Center for Cognitive Brain Imaging

at Carnegie Mellon University

Projects

4CAPS Cognitive Neuroarchitecture

4CAPS is a cognitive architecture whose models can account for both traditional behavioral data and, more interestingly, the results of neuroimaging studies; in this sense it is a neuroarchitecture (Just & Varma, 2007). Cognitively speaking, it is a hybrid architecture that combines symbolic and connectionist mechanisms in a resource-constrained environment. Cortically speaking, it moves beyond the localism of most neuroscience accounts, proposing that thinking is a network phenomenon. 4CAPS is particularly well-suited for specifying models of high-level forms of cognition.

This research has been supported by the Office of Naval Research Grant N00014-02-1-0037 and the Multidisciplinary Research Program of the University Research Initiative (MURI) Grant N00014-01-1-0677.

History
Operating Principles
Source Code & Documentation

Reference
Just, M. A., & Varma, S. (2007). The organization of thinking: What functional brain imaging reveals about the neuroarchitecture of complex cognition. Cognitive, Affective, & Behavioral Neuroscience, 7, 153-191.

History

4CAPS is the most recent member of an architectural family that includes CAPS and 3CAPS.

The original CAPS architecture (Thibadeau et al., 1982) synthesizes symbolic and activation-based processing as it was understood in the early 1980s, and in this regard resembles other hybrid efforts of the time (Anderson, 1983; Hofstadter et al., 1983; Holland et al., 1985; Erman et al., 1980; Minsky, 1985; Rumelhart & McClelland, 1982). Its computational mechanisms include: variable-binding, constituent-structured representations, graded activations, weights, thresholds, and parallel processing. The suitability of CAPS for accounting for high-level cognition has been demonstrated by successful models of language comprehension (Just & Carpenter, 1987; Thibadeau et al., 1982), mental rotation (Just & Carpenter, 1985), and problem solving (Carpenter et al., 1990).

CAPS was succeeded by 3CAPS (Just & Carpenter, 1992; Just & Varma, 2002), which adds constraints on the resources available for maintaining and processing representations. This enables computational explorations of individual differences on a number of tasks: sentence comprehension in young adults of different working memory capacities (Just & Carpenter, 1992); sentence comprehension in intact neurotypicals and aphasics (Haarmann et al., 1997); discourse comprehension in young adults (Goldman & Varma, 1995); problem solving in neurotypical adults with different fluid intelligence (Just et al., 1996); problem solving in intact neurotypicals and patients with frontal lobe lesions (Goel et al., 2001); and human-computer interaction (Byrne & Bovair, 1997; Huguenard et al., 1997). The success of these models furthers the case that human information processing employs hybrid computational mechanisms in a capacity-constrained environment.

CAPS and 3CAPS models account for behavioral measures of high-level cognition collected from neurotypical young adults and neuropsychological patients, broadly defined. 4CAPS, the latest member of the CAPS family, extends to new measures and new populations. Like their predecessors, 4CAPS models account for the time course of cognition and for individual differences. Unlike their predecessors, they also account for neuroimaging measures of normal cognition, and they provide much more precise accounts of the behavioral consequences of cortical lesions.

