Easy tasks can route around the global workspace, hard ones or ones that produce error have to go through it. That’s the previous idea. Now, this paper has begun to shift my thinking. For a specific set of tasks, it claims to show that training doesn’t shift activity away from a bottleneck location, but instead makes the processing at the point of the bottleneck more efficient.
Interesting! This would make a lot of intuitive sense—after the subsystems responsible for some task have been sufficiently trained, they can mostly just carry out the task using their trained-up predictive models, and need a lot less direct sensory data.
This might also explain some aspects of meditation: for example, The Mind Illuminated talks about “increasing the power of consciousness”, which it describes as literally increasing the amount of experience-moments per unit of time. I was never quite sure of how exactly to explain that in terms of a global workspace model… but maybe if the system for generating moments of introspective awareness also got more efficient somehow? Hmm...
If subsystems had to route through the GNW to trigger motor actions, then this system or some variation could totally account for the serial conflict resolution function. But if subsystems can directly send motor commands without going through the GNW, how would would subsystems in conflict be “told to stop” while the conflict resolution happens? The GNW is not a commander, it can’t order subsystems around. Though it may be central to consciousness, it’s not the “you” that commands and thinks.
All this leaves me thinking that I’m either missing a big obvious chunk of research, or that various motor-planning parts of the brain can’t send motor commands except via the GNW. Please point me at any relevant research that you know of.
So AFAIK, the command bottleneck is in the basal ganglia, which are linked to the GNW. A lot of the brain works by lateral inhibition, where each of neurons A, B and C may fire, but any of them firing sends inhibitory signals to the others, causing only one of them to be active at a time.
My understanding from the scholarpedia article is that something similar is going on in the basal ganglia—different subsystems send various motor command “bids” to the BG, which then get different weights depending on various background factors (e.g. food-seeking behaviors get extra weight when you are hungry). Apparently there’s a similar mechanism of a strong bid for one system inhibiting the bidding signals for all the others. So if multiple subsystems are issuing conflicting bids at the same time, their bids would end up inhibiting each other and none of them would reach the level necessary for carrying out actions.
Scholarpedia links to Prescott et al. 2006 (sci-hub) as offering a more detailed model, including a concrete version that was implemented in a robot. I’ve only skimmed it, but they note that their model has some biologically plausible behaviors. In some situations where the robot experiences conflicting high-weight bids, it seems to “react to uncertainty by going half-speed”: doing a bit of one one response and then a bit of another, and failing to fully commit to any single procdure:
The control architecture of the robot includes five behaviors, or action sub-systems, which it can switch between at any time. These are: searching for cylinders (cylinder-seek or Cs), picking up a cylinder (cylinderpickup, Cp), looking for a wall (wall-seek, Ws), following alongside a wall (wall-follow, Wf), and depositing the cylinder in a corner (cylinder-deposit, Cd). [...]
three of the five action sub-systems—cylinder-seek, wall-seek, and wall-follow— map patterns of input from the peripheral sensor array into movements that orient the robot towards or away from specific types of stimuli (e.g. object contours). [...]
By comparison, the fixed action pattern for cylinder-pickup in the robot model constitutes five sequential elements: (i) slowly approach the cylinder (to ensure correct identification and good alignment for pickup), (ii) back away (to allow room to lower the arm) whilst opening the gripper, (iii) lower the arm to floor level, (iv) close the gripper (hopefully around the cylinder), (v) return the arm to vertical. [...]
A centralized action selection system requires mechanisms that can assimilate relevant perceptual, motivational, and contextual signals to determine, in some form of ‘common currency’ the relative salience or urgency of each competing behavior (McFarland, 1989; Redgrave et al., 1999a). In the embedding architecture of our model, at each time-step, a salience value for each action sub-system is calculated as a weighted sum of relevant perceptual and motivational variables, and may also be dependant on the current activity status of the action sub-system itself. [...]
… a breakdown of clean selection can occur in the disembodied model when two competitors have high salience levels. To examine the behavioral consequences of this pattern of selection, the robot model was tested over five trials of 120s (approximately 800 robot time-steps) in which the salience of every channel was increased, on every time-step, by a constant amount (C0.4) [...]
During a continuous sequence of high salience competitions the robot exhibited patterns of behavioral disintegration characterized by: (i) reduced efficiency and distortion of a selected behavior, and (ii) rapid switching and incomplete foraging behavior.
