The sensory information that conveys everyday objects and events tends to be multidimensional and probabilistic; often no single sensory cue is sufficient for guiding behavior. For example, edible mushrooms may tend to have a typical shape, or smell, or color but none of these cues, on its own, may be entirely diagnostic of a safe meal. To generate categories, imperfect and complex regularities present in the sensory input must be extracted and learned, but this knowledge then needs to be consolidated into long-term memory to be called upon to guide behavior when encountering novel objects and events with similar properties.

Natural environments add to this challenge because learning tends to proceed across multiple simultaneously present forms of sensory input, typically with no explicit instruction or feedback as a guide to what is relevant or important. Under these conditions, individuals may be unaware that categories exist or that category decisions are called for. Yet, most studies have examined category learning under conditions in which learners overtly direct attention to explicit category decisions and receive feedback about the correctness of their decisions (Holt and Lotto, 2006; Goudbeek et al., 2009; Guenther et al., 1999; Holt et al., 2004; Mirman et al., 2004; Earle and Myers, 2015; Earle et al., 2017; Ashby and Maddox, 2005; Love, 2002; Love et al., 2004).

However, there is ample evidence that category learning can proceed even under incidental conditions when the regularities underlying sensory input categories align with successful behavior on a primary task ostensibly unrelated to a need for categorization. Adult listeners are capable of generating multidimensional auditory (Gabay et al., 2015; Leech et al., 2009; Lim and Holt, 2011; Roark and Holt, 2018; Wade and Holt, 2005; Roark et al., 2022) and phonetic speech (Lim and Holt, 2011; Lim et al., 2015) categories when auditory categories incidentally align with successful behavior in a visuomotor task (i.e. when the task can be completed without recourse to the auditory input). Moreover, this incidentally acquired auditory category knowledge can generalize to encompass novel category exemplars and has been shown to draw on cortical and cortico-striatal networks associated with speech-related category expertise (Leech et al., 2009; Liu and Holt, 2011; Lim et al., 2019); this supports the notion that incidental auditory category learning draws upon internal feedback associated with success in the primary task (Lim et al., 2019; Roark et al., 2020). Yet, although such incidental learning is well-attested (Gabay et al., 2015; Lim and Holt, 2011; Wade and Holt, 2005; Lim et al., 2015; Vlahou et al., 2012; Seitz et al., 2010; Gabay et al., 2023), we understand very little about the consolidation, retention, and generalization of category learning under the incidental learning conditions that characterize most natural learning environments.

Here, we examine whether and how auditory categories that align, incidentally, with visuomotor task demands can lead to long-term auditory category knowledge. We capitalize upon previous studies showing that auditory categories can be generated during the performance of a simple visuomotor target detection task – the Systematic Multimodal Association Reaction Time (SMART) task (Gabay et al., 2015). In the SMART task participants practice the rapid detection of a visual target in one of four possible screen locations and report its position by pressing a key corresponding to the visual location (Figure 1). A brief sequence of sounds, ostensibly unrelated to the simple demands of detecting the suprathreshold visual target, precedes each visual target. Unknown to participants, the sounds are drawn from one of four distinct nonspeech sound categories (Figure 1a; Wade and Holt, 2005). Thus, there is a multimodal (auditory category to visual location/response) correspondence that relates the different exemplars of a specific sound category to a consistent visual target location and response – a many-to-one mapping. The sound categories predict the visual target’s location and the action required to complete the visual detection task. Incidentally learning to treat the acoustically variable sounds as functionally equivalent (and predictive of the upcoming location of a visual target) thus may facilitate visual detection. Overt sound categorization decisions are not required and explicit feedback about category membership to direct learning is not provided. The SMART task therefore makes it possible to investigate how participants learn auditory categories incidentally during the practice of a visuomotor task.

Figure 1

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We first addressed the contributions of the visuomotor task demands (task practice without sounds) to within-session, online gains in task performance and tested for putative offline (post-session) learning gains, the processes that occur in the immediate post-learning interval after training has ended, and their long-term retention. We next addressed the possibility that a post-learning consolidation phase follows incidental auditory category learning (practice that includes incidental experience with the sounds), and that category knowledge can be retained and serve the categorization of novel stimuli. The underlying motivation was to examine the potential for a theoretical bridge for considering incidental category learning in the light of accounts of task-relevant perceptual and motor skill mastery; delayed (offline) gains in task performance and their retention are considered a behavioral expression of memory consolidation processes (Karni, 1996) and signature indicators of the establishment of robust and efficient long-term ‘how to’ memory representations (Karni, 1996; Dorfberger et al., 2007; Karni and Bertini, 1997; Dudai et al., 2015; Seitz and Dinse, 2007).