References
Anderson, J. R. (1983). The architecture of cognition. Cambridge, MA: Harvard University Press.
Byrne, M. D., & Bovair, S. (1997). A working memory model of a common procedural error. Cognitive Science, 21, 31-61.
Carpenter, P. A., Just, M. A., & Shell, P. (1990). What one intelligence test measures: A theoretical account of the processing in the Raven Progressive Matrices Test. Psychological Review, 97 404-431.
Erman, L. D., Hayes-Roth, F., Lesser, V. R., & Reddy, D. R. (1980). The Hearsay-II speech understanding system: Integrating knowledge to resolve uncertainty. ACM Computing Surveys, 12, 213-253.
Goel, V., Pullara, S. D., & Grafman, J. (2001). A computational model of frontal lobe dysfunction: working memory and the Tower of Hanoi task. Cognitive Science, 25, 287-313.
Goldman, S. R., & Varma, S. (1995). CAPping the construction-integration model of discourse comprehension. In C. Weaver, S. Mannes, & C. Fletcher (Eds.), Discourse comprehension: Essays in honor of Walter Kintsch (pp. 337-358). Hillsdale, NJ: Erlbaum.
Haarmann, H. J., Just, M. A., & Carpenter, P. A. (1997). Aphasic sentence comprehension as a resource deficit: A computational approach. Brain and Language, 59, 76-120.
Hofstadter, D., & The Fluid Analogies Research Group. (1995). Fluid concepts and creative analogies: Computer models of the fundamental mechanisms of thought. New York: Basic Books.
Holland, J. H., Holyoak, K. J., Nisbett, R. E., & Thagard, P. R. (1986). Induction: Processes of inference, learning, and discovery. Cambridge, MA: The MIT Press.
Huguenard, B. R., Lerch, F. J., Junker, B. W., Patz, R. J., & Kass, R. E. (1997). Working Memory failure in phone-based interaction. ACM Transactions on Computer-Human Interaction, 4, 67-102.
Just, M. A., & Carpenter, P. A. (1985). Cognitive coordinate systems: Accounts of mental rotation and individual differences in spatial ability. Psychological Review, 92, 137-172.
Just, M. A., & Carpenter, P. A. (1987). The psychology of reading and language comprehension. Boston: Allyn and Bacon.
Just, M. A., & Carpenter, P. A. (1992). A capacity theory of comprehension: Individual differences in working memory. Psychological Review, 99, 122-149. Just, M. A., Carpenter, P. A., & Hemphill, D. D. (1996). Constraints of processing capacity: Architectural or implementational? In D. Steier & T. M. Mitchell (Eds.), Mind matters: A tribute to Allan Newell (pp. 141-178). Hillsdale, NJ: Erlbaum.
Just, M. A., & Varma, S. (2002). A hybrid architecture for working memory: Reply to MacDonald and Christiansen (2002). Psychological Review, 109, 55-65.
Minsky, M. (1985). Society of mind. New York: Simon & Schuster. Thibadeau, R., Just, M. A., & Carpenter, P. A. (1982). A model of the time course and content of reading. Cognitive Science, 6, 157-203.
Rumelhart, D. E., & McClelland, J. L. (1982). An interactive activation model of context effects in letter perception: Part 2. The contextual enhancement effect and some tests and extension of the model. Psychological Review, 89, 60-94.

Operating Principles of 4CAPS

4CAPS embodies six operating principles that specify the nature of cognitive and cortical information processing.

An initial principle is intended to capture the current consensus of the field.

1. Thinking is the product of the concurrent activity of multiple brain areas that collaborate in a large-scale cortical network.

The next four principles, which constitute the theoretical core of our proposal, are relatively novel.

2. Each cortical area can perform multiple cognitive functions, and conversely, many cognitive functions can be performed by more than one area.

3. Each cortical area has a limited capacity of computational resources, constraining its activity.

4. The topology of a large-scale cortical network changes dynamically during cognition, adapting itself to the resource limitations of different cortical areas and to the functional demands of the task at hand.

5. The communications infrastructure that supports collaborative processing is also subject to resource constraints, construed here as bandwidth limitations.

Finally, we propose a measurement assumption that enables our theoretical constructs to make contact with neuroimaging data.

6. The activation of a cortical area as measured by imaging techniques such as fMRI and PET varies as a function of its cognitive workload. The reader interested in the technical details of how these principles are realized within a hybrid symbolic-connectionist architecture is directed to Just and Varma (2007).

Source Code & Documentation for 4CAPS and Models

4CAPS is written in ANSI Common Lisp. In theory, it should run in any compliant implementation of the language. In practice, it has been tested in two commercial products, Digitool's Macintosh Common Lisp (through version 5.0) and Franz's Allegro Common Lisp (through version 6.5). A list of free and commercial Common Lisp implementations and useful information about the language are available at the Association of Lisp Users website.

4CAPS
Models
Sentence Comprehension
Tower of London
Mental Rotation
Driving
Tower of Hanoi
Dual Sentence Comprehension and Mental Rotation
Dual Sentence Comprehension and Driving
Dual Auditory and Visual Sentence Comprehension

4CAPS Source Code & Documentation

The source code for 4CAPS is available here. To run 4CAPS, load this file into your Common Lisp environment. There are two caveats to be aware of.

* The first concerns interpretation vs. compilation. Some Common Lisps environments (e.g., Digitool's) automatically compile all source code. However, others (e.g., Franz's) use the interpreter by default. In this case, you will want to compile the 4CAPS source code before loading it. This is done via the compile-file function or via a menu choice; the result should be a so-called "fasl" (fast load) file. Compilation is not necessary, but will greatly speed performance.

