Activation in the neural network responsible for categorization and recognition reflects parameter changes

Nosofsky, Robert M.; Little, Daniel R.; James, Thomas W.

doi:10.1073/pnas.1111304109

Cited by 78 publications

(87 citation statements)

References 37 publications

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“…Both categorization and recognition rely on the comparison of a test item with the contents of memory, and rely on brain regions that tend to overlap (Nosofsky, Little, & James, 2012). Recognition decisions are typically binary ("old" vs. "new"), so we consider an extension of our model to binary categorization, where participants study items from either category A or category B.…”

Section: Categorizationmentioning

confidence: 99%

“…Recently, much progress has been made in cognitive neuroscience by interpreting measures of neural function in terms of quantitative cognitive models, including in the domains of categorization and recognition (e.g., Nosofsky et al, 2012;Mack, Preston, & Love, 2013;Turner et al, 2013). By correlating model parameters with neural activity, it is possible to make stronger inferences about the role that a particular area plays while engaged in a particular task.…”

Section: Prospects For Neurosciencementioning

confidence: 99%

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A Dynamic Approach to Recognition Memory

Cox¹,

Shiffrin²

2017

Preprint

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ii Acknowledgements First, I extend my thanks to my committee members for their time, consideration, and guidance. I would especially like to thank my advisors, Rich Shiffrin and Mike Jones, for first convincing me to come to Indiana, making me feel welcome, and guiding me into the world I now inhabit. I must also thank Isaiah Harbison, Michael Dougherty, and Sharona Atkins for introducing me to the very idea of mathematical psychology and for teaching me and trusting me. show how the dynamic model can be extended to account for paradigms like associative recognition and list discrimination, leading to another novel test of the presence of recall-like processes. Associative recognition, list discrimination, recognition of similar foils, and source exclusion are all better explained by the formation of a compound cue rather than recall, although source memory is found to be better modeled by a recall process.

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Section: Categorizationmentioning

confidence: 99%

Section: Prospects For Neurosciencementioning

confidence: 99%

A Dynamic Approach to Recognition Memory

Cox¹,

Shiffrin²

2017

Preprint

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“…Although we focus on rule-plus-exception learning, there are many different category learning tasks that have been studied; some tasks, like rule-based and prototype learning tasks, engage MTL-PFC circuitry (e.g., Nomura et al 2007;Ziethamova et al 2008), but others depend upon implicit neurobiological systems that do not include the MTL (Ashby and Maddox 2005;Poldrack and Foerde 2008;Smith and Grossman 2008;Seger and Miller 2010; but see Gureckis et al 2010;Nosofsky et al 2012). As our theory is intended to describe the function of the MTL-PFC circuit, it may draw together aging-related findings in rule-based and prototype learning (Hess 1982;Hess and Slaughter 1986;Filoteo and Maddox 2004;Maddox et al 2010;Glass et al 2012).…”

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confidence: 99%

Age-related declines in the fidelity of newly acquired category representations

Davis¹,

Love

Maddox

2012

Learn. Mem.

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We present a theory suggesting that the ability to build category representations that reflect the nuances of category structures in the environment depends upon clustering mechanisms instantiated in an MTL-PFC-based circuit. Because function in this circuit declines with age, we predict that the ability to build category representations will be impaired in older adults. Consistent with this prediction, we find that older adults are impaired relative to younger adults at learning nuanced category structures that contain exceptions to the rule. Model-based analysis reveals that this deficit arises from older adults' failure to engage clustering mechanisms to separate exception and rule-following items in memory.[Supplemental material is available for this article.]Is it a Tea Party or Occupy Wall Street Rally? Taquito or flauta? Throughout our lifespans, we are continuously bombarded with novel categories to learn. These categories perform a key role in day-to-day cognition by facilitating generalization and inference.One critical aspect of category learning is the ability to flexibly build category representations that reflect the nuances of category structures as they exist in nature. Many category structures in the world are nuanced such that they cannot be easily described by simple rules or statistical averages. For example, most members of the categories, birds and mammals, can be accurately categorized based on the rule, "If it has wings, then it's a bird," but there are exceptions to this rule (e.g., bats) that must also be accommodated.Cluster-based category learning models offer psychological accounts and formal methods of measuring the cognitive and neural mechanisms that allow people to build category representations that reflect the structure of categories in the environment (Anderson 1991;Love et al. 2004;Vanpaemel and Storms 2008). Cluster-based models represent categories by clusters that code a conjunction of an object's features and category membership. Clusters can take various forms to represent category structures, ranging from a single example to an abstraction, like a rule. For example, cluster-based models might represent the categories birds and mammals, with a cluster that contains winged animals for birds and another that contains nonwinged animals for mammals but require an additional cluster to form a separate representation for bats.Neurobiologically, the ability to form new cluster representations is thought to depend on a circuit comprising the medial temporal lobes (MTL) and prefrontal cortex (PFC) (Love and Gureckis 2007;Davis et al. 2012a). Within this network, the MTL is thought to be the primary location where clusters are formed and stored. The PFC plays a critical role in directing the encoding of new clusters in response to surprise or prediction error and engages controlled retrieval processes. Recent fMRI studies (Davis et al. 2012a,b) have tested these neurobiological predictions using rule-plus-exception tasks. Rule-plus-exception tasks, like the birds and mammals exam...

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“…Testing patients with documented changes in the biased competition process of attentional selection offers a critical methodology in order to test theoretical assumptions on visual attention and mnemonic processing that are made by cognitive models. For instance, the critical assumption of the ITAM model [106] that attentional selection and memory categorization rely on the same competitive race, could be tested by the use of paradigms that allow us to quantify parameters for attentional selection [50] as well as memory categorization [111]. Furthermore, the neuroanatomical AtoM model [86] could be tested by the assessment of patients with relatively homogeneous neurodegeneration within the parietal lobe system when neuroscientific methods (e.g.…”

Section: Conclusion: Unified Biased Competition Account Of Visual Attmentioning

confidence: 99%

A biased competition account of attention and memory in Alzheimer's disease

Finke

Myers

Bublak

et al. 2013

Phil. Trans. R. Soc. B

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The common view of Alzheimer's disease (AD) is that of an age-related memory disorder, i.e. declarative memory deficits are the first signs of the disease and associated with progressive brain changes in the medial temporal lobes and the default mode network. However, two findings challenge this view. First, new model-based tools of attention research have revealed that impaired selective attention accompanies memory deficits from early pre-dementia AD stages on. Second, very early distributed lesions of lateral parietal networks may cause these attention deficits by disrupting brain mechanisms underlying attentional biased competition. We suggest that memory and attention impairments might indicate disturbances of a common underlying neurocognitive mechanism. We propose a unifying account of impaired neural interactions within and across brain networks involved in attention and memory inspired by the biased competition principle. We specify this account at two levels of analysis: at the computational level, the selective competition of representations during both perception and memory is biased by AD-induced lesions; at the large-scale brain level, integration within and across intrinsic brain networks, which overlap in parietal and temporal lobes, is disrupted. This account integrates a large amount of previously unrelated findings of changed behaviour and brain networks and favours a brain mechanism-centred view on AD.

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Activation in the neural network responsible for categorization and recognition reflects parameter changes

Cited by 78 publications

References 37 publications

A Dynamic Approach to Recognition Memory

A Dynamic Approach to Recognition Memory

Age-related declines in the fidelity of newly acquired category representations

A biased competition account of attention and memory in Alzheimer's disease

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