A recent study mentions that when people are in good mood, they are likely to choose from amongst the first of the options presented, if asked to choose on the run. However, if they are asked to withhold evaluation till all the alternatives are presented, then they chose the last item presented.
A new study in the February issue of the Journal of Consumer Research people finds that consumers in a good mood are more likely than unhappy consumers to choose the first item they see, especially if all the choices are more or less the same.
The researchers also found that when happy consumers were asked to withhold judgment until all options were presented, they tended to prefer the last option they saw.
To me this appears very much like the recency and primacy effects. Their working memory is so much compromised , due to their good mood that they resort to the heuristics of recency/ primacy to determine their decisions.
The above theory may seem outrageous at first glance, but there are studies suggesting that people are bad decision makers when in good mood and that working memory compromise may be the underlying factor.
A good mood may be bad for people faced with problem-solving tasks that demand a high degree of logical thought and planning, according to a study.
Researchers say the brain may be too busy retrieving “feelgood” memories to enhance the positive mood to focus fully on the task in hand. Someone in a neutral mood can devote themself solely to problem solving, they argue.
According to Mike Oswald, when in good mood, good memories are brought into consciousness and this intrudes with the limited working memory thus temporarily incapacitating it.
Dr Oaksford, who will receive the BPS Spearman Medal today for his work on human reasoning, said that the positive mood state may be affecting the brain’s capacity for “working memory” – a space devoted to thinking, planning, and problem solving – as good memories are being retrieved at the same time.
“It is like a having a blackboard to work your problems out on but your memory is writing on that blackboard at the same time,” he said
This compromising of working memory due to good mood may also explain the working memory deficits found in those suffering from Mania/ psychosis. This may also underlie their jumping to conclusions sort of thinking as they pick the first alternative that comes to mind. Also this may explain their irritable and impatient mood, where they just go for decision making without withholding judgment as the first option itself seems promising and does not get critical evaluation. The direction may even be reverse- due to irritability and good mood (manic style) associations, one may choose the first alternative and this may appear like the primacy effect. However the directionality may be it seems evident that good mood comes accompanied with bad decisions. If the relation is exclusively that of working memory overrode with primacy and recency heuristics we can devise better decision making guidelines for those suffering from Mania.
Descartes held that non-human animals are automata: their behavior is explicable wholly in terms of physical mechanisms. He explored the idea of a machine which looked and behaved like a human being. Knowing only seventeenth century technology, he thought two things would unmask such a machine: it could not use language creatively rather than producing stereotyped responses, and it could not produce appropriate non-verbal behavior in arbitrarily various situations (Discourse V). For him, therefore, no machine could behave like a human being. (emphasis mine)
To me this seems like a very reasonable and important speculation: although we have learned a lot about how we are able to generate an infinite variety of creative sentences using the generative grammar theory of Chomsky (I must qualify, we only know how to create a new grammatically valid sentence-the study of semantics has not complimented the study in syntax – so we still do not know why we are also able to create meaningful sentences and not just grammatically correct gibberish like “Colorless green ideas flow furiously” : the fact that this grammatically correct sentence is still interpretable by using polysemy , homonymy or metaphorical sense for ‘colorless’, ‘green’ etc may provide the clue for how we map meanings -the conceptual Metaphor Theory- but that discussion is for another day), we still do not have a coherent theory of how and why we are able to produce a variety of behavioral responses in arbitrarily various situations.
If we stick to a physical, brain-based, reductionist, no ghost-in-the-machine, evolved-as-opposed-to-created view of human behavior, then it seems reasonable that we start from the premise of humans as an improvement over the animal models of stimulus-response (classical conditioning) or response-reinforcement (operant conditioning) theories of behavior and build upon them to explain how and what mechanism Humans have evolved to provide a behavioral flexibility as varied, creative and generative as the capacity for grammatically correct language generation. The discussions of behavioral coherence, meaningfulness, appropriateness and integrity can be left for another day, but the questions of behavioral flexibility and creativity need to be addressed and resolved now.
I’ll start with emphasizing the importance of response-reinforcement type of mechanism and circuitry. Unfortunately most of the work I am familiar with regarding the modeling of human brain/mind/behavior using Neural Networks focuses on the connectionist model with the implicit assumption that all response is stimulus driven and one only needs to train the network and using feedback associate a correct response with a stimulus. Thus, we have an input layer for collecting or modeling sensory input, a hidden association layer and an output layer that can be considered as a motor effector system. This dissociation of input acuity, sensitivity representation in the form of input layer ; output variability and specificity in the form of an output layer; and one or more hidden layers that associate input with output and may be construed as an association layer maps very well to our intuitions of a sensory system, a motor system and an association system in the brain to generate behavior relevant to external stimuli/situations. However, this is simplistic in the sense that it is based solely on stimulus-response types of associations (the classical conditioning) and ignores the other relevant type of association response-reinforcement. Let me clarify that I am not implying that neural networks models are behavioristic: in the form of hidden layers they leave enough room for cognitive phenomenon, the contention is that they not take into account the operant conditioning mechanisms. Here it is instructive to note that feedback during training is not equivalent to operant-reinforcement learning: the feedback is necessary to strengthen the stimulus-response associations; the feedback only indicates that a particular response triggered by the particular stimuli was correct.
For operant learning to take place, the behavior has to be spontaneously generated and based on the history of its reinforcement its probability of occurrence manipulated. This takes us to an apparently hard problem of how behavior can be spontaneously generated. All our life we have equated reductionism and physicalism with determinism, so a plea to spontaneous behavior seems almost like begging for a ghost-in-the-machine. Yet on careful thinking the problem of spontaneity (behavior in absence of stimulus) is not that problematic. One could have a random number generator and code for random responses as triggered by that random number generator. One would claim that introducing randomness in no way gives us ‘free will’, but that is a different argument. What we are concerned with is spontaneous action, and not necessarily, ‘free’ or ‘willed’ action.