Fig. 11 shows the effect of increased salience intensity on exploration of the winner/most-salient-loser salience-space over all trials. The graph demonstrates that virtually all (w4000) salience competitions appeared in the region of salience space (compare with Fig. 7) where reduced efficiency and distorted selection can be expected
The initial avoidance sequence followed the expected pattern but the transition to foraging activity did not begin cleanly, instead showing reduced efficiency and intermittent, partial selection of (losing) avoidance behaviors. To the observer the movement of the robot behavior during the transition appeared somewhat slowed and ‘tentative’. During the foraging bout there was an extended period of rapid switching between cylinder-seek and cylinder-pickup with the robot repeatedly approaching the cylinder but failing to grasp it. The pattern initially observed (t=60–85s) was for the robot to approach the cylinder; back up as if to collect it in the gripper; then move forward without lowering the gripper-arm, pushing the cylinder forward slightly. Later (t=85–90s, 110–115s), where both behaviors showed some partial selection, the robot would lower the arm whilst moving forward but fail to grasp the cylinder due to being incorrectly aligned.
In all five trials, the selection behavior of the robot was similarly inefficient and distorted with the robot frequently displaying rapid alternation of foraging acts. This is illustrated in the transition matrix in Table 2, which shows that the behavior of the robot was dominated by the sequence Cs–Cp–Cs–Cp. with no trials leading to a successful foraging sequence. [...]
Whilst the performance of the model in these circumstances is clearly sub-optimal from a purely action selection viewpoint, it shows interesting similarities to the findings of a large number of studies investigating the behavior of animals in conflict situations (Fentress, 1973; Hinde, 1953, 1966; Roeder, 1975). For instance, Hinde (1966) describes a number of possible outcomes that have been observed in ethological studies of strong behavioral conflicts: (i) inhibition of all but one response, (ii) incomplete expression of a behavior (generally the preparatory stages of behavior are performed), (iii) alternation between behaviors (or ‘dithering’), (iv) ambivalent behavior (a mixture of motor responses), (v) compromise behavior (similar to ambivalence, except that the pattern of motor activity is compatible with both behavioral tendencies), (vi) autonomic responses (for instance defecation or urination), (vii) displacement activity (expression of a behavior that seems irrelevant to the current motivational context, e.g. grooming in a ‘fight or flight’ conflict situation) . Of these outcomes, several show clear similarities with the behavior of the robot in the high salience condition. Specifically, the distortion observed in the early stages of the trial could be understood as a form of ambivalent behavior (iv), whilst the later repetitive behavioral switching has elements of both incomplete expression of behavior (ii) and alternation (iii).
More generally, the behavior of the embodied basal ganglia model is consistent a wide range of findings in psychology and ethology demonstrating that behavioral processes are most effective at intermediate levels of activation (Berlyne, 1960; Bindra, 1969; Fentress, 1973; Malmo, 1959), These findings can also be viewed as expressing the Yerkes-Dodson law (Yerkes & Dodson, 1908) that predicts an ‘inverted U’-shaped relationship between arousal and performance. Our model is consistent with this law in that the robot shows little or no behavioral expression when only low salience inputs are present, demonstrates effective action selection for a range of intermediate level salience inputs (and for high salience inputs where there is no high salience competitor), and exhibits disintegrated behavior in circumstance of conflict between multiple high-salience systems. The robot model, therefore, suggests that the basal ganglia form an important element of the neural substrate mediating the effects of arousal on behavioral effectiveness.
Connecting this with the GNW, several of the salience cues used in the model are perceptual signals, e.g. whether or not a wall or a cylinder is currently perceived. We also know that signals which get to the GNW have a massively boosted signal strength over ones that do not. So while the GNW does not “command” any particular subsystem to take action, salience cues that get into the GNW can get a significant boost, helping them win the action selection process.
Compare e.g. the Stanford Marshmallow Experiment, where the children used a range of behaviors to distract themselves from the sight/thought of the marshmallow—or any situation where you yourself keep getting distracted by signals making their way to consciousness:
The three separate experiments demonstrate a number of significant findings. Effective delay of gratification depends heavily on the cognitive avoidance or suppression of the reward objects while waiting for them to be delivered. Additionally, when the children thought about the absent rewards, it was just as difficult to delay gratification as when the reward items were directly in front of them. Conversely, when the children in the experiment waited for the reward and it was not visibly present, they were able to wait longer and attain the preferred reward. The Stanford marshmallow experiment is important because it demonstrated that effective delay is not achieved by merely thinking about something other than what we want, but rather, it depends on suppressive and avoidance mechanisms that reduce frustration.
The frustration of waiting for a desired reward is demonstrated nicely by the authors when describing the behavior of the children. “They made up quiet songs…hid their head in their arms, pounded the floor with their feet, fiddled playfully and teasingly with the signal bell, verbalized the contingency…prayed to the ceiling, and so on. In one dramatically effective self-distraction technique, after obviously experiencing much agitation, a little girl rested her head, sat limply, relaxed herself, and proceeded to fall sound asleep.”
