The effects of prior knowledge on incidental category learning.

Clapper, John P.

doi:10.1037/a0028457

Cited by 4 publications

(5 citation statements)

References 44 publications

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“…This buttressed learning beyond what has been observed for these same stimuli under passive, unsupervised learning conditions (Emberson et al, 2013; Wade & Holt, 2005). Nevertheless, there is evidence that quite complex perceptual categories can be acquired through unsupervised learning (for examples in the visual domain see Clapper, 2012; Love, 2002). It will be important to unravel the relative influence of stimulus input distributions, categorization training task, the influence of an active task, and the presence of different types of feedback in future work.…”

Section: Discussionmentioning

confidence: 99%

Incidental auditory category learning.

Gabay

Dick

Zevin

et al. 2015

Journal of Experimental Psychology: Human Perception and Perfor

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Very little is known about how auditory categories are learned incidentally, without instructions to search for category-diagnostic dimensions, overt category decisions, or experimenter-provided feedback. This is an important gap because learning in the natural environment does not arise from explicit feedback and there is evidence that the learning systems engaged by traditional tasks are distinct from those recruited by incidental category learning. We examined incidental auditory category learning with a novel paradigm, the Systematic Multimodal Associations Reaction Time (SMART) task, in which participants rapidly detect and report the appearance of a visual target in one of four possible screen locations. Although the overt task is rapid visual detection, a brief sequence of sounds precedes each visual target. These sounds are drawn from one of four distinct sound categories that predict the location of the upcoming visual target. These many-to-one auditory-to-visuomotor correspondences support incidental auditory category learning. Participants incidentally learn categories of complex acoustic exemplars and generalize this learning to novel exemplars and tasks. Further, learning is facilitated when category exemplar variability is more tightly coupled to the visuomotor associations than when the same stimulus variability is experienced across trials. We relate these findings to phonetic category learning.

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Section: Discussionmentioning

confidence: 99%

Incidental auditory category learning.

Gabay

Dick

Zevin

et al. 2015

Journal of Experimental Psychology: Human Perception and Perfor

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“…From the perspective of the category invention framework, the main difference between supervised and unsupervised learning is that providing people with the category membership of each instance, as in supervised learning, eliminates the discovery/aggregation problem that is so central to unsupervised learning. Consistent with this, Clapper and Bower (2002) and Clapper (2012) showed that providing diagnostic category labels with each instance in the exemplar-memory task substantially improved learning in interleaved conditions-that is, much or all of the interleaving effect was eliminated by providing diagnostic labels.…”

Section: Relation To Supervised Learningmentioning

confidence: 53%

“…Means and standard deviations for consistent and variable features are shown in Table 1, while consistent feature recalls are shown plotted over trials in Figure 2. Following procedures reported in Clapper (2007Clapper ( , 2012, the bottom 25% of participants from each condition (a total of six participants, three per condition) were excluded from Figure 2 and all subsequent data analyses. This procedure is intended to correct for inattentive responding (Meade & Craig, 2012), which can add random noise to the data and substantially decrease the power of an experiment (e.g., Osborne & Blanchard, 2010).…”

Section: Resultsmentioning

confidence: 99%

The impact of training sequence and between-category similarity on unsupervised induction

Clapper

2015

Quarterly Journal of Experimental Psychology

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Studies of supervised categorization often show better learning when examples are presented in random alternation rather than massed by category, but such interleaving impairs learning in unsupervised tasks. The exemplar comparison hypothesis explains this result by assuming that people in unsupervised tasks discover generalizations about categories by comparing individual examples, and that interleaving increases the difficulty of such within-category comparisons. The category invention hypothesis explains the interleaving effect by assuming that people are more likely to merge or aggregate potentially separable categories when they are interleaved, and this initial failure to recognize separate categories then acts as an effective barrier to further learning. The present experiments show that the interleaving effect depends on the similarity or alignability of the presented categories. This result provides evidence in favour of the category invention hypothesis, which expects that highly dissimilar (nonalignable) categories will resist aggregation and hence will not be affected by interleaving. The nonmonotonic pattern of learning, and the interaction between sequence and similarity, observed in the alignable conditions of Experiment 3 were also consistent with category invention, but not with exemplar comparison. Implications are discussed for real-world learning, especially the relationship between exposure and learning and between supervised and unsupervised learning.

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“…In these studies, researchers have taken the approach of examining learning in contexts in which participants interact actively with the category-relevant information in the context of an engaging primary task (Clapper, 2012;Gabay et al, 2015;Lim et al, 2019;Lim & Holt, 2011;Protopapas et al, 2017;Seitz et al, 2010;Vlahou et al, 2012;Wade & Holt, 2005). Notably, this primary task does not require overt category decisions, instruction about the existence of categories, or explicit feedback about categorization.…”

Section: Incidental Category Learningmentioning

confidence: 99%

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

Roark¹,

Lehet²,

Dick³

et al. 2020

Preprint

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The ability to form coherent acoustic categories is thought to be a fundamental cognitive process underlying speech perception. Auditory categories can be learned via passive exposure or overt training. Recent investigations have shown that listeners can learn categories incidentally based on associations between sounds, visual objects, actions, and events in the world. However, much remains unknown about the conditions under which this incidental learning occurs. Across five conditions, we manipulated how auditory categories are associated with visual features to inform behavioral responses in an incidental learning task. Category learning was unaffected by irrelevant visual features and was robust to manipulations of motor response. However, learning was disrupted when auditory categories were associated with task-irrelevant visual features. Together, this study demonstrates that incidental learning is driven by consistent relationships between auditory category information and task-relevant visual features.

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The effects of prior knowledge on incidental category learning.

Cited by 4 publications

References 44 publications

Incidental auditory category learning.

Incidental auditory category learning.

The impact of training sequence and between-category similarity on unsupervised induction

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

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