To keep things simple, consider a periodic oscillator in your neural network. Lets us say it has a duration of 12 hours and it takes 12 hours to complete one oscillation (i.e. it is a simple inductor-capacitor pair and it takes 6 hours for capacitor to discharge and another 6 hours for it to recharge) ; now we can make connections a priori between this 12 hr clock in the hidden layer and one of the outputs in the output layer that gets activated whenever the capacitor has fully discharged i.e. at a periodic interval of 12 hours. Suppose that this output response is labeled ‘eat’. Thus we have coded in our neural networks a spontaneous mechanism by which it ‘eats’ at 12 hour durations.
Till now we haven’t really trained our neural net, and moreover we have assumed a circuitry like a periodic oscillator in the beginning itself, so you may object to this saying this is not how our brain works. But let us be reminded that just like normal neurons in the brain which form a model for neurons in the neural network, there is also a suprachiasmatic nuclei that gives rise to circadian rhythms and implements a periodic clock.
As for training, one can assume the existence of just one periodic clock of small granularity, say 1 second duration in the system, and then using accumulators that code for how many ticks have elapsed since past trigger, one can code for any arbitrary periodic response of greater than one second granularity. Moreover, one need not code for such accumulators: they would arise automatically out of training from the other neurons connected to this ‘clock’ and lying between the clock and the output layer. Suppose, that initially, to an output marked ‘eat’ a one second clock output is connected (via intervening hidden neuron units) . Now, we have feedback in this system also. Suppose, that while training, we provide positive feedback only on 60*60*12 trials (and all its multiples) and provide negative feedback on all other trials, it is not inconceivable to believe that an accumulator neural unit would get formed in the hidden layer and count the number of ticks that come out of the clock: it would send the trigger to output layer only on every 60*60*12 th trial and suppress the output of the clock on every other trial. Viola! We now have a 12 hour clock (which is implemented digitally using counting ticks) inside our neural network coding for a 12 hour periodic response. We just needed to have one ‘innate’ clock mechanism and using that and the facts of ‘operant conditioning’ or ‘response-reinforcement’ pairing we can create an arbitrary number of such clocks in our body/brain. Also, please notice the fact, that we need just one 12 hour clock, but can flexibly code for many different 12 hour periodic behaviors. Thus, if the ‘count’ in accumulator is zero, we ‘eat’; if the count is midway between 0 and 60*60*12, we ‘sleep’. Thus, though both eating and sleeping follow a 12 hour cycle, they do not occur concurrently, but are separated by a 6 hour gap.
Suppose further, that one reinforcement that one is constantly exposed to and that one uses for training the clock is ‘sunlight’. The circadian clock is reinforced, say only by the reinforcement provided by getting exposed to the mid noon sun, and by no other reinforcements. Then, we have a mechanism in place for the external tuning of our internal clocks to a 24 hour circadian rhythm. It is conceivable, that for training other periodic operant actions, one need not depend on external reinforcement or feedback, but may implement an internal reinforcement mechanism. To make my point clear, while ‘eat’ action, i.e. a voluntary operant action, may get generated randomly initially, and in the traditional sense of reinforcement, be accompanied by intake of food, which in the classical sense of the word is a ‘reinforcement’; the intake of food, which is part-and-parcel of the ‘eat’ action should not be treated as the ‘feedback’ that is required during training of the clock. During the training phase, though the operant may be activated at different times (and by the consequent intake of food be intrinsically reinforced) , the feedback should be positive only for the operant activations inline with the periodic training i.e. only on trials on which the operant is produces as per the periodic training requirement; and for all other trails negative feedback should be provided. After the training period, not only would operant ‘eat’ be associated with a reinforcement ‘food’: it would also occur as per a certain rhythm and periodicity. The goal of training here is not to associate a stimulus with a response ( (not the usual neural networks association learning) , but to associate a operant (response) with a schedule(or a concept of ‘time’). Its not that revolutionary a concept, I hope: after all an association of a stimulus (or ‘space’) with response per se is meaningless; it is meaningful only in the sense that the response is reinforced in the presence of the stimulus and the presence of the stimulus provides us a clue to indulge in a behavior that would result in a reinforcement. On similar lines, an association of a response with a schedule may seem arbitrary and meaningless; it is meaningful in the sense that the response is reinforced in the presence of a scheduled time/event and the occurrence of the scheduled time/event provides us with a reliable clue to indulge in a behavior that would result in reinforcement.
To clarify, by way of an example, ‘shouting’ may be considered as a response that is normally reinforcing, because of say its being cathartic in nature . Now, ‘shouting’ on seeing your spouse”s lousy behavior may have had a history of reinforcement and you may have a strong association between seeing ‘spouse’s lousy behavior’ and ‘shouting’. You thus have a stimulus-response pair. why you don’t shout always, or while say the stimuli is your ‘Boss’s lousy behavior’, is because in those stimulus conditions, the response ‘shouting’, though still cathartic, may have severe negative costs associated, and hence in those situations it is not really reinforced. Hence, the need for an association between ‘spouse lousy behavior’ and ‘shouting’ : only in the specific stimulus presence is shouting reinforcing and not in all cases.
Take another example that of ‘eating’, which again can be considered to be a normally rewarding and reinforcing response as it provides us with nutrition. Now, ‘eating’ 2 or 3 times in a day may be rewarding; but say eating all the time, or only on 108 hours periodicity may not be that reinforcing a response, because that schedule does not take care of our body requirements. While eating on a 108 hours periodicity would impose severe costs on us in terms of under nutrition and survival, eating on 2 mins periodicity too would not be that reinforcing. Thus, the idea of training of spontaneous behaviors as per a schedule is not that problematic.
