions. This raises the intriguing possibility that people who are less fearful than others might make better investors, which is precisely what George Loewenstein and four other researchers found when they carried out a series of experiments with a group of patients who had suffered brain damage.
Further, the article notes that people with orbitofrontal, right insular or amygdala damage, are less fearful or are less able to integrate the fearful or ‘emotional’ response of the brain and are thus able to make decisions that are more risky then their normal counterparts. Thus, the counterintuitive conclusion that damages to these areas may make one a better investor/ gambler etc.
Each of the patients had a lesion in one of three regions of the brain that are central to the processing of emotions: the amygdala, the orbitofrontal cortex, or the right insular cortex. The researchers presented the patients with a series of fifty-fifty gambles, in which they stood to win a dollar-fifty or lose a dollar. This is the type of gamble that people often reject, owing to loss aversion, but the patients with lesions accepted the bets more than eighty per cent of the time, and they ended up making significantly more money than a control group made up of people who had no brain damage. “Clearly, having frontal damage undermines the over-all quality of decision-making,” Loewenstein, Camerer, and Drazen Prelec, a psychologist at M.I.T.’s Sloan School of Management, wrote in the March, 2005, issue of the Journal of Economic Literature. “But there are situations in which frontal damage can result in superior decisions.”
If we club the two studies together, one may come to a surprising conclusion that to become a good speculative investor or gambler you may need to temporarily knock out your parts of the brain involved in emotional decision making (one may use TMS here) and also additionally take a dopamine does to learn the rewarding strategies and actions early on. This may be the only way for us to counter the tyranny of loss aversion that nature has imposed on us and move towards that ideal of Homo Economicus.
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Effect of enriched environments on the brain
5 years ago
Nature Reviews Neuroscience has an interesting article that summarizes the latest findings about neurogenesis and synaptic plasticity in adult mice and how exposure to enriched environments and experience leads to later onset of diseases in transgenic mice models of human diseases like Huntington’s disease, Alzheimer’s disease and Parkinson’s disease, fragile X and Down syndrome, as well as various forms of brain injury.
This is exciting news and lends credence to the fact that for full flowering and upkeep of your mental faculties, mental exercises and stimulating mental environment is a must.
Hat Tip : The Frontal Cortex
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The Cognitive Map : How Mice and Men learn when in the Mouse Trap
5 years ago
As a continuation of the Mouse Trap theme, would like to share links to a very insightful paper in Psychology that had marked a departure from behavioral to cognitive explanations and provided a very relevant concept of Cognitive Maps. This original article by Tolman is a delightful and easy read, though some background in classical behaviorist theories of Instrumental and Operant Conditioning would help. What delights is that Tolman uses and explains these concepts without the associated jargon.
I find the 5 different cognitive modes of learning, he identifies, quite instructive and intriguing:
1) Latent Learning (the mice are not blind, though they may act as such if not motivated enough to have eyes:-): They learn the maze, though if not rewarded they may not exhibit that learning in their behavior. This type of learning helps to clarify the difference between learning something and behavior in-line with that learning; and it is clear that the appropriate behavior is mediated by motivation. Learning happens automatically, irrespective of mediation by goal-directed or reward-presence, maybe subconscious in nature, and in the form of a Cognitive Map that is formed latently; but if no incentives are there to make use of the Cognitive Map then that learning is not reflected in the behavior. In the presence of motivation, learning may become conscious and manifest in behavior.
2) Vicarious Trial and Error Learning (The million dollar question in the Matrix: Which door leads to the Source:-) and the billion dollar question superseding that : which door would Neo choose? 