* The second concerns packages. 4CAPS will be loaded into the "CL-USER" package. To gain access to its functionality, you will have to operate within this package. Perhaps the simplest way to do this is to type (in-package "CL-USER") after loading 4CAPS.

There is no 4CAPS tutorial or manual. Rather, there exist several manuscripts written by several people over the years, each documenting a slightly different version of the cognitive architecture.
Sashank Varma has written three manuals of varying completeness.
Varma manual 1
Varma manual 2
Varma manual 3
Scott Sanner has also written a 4CAPS manual.
Sanner manual

Sentence Comprehension Model

The Sentence Comprehension Model is briefly described in Just et al. (1999) and comprehensively described in Just and Varma (2007). Its source code is available here.

At the bottom of the file are a number of commands that are defined for simulating the comprehension of sentences used in a number of empirical studies. Some are for behavioral studies of neurotypical young adults: King & Just (1991): (king1991 &optional (summ-p t)) MacDonald et al. (1992): (macdonald1992 &optional (summ-p t)). One is for a behavioral study of lesion patients: Haarmann et al. (1997): (haarmann1997 &optional (summ-p t)). One is for an fMRI study that employs a block design: Just et al. (1996): (just1996 &optional (summ-p t)). Others are for event-related fMRI studies: Mason et al. (2003): (mason2003 &optional (summ-p t)) Caplan et al. (2001): (caplan2001 &optional (summ-p t)). One is for a block-design fMRI study of a lesion patient: Thulborn et al. (1999):(thulborn1999 &optional (summ-p t)). The :summ-p parameter is optional. It can be either t or nil. When t - its default value - the summ command is called after every simulation to pretty-print the results. The model defines a sim command for simulating comprehension of a sentence and a summ command for pretty-printing the temporal and resource utilization results. These are the components from which the above study-specific commands are built. The user can use them to write commands for simulating different studies. (The user must also extend the model's lexicon, which is defined in a straightforward fashion in the middle of the file.)

References
Caplan, D., Vijayan, S., Kuperberg, G., West, C., Waters, G., Greve, D., & Dale, A. M. (2001). Vascular responses to syntactic processing: Event-related fMRI study of relative clauses. Human Brain Mapping, 15, 26-38.
Haarmann, H. J., Just, M. A., & Carpenter, P. A. (1997). Aphasic sentence comprehension as a resource deficit: A computational approach. Brain and Language, 59, 76-120.
Just, M. A., Carpenter, P. A., Keller, T. A., Eddy, W. F., & Thulborn, K. R. (1996). Brain activation modulated by sentence comprehension. Science, 274, 114-116.
King, J., & Just, M. A. (1991). Individual differences in syntactic processing: The role of working memory. Journal of Memory and Language, 30, 580-602.
MacDonald, M. C., Just, M. A., & Carpenter, P. A. (1992). Working memory constraints on the processing of syntactic ambiguity. Cognitive Psychology, 24, 56-98.
Mason, R. A., Just, M. A., Keller, T. A., & Carpenter, P. A. (2003). Ambiguity in the brain: How syntactically ambiguous sentences are processed. Journal of Experimental Psychology: Learning, Memory, & Cognition, 29, 1319-1338.
Thulborn, K. R., Carpenter, P. A., & Just, M. A. (1999). Plasticity of language-related brain function during recovery from stroke. Stroke, 30, 749-754. &
Just, M. A., & Varma, S. (2007). The cognitive neuroarchitecture of sentence comprehension. Cognitive, Affective, & Behavioral Neuroscience, 7, 153-191.

Tower Of London

The Tower of London (TOL) Model is described in Newman et al. (2003) and Just and Varma (2007). Its source code is available here.

At the bottom of the file, five commands are defined for testing the model's performance on problems of increasing complexity. These are invoked as: (test1) (test2) (test3) (test4) (test5). The model defines a sim command for simulating the solution of a given problem and a summ command for pretty-printing the temporal and resource utilization results. These are defined at the bottom of the file and used by the various test commands. It is easy to infer how these work and to write new commands that have the model solve different problems.