Having taken a long diversion, arguing for a case for ‘operant conditioning’ based training of neural networks, let me come to my main point.
While ‘stimulus’ and the input layer represent the external ‘situation’ that the organism is facing, the network comprising of the clocks and accumulators represent the internal state and ‘needs’ of the organism. One may even claim, a bit boldly, that they represent the goals or motivations of the organism.
A ‘eat’ clock that is about to trigger a ‘eat’ response, may represent a need to eat. This clock need not be a digital clock, and only when the 12 hour cycle is completed to the dot, an ‘eating’ act triggered. Rather, this would be a probabilistic, analog clock, with the ‘probability’ of eating response getting higher as the 12 hour cycle is coming to an end and the clock being rest, whenever the eating response happens. If the clock is in the early phases of the cycle (just after an eating response) then the need for eating (hunger) is less; when the clock is in the last phases of the cycle the hunger need is strong and would likely make the ‘eating’ action more and more probable.
Again, this response-reinforcement system need not be isolated from the stimulus-response system. Say, one sees the stimulus ‘food’, and the hunger clock is still showing ‘medium hungry’. The partial activation of the ‘eat’ action (other actions like ‘throw the food’, ignore the food, may also be activated) as a result of seeing the stimulus ‘food’ may win over other competing responses to the stimuli, as the hunger clock is still activating a medium probability of ‘hunger’ activation and hence one may end up acting ‘eat’. This however, may reset the hunger clock and now a second ‘food’ stimulus may not be able to trigger ‘eat’ response as the activation of ‘eat’ due to ‘hunger clock’ is minimal and other competing actions may win over ‘eat’.
To illustrate the interaction between stimulus-response and response-reinforcement in another way, on seeing a written word ‘hunger’ as stimulus, one consequence of that stimulus could be to manipulate the internal ‘hunger clock’ so that its need for food is increased. this would be simple operation of increasing the clock count or making the ‘need for hunger’ stronger and thus increasing the probability of occurrence of ‘eat’ action.
I’ll also like to take a leap here and equate ‘needs’ with goals and motivations. Thus, some of the most motivating factors for humans like food, sex, sleep etc can be explained in terms of underlying needs or drives (which seem to be periodic in nature) and it is also interesting to note that many of them do have cycles associated with them and we have sleep cycles or eating cycles and also the fact that many times these cycles are linked with each other or the circadian rhythm and if the clock goes haywire it has multiple linked effects affecting all the motivational ‘needs’ spectrum. In a mainc pahse one would have low needs to sleep, eat etc, while the opposite may be true in depression.
That brings me finally to Marvin Minsky and his AI attempts to code for human behavioral complexity.
In his analysis of the levels of mental activity, he starts with the traditional if, then rule and then refines it to include both situations and goals in the if part. To me this seems intuitively appealing: One needs to take into account not only the external ‘situation’, but also the internal ‘goals’ and then come up with a set of possible actions and maybe a single action that is an outcome of the combined ‘situation’ and ‘goals’ input.
However, Minsky does not think that simple if-then rules, even when they take ‘gaols’ into consideration would suffice, so he posits if-then-result rules. To me it is not clear how introducing a result clause makes any difference: Both goals and stimulus may lead to multiple if-then rule matches and multiple actions activation. These action activations are nothing but what Minsky has clubbed in the result clause and we still have the hard problem of given a set of clauses, how do we choose one of them over other.
Minsky has evidently thought about this and says:
What happens when your situation matches the Ifs of several different rules? Then you’ll need some way to choose among them. One policy might arrange those rules in some order of priority. Another way would be to use the rule that has worked for you most recently. Yet another way would be to choose rules probabilistically.
To me this seems not a problem of choosing which rule to use, but that of choosing which response to choose given several possible responses as a result of application of several rules to this situation/ goal combination. It is tempting to assume that the ‘needs’ or ‘gaols’ would be able to uniquely determine the response given ambiguous or competing responses to a stimulus; yet I can imagine a scenario where the ‘needs’ of the body do not provide a reliable clue and one may need the algorithms/heuristics suggested by Minsky to resolve conflicts. Thus, I see the utility of if-then-result rules: we need a representation of not only the if part (goals/ stimulus) in the rule; which tells us what is the set of possible actions that can be triggered by this stimulus/ situation/ needs combo; but also a representation of the results part of the rule: which tells us what reinforcement values these response(actions) have for us and use this value-response association to resolve the conflict and choose one response over the other. This response-value association seems very much like the operant-reinforcement association, so I am tempted once more to believe that the value one ascribes to a response may change with bodily needs and rather is reflective of bodily needs, but I’ll leave that assumption for now and instead assume that somehow we do have different priorities assigned to the responses ( and not rules as Minsky had originally proposed) and do the selection on the basis of those priorities.
Though I have posited a single priority-based probabilistic selection of response, it is possible that a variety of selection mechanisms and algorithms are used and are activated selectively based on the problem at hand.
This brings me to the critic-selector model of mind by Minsky. As per this model, one needs both critical thinking and problem solving abilities to act adaptively. One need not just be good at solving problems- one also has to to understand and frame the right problem and then use the problem solving approach that is best suited to the problem.
Thus, the first task is to recognize a problem type correctly. After recognising a problem correctly, we may apply different selctors or problem solving strategies to different problems.
He also posits that most of our problem solving is analogical and not logical. Thus, the recognizing problem is more like recognizing a past analogical problem; and the selecting is then applying the methods that worked in that case onto this problem.
