Attention has been studied heavily and as per a popular model of attention by Posner et al, we have 3 systems for attention: alerting, orienting and an executive control network.
Now let me propose a radical fourth network for the same, but before I do that I want to clear some misconceptions about attention. Way back in 2009 I had blogged and elaborated on a couple of posts that attention allocation and action selection utilized the same mechanisms and were conceptually similar; at that time I was not much aware of the Posner et al model of attention. Today I want to go further and claim that attention processes are involved in action initiation and selection.
But lets start from first principles.
We can always be in either an alert state on the lookout for stimuli or in a more sleepy/drowsy state where we will probably ignore stimulus, the extreme being when we are sleeping and ignoring all stimuli. This system is also know as arousal system and is fairly unequivocally associated with Norepinephrine (NE) system. Tonic NE levels (tonic meaning the baseline/spontaneous firing rate of NE neurons in LC (locus coerelus) ) as per one theory drive the activity of this alerting network in the brain. The higher the tonic NE and more alert you are; lower the NE and you get drowsy /sleepy. Too much NE/alertness and it actually becomes distractability where you cannot remain focused on task at hand but get distracted/ alerted by each and any irrelevant stimuli. So the relationship of alertness/ tonic NE with task performance follows inverted U of Yerkes Dodsan law.
So to detect a particular stimulus (of relevance) the first step is to be on the lookout for stimuli in general. And alerting system does this beautifully – it provides a knob to tune whether you want to ignore most stimuli or to attend to most stimuli in your current state. By playing around with tonic NE levels that can be easily accomplished.
Once you are in varied state of readiness to detect stimuli in general (on various levels of alertness), you may be primed to attend to a particular spatial location or a particular modality for a particular stimulus. On ANT (attention network test) this is accomplished by providing a spatial cue that indicates where the target will appear and primes the human/animal to turn its gaze either covertly or overtly to that location. In real world phenomenon, some CS will predict that an UCS is going to follow and teh animal/ human will react by directing attention to the location/ modality of that UCS prediction. For eg., when you hear thunder, you will be on lookout for a lightening visual to follow and will be using orienting attention to become primed for the same. Typically, orienting attention is based on prediction: there are hundreds of locations one can attend to, but due to predicting cues one orients to a particular location in space, in anticipation of a stimulus of relevance.
So the second step to detect a stimulus of relevance is to orient one’s attention keeping in mind the preceding cue(s). Its not clear whether this is accomplished by tonic NE levels or phasic NE levels, but what is known is that this is a faster response as indicated by pupil diameters (of eyes) which are somehow associated with NE levels and dilate faster in orienting than in alerting.
And once you are both alert and oriented then the stimulus of relevance appears (or doesn’t).
Once the stimulus appears, it rarely appears alone. Only in contrived lab settings does the stimulus appear alone; and even there in some conditions it is flanked by flankers (distractors) which can be either congruent or in-congruent. So we need to suppress irrelevant stimuli and attend selectively to relevant stimuli. Now one can debate what is relevant- for the purpose of this section it is the stimulus on whose lookout we re as per the previous step of orientation. Because it is surrounded by irrelevant background information/ stimuli executive networks kicks in and suppresses the flankers/ irrelevant information.
So the third step in detecting a stimuli is a ‘hot’ process where we latch onto the relevant stimuli and suppress irrelevant stimuli. And this is accomplished beautifully by phasic NE firings which ensure that once a decision is being made, it is widely amplified and winner takes all happens.
However the story is not complete yet.
The final stage in detecting a stimuli is doing deep processing of selected stimuli (a ‘cold’ process) and determining what action to take / response to met out. This is akin to final selection of stimulus as worthy of action and further processing.
So to summarize:
Readiness to detect stimuli (alerting)
Priming for a particular stimulus (orienting)
Suppressing irrelevant stimuli (executive control – conflict )
Selecting final stimuli for deep processing (executive control- decision)
So the above is the way in which action happens in detecting and selecting stimuli in my view.
However , the story is not complete even now.
Consider, the parallel path associated with response/ action.
