Effects of explicit knowledge and predictability on auditory distraction and target performance

Atten Percept Psychophys

Groth

Agostino

et al. 2019

Learning the statistical regularities of environmental events is a powerful tool for enhancing performance. However, it remains unclear whether this often implicit type of behavioral facilitation can be proactively modulated by explicit knowledge about temporal regularities. Only recently, Menceloglu and colleagues (Attention, Perception & Psychophysics, 79(1), 169-179, 2017) tested for differences between implicit versus explicit statistical learning of temporal regularities by using a within-paradigm manipulation of metacognitive temporal knowledge. The authors reported that temporal expectations were enhanced if participants had explicit knowledge about temporal regularities. Here, we attempted to replicate and extend their results, and to provide a mechanistic framework for any effects by means of computational modelling. Participants performed a letter-discrimination task, with target letters embedded in congruent or incongruent flankers. Temporal predictability was manipulated block-wise, with targets occurring more often after either a short or a long delay period. During the delay a sound was presented in half of the trials. Explicit knowledge about temporal regularities was manipulated by changing instructions: Participants received no information (implicit), information about the most likely cue-target delay (explicit), or received 100% valid cues on each trial (highly explicit). We replicated previous effects of target-flanker congruence and sound presence. However, no evidence was found for an effect of explicit knowledge on temporal expectations using Bayesian statistics. Concordantly, computational modelling suggested that explicit knowledge may only influence non-perceptual processing such as response criteria. Together, our results indicate that explicit metacognitive knowledge does not necessarily alter sensory representations or temporal expectations but rather affects response strategies.

Section: Comparison With the Previous Studysupporting

confidence: 89%

Section: Bottom-up Top-down or Bias?supporting

confidence: 56%

Explicitly versus implicitly driven temporal expectations: No evidence for altered perceptual processing due to top-down modulations

Atten Percept Psychophys

Groth

Agostino

et al. 2019

“…And most within-study comparisons of explicitly versus implicitly driven behaviour report the absence of modulating effects due to explicit knowledge. For instance, Max et al ( 2015 ) investigated the impact of distracting sounds (deviant pitch) within a sequence of standard sounds on auditory duration judgments. The authors found no evidence that instructions (explicit vs. no information) altered behavioural performance related to the distraction effect, though they observed changes in the EEG signals (i.e., a lower P3a amplitude when distractors were expected due to instructions).…”

Section: Discussionmentioning

confidence: 99%

Minimal interplay between explicit knowledge, dynamics of learning and temporal expectations in different, complex uni- and multisensory contexts

Atten Percept Psychophys

Spuerck

Noesselt

2021

While temporal expectations (TE) generally improve reactions to temporally predictable events, it remains unknown how the learning of temporal regularities (one time point more likely than another time point) and explicit knowledge about temporal regularities contribute to performance improvements; and whether any contributions generalise across modalities. Here, participants discriminated the frequency of diverging auditory, visual or audio-visual targets embedded in auditory, visual or audio-visual distractor sequences. Temporal regularities were manipulated run-wise (early vs. late target within sequence). Behavioural performance (accuracy, RT) plus measures from a computational learning model all suggest that learning of temporal regularities occurred but did not generalise across modalities, and that dynamics of learning (size of TE effect across runs) and explicit knowledge have little to no effect on the strength of TE. Remarkably, explicit knowledge affects performance—if at all—in a context-dependent manner: Only under complex task regimes (here, unknown target modality) might it partially help to resolve response conflict while it is lowering performance in less complex environments.

“…However, a difference in behaviour or neural activation patterns – when based on different experimental paradigms or studies without assessment of participants’ explicit knowledge – would be insufficient to conclude that explicit knowledge truly affects e. g. performance and learning patterns (Ball et al, 2019); and most within-study comparisons of explicitly- vs. implicitly-driven behaviour report the absence of modulating effects due to explicit knowledge. For instance, Max and colleagues (2015) investigated the impact of distracting sounds (deviant pitch) within a sequence of standard sounds on auditory duration judgments. The authors found no evidence that instructions (explicit vs. no-information) altered behavioural performance related to the distraction effect, though they observed changes in the EEG signals (i.e.…”

Section: Discussionmentioning

confidence: 99%

Minimal interplay between explicit knowledge, dynamics of learning and temporal expectations in different, complex uni- and multisensory contexts

Spuerck

Noesselt

2021

Preprint

While temporal expectations (TE) generally improve reactions to temporally predictable events, it remains unknown how temporal rule learning and explicit knowledge about temporal rules contribute to performance improvements and whether any contributions generalise across modalities. Here, participants discriminated the frequency of diverging auditory, visual or audiovisual targets embedded in auditory, visual or audiovisual distractor sequences. Temporal regularities were manipulated run-wise (early vs. late target within sequence). Behavioural performance (accuracy, RT) plus measures from a computational learning model all suggest that temporal rule learning occurred but did not generalise across modalities, that dynamics of learning (size of TE effect across runs) and explicit knowledge have little to no effect on the strength of TE, and that explicit knowledge affects performance - if at all - in a context dependent manner: only under complex task regimes (unknown target modality) might it partially help to resolve response conflict while it is lowering performance in less complex environments.