: This learning behavior that Mice exhibit on a decision or choice point should not have been called Vicarious Trial and Error Learning. The name somehow misses the point. I also do not agree with the explanations. My own two cents: the mice ‘choose’ between the options presented on a choice point and this VTE is just an observable external behavior reflecting their internal dilemma : whether to choose food or to choose the locked door and fool the Experimenter:-) As mentioned in the Hitchhikers guide to the galaxy, it may end up that mice were experimenting on us all the way! On a more serious note, if we do assume that there may be reasons for mice to choose non-food at some times, lets say when it is satiated and does not really need food but prefers the thrill of bungee jumping back to the start point, then it makes sense that VTE would be observed more in conditions in which the Mice is able to differentiate easily between the options and thus use discretion /discrimination. These are the easy learning situations of the contrasting Black and White doors. For the dark grey and Black doors situation, the learning task is more difficult and so when the Mice doesn’t really know which door would lead to food and which to non-food, there is little point in deliberation and he may as well choose any door (or rather the choice he makes may be factually incorrect, so he need not hesitate to choose deliberately – he may as well choose randomly – as he doesn’t really have that much control ). Only when choice is real- that is he knows that he has sufficient information to make an informed choice, would there be observed deliberation and choice and associated VTE. Interestingly VTE starts increasing in difficult tasks too, as learning starts to happen and choice become real. An intriguing observation is that the stupid mice do more VTEing in mazes than intelligent ones: are they the really intelligent mice who are acting just stupid and experimenting on Humans?
3. Searching for Stimulus (Who let the dogs out? ): This seems related to the fact that mice would actually indulge in some behavior (in this case looking around their cage for the preponderant stimuli) that is directed towards identifying the salient features of the environment that are associated with their immediate prior experience. They are interested in finding the cause of the effects that they have just witnessed. This interactivity/connectionless view is limited as the experiments focused mostly on avoidance learning and new insights may be available from the similar behavior observed during escape/approach learning if that too exhibits the learning style typical to this avoidance learning : viz searching for stimulus that can be associated with the experience post facto. Interesting to note that when the stimuli following the responses are random, then as the mice’s ‘search for stimulus’ throws an exception, the mice acquires ‘learned helplessness’ whereby it stops monitoring/analyzing its environment. Thi sis presumed to be the mechanism behind clinical depression in humans.
4. The ‘Hypothesis’ experiments (the search for patterns/ pattern recognition : to look for the most apt Cognitive Map relevant to this situation): As per this type of learning the mice presumes or forms a hypothesis of what the desired sequence of steps leading to correct outcome should be, actively indulges in systematic exploration to verify the hypothesis and gives up and tries another hypothesis if the results are not favorable. This is the classical ‘scientific method’ and it is amazing that the mice use that! It is limited in the sense that the experiment is restricted to approach learning. Interesting to note that this type of learning presupposes the existence of ‘concepts’ like left, right, light and dark in the mice and presupposes an ability to sequence these in temporal fashion and act accordingly.
5. ‘Spatial Orientation’ learning (Let there be light!): This particular dimension of learning was the most instrumental in Tolman coming up with the Cognitive Map concept. In this, the mice while exploring the maze and learning the sequence of steps that lead to the food box (or goal, which significantly is paired with light in this case), also apparently learnt the precise spatial location of the food box, so that if the maze was replaced by a radiating spokes of alleys, then after some exploration of each alley, the Mice would finally ‘choose’ one of the alleys and run all the way down the alley (without looking back till it escapes from the alley to find wither the food box or the alley ends into an opening ) and in one particular experimental setup when no other intervening variables were present, the alley that was chosen was significantly related to the presence of food box (light source). Few points to note: Light (or an adequate point of reference like the Sun), seems essential is this sort of learning, maybe the different light/shadow interactions/ intensities are integrally required to from concepts of location. Also, in the first experiment only one light source ( and possibly diffractions effects maybe relevant, while in the second there are 2 sources of light – leaving scope for things like interferences effects to have been instrumental in the learning process. Haven’t yet figured out an explanation for the behavior exhibited in the second experiment, but one thing is evident – the presence of a third light (L3 labeled in the diagram) , would have indicated to the mice, that their starting positions and orientations have been changed, and thus they focused on the right direction (left and right …And thus perpendicular to the walls of the room/table) but missed out on