Given the complexity of everyday situations, ostensibly unrelated perceptual regularities that are not necessary for the execution of a given task may align with the task-related input or responses. The current results suggest that when such alignments occur, substantial learning can take place incidentally, rather than intentionally—without overt instruction, explicit call for perceptual decisions, directed attention, or feedback. After a relatively brief experience practicing a simple visuomotor task in which acoustic input is unnecessary for task performance, young adults learn about auditory category structure of the sounds and use it to hone visuomotor response. This learning extends beyond simple auditory-to-visuomotor mappings because it generalizes to support categorization of sounds not previously encountered. Participants thus process repeated information and establish new knowledge to support behavior, even when it is not strictly necessary – as in the SMART, a simple visuomotor task that can be completed with high accuracy without any reliance on the sounds.

The current results underscore an important outcome of the SMART training experience: category knowledge emerging from incidental experience is further elaborated and consolidated into long-term memory after the termination of the initial session. There was evidence for robust within-session, ‘online’, auditory category learning in Exp 3, however, there was no clear-cut evidence for auditory category learning during the first session of Exp 2. Nevertheless, by the second session of Exp 2 learners’ performance became significantly reliant on the availability of the sound categories, indicating that auditory category learning had occurred and, nine days later, their generalization of category knowledge was as robust as that of learners in Exp 3. Therefore, in addition to performance gains that can be observed as ‘online learning’ within a single training session, gains reflecting the setting up and utilization of the new sound categories occur post-session and can be expressed as ‘offline’ gains after the training experience has ended (Karni and Sagi, 1993).

These latter, delayed gains emerging in the post learning interval are believed to reflect memory consolidation—the process by which memories become less susceptible to interference and are honed to represent new ‘how to’ knowledge (Karni, 1996; Karni and Bertini, 1997; Dudai et al., 2015). Offline gains can be sleep-dependent (Stickgold, 2005; Karni et al., 1994), but can occur also after a wakefulness period (Roth et al., 2005). Since our protocol included a sleep interval, sleep-dependent consolidation may play a role, but future studies using polysomnography to relate sleep parameters with offline gains would be needed to resolve this question. At present, the results indicate the existence of a post-session memory consolidation phase in incidental category learning within which category knowledge is elaborated to a degree that it can be expressed in subsequent performance, even if it was not apparent by the end of the learning session.

The many-to-one correspondence of category exemplars and visuomotor task demands may have been instrumental in prompting these different learning trajectories. Overall, a change in the way that within-category exemplar variability was experienced (across or within trials) had a profound influence on the course of incidental learning. The distinct course of learning observed in Exp 2 and Exp 3 is especially notable considering that the experiments shared the same task settings and demands, identical acoustic stimuli, equivalent overall exemplar variability (across the experiment) and the same protocol of visuomotor practice. A tighter coupling of exemplar variability to visuomotor task demands (Exp 3) proved advantageous in early learning, but ultimately did not confer a benefit to long-term retention and generalization at the final session. This pattern of results is resonant with the notion that better within-session (online) learning may not necessarily lead to better retention across longer time periods (Bjork and Bjork, 2011) and with previous research reporting greater overnight consolidation of auditory regularities among poor online learners (Ballan et al., 2023) as well as reports that difficult auditory regularities are more likely to be consolidated if a night of sleep is afforded (Durrant et al., 2011). Nonetheless, we observe that participants’ early learning in the first session was predictive of learning evident in subsequent sessions.

Importantly, what is consolidated and retained in the long run appears to constitute category knowledge rather than a lasting mnemonic trace of the specific sound exemplars that were actually encountered, since learners were able to generalize to novel exemplars nine days after the initial learning experience. Prior studies of incidental learning in the SMART task found that the RT Cost was absent when sounds provided no category-to-visual target regularity (Gabay et al., 2023) or when sounds deterministically mapped to targets, but not in a manner that preserved underlying category regularities (Gabay et al., 2015). RT Costs were also absent when novel category-consistent exemplars were introduced in the SMART (Gabay et al., 2015). Collectively, these findings point to the development of category knowledge that extends beyond auditory-visual-motor associations; the findings cannot be explained by a non-specific cost to performance resulting from a change in the auditory environment. Relatedly, prior studies make clear that category learning does not reliably occur across passive observation of the auditory-visual patterns in the SMART task, or when participants make a category-nonspecific, generic motor response to report simply the presence of a visual target. Instead, the relationship of auditory regularities defining a category to distinct visuomotor task demands appears to be the ‘representational glue’ that binds distinct exemplars together to develop category knowledge that generalizes to novel exemplars consistent with the category regularities (Gabay et al., 2015).