How does that relate to our discussions of behavioral flexibility? I believe that every time we are presented with a stimulus or have to decide how to behave in response to that stimulus, we are faced with a problem- that of choosing one response over all others. We need to activate a selection mechanism and that selection mechanism may differ based on the critics we have used to define the problem. If the selection mechanism was fixed and hard-wired then we wont have the behavioral flexibility. Because the selection mechanism may differ based on our framing of the problem in terms of the appropriate critics, hence our behavioral response may be varied and flexible. At times, we may use the selector that takes into account only the priorities of different responses in terms of the needs of the body; at other times the selector may be guided by different selection mechanisms that involve emotions and values us the driving factors.
Minsky has also built a hierarchy of critics-selector associations and I will discuss them in the context of developmental unfolding in a subsequent post. For now, it is sufficient to note that different types of selection mechanisms would be required to narrow the response set, under different critical appraisal of the initial problem. To recap, a stimulus may trigger different responses simultaneously and a selection mechanism would be involved that would select the appropriate response based on the values associated with the response and the selection algorithm that has been activated based on our appraisal of the reason for conflicting and competing responses. while critics help us formulate the reason for multiple responses to the same stimuli, the selector helps us to apply different selection strategies to the response set, based on what selection strategy had worked on an earlier problem that involved analogous critics.
One can further dissociate this into two processes: one that is grammar-based, syntactical and uses the rules for generating a valid behavioral action based on the critic and selector predicates and the particular response sets and strategies that make up the critic and selector clause respectively. By combining and recombining the different critics and selectors one can make an infinite rules of how to respond to a given situation. Each such rule application may potentially lead to different action. The other process is that of semantics and how the critics are mapped onto the response sets and how selectors are mapped onto different value preferences.
Returning back to the response selection, given a stimulus, clearly there are two processes at work : one that uses the stored if-then rules (the stimulus-response associations) to make available to us a set of all actions that are a valid response to the situation; and the other that uses the then-result rules (and the response-value associations, that I believe are dynamic in nature and keep changing) to choose one of the response from that set as per the ‘subjective’ value that it prefers at the moment. This may be the foundation for the ‘memory’ and ‘attention’ dissociations in working memory abilities used in stroop task and it it tempting to think that the while DLPFC and the executive centers determine the set of all possible actions (utilizing memory) given a particular situation, the ACC selects the competing responses based on the values associated and by selectively directing attention to the selected response/stimuli/rule.
Also, it seems evident that one way to increase adaptive responses would be to become proficient in discriminating stimuli and perceiving the subjective world accurately; the other way would be to become more and more proficient in directing attention to a particular stimulus/ response over others and directing attention to our internal representations of them so that we can discriminate between the different responses that are available and choose between them based on an accurate assessment of our current needs/ goals.
Using his ideas of sensorimotor function, Hughlings-Jacksondescribed two “halves” of consciousness, a subject half (representationsof sensory function) and an object half (representations ofmotor function). To describe subject consciousness, he usedthe example of sensory representations when visualizing an object. The object is initially perceived at all sensory levels.This produced a sensory representation of the object at allsensory levels. The next day, one can think of the object andhave a mental idea of it, without actually seeing the object.This mental representation is the sensory or subject consciousnessfor the object, based on the stored sensory information of theinitial perception of it.
What enables one to think of the object? This is the other halfof consciousness, the motor side of consciousness, which Hughlings-Jacksontermed “object consciousness.” Object consciousness is the facultyof “calling up” mental images into consciousness, the mentalability to direct attention to aspects of subject consciousness.Hughlings-Jackson related subject and object consciousness asfollows:
The substrata of consciousness are double, as we might inferfrom the physical duality and separateness of the highest nervouscentres. The more correct expression is that there are two extremes.At the one extreme the substrata serve in subject consciousness.But it is convenient to use the word “double.”
Hughlings-Jackson saw the two halves of consciousness as constantly interacting with each other, the subjective half providing a store of mental representations of information that the objective half used to interact with the environment.
The term “subjective” answers to what is physically the effect of the environment on the organism; the term “objective” to what is physically the reacting of the organism on the environment.
Hughlings-Jackson’s concept of subjective consciousness is akin to the if-then representation of mental rules.One needs to perceive the stimuli as clearly as possible and to represent them along with their associated actions so that an appropriate response set can be activated to respond to the environment. His object consciousness is the attentional mechanism that is needed to narrow down the options and focus on those mental representations and responses that are to be selected and used for interacting with the environment.
As per him, subject and object consciousness arise form a need to represent the sensations (stimuli) and movements (responses) respectively and this need is apparent if our stimulus-response and response-reinforcement mappings have to be taken into account for determining appropriate action.
All nervous centres represent or re-represent impressions andmovements. The highest centres are those which form the anatomicalsubstrata of consciousness, and they differ from the lower centresin compound degree only. They represent over again, but in morenumerous combinations, in greater complexity, specialty, andmultiplicity of associations, the very same impressions andmovements which the lower, and through them the lowest, centresrepresent.
He had postulated that temporal lobe epilepsy involves a loss in objective consciousness (leading to automatic movements as opposed to voluntary movements that are as per a schedule and do not happen continuously) and a increase in subjective consciousness ( leading to feelings like deja-vu or over-consciousness in which every stimuli seems familiar and triggers the same response set and nothing seems novel – the dreamy state). These he described as the positive and negative symptoms or deficits associated with an epileptic episode. It is interesting to note that one of the positive symptom he describes of epilepsy, that is associated with subjective consciousness of third degree, is ‘Mania’ : the same label that Minsky uses for a Critic in his sixth self-consciousness thinking level of thinking. The critic Minsky lists is :
Self-Conscious Critics. Some assessments may even affect one’s current image of oneself, and this can affect one’s overall state:
None of my goals seem valuable. (Depression.) I’m losing track of what I am doing. (Confusion.)
I can achieve any goal I like! (Mania.) I could lose my job if I fail at this. (Anxiety.)
Would my friends approve of this? (Insecurity.)