the location. Interestingly this second experiment too exhibits ‘escape’ behavior, whereby once a mice has chosen an alley, it goes all the way down that alley. I would sum this up mostly as Insight learning regarding the 2-dimensional nature of table-space, wherein the mice learn the 2-dimensional spatial location of an object (food box) and either use the r, theta co-ordinates (angular geometry) to guess its location and behave accordingly, or use the other ‘Cartesian’ X,Y co-ordinate system to guess the location of the object and take an alley which is perpendicular to the walls of the table (which serve as reference co-ordinates) in its search of the object. In both cases, if my hypothesis is correct, the Rats should ideally not go all along the way down the alley chosen and exhibit the strong escape behavior; but in Radius, theta case should stop/ hesitate when the radius they have covered is sufficiently greater than the actual radial distance; and in the X,Y case, should stop/ hesitate and look for a turn when the reasonable X, or Y distance has been covered, and if the alley does have a perpendicular turn / choice point after some time, than they should turn in the right direction of where the food is located). Interesting hypothesis, but I am afraid I cannot verify these. Isf someone can conducts these experiments for me and inform me of outcome I will be really grateful. In any case, in the present circumstances, this learning mechanism seems to mostly mediate escape behaviors and that too in the presence of light source and is restricted to learning about spatial locations and the nature of Space (2- D for rats)
Interestingly in another article on the web in TIP , these mechanisms are represented in the reverse order and that too with one important transposition wherein the order of Escape and Approach is reversed. : 1) approach 2) escape, 3) avoidance 4) choice point and 5) latent. This is described under ‘sign learning’ and I find that fascinating as I am currently hooked to things like Da Vince Code ( haven’t read the book or movie, jut documentaries on the same) or tarot and looking for subtle signs that would help in uncracking the code/ breaking the matrix. A tidbit from this page that I find intriguing is that Tolman was also investigating motivation for war.
Resuming discussion on the original article, it also mentions 3 mechanism related to Cognitive Maps that are observable in Humans ( I presume this is over and above the 5 Learning mechanism that are definitely present in both Mice and Men). These, of a different dimension, are briefly mentioned below, but seem to be based on Freudian defense mechanism and are generally speaking unhealthy (defense) mechanism that we may use by referring to Cognitive Maps that are not in touch with reality ( and the learning and behavior instead of being based on Reality principle are based on Pleasure principal). These are summarized below (though haven’t though about them in details and will follow up in a later mail with more elaborations) and this sort of learning ideally needs to be unlearned in order for effective behavior.
6) Regression : Reverting back to earlier learned cognitive maps that are no more relevant in the present situations when dealing with things like emotional loss ( the exact example given is of a loss of spouse…And this concrete example may have much to do with the situations in which this ‘learning’ becomes relevant)
7) Fixation : Using one particular Cognitive Map in all situations as it may have served well in one particular situation in which it was associated with high reward/ motivation.
Displacement of aggression onto outgroup : This, on first look, appears to be the classical displacement (of anger) that is touted as a defense mechanism when a man that returns home angry on the boss, shouts on his wife; but this is made contingent to the fact that aggression is directed on outgroup and is thus qualitatively different. This assumes concepts like group and belonging to group as well as ability to differentiate and discriminate between group and outgroup. It also involves deliberate hate ( and not just prejudice of the outgroup) towards the outgroup, who is made a willing target of aggression in case of frustrations within the group.
All the above 3 negative learned mechanisms Tolman maintains is a result of narrow cognitive Maps. The article ends on a very philosophical note!!
Before concluding, I would just like to briefly summarize my position on the experiments done in the mouse traps:-). I believe the correct order ( an this does matter in the theory I am trying to build up) should be
1) Latent 2) Choice point 3) Avoidance 4) Escape and 5) Approach
6) regression 7) fixation Displacement to outgroup
the only difference I propose in ordering is that 4) Escape (learning related to Space) comes before 5) Learning related to time ( sequence of steps / patterns in time that lead to correct outcome)
Also, to briefly give you an idea from where I come from
I believe in the eight stage developmental theory of Erikson with the 5th step of ego formation being a definitive step in development of self, whereby the adolescent tries many roles in different situations, and achieves ego integrity (or finds the role that he is supposed to play) on successful completion of this step. The rest 3 development tasks of Intimacy, efficacy and Integrity are of qualitatively different nature that the preceding five tasks. Of course I don’t take the Freudian background or explanations of these stages/ tasks but am more into the cognitive developmental theories fir eg of Piaget.
Do comment and let us rock!!
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