RT facilitation in the SMART task may capture both the development of auditory category knowledge and more general skills such as the visual stimulus-response mapping. We attempted to dissociate these types of knowledge by comparing RT in a block in which category-to-target regularities were present and a subsequent block in which the regularities were disrupted, yielding a RT Cost. The RT Cost afforded a graded measure of how much a participant’s performance depended on the correspondence of the auditory category and the visuomotor task demands. Importantly, the RT Cost is elicited only with some level of category knowledge. We included a final block re-introducing the category-target regularities to enable participants to return to the trained task conditions. This regularity-random-regularity structure allowed us to assess the development of a specific reliance (in SMART execution) on (emerging) auditory category knowledge across time, independently of the general gains that could be reflected in the random block performance. Despite its advantages, this approach leaves open the possibility that category reactivation across the SMART task may have influenced generalization assessed in the final session. It will be important for future investigations to refine our understanding of a possible contribution of retained category knowledge to generalization, independent of the potential for mnemonic ‘reactivation’ by re-performing the SMART task.

The nature of the category regularity associated with the visuomotor target had an influence on learning, at least in Exp 3 where post-test categorization was more accurate for unidimensional, compared to multidimensional, categories. This is consistent with prior research (Gabay et al., 2015) under single-session incidental training conditions. It has been suggested that there is a complex relationship between how categories are defined by a single, or multiple, input dimensions, and whether the distributions defining the categories are deterministic (as they were here) or probabilistic in their sampling on the one hand, and whether training is incidental or driven by overt feedback on the other (Roark and Holt, 2018). It will be important for future research to assess whether – as in visual category learning under overt training conditions (Ashby and Maddox, 2005) – an advantage for unidimensional category learning in incidental training relates to the affordance of being more easily verbalizable.

Our data presents a cautionary note for learning research, very generally. The observation of offline learning gains even for the simple visuomotor task without auditory stimuli in Exp 1 makes the case that we must be attentive to how even the simplest task demands can trigger task-specific learning that may masquerade as other forms of learning. Without Exp 1 as a baseline, it would be easy to attribute the robust facilitation of RT across sessions to auditory category learning. Instead, this facilitation appears to be largely driven by robust post-session, consolidation phase changes in the execution of the visuomotor task—visuomotor learning unrelated to the availability of an auditory input. This can explain the finding of no relationship between the RT facilitation across sessions and category generalization in the final session in Exp 2 and 3. Nevertheless, there remains the prospect that visuomotor learning interacts with incidental auditory category learning. In the context of the more difficult category learning challenge afforded in Exp 2, the offline gains evident in RT facilitation between-sessions were smaller than those observed in either the purely visuomotor task of Exp 1 or the less challenging auditory category learning of Exp 3. This leaves open the possibility that incidental auditory category learning and visuomotor learning may interact, so that overall SMART performance is constrained under conditions of more challenging auditory category learning.

At the broadest level, the present results speak to debates on how sensory experiences – across any modality and, indeed, between modalities—accumulate to convey regularities that ultimately structure knowledge. Understanding how organisms come to treat physically distinct objects that share deeper statistical structure as functionally equivalent is central to understanding cognition. Yet, most of what we know about category learning has come from studies examining learning under explicit training with overt feedback. In these situations, participants typically know of the existence of categories (at least via the number of response options), actively make category decisions via a motor response, and learn from explicit feedback about the correctness of the decisions. We know much less about incidental learning, when active engagement in behavior involves multiple sources of sensory input, some of which may be, unbeknownst to learner or teacher, coherently structured and not explicitly categorized.

