Implicit learning of between-group intervals in auditory temporal structures

Abstract

Implicit learning of temporal structure has primarily been reported when events within a sequence (e.g., visualspatial locations, tones) are systematically ordered and correlated with the temporal structure. An auditory serial reaction time task was used to investigate implicit learning of temporal intervals between pseudorandomly ordered syllables. Over exposure, participants identified syllables presented in sequences with weakly metrical temporal structures. In a test block, the temporal structure differed from exposure only in the duration of the interonset intervals (IOIs) between groups. It was hypothesized that reaction time (RT) to syllables following between-group IOIs would decrease with exposure and increase at test. In Experiments 1 and 2, the sequences presented over exposure and test were counterbalanced across participants (Pattern 1 and Pattern 2 conditions). An RT increase at test to syllables following between-group IOIs was only evident in the condition that presented an exposure structure with a slightly stronger meter (Pattern 1 condition). The Pattern 1 condition also elicited a global expectancy effect: Test block RT slowed to earlier-than-expected syllables (i.e., syllables shifted to an earlier beat) but not to later-than-expected syllables. Learning of between-group IOIs and the global expectancy effect extended to the Pattern 2 condition when meter was strengthened with an external pulse (Experiment 2). Experiment 3 further demonstrated implicit learning of a new weakly metrical structure with only earlier-than-expected violations at test. Overall findings demonstrate learning of weakly metrical rhythms without correlated event structures (i.e., sequential syllable orders). They further suggest the presence of a global expectancy effect mediated by metrical strength.

Temporal learning in the suprasecond range: insights from cognitive style

“…These findings are in line with evidence that healthy individuals are generally able to extract relevant organizational patterns from an apparently ambiguous stream of stimulation, in both the auditory and visual domain, and in a variety of contexts and settings (see Aslin & Newport, 2012, for a review). In the domain of timing, specifically, this effect has been previously demonstrated for temporal order statistics (Baker et al, 2014;Schapiro et al, 2012), as well as for motor learning of subsecond temporal patterns (Brandon et al, 2012;Karabanov & Ullén, 2008;Salidis, 2001;Schultz et al, 2013;Terry et al, 2016;Tillmann et al, 2011). Present results extend previous findings, suggesting that a rapid and effective learning of temporal regularities following repeated exposure also occurs in the second range, in line with indirect evidence provided by temporal expectation studies (Bueti & Macaluso, 2010;Herbst & Obleser, 2017, among others).…”

“…These studies generally showed an improvement in performance and a reduction of variability (Bartolo & Merchant, 2009 ; Ivry & Hazeltine, 1995 ; Laje et al, 2011 ). Even though many of these motor learning studies involved an explicit training, a gradual improvement in the reproduction of temporal sequences also occurs within single task sessions, and without an explicit instruction to encode the embedded temporal regularity (Brandon et al, 2012 ; Karabanov & Ullén, 2008 ; Salidis, 2001 ; Schultz et al, 2013 ; Terry et al, 2016 ; Tillmann et al, 2011 ; Ullén & Bengtsson, 2003 ).…”

Unravelling individual rhythmic abilities using machine learning

“…Thus model PMI can capture subtle differences in rhythmic abilities linked to informal musical activities, such as playing a musical instrument as an amateur musician would do. Notably, Western listeners, musicians, and non-musicians alike, can acquire implicitly complex musical features (melody, harmony, rhythm) [77][78][79][80][81] from mere exposure to music 82 , shaping their perception 77 and production 83 . This implicit knowledge paves the way for non-musicians' perception of the relations between musical events, thus creating expectations for upcoming events that in turn, influence their processing 84,85 .…”

Unravelling individual rhythmic abilities using machine learning

“…Western listeners, musicians, and non-musicians alike, can acquire implicitly complex musical features (melody, harmony, rhythm) from mere exposure to music (Rohrmeier & Rebuschat, 2012). For example, non-musicians can develop implicit knowledge of pitch regularities characteristic of the Western tonal system (tonal knowledge; Tillmann, 2005; Tillmann et al, 2000) and of the temporal structure of event sequences (Bigand & Poulin-Charronnat, 2006; Terry et al, 2016; Tillmann et al, 2011). This implicit knowledge paves the way for non-musicians’ perception of the relations between musical events, thus creating expectations for upcoming events that in turn, influence their processing (Jones et al, 2002; Selchenkova et al, 2014).…”