Interesting to note that this Critic or subjective appraisal of the problem in terms of Mania can lead to a subjective consciousness that is characterized as Mania.
If Hughlings-Jackson has been able to study epilepsy correctly and has been able to make some valid inferences, then this may tell us a lot about how we respond flexibly to novel/ familiar situations and how the internal complexity that is required to ensure flexible behavior, leads to representational needs in brain, that might lead to the necessity of consciousness.
In a nutshell, in this study incongruent stimuli like a red spade card or a black heart card was presented for brief durations and the subjects asked to identify the stimuli completely – the form or shape (heart/spade/club/diamond), the color (red/black) and the number( 1..10…face cards were not used) of the stimuli.
The trial used both congruent ( for eg a red heart, a black club) as well as incongruent stimuli (a black heart, a red spade).
To me this appears to be a form of stroop task , in which, if one assumes that form is a more salient stimulus than color, then a presentation of a spade figure would automatically activate the black color perception and the prepotent color naming response would be black, despite the fact that the spade was presented in red color. This prepotent ‘black’ verbal response would, as per standard stroop effect explanations, be inhibited for the successful ‘red’ verbal response to happen. I am making an analogy here that the form of a suit is equivalent to the linguistic color-term and that this triggers a prepotent response.
In these lights, the results of the experiment do seem to suggest a stroop effect in this playing-deck task, with subjects taking more trials to recognize incongruent stimuli as compared to congruent stimuli.
Perhaps the most central finding is that the recognition threshold for the incongruous playing cards (whose with suit and color reversed) is significantly higher than the threshold for normal cards. While normal cards on the average were recognized correctly — here defined as a correct response followed by a second correct response — at 28 milliseconds, the incongruous cards required 114 milliseconds. The difference, representing a fourfold increase in threshold, is highly significant statistically, t being 3.76 (confidence level < .01).
Further interesting is the fact that this incongruence threshold decreases if one or more incongruent trials precede the incongruent trial in question; or increases if the preceding trials are with normal cards. This is inline with current theories of stroop effect as involving both memory and attention, whereby the active maintenance of the goal (ignore form and focus on color while naming color) affects performance on all trials and also affects the errors , while the attentional mechanism to resolve incongruence affects only reaction times (and leads to RT interference).
As in the playing card study, no reaction time measures were taken, but only the threshold reached to correctly recognize the stimuli were used, so we don’t have any RT measures, but a big threshold is indicative of and roughly equal to an error on a trial. The higher thresholds on incongruent trial means that the errors on incongruent trial were more than on congruent trials. The increase in threshold , when normal card precede and a decrease when incongruent cards precede is analogous to the high-congruency and low-congruency trials described in Kane and Engel study and analyzed in my previous posts as well as in a Developing Intelligence post. It is intuitive to note that when incongruent trials precede, then the goal (ignore form and focus on color while naming color) becomes more salient; when normal cards precede one may have RT facilitation and the (implicit) goal to ignore color may become less salient.
Experience with an incongruity is effective in so far as it modifies the set of the subject to prepare him for incongruity. To take an example, the threshold recognition time for incongruous cards presented before the subject has had anything else in the tachistoscope — normal or incongruous — is 360 milliseconds. If he has had experience in the recognition of one or more normal cards before being presented an incongruous stimulus, the threshold rises slightly but insignificantly to 420 milliseconds. Prior experience with normal cards does not lead to better recognition performance with incongruous cards (see attached Table ). If, however, an observer has had to recognize one incongruous card, the threshold for the next trick card he is presented drops to 230 milliseconds. And if, finally, the incongruous card comes after experience with two or three previously exposed trick cards, threshold drops still further to 84 milliseconds.
Thus clearly the goal maintenance part of stroop effect is clearly in play in the playing-card task and affects the threshold for correct recognition.
The second part of explanation of stroop task is usually based on directed inhibition and an attentional process that inhibits the perpotent response. This effect comes into play only on incongruent trials. An alternate explanation is that their is increased competition of competing representations on incongruent trials and instead of any top-down directed inhibition, inline with the goal/expectation, their is only localized inhibition. The dissociation of a top-down goal maintenance mechanism ad another attentional selection mechanism seems to be more inline with the new model, wherein inhibition is local and not top-directed.
While RT measures are not available it is intersecting to take a look at some of the qualitative data that supports a local inhibition and attentional mechanism involved in reacting to incongruent stimuli. The authors present evidence that the normal course of responses that are generated by the subjects for (incongruent) stimuli is dominance, compromise, disruption and finally recognition.
Generally speaking, there appear to be four kinds of reaction to rapidly presented incongruities. The first of these we have called the dominance reaction. It consists, essentially, of a “perceptual denial” of the incongruous elements in the stimulus pattern. Faced with a red six of spades, for example, a subject may report with considerable assurance, “the six of spades” or the “six of hearts,” depending upon whether he is color or form bound (vide infra). In the one case the form dominates and the color is assimilated to it; in the other the stimulus color dominates and form is assimilated to it. In both instances the perceptual resultant conforms with past expectations about the “normal” nature of playing cards.
A second technique of dealing with incongruous stimuli we have called compromise. In the language of Egon Brunswik , it is the perception of a Zwischengegenstand or compromise object which composes the potential conflict between two or more perceptual intentions. Three examples of color compromise: (a) the red six of spades is reported as either the purple six of hearts or the purple six of spades; (b) the black four of hearts is reported as a “grayish” four of spades; (c) the red six of clubs is seen as “the six of clubs illuminated by red light.”
A third reaction may be called disruption. A subject fails to achieve a perceptual organization at the level of coherence normally attained by him at a given exposure level. Disruption usually follows upon a period in which the subject has failed to resolve the stimulus in terms of his available perceptual expectations. He has failed to confirm any of his repertory of expectancies. Its expression tends to be somewhat bizarre: “I don’t know what the hell it is now, not even for sure whether it’s a playing card,” said one frustrated subject after an exposure well above his normal threshold.