Our results show that incidental learning continues to evolve after the learning experience has ended, i.e., in the interval following the learning-training session. Brief, incidental experience with novel sound categories that align with a very simple visuomotor task led young adult participants to capitalize on the presence of auditory stimuli, despite the lack of a simple stimulus-response association, to support visuomotor task performance. Our results show clearly that the learning process initiated in the session continues in the post-session interval resulting in delayed gains in the ability of the learners to subsequently employ the new auditory knowledge in performing the visuomotor task. In capitalizing on the auditory stimuli learners seem to build lasting category representations that support both the long-term retention of the new auditory category knowledge and its generalization to similar, novel sensory experiences nine days later. Consistent with prior reports of consolidation phase offline gains for category knowledge (Djonlagic et al., 2009; Maddox et al., 2009; Barsky et al., 2015), our findings are resonant with research in motor (Karni et al., 1998) and perceptual domains (Karni and Sagi, 1993; Durrant et al., 2011; de la Chapelle et al., 2022; López-Barroso et al., 2016) indicating the existence of a consolidation phase in the development of skills (Karni, 1996; Dudai et al., 2015; Stickgold, 2005; Robertson et al., 2004) and extend these findings to incidental category learning. In this regard, the results may be particularly relevant in understanding speech category learning, which proceeds incidentally without explicit feedback. (Lim and Holt, 2011; Lim et al., 2014).

Anonymized data and code to reproduce the results presented here are available at https://osf.io/7y2nx/.

1. 1. Gabay Y
   2. Karni A
   3. Holt L

   (2022) Open Science Framework

   Memory for Incidentally Learned Categories Evolves in the Post-Learning Interval.

   https://doi.org/10.31234/osf.io/a8ksm

1. 1. Ashby FG
   2. Maddox WT

   (2005) Human category learning

   Annual Review of Psychology 56:149–178.

   https://doi.org/10.1146/annurev.psych.56.091103.070217

   • PubMed
   • Google Scholar
2. 1. Ballan R
   2. Durrant SJ
   3. Manoach DS
   4. Gabay Y

   (2023) Failure to consolidate statistical learning in developmental dyslexia

   Psychonomic Bulletin & Review 30:160–173.

   https://doi.org/10.3758/s13423-022-02169-y

   • PubMed
   • Google Scholar
3. 1. Barsky MM
   2. Tucker MA
   3. Stickgold R

   (2015) Rem sleep enhancement of probabilistic classification learning is sensitive to subsequent interference

   Neurobiology of Learning and Memory 122:63–68.

   https://doi.org/10.1016/j.nlm.2015.02.015

   • PubMed
   • Google Scholar
4. 1. Bjork EL
   2. Bjork RA

   (2011)

   Making things hard on yourself, but in a good way: creating desirable difficulties to enhance learning

   Psychology and the Real World: Essays Illustrating Fundamental Contributions to Society 2:56–64.

   • Google Scholar
5. 1. de la Chapelle A
   2. Savard MA
   3. Restani R
   4. Ghaemmaghami P
   5. Thillou N
   6. Zardoui K
   7. Chandrasekaran B
   8. Coffey EBJ

   (2022) Sleep affects higher-level categorization of speech sounds, but not frequency encoding

   Cortex; a Journal Devoted to the Study of the Nervous System and Behavior 154:27–45.

   https://doi.org/10.1016/j.cortex.2022.04.018

   • PubMed
   • Google Scholar
6. 1. Djonlagic I
   2. Rosenfeld A
   3. Shohamy D
   4. Myers C
   5. Gluck M
   6. Stickgold R

   (2009) Sleep enhances category learning

   Learning & Memory 16:751–755.

   https://doi.org/10.1101/lm.1634509

   • PubMed
   • Google Scholar
7. 1. Dorfberger S
   2. Adi-Japha E
   3. Karni A

   (2007) Reduced susceptibility to interference in the consolidation of motor memory before adolescence

   PLOS ONE 2:e240.

   https://doi.org/10.1371/journal.pone.0000240

   • PubMed
   • Google Scholar
8. 1. Dudai Y
   2. Karni A
   3. Born J

   (2015) The consolidation and transformation of memory

   Neuron 88:20–32.

   https://doi.org/10.1016/j.neuron.2015.09.004

   • PubMed
   • Google Scholar
9. 1. Durrant SJ
   2. Taylor C
   3. Cairney S
   4. Lewis PA

   (2011) Sleep-dependent consolidation of statistical learning

   Neuropsychologia 49:1322–1331.

   https://doi.org/10.1016/j.neuropsychologia.2011.02.015

   • PubMed
   • Google Scholar
10. 1. Earle FS
    2. Myers EB

    (2015) Sleep and native language interference affect non-native speech sound learning

    Journal of Experimental Psychology. Human Perception and Performance 41:1680–1695.

    https://doi.org/10.1037/xhp0000113

    • PubMed
    • Google Scholar
11. 1. Earle FS
    2. Landi N
    3. Myers EB

    (2017) Sleep duration predicts behavioral and neural differences in adult speech sound learning

    Neuroscience Letters 636:77–82.

    https://doi.org/10.1016/j.neulet.2016.10.044

    • PubMed
    • Google Scholar
12. 1. Emberson LL
    2. Liu R
    3. Zevin JD

    (2013) Is statistical learning constrained by lower level perceptual organization?

