Rule abstraction, model-based choice, and cognitive reflection

Don, Hilary J.; Goldwater, Micah B.; Otto, A. Ross; Livesey, Evan J.

doi:10.3758/s13423-016-1012-y

Cited by 28 publications

(40 citation statements)

References 32 publications

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“…There is now evidence from a range of studies that this eliminative inferential account fails to capture many of the properties of the effect and makes corollary predictions Don, Goldwater, Otto, & Livesey, 2016;Shanks & Darby, 1998;Wills, Graham, Koh, McLaren & Rolland, 2011). This may be the reason why both learning models do a less than impressive job of capturing the pattern of choice data in its entirety.…”

Section: Rule Based-processes In the Inverse Base-rate Effectmentioning

confidence: 99%

Learned predictiveness models predict opposite attention biases in the inverse base-rate effect.

Don¹,

Beesley²,

Livesey³

2019

Journal of Experimental Psychology: Animal Learning and Cogniti

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Several attention-based models of associative learning are built upon the learned predictiveness principle, whereby learning is optimised by attending to the most predictive features and ignoring the least predictive features. Despite their functional similarity, these models differ in their formal mechanisms, and thus may produce very different predictions in some circumstances. As we demonstrate, this is particularly evident in the inverse base-rate effect. Using simulations with a modified Mackintosh model and the EXIT model, we found that models based on the learned predictiveness principle can account for rare-outcome choice biases associated with the inverse baserate effect, despite making opposite predictions for relative attention to rare versus common predictors. The models also make different predictions regarding changes in attention across training, and effects of context associations on attention to cues. Using a human causal learning task, we replicated the inverse base-rate effect and a recently reported reduction in this effect when the context is not predictive of the common outcome, and used eye-tracking to test model predictions about changes in attention both prior to making a decision, and during feedback. The results support the predictions made by EXIT, where the rare predictor commands greater attention than the common predictor throughout training. In addition, patterns of attention prior to making a decision differed to those during feedback, where effects of using a partially predictive context were evident only prior to making a prediction.

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Section: Rule Based-processes In the Inverse Base-rate Effectmentioning

confidence: 99%

Learned predictiveness models predict opposite attention biases in the inverse base-rate effect.

Don¹,

Beesley²,

Livesey³

2019

Journal of Experimental Psychology: Animal Learning and Cogniti

Self Cite

View full text Add to dashboard Cite

show abstract

“…This difference in the response patterns between groups was not attributable to differences in learning efficiency, indicating that the result was not simply due to differences in learned associations or selective attention. Second, the researchers classified participants as either rule-based or featurebased, according to their generalization performance in a separate patterning task, in which rule transfer is associated with higher working memory capacity (Wills, Barrasin, & McLaren, 2011), greater cognitive reflection, and more strategic Bmodel-based^choices in reinforcement learning (Don, Goldwater, Otto, & Livesey, 2016). Only those participants who were able to extract and apply an abstract rule in the patterning task exhibited an inverse base-rate effect.…”

Section: Inferential Explanationsmentioning

confidence: 99%

Effects of outcome and trial frequency on the inverse base-rate effect

Don

Livesey

2016

Mem Cogn

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The inverse base-rate effect is a bias in contingency learning in which participants tend to predict a rare outcome for a conflicting set of perfectly predictive cues. Although the effect is often explained by attention biases during learning, inferential strategies at test may also contribute substantially to the effect. In three experiments, we manipulated the frequencies of outcomes and trial types to determine the critical conditions for the effect, thereby providing novel tests of the reasoning processes that could contribute to it. The rare bias was substantially reduced when the outcomes were experienced at equal rates in the presence of predictive-cue frequency differences (Exp. 2), and when the predictive cues were experienced at equal rates in the presence of outcome frequency differences (Exp. 3). We also found a consistent common-outcome bias for novel cue compounds. The results indicate the importance of both cue and outcome frequencies to the inverse base-rate effect, and reveal a combination of necessary conditions that are not well captured by appealing to inferential strategies at test. Although both attention-based and inferential theories explain some aspects of these data, no existing theory fully accounts for these effects of relative novelty.

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“…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.

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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.

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Rule abstraction, model-based choice, and cognitive reflection

Cited by 28 publications

References 32 publications

Learned predictiveness models predict opposite attention biases in the inverse base-rate effect.

Learned predictiveness models predict opposite attention biases in the inverse base-rate effect.

Effects of outcome and trial frequency on the inverse base-rate effect

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

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