Thank you for pointing to the basal ganglia’s relation to motor control! This feels like one of those things that’s obvious but I just didn’t know because I haven’t “broadly studied” neuroanatomy. Relatedly, if anyone knows of any resources of neuroanatomy that really dig into why we think of this or that region of the brain as being different, I’d love to hear. I know there’s both a lot of “this area defs has this structure and does this thing” and “an fMRI went beep so this is the complain-about-ants part of your brain!”, and I don’t yet have the knowledge to tell them apart.
Also:
Connecting this with the GNW, several of the salience cues used in the model are perceptual signals, e.g. whether or not a wall or a cylinder is currently perceived. We also know that signals which get to the GNW have a massively boosted signal strength over ones that do not. So while the GNW does not “command” any particular subsystem to take action, salience cues that get into the GNW can get a significant boost, helping them win the action selection process.
This was a very helpful emphasis shift for me. Even though I wasn’t conceptualizing GNW as a commander, I was still thinking of it as a “destination”, probably because of all the claims about its connection to consciousness. The “signal boosting” frame feels like a much better fit. Subsystems are already plugged into the various parts of your brain that they need to be connected to; the GNW is not a universal router. It’s only when you’re doing Virtual Machine esque conscious thinking that it’s a routing bottleneck. Other times it might look like a bottle neck, but maybe it’s more “if you get a signal boost from the GNW, you’re probs gonna win, and only one thing can get boosted at a time”.
Interesting! This would make a lot of intuitive sense—after the subsystems responsible for some task have been sufficiently trained, they can mostly just carry out the task using their trained-up predictive models, and need a lot less direct sensory data.
This might also explain some aspects of meditation: for example, The Mind Illuminated talks about “increasing the power of consciousness”, which it describes as literally increasing the amount of experience-moments per unit of time. I was never quite sure of how exactly to explain that in terms of a global workspace model… but maybe if the system for generating moments of introspective awareness also got more efficient somehow? Hmm...
So AFAIK, the command bottleneck is in the basal ganglia, which are linked to the GNW. A lot of the brain works by lateral inhibition, where each of neurons A, B and C may fire, but any of them firing sends inhibitory signals to the others, causing only one of them to be active at a time.
My understanding from the scholarpedia article is that something similar is going on in the basal ganglia—different subsystems send various motor command “bids” to the BG, which then get different weights depending on various background factors (e.g. food-seeking behaviors get extra weight when you are hungry). Apparently there’s a similar mechanism of a strong bid for one system inhibiting the bidding signals for all the others. So if multiple subsystems are issuing conflicting bids at the same time, their bids would end up inhibiting each other and none of them would reach the level necessary for carrying out actions.
Scholarpedia links to Prescott et al. 2006 (sci-hub) as offering a more detailed model, including a concrete version that was implemented in a robot. I’ve only skimmed it, but they note that their model has some biologically plausible behaviors. In some situations where the robot experiences conflicting high-weight bids, it seems to “react to uncertainty by going half-speed”: doing a bit of one one response and then a bit of another, and failing to fully commit to any single procdure:
Connecting this with the GNW, several of the salience cues used in the model are perceptual signals, e.g. whether or not a wall or a cylinder is currently perceived. We also know that signals which get to the GNW have a massively boosted signal strength over ones that do not. So while the GNW does not “command” any particular subsystem to take action, salience cues that get into the GNW can get a significant boost, helping them win the action selection process.
Compare e.g. the Stanford Marshmallow Experiment, where the children used a range of behaviors to distract themselves from the sight/thought of the marshmallow—or any situation where you yourself keep getting distracted by signals making their way to consciousness:
Thank you for pointing to the basal ganglia’s relation to motor control! This feels like one of those things that’s obvious but I just didn’t know because I haven’t “broadly studied” neuroanatomy. Relatedly, if anyone knows of any resources of neuroanatomy that really dig into why we think of this or that region of the brain as being different, I’d love to hear. I know there’s both a lot of “this area defs has this structure and does this thing” and “an fMRI went beep so this is the complain-about-ants part of your brain!”, and I don’t yet have the knowledge to tell them apart.
Also:
This was a very helpful emphasis shift for me. Even though I wasn’t conceptualizing GNW as a commander, I was still thinking of it as a “destination”, probably because of all the claims about its connection to consciousness. The “signal boosting” frame feels like a much better fit. Subsystems are already plugged into the various parts of your brain that they need to be connected to; the GNW is not a universal router. It’s only when you’re doing Virtual Machine esque conscious thinking that it’s a routing bottleneck. Other times it might look like a bottle neck, but maybe it’s more “if you get a signal boost from the GNW, you’re probs gonna win, and only one thing can get boosted at a time”.