Grouping in working memory guides chunk formation in long-term memory: Evidence from the Hebb effect.

The Hebb effect refers to the improvement in immediate memory performance on a repeated list compared to unrepeated lists. That is, participants create a long-term memory representation over repetitions, on which they can draw in working memory tests. These long-term memory representations are likely formed by chunk acquisition: The whole list becomes integrated into a single unified representation. Previous research suggests that the formation of such chunks is rather inflexible and only occurs when at least the beginning of the list repeats across trials. However, recent work has shown that repetition learning strongly depends on participants recognizing the repeated information. Hence, successful chunk formation may depend on the recognizability of the repeated part of a list, and not on its position in the list. Across six experiments, we compared these two alternatives. We tested immediate serial recall of eight-letter lists, some of which partially repeated across trials. We used different partial-repetition structures, such as repeating only the first half of a list, or only every second item. We manipulated the salience of the repeating structure by spatially grouping and coloring the lists according to the repetition structure. We found that chunk formation is more flexible than previously assumed: Participants learned contiguous repeated sequences regardless of their position within the list, as long as they were able to recognize the repeated structure. Even when the repeated sequence occurred at varying positions over repetitions, learning was preserved when the repeated sequence was made salient by the spatial grouping. These findings suggest that chunk formation requires recognition of which items constitute a repeating group, and demonstrate a close link between grouping of information in working memory, and chunk formation in long-term memory.

Testing expectations and retrieval practice modulate repetition learning of visuospatial arrays.

One of the best-known demonstrations of long-term learning through repetition is the Hebb effect: Immediate recall of a memory list repeated amidst nonrepeated lists improves steadily with repetitions. However, previous studies often failed to observe this effect for visuospatial arrays. Souza and Oberauer (2022) showed that the strongest determinant for producing learning was the difficulty of the test: Learning was consistently observed when participants recalled all items of a visuospatial array (difficult test) but not if only one item was recalled, or recognition procedures were used (less difficult tests). This suggests that long-term learning was promoted by increased testing demands over the short term. Alternatively, it is possible that lower testing demands still lead to learning but prevented the application of what was learned. In four preregistered experiments (N = 981), we ruled out this alternative explanation: Changing the type of memory test midway through the experiment from less demanding (i.e., single item recall or recognition) to a more demanding test (i.e., full item recall) did not reveal hidden learning, and changing it from the more demanding to a less demanding test did not conceal learning. Mixing high and low demanding tests for nonrepeated arrays, however, eventually produced Hebb learning even for the less demanding testing conditions. We propose that testing affects long-term learning in two ways: Expectations of the test difficulty influence how information is encoded into memory, and retrieval consolidates this information in memory. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Delayed memory for complex visual stimuli does not benefit from distraction during encoding.

The covert retrieval model (McCabe, Journal of Memory and Language 58(2), 480-494, 2008) postulates that delayed memory performance is enhanced when the encoding of memoranda in working memory (WM) is interrupted by distraction. When subjects are asked to remember stimuli for an immediate memory test, they usually remember them better when the items are presented without distraction, compared to a condition in which a distraction occurs following each item. In a delayed memory test, this effect has been shown to be reversed: Memory performance is better for items followed by distraction than without. Yet, this so-called McCabe effect has not been consistently replicated in the past. In an extensive replication attempt of a previous study showing the effect for complex visual stimuli, we investigated five potential boundary conditions of the predictions of the covert retrieval model: (1) Type of Stimuli (doors vs. faces), (2) type of distractor (pictures vs. math equations), (3) expectation about task difficulty (mixed vs. blocked lists), (4) memory load in WM (small vs. large), and (5) expectation about the long-term memory (LTM) test (intentional vs. incidental encoding). Across four experiments we failed to replicate the original findings and show that delayed memory for faces and other complex visual stimuli does not benefit from covert retrieval during encoding - as suggested as being induced by distractors. Our results indicate that the transfer of information from WM to LTM does not seem to be influenced by covert retrieval processes, but rather that a fixed proportion of information is laid down as a more permanent trace.

Repetition learning is neither a continuous nor an implicit process.

Learning advances through repetition. A classic paradigm for studying this process is the Hebb repetition effect: Immediate serial recall performance improves for lists presented repeatedly as compared to nonrepeated lists. Learning in the Hebb paradigm has been described as a slow but continuous accumulation of long-term memory traces over repetitions [e.g., Page & Norris, Phil. Trans. R. Soc. B 364, 3737-3753 (2009)]. Furthermore, it has been argued that Hebb repetition learning requires no awareness of the repetition, thereby being an instance of implicit learning [e.g., Guérard et al., Mem. Cogn. 39, 1012-1022 (2011); McKelvie,  J. Gen. Psychol. 114, 75-88 (1987)]. While these assumptions match the data from a group-level perspective, another picture emerges when analyzing data on the individual level. We used a Bayesian hierarchical mixture modeling approach to describe individual learning curves. In two preregistered experiments, using a visual and a verbal Hebb repetition task, we demonstrate that 1) individual learning curves show an abrupt onset followed by rapid growth, with a variable time for the onset of learning across individuals, and that 2) learning onset was preceded by, or coincided with, participants becoming aware of the repetition. These results imply that repetition learning is not implicit and that the appearance of a slow and gradual accumulation of knowledge is an artifact of averaging over individual learning curves.