Effects of concurrent load on feature- and rule-based generalization in human contingency learning.

Wills, Andy J.; Graham, Steven; Koh, Zhisheng; McLaren, Ian A.; Rolland, Matthew D.

doi:10.1037/a0023120

Cited by 34 publications

(71 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although Lamberts and Kent (2007) showed that neither the inclusion of a secondary task nor forcing speeded responses during test disrupted the IBRE, the effect requires specific, asymmetrical learning conditions that suggest its occurrence is unlikely to be a product of test strategy alone (Don and Livesey, 2016). Rather, experiments in category learning (Waldron and Ashby, 2001;Zeithamova and Maddox, 2006) and predictive learning (Wills et al, 2011) suggest that adding cognitive load during the learning phase may disrupt rule acquisition and result in more habitual, feature-based generalization patterns at test.…”

Section: Cc-by-nc-ndmentioning

confidence: 99%

Model-based fMRI Reveals Dissimilarity Processes Underlying Base Rate Neglect

O’Bryan

Worthy

Livesey

et al. 2017

Preprint

View full text Add to dashboard Cite

The inverse base-rate effect (IBRE) describes an apparent irrationality in human decision making whereby people tend to ignore category base rates and choose rarer options when classifying ambiguous stimuli. According to associative learning theories, people choose rare categories for ambiguous stimuli because rare cues draw more attention. Alternatively, inferential theories predict that people choose rare categories because ambiguous stimuli contrast more with well-established rules that predict membership in the common category. In this experiment, we used multi-voxel pattern analysis to decode which features of ambiguous stimuli, common or rare, participants are activating during an fMRI version of the IBRE task. Participants learned to predict four hypothetical diseases based on pairings of face, object, and scene cues. Objects and scenes were diagnostic cues, and predicted either a common or rare disease. Prior to the task, we collected independent localizer scans to characterize activation patterns associated with each of the different visual cues in object-selective cortex. During a test phase, ambiguous trials were presented in which a cue for the rare disease was paired with a cue for the common disease. We show that individuals engage qualitatively distinct neural processes when making rare versus common responses: choosing the rare category involved activation of cues associated with the common category. Consistent with inferential theories of base-rate neglect, our findings suggest that this surprising behavior involves a deliberative mechanism not explained by purely associative models.

show abstract

Section: Cc-by-nc-ndmentioning

confidence: 99%

Model-based fMRI Reveals Dissimilarity Processes Underlying Base Rate Neglect

O’Bryan

Worthy

Livesey

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…Cobos et al (2016) showed the same is true for humans when using a cued-response priming task, whereas verbal ratings were consistent with rule-based generalization. Furthermore, the use of rule-based generalization has been shown to be related to working memory, cognitive reflection, and strategic model-based choice in other instrumental learning tasks (Wills et al, 2011a,b; Don et al, 2015, 2016). However, as with the Perruchet effect, researchers have not yet explored whether these competing forms of generalization have an impact on the strength of future learning.…”

Section: Issues Limitations and Future Directionsmentioning

confidence: 99%

Three Ways That Non-associative Knowledge May Affect Associative Learning Processes

Thorwart

Livesey

2016

Front. Psychol.

View full text Add to dashboard Cite

Associative learning theories offer one account of the way animals and humans assess the relationship between events and adapt their behavior according to resulting expectations. They assume knowledge about event relations is represented in associative networks, which consist of mental representations of cues and outcomes and the associative links that connect them. However, in human causal and contingency learning, many researchers have found that variance in standard learning effects is controlled by “non-associative” factors that are not easily captured by associative models. This has given rise to accounts of learning based on higher-order cognitive processes, some of which reject altogether the notion that humans learn in the manner described by associative networks. Despite the renewed focus on this debate in recent years, few efforts have been made to consider how the operations of associative networks and other cognitive operations could potentially interact in the course of learning. This paper thus explores possible ways in which non-associative knowledge may affect associative learning processes: (1) via changes to stimulus representations, (2) via changes to the translation of the associative expectation into behavior (3) via a shared source of expectation of the outcome that is sensitive to both the strength of associative retrieval and evaluation from non-associative influences.

show abstract

“…Feature-based generalization depends upon the surface similarity between separate stimuli and compounds. As such, it is assumed that rule-based generalization is more complex and might require greater understanding of the discrimination (Shanks and Darby, 1998) or more working memory capacity (Wills et al, 2011). …”

Section: Learning About Constituent Elements or Configurationsmentioning

confidence: 99%

“…Rule-based generalization was associated with strong initial discrimination learning (Shanks and Darby, 1998). Wills et al (2011) found that individuals who completed a concurrent task while learning the same initial discrimination were more likely to show feature-based generalization (Wills et al, 2011). As such, it may be that greater working memory capacity is associated with stronger non-linear discrimination learning and rule-based generalization.…”

Section: Learning About Constituent Elements or Configurationsmentioning

confidence: 99%

Accounting for individual differences in human associative learning

Byrom¹

2013

Front. Psychol.

View full text Add to dashboard Cite

Associative learning has provided fundamental insights to understanding psychopathology. However, psychopathology occurs along a continuum and as such, identification of disruptions in processes of associative learning associated with aspects of psychopathology illustrates a general flexibility in human associative learning. A handful of studies have looked specifically at individual differences in human associative learning, but while much work has concentrated on accounting for flexibility in learning caused by external factors, there has been limited work considering how to model the influence of dispositional factors. This review looks at the range of individual differences in human associative learning that have been explored and the attempts to account for, and model, this flexibility. To fully understand human associative learning, further research needs to attend to the causes of variation in human learning.

show abstract

Effects of concurrent load on feature- and rule-based generalization in human contingency learning.

Cited by 34 publications

References 60 publications

Model-based fMRI Reveals Dissimilarity Processes Underlying Base Rate Neglect

Model-based fMRI Reveals Dissimilarity Processes Underlying Base Rate Neglect

Three Ways That Non-associative Knowledge May Affect Associative Learning Processes

Accounting for individual differences in human associative learning

Contact Info

Product

Resources

About