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Parallel Processing in Rat Cognition:

Multiple Cognitive Processes in Complex Behavior

 
 

Hierarchically organized serial patterns.  One prediction from the rule-learning view is that a highly organized, hierarchically structured sequence should be easier to learn than a sequence having little or no higher-order structure.  We designed several studies to explore whether pattern structure would determine the ease or difficulty of learning long and elaborate patterns.  

In one experiment (Fountain et al., 1995b), we tested whether pattern structure described as “runs” (e.g., 1-2-3-4-5-…) or “trills” (e.g., 1-2-1-2-1-…) would determine the ease or difficulty of anticipating a final sequence item that either conformed to the implied structure of the sequence or violated pattern structure.  Rats received patterns having either perfect structure or one sequence element (the last in the series) that violated an otherwise perfect structure:

"Perfect Runs"            *************** 567 678 781 812 ...

"Violation Runs"          *************** 567 678 781 818 ... (violation element indicated)

"Perfect Trills"             121 232 343 454 565 676 787 818 ...

"Violation Trills"           121 232 343 454 565 676 787 812 ... (violation element indicated)

A 1-s ITI was used except where spaces indicate 3-s phrasing cues.  [Note once again that Lever 1 is immediately to the right of Lever 8, so that, for example, a 6-7-8-1-2 sequence would be a quite natural "run" series.]  As shown in Figure 3, high error rates were observed in acquisition on the violation trial (the last trial of the pattern) for both Violation Runs and Violation Trills patterns, despite the fact that one view might predict generalization of associations from other parts of the pattern should have predisposed animals to learn the violation patterns easily.  For example, in the Violation Trills pattern a correct response on lever 1 should always predict that the next response should be to lever 2, yet rats had great difficulty learning to respond on Lever 2 on the last trial of the pattern but not the second trial of the pattern.  No comparable errors were observed for the Perfect Runs or Perfect Trills patterns. An alternative view is that rats learned about the highly repetitive structure of the sequence that resulted in highly repetitive patterns of response to learn the sequence even when doing so produced errors at the violation element, even though these errors might have been avoided by adopting another strategy.  CF1 mice show the same pattern of results as rats when learning the perfect and violation runs patterns described here(Fountain et al., 1999).           

Figure 3.   Rats' mean percentage of pattern tracking errors for hierarchical (top panel) and linear (bottom panel) patterns as a function of the 24 items of the patterns collapsed across 7 days of training.  “Runs” versus “trills” structure predicted difficulty and types of errors observed for the final violation element of the violation patterns (Fountain & Rowan, 1995b). 

In another set of studies, we tested whether pattern structure would determine the ease or difficulty of pattern learning by developing patterns with hierarchical structure, then reordering chunks of the pattern to produce “linear” structure, that is, a sequence of unrelated chunks.  The Hierarchical (H) and Linear (L) patterns were:

H Pattern: *************** 567 876 765 654 543 432...

L Pattern: 123 234 543 456 567 876 765 654 345 432...

For both groups, the digits indicate the clockwise position of the correct response on successive trials and spaces indicate brief pauses. 

The completely nested H pattern is described by a simple hierarchical rule structure: elements within 3-element chunks are related by first-order rules, chunks within the first and second halves of the pattern, respectively, are related to each other by second-order rules, and the first half of the pattern is related to the second half of the pattern by a third-order rule.  A formal description of this pattern is (M(T+14(T+12(1)))), where “1” refers to the starting lever, T+n represents a “transpose” rule (i.e., to move n units in the indicated direction, where + indicates clockwise), M represents a “mirror image” rule, and superscripts reflect the number of repeated applications of the rule that are required.  Because of the nested structural organization, the second-order T+1 rule applies a “+1” rule to each item in the first chunk to generate the second chunk, and so on.  The third-order M rule produces a “mirror image” (a more complex form of a “reverse” rule) of the first half of the pattern to generate the entire second half of the pattern. 

The incompletely nested L pattern was generated by exchanging the two underlined 3-element chunks in the H pattern.  In so doing, however, it should be noted that all pairwise associations were maintained; rats were always required to press a lever immediately to the left or right of the last correct response in both patterns, and the number of transitions from a given lever to any other was conserved across patterns.  In this structure, elements within any chunk are related by a rule, but chunks are not related to each other in any systematic way.  A formal description of this pattern is (T+1(T+12(1))) - (T-12(5)) - (T+1(T+12(4))) - (T-12(T-12(8))) - (T+12(3)) - (T-12(4)), where T+n and T-n represent rules indicating to “transpose” clockwise and counter-clockwise, respectively.  Dashed lines indicate connections that must be learned by non-hierarchical rules or non-rule-based associations between rule-governed chunks or chunk subsets.  Note that this complicated structure resulted from changing the serial positions of only 4 of 30 pattern elements compared to the H pattern. 