Finally, there is recognition of incongruity, the fourth, and viewed from the experimenter’s chair, most successful reaction. It too is marked by some interesting psychological by-products, of which more in the proper place.
This sequence points towards a local inhibition mechanism in which either one of the responses is selected and dominates the other; or both the responses mix and yield to give a mixed percept —this is why a gray banana may appear yellowish—or why a banana matched to gray background by subjects may actually be made bluish—as that of a blackish red perception of suit color; or in some cases there may be frustration when the incongruent stimuli cannot be adequately reconciled with expectations- leading to disruption- in the classical stroop task this may explain the skew in RT for some incongruent trials—-some take a lot of time as maybe one has just suffered from disruption—; and finally one may respond correctly but only after a reasonable delay. This sequence is difficult to explain in terms of top-down expectation model and directed inhibition.
Finally, although we have been discussing the playing card task in terms of stroop effect, one obvious difference is striking. In the playing cards and t e pink-banana experiments the colors and forms or objects are tightly coupled- we have normally only seen a yellow banana or a red heart suit. This is not so for the printed grapheme and linguistic color terms- we have viewed then in all colors , mostly in black/gray- but the string hue association that we still have with those colors is on a supposedly higher layer of abstraction.
Thus, when an incongruent stimuli like a red heart is presented , then any of the features of the object may take prominence and induce incongruence in the other feature. For eg, we may give more salience to form and identity it as a black spade; alternately we may identify the object using color and perceive incongruence in shape- thus we may identify it as a red spade. Interestingly, both kind of errors were observed in the Bruner study. Till date, one hast not really focussed on the reverse stroop test- whereby one asks people to name the color word and ignore the actual color- this seems to be an easy task as the linguistic grapheme are not tied to any color in particular- the only exception being black hue which might be reasonably said to be associated with all grapheme (it is the most popular ink). Consistent with this, in this reverse stroop test, sometimes subjects may respond ‘black’ when watching a ‘red’ linguistic term in black ink-color. This effect would be for ‘black’ word response and black ink-color only and for no other ink color. Also, the response time for ‘black’ response may be facilitated when the ink-color is black (and the linguistic term is also ‘black’) compared to other ink-colors and other color-terms. No one has conducted such an experiment, but one can experiment and see if there is a small stroop effect involved here in the reverse direction too.
Also, another important question of prime concern is whether the stroop interference in both cases, the normal stroop test, and the playing card test, is due to a similar underlying mechanism, whereby due to past sensory (in case of playing cards) or semantic associations (in case of linguistic color terms) the color terms or forms (bananas/ suits) get associated with a hue and seeing that stimulus feature automatically activates a sensory or semantic activation of the corresponding hue. This prepotent response then competes with the response that is triggered by the actual hue of the presented stimulus and this leads to local inhibition and selection leading to stroop interference effects.
If the results of the non-verbal stroop test, comprising of natural or man-made objects, with strong color associations associated with them, results in similar results as observed in the classical stroop test, then this may be a strong argument for domain-general associationist/ connectionist models of language semantics and imply that linguistic specificity may be over hyped and at least the semantics part of language acquisition, is mostly a domain general process. On the other hand, dissimilar results on non-verbal stroop tests form the normal stroop test, may indicate that the binding of features in objects during perception; and the binding of abstract meaning to linguistic words in a language have different underlying mechanisms and their is much room for linguistic specificity. Otherwise, it is apparent that the binding of abstract meaning to terms is different a problem from that of binding of different visual features to represent and perceive an object. One may use methods and results from one field and apply them in the other.
To me this seems extremely interesting and promising. The evidence that stroop test is due to two processes – one attentional and the other goal maintenance/ memory mediated – and its replication in a non-verbal stroop tests, would essentially help us a lot by focusing research on common cognitive mechanisms underlying working memory – one dependent on memory of past associations and their active maintenance- whether verbal/abstract or visual/sensory- and the other dependent on a real-time resolution of incongruity/ambiguity by focusing attention on one response to the exclusion of the other. This may well correspond to the Gc and Gf measures of intelligence. One reflecting how good we are at handling and using existing knowledge; the other how good we are able to take into account new information and respond to novel situations. One may even extend this to the two dissociated memory mechanisms that have been observed in parahippocampal regions- one used when encountering familiar situations/stimuli and the other when encountering novel stimuli. One essentially a process of assimilation as per existing schema/ conceptual metaphors; the other a process of accommodation, involving perhaps, an appreciation/formation of novel metaphors and constructs.
Enough theorizing and speculations for now. Maybe I should act on this and make an online non-verbal stroop test instead to test my theories!
Endgame: Another interesting twist to the playing cards experiment could be in terms of motivated perception. Mixing Memory discusses another classical study by Bruner in this regard. Suppose that we manipulate motivations of people so that they are either expecting to see a heart or a red color as the next stimuli- because only this desired stimuli would yield them a desired outcome, say, orange juice; then in this case when presented with an incongruent stimuli – a red spade- would we be able to differentially manipulate the resolution of incongruence; that is those motivated to see red would report seeing a ‘red spade’ and those motivated to see a heart would report a ‘black heart’ . Or is the effect modality specific with effects on color more salient than on form. Is it easier to see a different color than it is to see a different form? And is this related to themodality specific Sham’s visual illusion that has asymmetry in the sense that two beeps, one flash leads to perception of two flashes easily but not vice versa.
As per this study, as we have normally only seen a yellow banana and that color association is quite strong in our minds, hence when we perceive a ‘different’ colored banana, we are bound to see it more yellowish than is the actual hue in which the different color banana is presented.
