Neural correlates of the inverse base rate effect

Inkster, Angus; Milton, Fraser; Edmunds, Charlotte; Benattayallah, Abdelmalek; Wills, Andy J.

doi:10.1002/hbm.25729

Cited by 6 publications

(7 citation statements)

References 43 publications

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“…Wills et al (2014) previously reported preferentially common responding for cue compound DE, which was not observed in this experiment. This is the third time we have failed to replicate this particular aspect of our previous work under closely similar procedures; the other two were Experment 3 of Inkster (2019), and the experiment reported by Inkster, Milton, Edmunds, Benattayallah, and Wills (2021). Thus, contrary to Wills et al (2014), it seems likely that DE does not produce preferentially common responding in this procedure.…”

Section: The Exit Modelmentioning

confidence: 53%

Effect of a context shift on the inverse base-rate effect

Inkster¹,

Mitchell²,

Schlegelmilch³

et al. 2022

OJEPN

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The Inverse Base Rate Effect (IBRE) is a non-rational behavioural phenomenon in predictive learning. In the IBRE, participants learn that a stimulus compound AB leads to one outcome and that another compound AC leads to a different outcome. Importantly, AB and its outcome are presented three times as often as AC (and its outcome). On test, when asked which outcome to expect on presentation of the novel compound BC, participants preferentially select the rarer outcome, previously associated with AC. This is irrational because, objectively, the common outcome is more likely. Usually, the IBRE is attributed to greater attention paid to cue C than to cue B, and so is an excellent test for attentional learning models. The current experiment tested a simple account of attentional learning proposed by Le Pelley, Mitchell, Beesley, George, and Wills (2016) where attention paid to a stimulus is determined by its associative strength. This account struggles to capture the IBRE, but a potential solution presented by Le Pelley et al. (2016) appeals to the role of experimental context. In the present paper, we derived four predictions from the context explanation concerning the effect of changing to a novel experimental context at test, and examined these predictions empirically. Only one of the predictions, concerning the effect of a context shift on responding to a novel cue, was supported. These results fail to support both the context explanation suggested by Le Pelley et al. (2016) and the current leading account of the IBRE, EXIT (Kruschke, 2001b), but provide avenues for further research. Keywords: Inverse base-rate effect, EXIT, predictive learning, categorization

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Section: The Exit Modelmentioning

confidence: 53%

Effect of a context shift on the inverse base-rate effect

Inkster¹,

Mitchell²,

Schlegelmilch³

et al. 2022

OJEPN

View full text Add to dashboard Cite

show abstract

“…In the current experiment, each test stimulus was presented 12 times. Our previous work on the IBRE with EEG and MRI methodologies used similarly long test phases and the group-level results remained robust in these procedures (Inkster et al, 2021;Wills, Lavric, Hemmings, & Surrey, 2014), so we were confident that extending the test phase in this way would preserve the group-level results.…”

Section: Methodsmentioning

confidence: 73%

“…However, when participants are sequentially trained and tested on a combination of conflicting cues, the group-level result is that participants prefer the rare category (Kruschke, 1996). The IBRE has been observed in many experiments across a range of G-DISTANCE 20 scenarios (e.g., Shanks, 1992;Don & Livesey, 2017;Inkster, Milton, Edmunds, Benattayallah, & Wills, 2021;Inkster, Mitchell, Schlegelmilch, & Wills, 2022;Johansen, Fouquet, & Shanks, 2007, 2010Kalish, 2001;Sherman et al, 2009;Wills, Lavric, Croft, & Hodgson, 2007); for an excellent recent review, see Don, Worthy, and Livesey (2021).…”

Section: The Inverse Base-rate Effect (Ibre)mentioning

confidence: 99%

“…In the context of IBRE research, the paradigm example of an attentional-associative model is the EXemplar-based attention to distinctive InpuT (EXIT) model (Kruschke, 2001b). EXIT has been extensively examined through behavioral, eye-tracking, EEG, and fMRI methodologies, and is sometimes considered to be the most adequate of the available formal accounts of the IBRE (Don, Beesley, & Livesey, 2019;Don & Livesey, 2017;Inkster et al, 2021Inkster et al, , 2022Kruschke, 2001a;Wills et al, 2017). It is thus hard to imagine any assessment of the relative adequacy of models of the IBRE without including EXIT.…”

Section: Models Of the Ibrementioning

confidence: 99%

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g-distance: On the comparison of model and human heterogeneity

Dome¹,

Wills²

2023

Preprint

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Models are often evaluated when their behavior is at its closest to a single group-averaged empirical result, but this evaluation neglects the fact that both model and human behavior can be heterogeneous. Here we develop a measure, g-distance, which considers model adequacy as the extent to which models exhibit a similar range of behaviors to the humans they model. We demonstrate an application of this measure to five models of an irrational learning effect, the inverse base-rate effect. None of the five models we evaluate is substantially better than a random model on our metric. In the process of analyzing the human data, we also discovered that the group-level result was observed in less than 1% of individuals. We discuss the implications of our findings for model evaluation generally, and for models of the inverse base-rate effect specifically. We end by suggesting future avenues of research in computational modeling.

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“…Johansen et al (2007) report a sample size of 16. If we use an effect size of d = 0.46 observed by Inkster, Milton, Edmunds, Benattayallah, and Wills (2022) and an α of 0.05 with a non-directional alternative hypothesis, the experiment has 24% power 1 . Given this information, this pilot experiment is underpowered.…”

Section: Related Workmentioning

confidence: 99%

Errorless irrationality: removing error-driven components from the inverse base-rate effect paradigm

Dome¹,

Wills²

2023

Preprint

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The inverse base-rate effect is a robust irrational bias that arises when people face ambiguity. The most prominent theories of this irrational bias depend on prediction error. In this study, we gradually removed elements of a predictive learning design to test the extent to which error-driven processes underlie this bias. In our first experiment, we removed explicit feedback by implementing the inverse base-rate effect in an observational learning procedure. In our second study, we further removed any causal relationship between stimulus features and category labels by moving towards an unsupervised learning procedure. This removed any information participants could use to identify category labels. In both experiments, the inverse base-rate effect persisted and remained robust. This outcome suggests that this irrational bias is independent of supervised learning procedures. We propose that any theories and models of the inverse base-rate effect must manage information encoding and connection updates without explicit prediction error. We end by proposing two clear paths for future investigations.

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Neural correlates of the inverse base rate effect

Cited by 6 publications

References 43 publications

Effect of a context shift on the inverse base-rate effect

Effect of a context shift on the inverse base-rate effect

g-distance: On the comparison of model and human heterogeneity

Errorless irrationality: removing error-driven components from the inverse base-rate effect paradigm

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