The results showed that, for rats, pattern complexity predicted pattern learning difficulty (Fountain & Rowan, 1995a).  The formally simpler H pattern was easier to learn than the formally more complex L pattern.  In addition, rats in H were sensitive to the hierarchical structure of their pattern.  For rats, as in humans, in the H pattern groups, the difficulty of learning to respond appropriately on any trial was a function of the hierarchical level of the rule required to predict the item.  Figure 4 shows rats' group mean element-by-element error rates for Week 1 of the experiment for the H group (top panel) and the L group (bottom panel).  As shown in Figure 4, during Week 1, rats produced significantly more errors on the first trial of Chunks 1 and 6 than on all other trials.  These trials corresponded to the highest-order rule transitions in the pattern structure (i.e., third-order rule transitions).  Fewer errors were observed on the first trial of other chunks; these trials corresponded to second-order rule transitions.  The fewest errors occurred within chunks, where trials corresponded to first-order rule transitions.  Thus, in the completely hierarchical pattern, the difficulty of learning to respond appropriately on any trial was a function of the hierarchical level of the rule required to predict the item.

Figure 4. Rats' mean percentage of pattern tracking errors for hierarchical (top panel) and linear (bottom panel) patterns as a function of the 30 items of the patterns.  Mean percentage of errors are shown for the first week of training.  Pattern complexity predicted pattern learning difficulty and features of pattern structure predicted the kinds of “intrusion” errors observed (Fountain & Rowan, 1995a). 

Rats in L did not show the 3-level hierarchical pattern of errors observed for H rats.  L rats found trials within chunks easier than the first trial of each chunk, but their response to the first trial of chunks was disorganized.  That is, L rats, unlike H rats, showed no differential response for the first trial of Chunks 1 and 6 (corresponding to third-order rule transitions in the completely nested pattern) versus the first trials of other chunks  (corresponding to second-order  rule  transitions  in  the  completely nested pattern).   However, error rates for the second and third elements of each chunk (with the exception of the second element of Chunks 3 and 10) were significantly lower than for the first element of each chunk.  These results support the view that L rats were sensitive to the actual pattern structure; they recognized that elements within a given chunk were related by a single rule, but that chunks were somewhat haphazardly arranged. 

In the hierarchical versus linear structure experiment just described, rats demonstrated sensitivity to multi-level hierarchically-organized pattern structure.  Rats found learning completely nested hierarchical patterns easier than learning less organized patterns even when pairwise associations and pattern length were conserved across patterns.  In another study from the same series (Fountain et al., 1995a), a 3-level hierarchy was easier to learn than a 4-level hierarchy when pattern length was conserved across patterns.  As a rule, then, pattern complexity was a better predictor of acquisition difficulty in these studies than was pattern length.  These acquisition results alone are strong evidence that pattern organization, that is, pattern complexity, was the primary determinant of pattern difficulty, as argued by a rule-learning view of sequential learning. 

Interleaved serial patternsOne question of significance for animal sequential learning research is whether animals are constrained to learn sequences on the basis of pair-wise associations between successive elements, for example, as in chaining (Skinner, 1934).  A significant body of evidence suggests that animals are able to be more flexible in representing sequential events, conceivably by coding hierarchical representations characterized by relations for nonadjacent events.  The mechanisms of learning involving nonadjacent events are not well-understood.  Terrace(1987), for example, indicated that little evidence existed that animals are able to spontaneously reorganize sequentially-presented items into chunks not presented by the experimenter.  As Terrace noted, such processes are readily observed in human free-recall (Tulving, 1962).  Additionally, it should be noted that chunking of nonadjacent items in human serial-pattern learning has been studied extensively using patterns of letters and digits (e.g., Hersh, 1974).  We have previously shown that rats, when presented a sequence of reward quantities, can spontaneously sort quantities from nonadjacent serial positions into chunks to facilitate learning (Fountain & Annau, 1984). A comparable strategy in humans would be to learn the pattern 2555455565558 by sorting pattern elements into 555 chunks and a 2468 chunk.  Other work also supports the view that rats have this capacity.  For example, Capaldi and Miller (1988) have shown that rats can keep count of different kinds of rewards by chunking nonadjacent items in series into different food categories. In two recent studies in our laboratory (Fountain, Rowan, & Benson, Jr., 1999), rats learned either a structured (ST) or unstructured (UNST) sequence interleaved with elements of a repeating (R) sequence in one experiment or an alternation (A) sequence in another experiment. The question was whether rats would learn the interleaved subpatterns at different rates as a function of subpattern complexity. 