One can be in various states of readiness to respond. If you are highly aroused and edgy you may jump at slightest stimuli or act with hyperactivity. On the other hand if you are aroused pretty less you may be sleepy/drowsy and sluggish in your responses. In the extreme case of sleep you wont respond at all. This arousal system as is well known is mediated by tonic NE levels. To me, this is the same alerting network – though this time attuned to responding and not detecting stimuli. And this is a non specific general readiness to respond.
So the first step to responding is being in various states of alertness as to whether and how fast you would respond to any stimuli in general.
The next step of course is based on cues you would be in state of readiness for a particular (set of ) action (s). The CS that leads to orienting response and attention to where the UCS may appear also primes a response set or UCR. To take our crude analogy, when you hear thunder you would be primed to seek shelter (or stay away from trees) etc). In a nutshell a response set will be activated and a goal needs to be maintained. This is the orienting network in action in the response leg. Note that all this is happening in parallel. That is Alerting stimulus leg and alerting response leg are running in parallel and so too are orienting stimulus leg and orienting response leg.
So the second step to responding is being primed with a particular response set. In ANT this would be responding with ether left hand or right hand .
And then the stimulus happens.
Given that the stimulus has happened a primary response gets activated, however there are competing responses : in the contrived laboratory settings this may be in the form of STROOP test where habitual response competes with more relevant response. So the third system kicks in suppressing irrelevant/ habitual responses that are not relevant. This as you would have guessed is the executive control – conflict system.
So the third step to responding is suppressing irrelevant responses. And this is beautifully accomplished by executive control – conflict system.
Finally, the ‘cold’ system needs to take over and finalize, initiate and execute the final action. This is the executive control- decsion network.
So the sequence is :
Readiness to respond (Alerting)
Priming for a particular response (Orienting)
Suppressing irrelevant responses (executive control – conflict)
Selecting final response for deep processing (executive control – decision)
I should again emphasize that the stimulus leg and response leg run in parallel. I believe there is great value in talking about both legs when we focus on attention and underlying attention networks.
In my next post I will elaborate a bit more on underlying brain structures / functional networks and neurotransmitters underlying these networks .
PS: Some of my musings are based on studying thesearticles in depth and I express my debt to them.
This is the second in the series about major conscious and unconscious processes in the brain. The first part can be found here. This post tries to document a few more processes / functions in the brain and their neural substrates. To recap, the major processes in brain (along with sample broad brain regions (grossly simplified) associated) are :
Learning (hippocampal formation in medial temporal)
Affective (amygdalar and limbic system)
These are supplanted by the following processes and mechanisms.
6. Modeling system/ Hemispheric laterlaization: Another system/ mechanism that the brain may find useful and develop is the ability to model the world and model the self and others . This presents the following problem. The world consist of objects that follow deterministic casual laws thus lending order to it as well as seeming agents that act by their own volition and thus leading to chaos. The modeling required to model causal, deterministic world may suffer from different design constraints than that required to model a chaotic, agentic world. The brain, I propose, solves this, by having two hemispheres, one specialized for interacting with the world based on the model of the world as orderly, deterministic , statistically regular world; while the other hemisphere specialized for interacting with the world assuming it as a chaotic , agentic, probabilistically undetermined world. The two hemispheres co-operate with each other and respond using the advantages offered by the different strategies of both hemispheres. To recap, left hemisphere is specialized for causal patterns, sequences, analysis and interpretation, classifying objects (categorical spatial represnetation) , verbal abilities depending on analysis of sequences, uses prototypes (statistical mean) and uses Match strategy of responding in a statistical pattern, Music lyrics, and works on local stimuli (components) and parses high spatial frequency and values relativity. The right brain on the other hand is specialized for random/unperdicatble associations, scenes, synthesis and documentation, acting on objects (co-ordinate spatial representation), Spatial abilities depending on synthesis of objects making the scene, uses exemplars (actual events) and uses Maximizing strategy of responding as per probability at the moment, Music melody, and works on global stimuli (wholes) and parses low spatial frequency and values absoluteness. To summarize, left hemisphere is best suited to model temporal dimensions in an analytical and causal manner, while right hemisphere is best suited to model the spatial dimensions in an holistic and agentic manner. This modeling, it needs to be emphasized, need not be conscious, but could be entirely unconscious and have unconscious effects.
