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

Abstract

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...

Category learning strategies in younger and older adults: Rule abstraction and memorization.

“…For example, Davis et al (2012) showed that older adults were not impaired in their ability to categorize exemplars using a rule that was based on a single stimulus dimension. However, older adults' learning is consistently impaired when the correct rule is difficult to verbalize, as with information integration categories (e.g., Filoteo & Maddox, 2004).…”

Familiarization May Minimize Age-Related Declines in Rule-Based Category Learning

“…Research has shown that older adults struggle with both RB and NRB category learning (Davis et al, 2012;Filoteo & Maddox, 2004;Maddox et al, 2010;Racine et al, 2006), with impairments in RB category learning increasing as rule complexity increases (e.g., learning rule-plus-exception category structures; Davis, 2012).…”

Model-based cognitive neuroscience

“…For example, Love and Gureckis (2007) proposed a theory linking aspects of the SUSTAIN clustering model of human categorization (Love, Medin, & Gureckis, 2004; Sakamoto & Love, 2004) to the functions of prefrontal cortex and the hippocampus. They simulating various populations, such as amnesics (Love & Gureckis, 2007), infants (Gureckis & Love, 2004), and the aged (Davis, Love, & Maddox, 2012), by adjusting model parameters hypothesized to relate to brain regions whose functions vary across populations. With the advent of model-based neuroscience, exact predictions of the theory were tested and confirmed with healthy young adults using fMRI (Davis et al, 2012; Davis, Love, & Preston, 2012a; Davis, Xue, Love, Preston, & Poldrack, 2014; Mack, Preston, & Love, in press).…”

“…They simulating various populations, such as amnesics (Love & Gureckis, 2007), infants (Gureckis & Love, 2004), and the aged (Davis, Love, & Maddox, 2012), by adjusting model parameters hypothesized to relate to brain regions whose functions vary across populations. With the advent of model-based neuroscience, exact predictions of the theory were tested and confirmed with healthy young adults using fMRI (Davis et al, 2012; Davis, Love, & Preston, 2012a; Davis, Xue, Love, Preston, & Poldrack, 2014; Mack, Preston, & Love, in press). The analyses revealed a number of phenomena that would not be possible to observe without the model, such as how the involvement of the hippocampus changes over learning trials, ramping up for familiar items (related to recognition) at the time of decision and ramping down at the time of feedback as the error signal abates (Davis et al, 2012).…”

“…With the advent of model-based neuroscience, exact predictions of the theory were tested and confirmed with healthy young adults using fMRI (Davis et al, 2012; Davis, Love, & Preston, 2012a; Davis, Xue, Love, Preston, & Poldrack, 2014; Mack, Preston, & Love, in press). The analyses revealed a number of phenomena that would not be possible to observe without the model, such as how the involvement of the hippocampus changes over learning trials, ramping up for familiar items (related to recognition) at the time of decision and ramping down at the time of feedback as the error signal abates (Davis et al, 2012). The model-based imaging work also confirmed more speculative hypotheses such as that prefrontal and hippocampus interactions would be strongest in the early stages of mastering a new learning task as attention weights are established (Mack et al, in press).…”