Basically, they used 2 extremely good experiments that show that when viewing a banana (which is generally yellow), the yellow color perception is automatically activated in our brains: thus a gray matched banana would appear yellowish; while the task that requires matching a pink banana to a gray background would result in a bluish-gray banana, as blue is the opponent color for yellow and blue is added to the background gray to compensate for the memory-activated yellow color perception.
It is interesting to draw parallels here with the stroop test. In this test, color words like ‘red’, ‘yellow’ etc also appear to invoke automatic activation of the corresponding color in the brain and thus interferes with the correct naming of the actual color in which the color word is presented. Developing Intelligence has a very interesting and promising post, in which he explores the current research and computation models, that seem to suggest that the mechanism underlying stroop interference is not directed inhibition of prepotent responses, but lateral excitation among color and linguistic perception modules, with color perception area of the brain being always activated when a color linguistic term is presented and in the incongruent trials more activation seen in this to-be-ignored module as the conflicting activations of color – one due to the actual color of the word and the other due to the color perception activated by the linguistic color word (‘red’ ) both competing against each other lead to more activation. This is in contrast to the view that the more activation is due to directed inhibition . The new explanation advocated seems also to fit with the brain anatomy, with there being only local inhibition processes and is reconcilable with a lack of long range inhibiting pathways in the neocortex.
Thus to me, it seems more and more possible that stroop effect may be due to actual ‘yellowish’ hue perception in brain on watching the linguistic term ‘yellow’. I know that the two examples are not the same– a yellow banana actually has yellow color and thus its memory may affect the perception of a strange colored banana; but maybe the ‘yellow’ linguistic term is also somehow related in our mind very strongly with actual yellow hue perception and maybe we are all synaesthetic to the extent that all of us literally see the linguistic color terms in color rather than in black-and-white (or whatever the text color).
A tantalizing study, published in Science, indicates that a SNP in a single gene KIBRA, could lead to a difference of as big as 25% in the outcome of a free recall test measuring the episodic memory. The KIBRA gene is expressed in the medial temporal lobe (the hippocampal region) and using fMRI the authors were able to demonstrate different levels of activation in this brain area for the carriers versus non-carriers of the T allele when they were engaged in a retrieval task.
Human memory is a polygenic trait. We performed a genome-widescreen to identify memory-related gene variants. A genomic locusencoding the brain protein KIBRA was significantly associatedwith memory performance in three independent, cognitively normalcohorts from Switzerland and the United States. Gene expressionstudies showed that KIBRA was expressed in memory-related brainstructures. Functional magnetic resonance imaging detected KIBRAallele–dependent differences in hippocampal activationsduring memory retrieval. Evidence from these experiments suggestsa role for KIBRA in human memory
This is an important work and could lead to much insight on the memory formation mechanisms involved.
I recently came across twostudies both of which were pointing towards a double dissociation between ACC and PFC, in the realm of Working Memory Attentional processes in one case and the learning mechanisms (or acquisition and performance of a cognitive skill) in the other case.
In the first study by Kane and Engle, a stroop interference task was used to find the different attentional factors at work that determine the successful execution of the task. Using some clever experiments, it was demonstrated that two selective attentional mechanisms were involved- one that was related to goal maintenance and was active pre-stimulus presentation and the other that was active post-stimulus presentation and was related to inhibition of inappropriate bottom-up responses (the automatic response as per the linguistic color-denoting word in incongruent condition instead of as per the actual color of the word that was demanded by the task) and selection of relevant response from the competing responses.
As per the abstract of the study:
Individual differences in working-memory (WM) capacity predicted performance on the Stroop task in 5 experiments, indicating the importance of executive control and goal maintenance to selective attention. When the Stroop task encouraged goal neglect by including large numbers of congruent trials (RED presented in red), low WM individuals committed more errors than did high WM individuals on the rare incongruent trials (BLUE in red) that required maintaining access to the “ignore-the-word” goal for accurate responding. In contrast, in tasks with no or few congruent trials, or in high-congruency tasks that followed low-congruency tasks, WM predicted response-time interference. WM was related to latency, not accuracy, in contexts that reinforced the task goal and so minimized the difficulty of actively maintaining it. The data and a literature review suggest that Stroop interference is jointly determined by 2 mechanisms, goal maintenance and competition resolution, and that the dominance of each depends on WM capacity, as well as the task set induced by current and previous contexts.
As per this line of reasoning, errors in the stroop task are thought to result from failure to actively maintain a goal in mind and may thus be related to memory retrieval per se. On the other hand, reaction time slowing is thought to result from a post-stimulus attentional process – a failure to quickly bias competition towards the correct representation rather than the incorrect representation and might also be perceived as an attentional control mechanism- whereby attention is not diverted to irrelevant stimuli that are not consistent with the goal in WM.
Developing Intelligence presents some additional observations to bolster the argument:
Across all subjects, the amount of RT facilitation (i.e., how much faster congruent trials are than neutral trials) correlates with error interference (i.e., how much more accurate neutral trials are than incongruent trials), suggesting that goal maintenance failure is behind both of these phenomena. In contrast, there is no correlation between the RT facilitation effect and RT interference, as would be expected if goal maintenance failure actually gives rise to all of these measures, nor is there a correlation between error & latency interference. The implication being that errors (and the related RT facilitation) are due to one process and response time latency/interference due to another process involved in attending to ambiguous (multiple response generating) stimuli.
On high-congruency Stroop tasks, schizophrenics show increased errors on incongruent relative to congruent trials, and increased facilitation on congruent relative to neutral trials. The implication being that in schizophrenics only one of the attention mechanism is selectively dysfunctional – that related to goal maintenance. As presumably, schizophrenics do not show abnormal patterns of reaction times (except for increased RT facilitation on congruent trails governed by the lack of maintenance of goal – ‘ignore-the-color’) , thus, the second mechanism involving selection of competing responses is intact.