7. Communciation system/ perisylvian area/ mirror neurons?: Once an organism has discovered/ realized unconsciously that there are other agents/ con specifics in the world , a brain system that communicates (on an unconscious level) with the others can evolve. A system can evolve that signals the emotional/internal state to others and can also sense the emotional/ internal state of others. This can be used as an aid to predict how the agent will act – as the agent is similar to oneself, one can predict how the other will act based on its internal state by simulating how one would act himself , given the same internal state. Sensing the internal state of others is one side of the coin, the other part is signalling your own internal state honestly to others to aid communication and enhance fitness by group selection. Remember that none of these consdireations need to be conscious. Even unicellular bacteria that live in colonies/ cultures evolve communication systems based on sensing and emitting chemicals etc. In humans the mirror neuron system activated by others actions, the emotional expression and contagious unconscious empathy may all be the unconscious communciation system driven by non-verbal communication based on mirroring and mirror neurons.
8. Attention system : The last (for now!) system to evolve might be related to directing attention or selectivity processing relevant inputs, actions, affects, evaluations, associations, models and communciations while suppressing irrelevant ones. At any time , one is bombarded by many (all unconscious ) different stimuli, urges, activated associations, body states, values, models and communications from con specifics- these may or may not be relevant to current situation/ goal. Not everything can be processed equally as the brain has limited computational resources. This leads to a mechanism/system to gauze relevance and thus bias the other systems by selectively processing some aspects in detail while ignoring others. This attentional/orientational mechanism may be covert, may be unconscious and might be triggered by external events/ voluntarily directed; important thing to realize is that attention seems to integrate the output and inputs of other brain systems/ mechanisms by selectivity highlighting a few features that are relevant and coherent. This also ultimately leads to opening the doors to the next higher level of processing by brain – the conscious processing, which is computationally more demanding and thus requires attention to restrict the inputs that it can process. The attentional system opens the floodgates of heaven (consciousness) for the humans/ animals that are able to use it appropriately.
The spotlight of attention once created leads to conscious experiences of perception, agency, memory, feelings, thoughts, self-awareness, inner speech and identity. That of course is material for another post!
The title of my above post is a scaiku (scientific haiku in 140 chars on twitter) that I posted last night on twitter.I am using this title as the inspiration for this post is twitter itself.
Last night, after a hard day full of tweeting (yes tweeting and keeping up with all the friends’ tweets is a lot of hard work- go check the 4-way conversation I had on cosnsciousness and free will), I was not able to relax myself, but found myself in a constant state of distraction and restlessness, and getting up in middle of night to update my status. Of course I have heard of twitter addiction and would rubbish that off; but I could not rubbish off the unique demands on attention and juggling that twittering makes on you. First off, you need to read a lot of tweets and find the needle in the haystack- the tweets that need to be retweeted/replied to and ignore/forget the rest of them as soon as possible. Secondly, I at least, juggle constantly between windows and tabs of tweetdeck and other application trying to do optimal scavenging (feeding off good content tweeted by others) and foraging (finding a good tweetable link myself).
So to sum up, I found that twitter had taxed, at least yesterday, my attentional system- leading to a habitual distractibility and also my motor system hat had constantly flitted between open windows and tabs leading to a habitual distractibility. I am sure that was a very short term and temporary phenomenon, but that set me thinking I have already devoted an entire post to how attention allocation and action selection may be similar and have drawn many parallels. The fundamental problem in both the cases is to choose an action/ stimuli to attend to, that can maximize the rewards from the world/ predictability of the world. At any given time, there are many more stimuli to attend to and acts to indulge in than are the attentional/intentional resources required for the same and thus one has to choose between alternatives. Mathematicaly, different acts have different probabilities associated with them that they would lead to a rewarding state- this wave function needs to be collapsed such that only one act is actually intended. One way to do is my maximizing Utility (ExV) associated with different acts and choosing the maximal one always; another solution is to randomly choose an act from the given set in accordance with the probability distribution that is a function of their utilities.I believe that instead of maximizers most of us are staisficers and especially in time-sensitive decisions go for an undeliberate choice that does’nt actually maximize the utility over all possible behavioral acts, but choses one of them randomly/probabilistically as per their prior known probabilities of rewards. Thus, we can be both maximizers as well as satisficers and which system we engage depends both on situational factors as well as our personality tendencies/ habits.