There are two TOL studies. The "old" study is the published study Newman et al. (2003). The "new" study is the unpublished study conducted by Sharlene Newman and Greg Sliwoski. It uses different problems and more strictly varies problem complexity. The file for simulating the problems of these studies is located here.
The following command simulates solution of all old problems:
(old-sims)
This one simulates all new problems,
(new-sims)
and this one all old and new problems:
(all-sims)

The columns have the following meanings:
START: The starting state number. The state space is given in Newman et al. (2003), with each state numbered. (A problem is defined by a starting state and an ending state.)
END: The ending state number.
RESULT: Whether the model solved the problem.
MIN?: Whether the problem was solved in a minimum number of moves.
MINMOV: The minimum number of moves required to solve the problem.
MODMOV: The number of moves required by the model.
CYCS: Time required by the model.
BLOPOS: Don't worry about this for now.

The following commands also simulate the solution of old and new problems, respectively, but display the results as a function of increasing minimum-length solutions.
(fig-old-sims)
(fig-new-sims)

They clarify the aspects of model performance that vary with increasing problem difficulty.

References
Just, M. A., & Varma, S. (2007). The organization of thinking: What functional brain imaging reveals about the neuroarchitecture of complex cognition. Cognitive, Affective, & Behavioral Neuroscience, 7, 153-191.
Newman, S. D., Carpenter, P. A., Varma, S., & Just, M. A. (2003). Frontal and parietal participation in problem solving in the Tower of London: fMRI and computational modeling of planning and high-level perception. Neuropsychologia, 41, 1668-1682.

Mental Rotation Model

The Mental Rotation (MR) Model is described in Just and Varma (2007). Its source code is available here.

At the bottom of the file is a command for simulating the solution of the Shepard and Metzler (1971) (SM) problems used by Carpenter et al. (1999):
Carpenter et al. (1999): (mr1999)

The model defines a sim command for simulating the solution of a given problem:
(sim &key (angle 40) (mirror-p nil))
The angle parameter is the angular disparity between the two SM figures. When mirror-p is nil, the figures are congruent; when it is t, they are mirror-images of one another. All simulations run by the sim command currently use the same SM figure (and its mirror image). Why? The model itself is perfectly general. However, it is quite cumbersome to define new SM figures using the Marr and Nishihara (1977) representational scheme that the model adopts. The structure of the figure is documented in the header comment for the sim command, and can be changed by people of sufficient bravery.

The model also defines a summ command for pretty-printing the temporal and resource utilization results. These are defined at the bottom of the file and used by the mr1999 command. They can be combined by the user into commands that simulate the results of other studies.

References
Carpenter, P. A., Just, M. A., Keller, T., Eddy, W. F., & Thulborn, K. R. (1999). Graded functional activation in the visuospatial system with the amount of task demand. Journal of Cognitive Neuroscience, 11, 9-24
Just, M. A., & Varma, S. (2007). The organization of thinking: What functional brain imaging reveals about the neuroarchitecture of complex cognition. Cognitive, Affective, & Behavioral Neuroscience, 7, 153-191.
Marr, D., & Nishihara, H. K. (1978). Representation and recognition of the spatial organization of three-dimensional shapes. Proceedings of the Royal Society of London B, 200, 269-294.
Shepard, R. N., & Metzler, J. (1971). Mental rotation of three-dimensional objects. Science, 171, 701-703

Driving Model

The Driving Model is not yet described in any publication. However, these slides provide an overview of its operation and its fit to the results of two (unpublished) fMRI studies. The Driving model implements the algorithm defined by Salvucci and Gray (2004). Its source code is available here.

At the bottom of the file is a command for simulating navigation of a road.
(sim &key (rv *ccbi-rv*) (time 60))

The :rv ("road view") parameter is the road to be driven. Several are defined in the model:
*ccbi-rv*
: Polynomial approximation of the road driven by subjects in CCBI driving experiments.
*easy-rv*
: A road defined by a single sin curve.
*medium-rv*
: A road defined by the sum of two sin curves of different frequencies.
*hard-rv*
: A road defined by the sum of three sin curves of different frequencies.

The idea of defining roads sinusoidally was taken from Strayer and Johnston (2001). New polynomial or sinusoidal roads can be defined using the provided Lisp functions. The :time parameter is the number of seconds of driving to be simulated.

The model also defines a summ command for pretty-printing the temporal and resource utilization results.