ERP studies of Stroop tasks have identified a wave that may originate from anterior cingulate (ACC) and appears to correspond to response selection and competition processes; in contrast, the activity of a different wave up to 800 ms before stimulus presentation predicts correct performance on the next stimulus (and appears to originate from polar or dorsolateral frontal cortex [dlPFC]). The implication being that dissociated brain regions are involved in priming for the response (goal maintain ace) and selection of response ( conflict resolution – inhibition of inappropriate responses)
Event-related fMRI shows a strong negative correlation between delay-period dlPFC activity and Stroop interference, whereas ACC activity is tied to the presentation of incongruent stimuli. The implication being that PFC activity is related to errors and thus the process of goal maintenance, while ACC activity is related to peculiarities arising from incongruence – that is when competing responses are available- and thus tied to the process of response selection (inhibition of inappropriate response).
A clinching observation that could seal the argument about two dissociated mechanisms would be observing a correlation between errors on incongruent trials under 0 congruence condition (where the effect of goal maintenance has been effectively factored out by forcing subjects to keep the goal in mind on every trial), or better to display the goal (the rule that you have to choose as per the color and not the linguistic word) while the stimuli are presented to ensure that the goal is maintained constantly, and observe the correlations between errors in preceding condition and response time latencies/interference in the normal stroop task. This correlation would ensure that there indeed is an independent attentional mechanism that is independent of goal maintenance and is dependent only on conflict resolution.
In the second study by Fincham and Anderson, a learning paradigm was used whereby some sports names were associated with some arithmetical rules (that were either implicitly learned or explicitly told) and in the trials the subjects were required to retrieve the rule and apply it. There were four conditions – a visible-rule and rule-retrieval condition (supposed to measure the effects of the rule retrieval process) and a reverse/ forward calculation condition (supposed to measure the effect of rule complexity condition – a forward-reverse application of rule introduces another control step).
The authors discovered that in the first experiment, where there were four different trial conditions, recall (rule-retrieval condition) had a significant effect on both latencies and errors, they also found (but glossed over) a minor effect found of direction (or complexity of rule application) on the errors and latency and found no recall by direction interaction. Thus, it is evident that recall (or rule retrieval) and direction (or rule complexity/manipulation) are two different factors affecting performance. However imaging studies were not that helpful. Instead of finding a selective ACC activation effect linked to direction (as per their proposal of ACC as an attentional control region) and a selective PFC activation effect linked to recall (as per their proposal of PFC as a region involved in retrieval), they found that both recall and direction had effects on ACC and PFC activations.
Their second experiment was done with the purpose of dissociating the recall (retrieval) and direction (control) components. However they confounded the study by simultaneously introducing two variables- an additional direction task supposedly requiring an additional control step and not affecting retrieval at all, and a practice variable supposedly only affecting recall (retrieval) and not affecting control (rule manipulation) at all. This however cannot be taken for granted. All 3 trials in this experiment were recall trials. They present results for initial trial, a forward direction trial after some practice and a reverse direction trial after some practice. In my opinion, they should also have provided a simple direction-neutral trial after some practice. Comparison between this and the initial trial (which were same in all respects accept for practice) would have enabled a conclusive association of practice with retrieval ease and with decrease in PFC activation.
Even if the two practice trials (reverse + forward combined) are taken as a substitute for that direction- neutral practice trial (which they unfortunately did not conduct), still one can only derive the decrease in PFC activation due to practice (or ease of retrieval) relationship as a conclusion of this study. The increase in ACC that they observed between the initial trial and the final trials (involving reverse/ forward direction manipulation) are the same results that they observed in experiment one (whereby forward and reverse manipulations in both recall/ explicit condition led to more errors/ latency/ ACC activation) . They prefer to explain this as implying that ACC activation was required because an additional control step was involved); a more parsimonious ( and more in line with the current views of the functions of ACC) explanations is that when the reverse/ forward direction condition is added , then the stimuli that is presented (and which also contains the cue as to in which direction the calculation needs to be done) leads to a stroop-like default automatic forward direction application of the rule and ACC activity is required to choose between the competing responses (if reverse direction cue is present than forward direction response needs to be inhibited). This would predict more RT and errors in the reverse condition (incongruent trials) as opposed to forward conditions (congruent trials). One can even have some control conditions whereby novel sports words (with a novel explicit rule with no directionality associated with it) are displayed in some trials and reactions times and errors measured on these. If the resulting results are same as in Stroop task, perhaps the same mechanisms are in work.
The greater activation in ACC could also be, paradoxically, due to practice. To rule this out, the initial trials having both forward and reverse direction conditions should be compared with later reveres and forward direction trials after practice. Only if no increase in ACC activity is found that can be attributed to practice alone, can the increase in ACC be attributed to the additional control step that was supposedly introduced in experiment 2. A possible scenario where practice could influence ACC activation (and post stimulus response selection mechanism) is where practice or learning could lead to greater salience of activated goal or a stronger top-down expectation resulting in a stronger inhibitory signal for any stimulus that doesn’t meet the top-down expectations. It is not unreasonable to suppose that strength of a rule (the probability with which that rule has been ingrained in memory) may directly reflect in the strength of the biasing that is a result of a top-down expectation of that rule application. In that case , ACC may paradoxically be more and more activated as a result of practice (as the response expectation associated with the stimulus increases in habit strength though learning) to bias the response selection more strongly in favor of the expected response (goal).
In summation, there seems much ground to believe that two attentional processes in working memory /learning and performance are involved – one ACC based and the other PFC based and that they are explained in terms of pre-stimulus goal maintenance and post-stimulus response selection / biasing.