Anyway that was a lot of digression from the main line of argument. To continue with the digression for some more time, if one extends the analogy to attending to stimuli, on can either attend to stimuli that leads to greatest predictability (P= ExR) ; or one can attend to a stimuli from a given set in accordance with a probability distribution that is a function of their prior predictabilities. again I haven’t even got into Bayesian models where thing should get more complicated; suffice it to note for now that both attention-allocation and action-selection involve choosing an act / stimuli from a set.
A look at the Utility function of acts (U=ExV) and Predictability function of stimuli (P = ExR) , immediately outlines the importance of dopamine in the above choosing mechanism as it encodes both (reward) expectancy as well as incentive salience/Value for acts; on the attentional side of things, it should encode both the strength of conditioned association (E) as well as (stimuli) Relevance for minimizing surprise. As such it should detect novelty in stimuli that can indicate that things have changed and the internal model needs updating.
I also talked in my last post about a general energy level that leads to more propensity to indulge in operant acts and a general arousal level that leads to more propensity to attend to external stimuli. Today I want to elaborate on that concept using ADHD as a guide – ADHD has primarily two varieties (and in most general case both co-exist) – the inattentive type and the hyperactive-impulsive type. In the inattentive type, one is easily distracted or to put in my conceptualization – has a high baseline arousal leading to more frequent monitoring to the world/ external stimuli . The attention-reallocation happens faster than controls and may be triggered by irrelevant stimuli too. In the hyperactive-impulsive type, one is overly active and impulsive or to put in mu conceptualization- has a high baseline energy level leading to more frequent shifts in activities and possibly triggering unvalued acts (impulses that are not really rewarding) .
It is important to note that dopamine and dopamine mediated regions like smaller PFC, cerebellum and basal ganglia, dopamine related genes like DAT1 and DRD4 and Ritalin that works primarily on dopamine have been implicated in ADHD. All the above points to a dopamine signalling aberration in ADHD. Once one embraces the overarching framework of action-allocation and action-selection as similar in nature and possibly involving dopamine neurons, it is easy to see why ADHD children should have both hyperactive-impulsive and inattentive syndromes and subgroups.
I have recently blogged a bit about action-selection and operant learning, emphasizing that the action one chooses, out of many possible, is driven by maximizing the utility function associated with the set of possible actions, so perhaps a quick read of last few posts would help appreciate where I come from .
To recap, whenever an organism makes a decision to indulge in an act (an operant behavior), there are many possible actions from which it has to choose the most appropriate one. Each action leads to a possibly different Outcome and the organism may value the outcomes differentially. this valuation may be both objective (how the organism actually ‘likes’ the outcome once it happens, or it may be subjective and based on how keenly the organism ‘wants’ the outcome to happen independent on whether the outcome is pleasurable or not. Also, it is never guaranteed that the action would produce the desired/expected outcome. There is always some probability associated that the act may or may not result in the expected outcome. Also, on a macro level the organism may lack sufficient energy required to indulge in the act or to carry it out successfully to completion. Mathematically, with each action one can associate a utility U= E x V (where U is utility of act; E is expectancy as to whether one would be able to carry the act and if so whether the act would result in desired outcome; and V is the Value (both subjective and objective0 that one has assigned to the outcome. The problem of action-selection then is simply to maximize the utility given different acts n and to choose the action with maximum utility.
