A rational model of the effects of distributional information on feature learning

Austerweil, Joseph L.; Griffiths, Thomas L.

doi:10.1016/j.cogpsych.2011.08.002

Cited by 21 publications

(62 citation statements)

References 47 publications

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“…Although counter-intuitive from the broader perceptual similarity literature, previous behavioral studies with infants and adults on feature correlation predict that intra-item feature similarity should facilitate categorization (e.g., Austerweil & Griffiths, 2011; 2013). Feature correlation studies have shown that objects with correlated features (e.g., bananas tend to be yellow and have a crescent shape) rather than uncorrelated features (e.g., jelly beans come in many colors) enable more robust representations of multi-part objects and categories.…”

mentioning

confidence: 88%

“…Feature correlation studies have shown that objects with correlated features (e.g., bananas tend to be yellow and have a crescent shape) rather than uncorrelated features (e.g., jelly beans come in many colors) enable more robust representations of multi-part objects and categories. Having stronger individual object representations due to consistent feature correlations aids categorization of these objects, because the features determining category diagnosticity are more reliable and thus less corrupted by noise (Austerweil & Griffiths, 2011; 2013; Goldstone, 2000; Younger & Cohen, 1986). Consistent feature correlations lead to narrower generalization based on specific features of existing category members, suggesting a tighter category boundary compared to that of uncorrelated features.…”

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confidence: 99%

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A neural signature of rapid category-based target selection as a function of intra-item perceptual similarity, despite inter-item dissimilarity

Pruitt

Runkle

et al. 2016

Atten Percept Psychophys

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Previous work on visual search has suggested that only a single attentional template can be prioritized at any given point in time. Grouping features into objects and objects into categories can facilitate search performance by maximizing the amount of information carried by an attentional template. From infancy to adulthood, earlier studies on perceptual similarity show that consistent features increase the likelihood of grouping features into objects (e.g., Quinn & Bhatt, 2009) and objects into categories (e.g., shape bias; Landau et al., 1988). Here we ask whether lower-level intra-item similarity facilitates higher-level categorization despite inter-item dissimilarity. Adults participated in four visual search tasks where targets were defined as either one item (a specific alien) or a category (any alien) with similar features (e.g., circle belly shape, circle back spikes) or dissimilar features (e.g., circle belly shape, triangle back spikes). Using behavioral and neural measures (i.e., N2pc ERP component which typically emerges 200 milliseconds post-stimulus), we found that intra-item feature similarity facilitated categorization, despite dissimilar features across category items. Our results demonstrate that feature similarity builds novel categories and activates a task-appropriate abstract categorical search template. In other words, grouping at the lower item level facilitates grouping at the higher category level, which allows us to overcome efficiency limitations in visual search.

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confidence: 88%

mentioning

confidence: 99%

A neural signature of rapid category-based target selection as a function of intra-item perceptual similarity, despite inter-item dissimilarity

Pruitt

Runkle

et al. 2016

Atten Percept Psychophys

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“…The parameter l plays the same role as a, controlling how the number of latent causes grows as more customers enter, and hence the 'complexity' of the model. Soto and colleagues used the Indian buffet process in their latent-cause model of compound conditioning [13], and it has also been used in models of perceptual feature learning [58][59][60].…”

Section: Understanding the Effects Of Different Extinction Proceduresmentioning

confidence: 98%

Discovering latent causes in reinforcement learning

Gershman

Norman

Niv

2015

Current Opinion in Behavioral Sciences

133

127

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“…On the other hand, the number of causes that could possibly be present ( K ) is not known in advance, as in the model presented by Gershman et al (2010). Following earlier work on models with simultaneously active latent causes of a priori unknown number (e.g., Austerweil & Griffiths, 2011; Navarro & Griffiths, 2008), we use the Indian Buffet Process (IBP; see Griffiths & Ghahramani, 2011) as an infinite-capacity distribution on Z :

Z \sim I B P (α)

…”

Section: The Modelmentioning

confidence: 99%

Explaining compound generalization in associative and causal learning through rational principles of dimensional generalization.

Soto

Gershman

Niv

2014

Psychological Review

128

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How do we apply learning from one situation to a similar, but not identical, situation? The principles governing the extent to which animals and humans generalize what they have learned about certain stimuli to novel compounds containing those stimuli vary depending on a number of factors. Perhaps the best studied among these factors is the type of stimuli used to generate compounds. One prominent hypothesis is that different generalization principles apply depending on whether the stimuli in a compound are similar or dissimilar to each other. However, the results of many experiments cannot be explained by this hypothesis. Here we propose a rational Bayesian theory of compound generalization that uses the notion of consequential regions, first developed in the context of rational theories of multidimensional generalization, to explain the effects of stimulus factors on compound generalization. The model explains a large number of results from the compound generalization literature, including the influence of stimulus modality and spatial contiguity on the summation effect, the lack of influence of stimulus factors on summation with a recovered inhibitor, the effect of spatial position of stimuli on the blocking effect, the asymmetrical generalization decrement in overshadowing and external inhibition, and the conditions leading to a reliable external inhibition effect. By integrating rational theories of compound and dimensional generalization, our model provides the first comprehensive computational account of the effects of stimulus factors on compound generalization, including spatial and temporal contiguity between components, which have posed longstanding problems for rational theories of associative and causal learning.

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A rational model of the effects of distributional information on feature learning

Cited by 21 publications

References 47 publications

A neural signature of rapid category-based target selection as a function of intra-item perceptual similarity, despite inter-item dissimilarity

A neural signature of rapid category-based target selection as a function of intra-item perceptual similarity, despite inter-item dissimilarity

Discovering latent causes in reinforcement learning

Explaining compound generalization in associative and causal learning through rational principles of dimensional generalization.

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