    Cognition 128:82–102.

    https://doi.org/10.1016/j.cognition.2012.12.006

    • PubMed
    • Google Scholar
13. 1. Faul F
    2. Erdfelder E
    3. Lang AG
    4. Buchner A

    (2007) G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences

    Behavior Research Methods 39:175–191.

    https://doi.org/10.3758/bf03193146

    • PubMed
    • Google Scholar
14. 1. Faul F
    2. Erdfelder E
    3. Buchner A
    4. Lang AG

    (2009) Statistical power analyses using G*power 3.1: tests for correlation and regression analyses

    Behavior Research Methods 41:1149–1160.

    https://doi.org/10.3758/BRM.41.4.1149

    • PubMed
    • Google Scholar
15. 1. Gabay Y
    2. Dick FK
    3. Zevin JD
    4. Holt LL

    (2015) Incidental auditory category learning

    Journal of Experimental Psychology. Human Perception and Performance 41:1124–1138.

    https://doi.org/10.1037/xhp0000073

    • PubMed
    • Google Scholar
16. Conference

    1. Gabay Y
    2. Karni A
    3. Holt LL

    (2018)

    Consolidation and retention of auditory categories acquired incidentally in performing a visuomotor task

    Proceedings of the 40th Annual Conference of the Cognitive Science Society.

    • Google Scholar
17. 1. Gabay Y
    2. Madlansacay M
    3. Holt LL

    (2023) Incidental auditory category learning and visuomotor sequence learning do not compete for cognitive resources

    Attention, Perception, & Psychophysics 85:452–462.

    https://doi.org/10.3758/s13414-022-02616-x

    • PubMed
    • Google Scholar
18. 1. Goudbeek M
    2. Swingley D
    3. Smits R

    (2009) Supervised and unsupervised learning of multidimensional acoustic categories

    Journal of Experimental Psychology. Human Perception and Performance 35:1913–1933.

    https://doi.org/10.1037/a0015781

    • PubMed
    • Google Scholar
19. 1. Guenther FH
    2. Husain FT
    3. Cohen MA
    4. Shinn-Cunningham BG

    (1999) Effects of categorization and discrimination training on auditory perceptual space

    The Journal of the Acoustical Society of America 106:2900–2912.

    https://doi.org/10.1121/1.428112

    • PubMed
    • Google Scholar
20. 1. Holt LL
    2. Lotto AJ
    3. Diehl RL

    (2004) Auditory discontinuities interact with categorization: implications for speech perception

    The Journal of the Acoustical Society of America 116:1763–1773.

    https://doi.org/10.1121/1.1778838

    • PubMed
    • Google Scholar
21. 1. Holt LL
    2. Lotto AJ

    (2006) Cue weighting in auditory categorization: implications for first and second language acquisition

    The Journal of the Acoustical Society of America 119:3059–3071.

    https://doi.org/10.1121/1.2188377

    • PubMed
    • Google Scholar
22. 1. Karni A
    2. Sagi D

    (1993) The time course of learning a visual skill

    Nature 365:250–252.

    https://doi.org/10.1038/365250a0

    • PubMed
    • Google Scholar
23. 1. Karni A
    2. Tanne D
    3. Rubenstein BS
    4. Askenasy JJ
    5. Sagi D

    (1994) Dependence on REM sleep of overnight improvement of a perceptual skill

    Science 265:679–682.

    https://doi.org/10.1126/science.8036518

    • PubMed
    • Google Scholar
24. 1. Karni A

    (1996) The acquisition of perceptual and motor skills: a memory system in the adult human cortex

    Brain Research. Cognitive Brain Research 5:39–48.

    https://doi.org/10.1016/s0926-6410(96)00039-0

    • PubMed
    • Google Scholar
25. 1. Karni A
    2. Bertini G

    (1997) Learning perceptual skills: behavioral probes into adult cortical plasticity