Today I had an epiphany; doesn’t the same logic apply to allocating attention to the various stimuli that bombard us. Assuming a spotlight view of attention, and assuming that there are limited attentional resources, one is constantly faced with the problem of finding which stimuli in the world are salient and need to be attended to. Now, the leap I am making is that attention-allocation just like choosing to act volitionally is an operant and not a reactive, but pro-active process. It may be unconscious, but still it involves volition and ‘choosing’. Remember, that even acts can be reactive and thus there is room for reactive attention; but what I am proposing is that the majority of attention is pro-active- actively choosing between stimuli and focusing on one to try and better predict the world. We are basically prediction machines that want to predict beforehand the state of the world that is most relevant to us and this we do by classical or pavlovian conditioning. We try to associate stimuli (CS) with stimuli(UCS) or response (UCR) and thus try to ascertain what state of world at time T would be given that stimulus (CS) has happened. Apart from prediction machines we are also Agents that try to maximize rewards and minimize punishments by acting on this knowledge and acting and interacting with the world. There are thousands of actions we can indulge in- but we choose wisely; there are thousands of stimuli in the external world, but we attend to salient features wisely.
Let me elaborate on the analogy. While selecting an action we maximize reward and minimize punishment, basically we choose the maximal utility function; while choosing which stimuli to attend to we maximize our foreknowledge of the world and minimize surprises, basically we choose the maximal predictability function; we can even write an equivalent mathematical formula: Predictability P = E x R where P is the increase in predictability due to attending to stimulus 1 ; E is probability that stimulus 1 correctly leads to prediction of stimulus 2; and R is the Relevance of stimulus 2(information) to us. Thus the stimulus one would attend, is the one that leads to maximum gain in predictability. Also, similar to the general energy level of organism that would bias as to whether, and how much, the organism acts or not; there is a general arousal level of the organism that biases whether and how much it would attend to stimuli.
So, what new insights do we gain from this formulation? First insight we may gain is by elaborating the analogy further. We know that basal ganglia in particular and dopamine in general is involved in action-selection. Dopamine is also heavily involved in operant learning. We can predict that dopamine systems , and the same underlying mechanisms, may also be used for attention-allocation. Dopamine may also be heavily involved in classical learning as well. Moreover, the basic computations and circuitry involved in allocating attention should be similar to the one involved in action-selection. Both disciplines can learn from each other and utilize methods developed in one field for understanding and elaborating phenomenon in the other filed. For eg; we know that dopamine while coding for reward-error/ incentive salience also codes for novelty and is heavily involved in novelty detection. Is the novelty detection driven by the need to avoid surprises, especially while allocating attention to a novel stimulus.
What are some of the prediction we can make form this model: just like the abundant literature on U= E x V in decision making and action selection literature, we should be able to show the independent and interacting effects of Expectancy and Relevance on attention-grabbing properties of stimulus. The relevance of different stimuli can be manipulated by pairing them with UCR/UCS that has different degrees of relevance. The expectancy can be differentially manipulated by the strength of conditioning; more trials would mean that the association between the CS and UCS is strong; also the level of arousal may bias the ability to attend to stimuli. I am sure that there is much to learn in attention research from the research on decision-making and action-selection and the reverse would also be true. It may even be that attention-allocation is actually conceptualized in the above terms; if so I plead ignorance of knowledge of this sub-field and would love to get a few pointers so that I can refine my thinking and framework.
Also consider the fact that there is already some literature implicating dopamine in attention and the fact that dopamine dysfunction in schizophrenia, ADHD etc has cognitive and attentional implications is an indication in itself. Also, the contextual salience of drug-related cues may be a powerful effect of dapomine based classical conditioning and attention allocation hijacking the normal dopamine pathways in addicted individuals.
Lastly, I got set on this direction while reading an article on chaining of actions to get desired outcomes and how two different brain systems ( a cognitive (Prefrontal) high road one based on model-based reinforcement learning and a unconscious low road one (dorsolateral striatal) based on model-free reinforcement learning)may be involved in deciding which action to choose and select. I believe that the same conundrum would present itself when one turns attention to the attention allocation problem, where stimuli are chained together and predict each other in succession); I would predict that there would be two roads involved here too! but that is matter for a future post. for now, would love some honest feedback on what value, if any, this new conceptualization adds to what we already know about attention allocation.
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.
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.