References
Salvucci, D. D., & Gray, R. (2004). A two-point visual control model of steering. Perception, 33, 1233-1248.
Strayer, D. L., & Johnston, W. A. (2001). Driven to distraction: Dual-task studies of simulated driving and conversing on a cellular telephone. Psychological Science, 12, 462-466.

Tower of Hanoi

The Tower of Hanoi (TOH) Model is described in Varma (2006). Its source code is available here.

At the bottom of the file, five commands are defined for testing the model's performance on problems of increasing complexity. These are invoked as:
(test3)
(test4)
(test5)


test3 simulates solution of the standard 3-disk problem, where a stack of 3 disks must be moved from the left peg to the right peg. test4 and test5 simulate solution of the standard 4-disk and 5-disk problems, respectively.

The model defines a sim command for simulating the solution of a given problem and a summ command for pretty-printing the temporal and resource utilization results. These are defined at the bottom of the file and used by the various test commands. It is easy to infer how they work and define commands for simulating the results of other studies.

There exist commands for simulating solution of problems used in behavioral studies of neurotypical adults (Anderson, 1993; Carpenter et al., 1990; Just et al., 1996; Ruiz, 1987), behavioral studies of patients with frontal lesions (Goel et al., 2001; Morris et al., 1997a; Morris et al., 1997b), and fMRI studies of neurotypical adults (Anderson et al., 2005; Fincham et al., 2002).

References
Anderson, J. R. (1993). Rules of the mind. Hillsdale, NJ: Erlbaum.
Anderson, J. R., Albert, M. V., & Fincham, J. M. (2005). Tracing problem solving in real time: fMRI analysis of the subject-paced Tower of Hanoi. Journal of Cognitive Neuroscience, 17, 1261-1274.
Carpenter, P. A., Just, M. A., & Shell, P. (1990). What one intelligence test measures: A theoretical account of the processing in the Raven Progressive Matrices Test. Psychological Review, 97 404-431.
Goel, V., Pullara, S. D., & Grafman, J. (2001). A computational model of frontal lobe dysfunction: working memory and the Tower of Hanoi task. Cognitive Science, 25, 287-313.
Fincham, J, M., Carter, C. S., van Veen, V., Stenger, V. A., & Anderson, J. R. (2002). Neural mechanisms of planning: A computational analysis using event-related fMRI. Proceedings of the National Academy of Sciences, 99, 3346-3351.
Just, M. A., Carpenter, P. A., & Hemphill, D. D. (1996). Constraints of processing capacity: Architectural or implementational? In D. Steier & T. M. Mitchell (Eds.), Mind matters: A tribute to Allan Newell (pp. 141-178). Hillsdale, NJ: Erlbaum.
Morris, R. G., Miotto, E. C., Feigenbaum, J. D., Bullock, P., & Polkey, C. E. (1997a). The effect of goal-subgoal conflict on planning after frontal- and temporal-lobe lesions in humans. Neuropsychologia, 35, 1147-1157. Morris, R. G., Miotto, E. C., Feigenbaum, J. D., Bullock, P., & Polkey, C. E. (1997b). Planning ability after frontal and temporal lobe lesions in Humans: The effects of selection quivocation and working memory load. Cognitive Neuropsychology, 14, 1007-1027.
Ruiz, D. (1987). Learning and problem solving: What is learned while solving the Tower of Hanoi (Doctoral dissertation, Stanford University, 1987). Dissertation Abstracts International, 42, 3438B.
Varma, S. (2006). A computational model of Tower of Hanoi problem solving. Unpublished doctoral dissertation, Vanderbilt University.

Dual Sentence Comprehension and Mental Rotation

The Dual Comprehension-Rotation Model is described in Just and Varma (2007). It is not a new model per se, but rather the result of loading two separate models, combined with a glue script found here. It is defined by the following steps: Start Common Lisp, load 4CAPS, and enter the "CL-USER" package. Type (setq *dual-task* t) Load the Sentence Comprehension Model (SCM) Load the Mental Rotation (MR) Model Load the glue script The glue script combines the SCM and mental rotation models into an aggregate model that can perform sentence comprehension or mental rotation in isolation or concurrently. The glue script first defines a new facility for defining a "model." This is necessary for informing the system which model or models of the multiple defined models are to run for the task at. It also defines a new version of the main recognize-act loop for matching productions against declarative memory that is sensitive to the presence of multiple models and that records the activity or dormancy of each. Finally, it defines a generalized sim command for running one or more simulations at the same time and a generalized summ command for pretty-printing the results of one or more simulations.