    Current Opinion in Neurobiology 7:530–535.

    https://doi.org/10.1016/s0959-4388(97)80033-5

    • PubMed
    • Google Scholar
26. 1. Karni A
    2. Meyer G
    3. Rey-Hipolito C
    4. Jezzard P
    5. Adams MM
    6. Turner R
    7. Ungerleider LG

    (1998) The acquisition of skilled motor performance: fast and slow experience-driven changes in primary motor cortex

    PNAS 95:861–868.

    https://doi.org/10.1073/pnas.95.3.861

    • PubMed
    • Google Scholar
27. 1. Leech R
    2. Holt LL
    3. Devlin JT
    4. Dick F

    (2009) Expertise with artificial nonspeech sounds recruits speech-sensitive cortical regions

    The Journal of Neuroscience 29:5234–5239.

    https://doi.org/10.1523/JNEUROSCI.5758-08.2009

    • PubMed
    • Google Scholar
28. 1. Liberman AM
    2. Delattre PC
    3. Cooper FS
    4. Gerstman LJ

    (1954) The role of consonant-vowel transitions in the perception of the stop and nasal consonants

    Psychological Monographs 68:1–13.

    https://doi.org/10.1037/h0093673

    • Google Scholar
29. 1. Lim S
    2. Holt LL

    (2011) Learning foreign sounds in an alien world: videogame training improves non-native speech categorization

    Cognitive Science 35:1390–1405.

    https://doi.org/10.1111/j.1551-6709.2011.01192.x

    • PubMed
    • Google Scholar
30. 1. Lim SJ
    2. Fiez JA
    3. Holt LL

    (2014) How may the basal ganglia contribute to auditory categorization and speech perception?

    Frontiers in Neuroscience 8:230.

    https://doi.org/10.3389/fnins.2014.00230

    • PubMed
    • Google Scholar
31. 1. Lim SJ
    2. Lacerda F
    3. Holt LL

    (2015) Discovering functional units in continuous speech

    Journal of Experimental Psychology. Human Perception and Performance 41:1139–1152.

    https://doi.org/10.1037/xhp0000067

    • PubMed
    • Google Scholar
32. 1. Lim SJ
    2. Fiez JA
    3. Holt LL

    (2019) Role of the striatum in incidental learning of sound categories

    PNAS 116:4671–4680.

    https://doi.org/10.1073/pnas.1811992116

    • PubMed
    • Google Scholar
33. 1. Liu R
    2. Holt LL

    (2011) Neural changes associated with nonspeech auditory category learning parallel those of speech category acquisition

    Journal of Cognitive Neuroscience 23:683–698.

    https://doi.org/10.1162/jocn.2009.21392

    • PubMed
    • Google Scholar
34. 1. López-Barroso D
    2. Cucurell D
    3. Rodríguez-Fornells A
    4. Diego-Balaguer R

    (2016) Attentional effects on rule extraction and consolidation from speech

    Cognition 152:61–69.

    https://doi.org/10.1016/j.cognition.2016.03.016

    • Google Scholar
35. 1. Love BC

    (2002) Comparing supervised and unsupervised category learning

    Psychonomic Bulletin & Review 9:829–835.

    https://doi.org/10.3758/bf03196342

    • PubMed
    • Google Scholar
36. 1. Love BC
    2. Medin DL
    3. Gureckis TM

    (2004) Sustain: a network model of category learning

    Psychological Review 111:309–332.

    https://doi.org/10.1037/0033-295X.111.2.309

    • PubMed
    • Google Scholar
37. 1. Maddox WT
    2. Glass BD
    3. Wolosin SM
    4. Savarie ZR
    5. Bowen C
    6. Matthews MD
    7. Schnyer DM

    (2009) The effects of sleep deprivation on information-integration categorization performance

    Sleep 32:1439–1448.

    https://doi.org/10.1093/sleep/32.11.1439

    • PubMed
    • Google Scholar
38. 1. Maquet P
    2. Laureys S
    3. Perrin F
    4. Ruby P
    5. Melchior G
    6. Boly M
    7. Vu TD
    8. Desseilles M
    9. Peigneux P

    (2003) Festina lente: evidences for fast and slow learning processes and a role for sleep in human motor skill learning

    Learning & Memory 10:237–239.

