A Standard Model of Mind: AAAI Technical Report FS-17-05 Towards A Unified Theory of Mind and Brain Stephen Grossberg Center for Adaptive Systems, 677 Beacon Street, Boston University, Boston, MA 02215 [email protected] Abstract A unified theory of how brains give rise to minds has been getting paradigms in order to achieve biological intelligence: steadily developed for 60 years. This theory has explained data Complementary Computing and Laminar Computing. from thousands of interdisciplinary experiments, and scores of its Complementary Computing. Grossberg (2000) describes predictions have been confirmed. The theory includes new com- how the brain is organized into complementary parallel putational paradigms called Complementary Computing and processing streams whose interactions generate biological- Laminar Computing that clarify the global organization of brain ly intelligent behaviors. A single cortical processing stream dynamics and behavior. It uses a small number of basic equations can individually compute some properties well, but cannot, and modules to form modal architectures that model different by itself, process other computationally complementary modalities of intelligence. One of its models, Adaptive Resonance Theory, or ART, is currently the most advanced cognitive and properties. Pairs of complementary cortical processing neural theory of how advanced brains incrementally and stably streams interact to generate emergent properties that over- learn to attend, recognize, and predict objects and events in a come their complementary deficiencies to compute com- changing world. All the basic ART predictions have been con- plete information with which to represent or control some firmed by psychological and neural data. These results provide a faculty of intelligent behavior. Thus, although brain anat- firm foundation for further development of a Standard Theory of omy embodies a great deal of functional specialization, the Mind. there are no "independent modules" in advanced brains. Unified Theory of Mind and Brain Complementary Computing clarifies how different brain regions can achieve a great deal of specialization without Major design principles, mechanisms, and architectures being independent modules. have been discovered and developed during the past 60 years as part of an emerging unified theory of biological The WHAT and WHERE Cortical Streams intelligence, notably how brain mechanisms give rise to are Complementary mental functions as emergent properties. As of this writing, thousands of psychological and neurobiological experi- For example, the category learning, attention, recognition, ments have been explained and predicted in a unified way, and prediction circuits of the ventral, or What, cortical pro- including data about perception, cognition, cognitive- cessing stream for perception and cognition are computa- emotional dynamics, and action in both normal individuals tionally complementary to those of the dorsal, or Where and clinical patients. Key articles may be downloaded at and How, cortical processing steam for spatial representa- htty://cns.bu.edu/~steve. These results include a systematic tion and action (Grossberg, 2000, 2013, 2017). One reason analysis of what is happening in individual brains when for What-Where complementarity is that the What stream they consciously see, hear, feel, or know something, and learns object recognition categories that are substantially how we can experience integrated moments of seeing, invariant under changes in an object's view, size, and posi- hearing, feeling, and knowing (Grossberg, 2017). tion. These invariant object categories enable our brains to recognize valued objects without experiencing a combina- Brain paradigms: Complementary Computing torial explosion. They cannot, however, locate and act up- on a desired object in space. Where stream spatial and mo- and Laminar Computing tor representations can locate objects and trigger actions towards them, but cannot recognize them. What stream The possibility of this synthesis is predicated upon the dis- dynamics embody many aspects of declarative learning covery that advanced brains embody novel computational and memory, whereas Where stream dynamics realize pro- cedural learning and memory. By interacting together, the 354 What and Where streams can recognize valued objects and obstacles and spatial navigation in the dark; invariant ob- direct appropriate goal-oriented actions towards them. ject and scenic gist learning, recognition, and search; pro- totype, surface, and boundary attention; gamma and beta Adaptive Resonance Theory oscillations during cognitive dynamics; learning of ento- rhinal grid cells and hippocampal place cells, including the Abundant psychological and neurobiological data have use of homologous spatial and temporal mechanisms in the confirmed all of my foundational predictions concerning medial entorhinal-hippocampal system for spatial naviga- how perceptual/cognitive processes in the What stream use tion and the lateral stream for adaptively timed cognitive- excitatory matching and match-based learning to create emotional learning; breakdowns in attentive vigilance dur- self-stabilizing categorical representations of objects and ing autism, medial temporal amnesia, and Alzheimer's dis- events, notably recognition categories that can be learned ease; social cognitive abilities such as the learning of joint quickly without experiencing catastrophic forgetting dur- attention and the use of tools from a teacher, despite the ing subsequent learning. In other words, this recognition different coordinate systems of the teacher and learner; a learning process solves the so-called stability-plasticity unified circuit design for all item-order-rank working dilemma. These processes enable increasing expertise, and memories that enable stable learning of recognition catego- an ever-expanding sense of self, to emerge throughout life. ries, plans, and expectations for the representation and con- Excitatory matching by object attention is embodied by the trol of sequences of linguistic, spatial, and motor infor- ART Matching Rule. This type of attentional circuit ena- mation; conscious speech percepts that are influenced by bles us to prime our expectations to anticipate objects and future context; auditory streaming in noise during source events before they occur, and to focus attention upon ex- segregation; and speaker normalization that enables lan- pected objects and events when they do occur. Good guage learning from adults after a critical period of bab- enough matches between expected and actual events trig- bled sounds by a child; cognitive-emotional dynamics that ger resonant states that can support learning of new recog- direct motivated attention towards valued goals; and adap- nition categories and refinement of old ones, while also tive sensory-motor control circuits, such as those that co- triggering conscious recognition of the critical feature pat- ordinate predictive smooth pursuit and saccadic eye terns that are attended as part of these percepts. Excitatory movements, and coordinate looking and reaching move- matching also controls reset of the attentional focus when ments. Brain regions that are functionally described in- bottom-up inputs significantly mismatch currently active clude visual and auditory neocortex; specific and nonspe- top-down expectations. Cycles of resonance and reset un- cific thalamic nuclei; inferotemporal, parietal, prefrontal, derlie much of the brain's perceptual and cognitive dynam- entorhinal, hippocampal, parahippocampal, perirhinal, and ics. motor cortices; frontal eye fields; supplementary eye These matching and learning laws have been articu- fields; amygdala; basal ganglia: cerebellum; superior col- lated as part of Adaptive Resonance Theory, or ART, liculus; and reticular formation. which has been progressively developed since it was first These results include many important particulars. reported in 1976. ART is a cognitive and neural theory of For example, all working memories obey an LTM Invari- how the brain autonomously learns to attend, recognize, ance Principle that enables them to temporarily store se- and predict objects and events in a changing world. ART is quences of events that can be stably chunked and remem- currently the most highly developed cognitive and neural bered. This fact implies that all linguistic, spatial, and mo- theory available, with the broadest explanatory and predic- tor working memories are variants of the same network tive range. Central to ART's predictive power is its ability design. Properties like bounded rationality readily follow to carry out fast, incremental, and stable unsupervised and from this shared working memory design. supervised learning in response to a changing world. ART ART also clarifies how so-called attentional bottle- specifies mechanistic links between processes of con- necks may arise from basic constraints on how object sciousness, learning, expectation, attention, resonance, and learning is regulated and dynamically stabilized by interac- synchrony (the CLEARS processes) during both unsuper- tions that use spatial and object attention. vised and supervised learning. I have predicted that all ART does not, however, describe many spatial and brains that can solve the stability-plasticity dilemma do so motor processes and their behaviors, which employ differ- using these predicted links between CLEARS processes. ent matching and learning laws. ART is thus not "a theory