This article considers how experience can influence the developing and mature brain and proposes a new categorization scheme based upon the type of information stored and the brain mechanisms that appear to be involved in storing it. In this scheme, experience-expectant information storage refers to incorporation of environmental information that is ubiquitous in the environment and common to all species members, such as the basic elements of pattern perception. Experience-expectant processes appear to have evolved as a neural preparation for incorporating specific information: in many sensory systems, synaptic connections between nerve cells are overproduced, and a subsequent selection process occurs in which aspects of sensory experience determine the pattern of connections that remains. Experience-dependent information storage refers to incorporation of environmental information that is idiosyncratic, or unique to the individual, such as learning about one's specific physical environment or vocabulary. The neural basis of experience-dependent processes appears to involve active formation of new synaptic connections in response to the events providing the information to be stored. Although these processes probably do not occur entirely independently of one another in development, the categories offer a new view more in accord with neural mechanisms than were terms like "critical" or "sensitive period."

Experience and brain development.

Glutamate receptor function in learning and memory

The Organization of Learning そして

Neuroanatomical and neurochemical substrates of timing.

The hippocampal formation (comprising the hippocampus proper, the dentate gyrus, and the subiculum) has been repeatedly implicated in information storage models of the mammalian brain. The precise nature of the hippocampal role in the storage of information has, however, remained elusive. Here it is proposed that the role of the hippocampus is to form and retain an index of neocortical areas activated by experiential events. The hippocampal index, thus, represents those unique cortical regions activated by specific events. The neuronal mechanism underlying the memory index is hypothesized to be long-term potentiation. It is asserted that the reactivation of the stored hippocampal memory index will serve to also reactivate the associated unique array of neocortical areas and thus will result in a memorial experience. This hippocampal reactivation of a neocortical array may also be involved in establishing a cortically based memory trace.

The hippocampal memory indexing theory.

This experiment determined if rats could extrapolate a familiar serial sequence of diminishing food quantities by accurately anticipating a novel quantity added to the end of the sequence. In 13 days of training, rats ran in a straight runway to obtain quantities of food pellets presented in sequential order. A strongly monotonic group received repetitions of a formally simple pattern of 14-7-3-1 pellets of food, while weakly monotonic and nonmonotonic groups received formally more complex 14-5-5-1 and 14-3-7-1 patterns, respectively. In subsequent transfer, a 0-pellet quantity was added to each pattern, thus extending pattern length to five elements. Results of the very first pattern repetition containing the added 0-pellet element indicated that rats in the strongly monotonic condition, but not in the others, anticipated the reduced quantity before actually experiencing it. This result supports a cognitive, rule-learning hypothesis for serial learning by rats.

https://link.springer.com/content/pdf/10.3758/BF03197847.pdf

Extrapolation of serial stimulus patterns by rats

Phrasing cues and hierarchical organization in serial pattern learning by rats.

Two studies used a stimulus tracking paradigm to test whether rats are sensitive to the rule-based formal properties of structured serial patterns. Hooded rats tracked 16-element patterns of flashing lights by pressing levers under an array of 6 indicator lights. In Experiment 1, rats tracked a pattern similar to one previously used with human subjects and yielded remarkably similar results. More errors and response omissions occurred at boundaries of structural "chunks" than within chunks, and errors often reflected anticipation of the next chunk or extrapolation of the preceding chunk. In Experiment 2, temporal "phrasing" cues encouraged different groups to encode a pattern as a series of either "runs" or "trills." Differential placement of pauses induced rats to encode different rule-based representations of the pattern. Results indicate that under appropriate conditions rats may encode a representation of formal structure when they learn organized response patterns.

Rule abstraction, item memory, and chunking in rat serial-pattern tracking

In 3 experiments, rats and humans learned serial patterns composed of 24, 30, or 36 items. Patterns had a 2-, 3-, or 4-level hierarchical rule structure. In Experiments 1 and 2, patterns had either perfect hierarchical structure or 2 modified chunks that violated hierarchical structure, thus producing linear structure (i.e., nonhierarchical structure). For both rats and humans, pattern structure predicted pattern learning difficulty and also the nature and relative frequency of errors. Both treated chunks that were inconsistent with hierarchical structure as violation chunks, that is, they made errors that reflected their "tendency to regularize the perception of an irregular pattern" (F. Restle & B. L. Burnside, 1972). The results support the view that rats can abstract and encode a representation of multilevel hierarchical structure in serial patterns in much the same way as humans do in analogous tasks.

Coding of hierarchical versus linear pattern structure in rats and humans.

Rats learned serial patterns composed of either "run" chunks (e.g., 123 234 ...) or "trill" chunks (e.g., 121 232 ...). For each type of pattern, 1 group of rats encountered an element at the end of the pattern that violated the run or trill structure. In both run and trill patterns, violations were unusually difficult for rats to learn, whereas corresponding elements in "perfect" patterns that did not violate pattern structure were easy. Additionally, rats' errors on violation elements conformed to the structure of the patterns in which they were embedded. Thus, rats were sensitive to the run or trill organization of their patterns and mastered the rules governing the pattern before learning "exceptions to the rule."

Stephen B. Fountain

Papers

Extrapolation of serial stimulus patterns by rats

Phrasing cues and hierarchical organization in serial pattern learning by rats.

Rule abstraction, item memory, and chunking in rat serial-pattern tracking

Coding of hierarchical versus linear pattern structure in rats and humans.

Sensitivity to violations of "run" and "trill" structures in rat serial-pattern learning.