    https://doi.org/10.1101/lm.64303

    • PubMed
    • Google Scholar
39. 1. Mirman D
    2. Holt LL
    3. McClelland JL

    (2004) Categorization and discrimination of nonspeech sounds: differences between steady-state and rapidly-changing acoustic cues

    The Journal of the Acoustical Society of America 116:1198–1207.

    https://doi.org/10.1121/1.1766020

    • PubMed
    • Google Scholar
40. 1. Roark CL
    2. Holt LL

    (2018) Task and distribution sampling affect auditory category learning

    Attention, Perception & Psychophysics 80:1804–1822.

    https://doi.org/10.3758/s13414-018-1552-5

    • PubMed
    • Google Scholar
41. Preprint

    1. Roark CL
    2. Lehet M
    3. Dick F
    4. Holt LL

    (2020) Factors Influencing Incidental Category Learning

    arXiv.

    https://doi.org/10.31234/osf.io/mhbxc

    • Google Scholar
42. 1. Roark CL
    2. Lehet MI
    3. Dick F
    4. Holt LL

    (2022) The representational glue for incidental category learning is alignment with task-relevant behavior

    Journal of Experimental Psychology. Learning, Memory, and Cognition 48:769–784.

    https://doi.org/10.1037/xlm0001078

    • PubMed
    • Google Scholar
43. 1. Robertson EM
    2. Pascual-Leone A
    3. Miall RC

    (2004) Current concepts in procedural consolidation

    Nature Reviews. Neuroscience 5:576–582.

    https://doi.org/10.1038/nrn1426

    • PubMed
    • Google Scholar
44. 1. Roth DAE
    2. Kishon-Rabin L
    3. Hildesheimer M
    4. Karni A

    (2005) A latent consolidation phase in auditory identification learning: time in the awake state is sufficient

    Learning & Memory 12:159–164.

    https://doi.org/10.1101/87505

    • PubMed
    • Google Scholar
45. 1. Seitz AR
    2. Dinse HR

    (2007) A common framework for perceptual learning

    Current Opinion in Neurobiology 17:148–153.

    https://doi.org/10.1016/j.conb.2007.02.004

    • PubMed
    • Google Scholar
46. 1. Seitz AR
    2. Protopapas A
    3. Tsushima Y
    4. Vlahou EL
    5. Gori S
    6. Grossberg S
    7. Watanabe T

    (2010) Unattended exposure to components of speech sounds yields same benefits as explicit auditory training

    Cognition 115:435–443.

    https://doi.org/10.1016/j.cognition.2010.03.004

    • PubMed
    • Google Scholar
47. 1. Stickgold R

    (2005) Sleep-dependent memory consolidation

    Nature 437:1272–1278.

    https://doi.org/10.1038/nature04286

    • PubMed
    • Google Scholar
48. 1. Stickgold R

    (2009) How do I remember? let me count the ways

    Sleep Medicine Reviews 13:305–308.

    https://doi.org/10.1016/j.smrv.2009.05.004

    • PubMed
    • Google Scholar
49. 1. Vlahou EL
    2. Protopapas A
    3. Seitz AR

    (2012) Implicit training of nonnative speech stimuli

    Journal of Experimental Psychology. General 141:363–381.

    https://doi.org/10.1037/a0025014

    • PubMed
    • Google Scholar
50. 1. Wade T
    2. Holt LL

    (2005) Incidental categorization of spectrally complex non-invariant auditory stimuli in a computer game task

    The Journal of the Acoustical Society of America 118:2618–2633.

    https://doi.org/10.1121/1.2011156

    • PubMed
    • Google Scholar

Author details

1. Yafit Gabay

   Department of Special Education and the Edmond J. Safra Brain Research Center for the Study of Learning Disabilities, University of Haifa, Abba Khoushy Ave 199, Haifa, Israel

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   ygabay@edu.haifa.ac.il

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7899-3044

2. Avi Karni

   Sagol Department of Neurobiology and the Edmond, J. Safra Brain Research Center for the Study of Learning Disabilities, University of Haifa, Haifa, Israel

   Contribution

   Conceptualization, Resources, Funding acquisition, Writing – review and editing

   Contributed equally with

   Lori L Holt

   Competing interests

   No competing interests declared

3. Lori L Holt

   Department of Psychology and Neuroscience Institute, Carnegie Mellon University, Pittsburgh, United States

   Contribution

   Conceptualization, Resources, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – review and editing

   Contributed equally with

   Avi Karni

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8732-4977

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