Figure 5.  Group mean element-by-element errors for the ST-R and UNST-R interleaved patterns averaged across Week 3 of training (Fountain, Rowan, & Benson, 1999).

The first experiment sought to determine whether rats would show signs of being sensitive to the organization of nonadjacent items from interleaved subpatterns when one subpattern was a composed of simple, repeating element and the second subpattern was either highly structured or not.  For rats in the Structured (ST) subpattern condition, a *************** 567 subpattern was interleaved with a Repeating (R) subpattern, 888 888 888 888 888, resulting in the ST-R pattern that rats were required to learn: 

182838 283848 384858 485868 586878.

For rats in the Unstructured (UNST) subpattern condition, a 153 236 345 426 547 subpattern was interleaved with the same R subpattern to create the UNST-R pattern in the same manner.  For both patterns, integers represent the clockwise position of correct levers in the octagonal chamber on successive trials and spaces represent pauses that served as phrasing cues. 

Acquisition of the interleaved structured pattern (i.e., ST-R) was significantly faster than for the interleaved unstructured pattern (i.e., UNST-R).  The unstructured pattern was generated by exchanging only two pairs of elements in the structured pattern, as described above.  In so doing, however, all pair‑wise associations in the interleaved patterns were maintained because all of the relocated items were preceded by “8” trials.  Nevertheless, Figure 5 shows that the effects of disrupting pattern structure were apparent throughout the pattern.  This was so even in the third (middle) chunk that was not altered in producing the unstructured pattern; rats found this chunk, 384858, harder to learn in the context of the UNST-R pattern than in the ST-R pattern. 

In the second experiment, rats learned two interleaved sequences where both were created from sets composed of more than one element.  As before, longer patterns were composed of two interleaved subpatterns; either a structured or unstructured subpattern was interleaved with a subpattern of two alternating elements.  For one group of rats, the structured (ST) subpattern, 1 2 3 4 5 6, was interleaved with the alternating (A) subpattern, 7 8 7 8 7 8 to create the ST-A pattern.  For another group of rats, the unstructured (UNST) subpattern, 1 5 3 4 2 6, was likewise interleaved with the same alternating subpattern to produce the UNST-A pattern.  Note that the unstructured subpattern was generated by exchanging two items of the structured subpattern.  Rats learned the subpatterns of their interleaved patterns at different rates both within and between pattern groups.  As predicted based on subpattern structure, in the case of the UNST-A pattern, the A subpattern was acquired faster than the UNST subpattern.  The A subpattern would be expected to be acquired faster because it is formally simple whereas the UNST subpattern has little structure.  Based on similar reasoning, it was expected that the ST subpattern should be easier to learn than the UNST subpattern, and this result was obtained.  In the case of the ST-A pattern, since both subpatterns were structured, it might be difficult to predict in advance based on subpattern structure alone whether rats should find either the ST or A subpattern easier to learn than the other.  However, if structural complexity is equated (i.e., if the same number of rules are needed to describe subpattern structure), rats might show the same predisposition that humans do (Kotovsky & Simon, 1973) to detect repeating items before other structural features of patterns.  In fact, evidence for the latter assertion was obtained in this experiment.  Rats in the ST-A pattern group showed better acquisition for A with its repeating “7” and “8” elements than ST subpatterns of their interleaved pattern despite the fact that both ST and A subpatterns have simple structure that can be described by a single rule (viz., a “+1” rule for the 123456 ST subpattern versus an “alternate” rule for the 787878 A subpattern).  The results of differential acquisition of ST and UNST subpatterns support the notion that accurate performance on these interleaved subpatterns was dependent on a mnemonic representation characterized by relations for nonadjacent events.  The results indicate that rats are sensitive to the organization of nonadjacent elements in serial patterns and that they can detect and sort structural relationships in interleaved patterns.  Pattern and subpattern structure appear to drive how animals sort, chunk together, and represent nonadjacent pattern elements that are related by common rules or features.

 
 
 

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Animal Cognition & Neuroscience

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