The sentence comprehension and mental rotation models are defined next, as are commands for simulating the single-task conditions. (jv2005): Simulates comprehension of the sentences described in Just and Varma (2007). (mr1999): Simulates solution of the Shepard-Metzler problems solved by the Carpenter et al. (1999) subjects. These commands demonstrate the utility of the generalized sim and summ commands.

Finally, a command for simulating the Just et al. (2001) study of dual sentence comprehension and mental rotation is defined. (dt2001 &optional (summ-p t)) The :summ-p parameter is optional. It can be either t or nil. When t - its default value - the summ command is called after every simulation to pretty-print the results.

References
Carpenter, P. A., Just, M. A., Keller, T., Eddy, W. F., & Thulborn, K. R. (1999). Graded functional activation in the visuospatial system with the amount of task demand. Journal of Cognitive Neuroscience, 11, 9-24.
Just, M. A., Carpenter, P. A., Keller, T. A., Emery, L., Zajac, H., & Thulborn, K. R. (2001). Interdependence of nonoverlapping cortical systems in dual cognitive tasks. NeuroImage, 14, 417-426.
Just, M. A., & Varma, S. (2007). The organization of thinking: What functional brain imaging reveals about the neuroarchitecture of complex cognition. Cognitive, Affective, & Behavioral Neuroscience, 7, 153-191.

Dual Sentence Comprehension and Driving

The Dual Comprehension-Driving Model is not yet described in any publication. However, these slides provide an overview of its operation and fit to the results of an (unpublished) fMRI study. It is not a new model per se, but rather the result of loading two separate models, combined with a glue script found here. It is defined by the following steps: Start Common Lisp, load 4CAPS, and enter the "CL-USER" package. Type (setq *dual-task* t) Load the Sentence Comprehension Model (SCM) Load the Driving Model Load the glue script The glue script combines the SCM and driving models into an aggregate model that can perform sentence comprehension or driving in isolation or concurrently. The glue script first defines a new facility for defining a "model." This is necessary for informing the system which model or models of the multiple defined models are to run for the task at hand. It also defines a new version of the main recognize-act loop for matching productions against declarative memory that is sensitive to the presence of multiple models and that records the activity or dormancy of each. Finally, it defines a generalized sim command for running one or more simulations at the same time and a generalized summ command for pretty-printing the results of one or more simulations.

The sentence comprehension and mental rotation models are defined next, as are commands for simulating the single-task conditions. (jv2005): Simulates comprehension of the sentences described in Just and Varma (2007). (drv2003 &key (time 15)): Simulates driving :time seconds on the *ccbi-rv* road view that subjects navigate in CCBI driving experiments These commands that demonstrate the utility of the generalized sim and summ commands.

Finally, a command for simulating dual-task sentence comprehension and driving is defined. (dt-driving &key (time (* 95 *secs-per-mcyc*))) The :time parameter is the number of seconds of driving to be simulated. It is converted in the parameter list to the temporal scale of 4CAPS, which is measured in cycles.

Dual Auditory and Visual Sentence Comprehension

The Dual (Auditory and Sentence) Comprehension (DC) model is not yet described in any publication. Its source code is available here.

The Dual Comprehension model differs from the other dual-task models in that it does not consist of two independent models running at the same time, but rather the same model processing two input streams simultaneously - think dual threads, not dual processes. It is defined as follows:
Start Common Lisp, load 4CAPS, and enter the "CL-USER" package.
Load the DC model.
Load the script found here.

The DC is a slight augmentation of the conventional SCM. It adds the task-goal class of declarative memory element - should the auditory input stream be attended, the visual input stream, or both - It also adds an Executive center to house these goals and Phonological and Orthographic centers to house percepts of the different modalities.

The sim command for simulating comprehension of a sentence has been elaborated into three separate commands:
(aud-sim sent)
(vis-sim sent)
(dual-sim aud-sent vis-sent)


The sent, aud-sent, and vis-sent arguments to these commands are lists of words. These commands are defined at the bottom of the file. The summ command has also been extended to pretty-print the resource utilizations of the three new centers (as well as the four existing centers). It is also defined at the bottom of the file.