The memory-prediction framework is a theory of brain function that was created by Jeff Hawkins and described in his 2004 book, ‘On Intelligence.’ This theory concerns the role of the mammalian neocortex and its associations with the hippocampus and the thalamus in matching sensory inputs to stored memory patterns and how this process leads to predictions of what will happen in the future.
The theory is motivated by the observed similarities between the brain structures (especially neocortical tissue) that are used for a wide range of behaviours available to mammals. It posits that the remarkably uniform physical arrangement of cortical tissue reflects a single principle or algorithm which underlies all cortical information processing.
The basic processing principle is hypothesized to be a feedback/recall loop which involves both cortical and extra-cortical participation (the latter from the thalamus and the hippocampus in particular). The memory-prediction framework provides a unified basis for thinking about the adaptive control of complex behavior. Although certain brain structures are identified as participants in the core ‘algorithm’ of prediction-from-memory, these details are less important than the set of principles that are proposed as basis for all high-level cognitive processing.
The central concept of the memory-prediction framework is that bottom-up inputs are matched in a hierarchy of recognition, and evoke a series of top-down expectations encoded as potentiations. Consider, for example, the process of vision. Bottom-up information starts as low-level retinal signals (indicating the presence of simple visual elements and contrasts). At higher levels of the hierarchy, increasingly meaningful information is extracted, regarding the presence of lines, regions, motions, etc.
Even further up the hierarchy, activity corresponds to the presence of specific objects – and then to behaviors of these objects. Top-down information fills in details about the recognized objects, and also about their expected behavior as time progresses. The sensory hierarchy induces a number of differences between the various layers. As one moves up the hierarchy, representations have increased: Extent (for example, larger areas of the visual field, or more extensive tactile regions); Temporal stability (lower-level entities change quickly, whereas, higher-level percepts tend to be more stable); and Abstraction (through the process of successive extraction of invariant features, increasingly abstract entities are recognized). The relationship between sensory and motor processing is an important aspect of the basic theory.
It is proposed that the motor areas of cortex consist of a behavioral hierarchy similar to the sensory hierarchy, with the lowest levels consisting of explicit motor commands to musculature and the highest levels corresponding to abstract prescriptions (e.g. ‘resize the browser’). The sensory and motor hierarchies are tightly coupled, with behavior giving rise to sensory expectations and sensory perceptions driving motor processes. Finally, it is important to note that all the memories in the cortical hierarchy have to be learnt – this information is not pre-wired in the brain. Hence, the process of extracting this representation from the flow of inputs and behaviors is theorized as a process that happens continually during cognition.
Hawkins has extensive training as an electrical engineer. Another way to describe the theory (hinted at in his book) is as a learning hierarchy of feed forward stochastic state machines. In this view, the brain is analyzed as an encoding problem, not too dissimilar from future-predicting error-correction codes. The hierarchy is a hierarchy of abstraction, with the higher level machines’ states representing more abstract conditions or events, and these states predisposing lower-level machines to perform certain transitions. The lower level machines model limited domains of experience, or control or interpret sensors or effectors. The whole system actually controls the organism’s behavior.
Since the state machine is ‘feed forward,’ the organism responds to future events predicted from past data. Since it is hierarchical, the system exhibits behavioral flexibility, easily producing new sequences of behavior in response to new sensory data. Since the system learns, the new behavior adapts to changing conditions. That is, the evolutionary purpose of the brain is to predict the future, in admittedly limited ways, so as to change it.
The hierarchies are theorized to occur primarily in mammalian neocortex. In particular, neocortex is assumed to consist of a large number of columns (as surmised also by Vernon Benjamin Mountcastle from anatomical and theoretical considerations). Each column is attuned to a particular feature at a given level in a hierarchy. It receives bottom-up inputs from lower levels, and top-down inputs from higher levels. To account for storage and recognition of sequences of patterns the thalamus acts as a ‘delay line.’
Another anatomically diverse brain structure which is hypothesized to play an important role in hierarchical cognition is the hippocampus. It is well known that damage to the hippocampus impairs the formation of long-term declarative memory; individuals with such damage are unable to form new memories of episodic nature, although they can recall earlier memories without difficulties and can also learn new skills.
In the current theory, the hippocampus is thought as the top level of the cortical hierarchy; it is specialized to retain memories of events that propagate all the way to the top. As such events fit into predictable patterns, they become memorizable at lower levels in the hierarchy. Thus, the hippocampus continually memorizes ‘unexpected’ events (that is, those not predicted at lower levels); if it is damaged, the entire process of memorization through the hierarchy is compromised.
The memory-prediction framework explains a number of psychologically salient aspects of cognition. For example, the ability of experts to effortlessly analyze and remember complex problems within their field is a natural consequence of their formation of increasingly refined conceptual hierarchies. Also, the procession from ‘perception’ to ‘understanding’ is readily understandable as a result of the matching of top-down and bottom-up expectations. Mismatches, in contrast, generate the exquisite ability of biological cognition to detect unexpected perceptions and situations. (Deficiencies in this regard are a common characteristic of current approaches to artificial intelligence.)