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.

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 non-repeated 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, 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., 2011; McKelvie, 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 new Bayesian hierarchical mixture model 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 increase with a variable time for the onset of learning across individuals, and that (2) learning onset depended on 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.

One of the best-known demonstrations of long-term learning through repetition is the Hebb effect: Immediate recall of a memory list repeated amidst non-repeated lists improves steadily with repetitions. However, previous studies often failed to observe this effect for visuo-spatial 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 visuo-spatial 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 mid-way 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 non-repeated 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.

When subjects are asked to remember stimuli (e.g., words) for an immediate memory test, they usually remember them better when the items are presented without interruption (simple span task), compared to a condition in which a distraction occurs between each item (complex span task). In a delayed memory test, this effect has been shown to be reversed: Memory performance is better after complex span tasks than after simple span tasks. This so-called McCabe effect has not been able to be replicated consistently in the past. Here we investigated five potential boundary conditions of the McCabe effect: (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) Set Size (small vs. large), and (5) Expectation about the LTM test (intentional vs. incidental encoding). Across four experiments, we never found a McCabe effect: delayed memory was either equal between simple, complex and slow span conditions, the order of performance across these conditions from the immediate task transferred to the delayed task (simple > slow > complex), or any benefits on delayed memory performance were equally well explained by the amount of free time in the retention interval (slow span). 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 encoded into LTM is laid down as a more permanent trace.