Model-based fMRI Reveals Dissimilarity Processes Underlying Base Rate Neglect

O’Bryan, Sean; Worthy, Darrell A.; Livesey, Evan J.; Davis, Tyler

doi:10.1101/130716

Cited by 4 publications

(8 citation statements)

References 76 publications

(75 reference statements)

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“…Many studies support the view that the lateral PFC plays a central role for concepts when they require high levels of abstraction (Badre and D'Esposito, 2009) and rule-based reasoning (Davis et al, 2017;O'Bryan et al, 2018). But lateral PFC has also been implicated in low-level, similarity-based categorization (Davis et al, 2017;O'Bryan et al, 2018) and representation of specific exemplars (Mack et al, 2013). Zeithamova (2018, 2019) suggested that distinct processes and types of representations can be involved in concept learning, with their relative contribution depending on the coherence among category members.…”

Section: Different Prefrontal Cortex Dynamics For Learning At Differementioning

confidence: 97%

Brain Mechanisms of Concept Learning

et al. 2019

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Concept learning, the ability to extract commonalities and highlight distinctions across a set of related experiences to build organized knowledge, is a critical aspect of cognition. Previous reviews have focused on concept learning research as a means for dissociating multiple brain systems. The current review surveys recent work that uses novel analytical approaches, including the combination of computational modeling with neural measures, focused on testing theories of specific computations and representations that contribute to concept learning. We discuss in detail the roles of the hippocampus, ventromedial prefrontal, lateral prefrontal, and lateral parietal cortices, and how their engagement is modulated by the coherence of experiences and the current learning goals. We conclude that the interaction of multiple brain systems relating to learning, memory, attention, perception, and reward support a flexible concept-learning mechanism that adapts to a range of category structures and incorporates motivational states, making concept learning a fruitful research domain for understanding the neural dynamics underlying complex behaviors.

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Section: Different Prefrontal Cortex Dynamics For Learning At Differementioning

confidence: 97%

Brain Mechanisms of Concept Learning

et al. 2019

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“…If the two unique features M and N are presented together, people prefer to categorize them as belonging to the rarer category B . Not surprisingly, this effect still has not met a satisfactory theoretical explanation (Markman, 1989; Johansen et al, 2007; O’Bryan et al, 2017; Don and Livesey, 2017). On the contrary, its counterintuitive appearance has spread to occupying the categorization modeling domain as well (Kruschke, 1996; Juslin et al, 2001).…”

Section: Verification Simulations – the Interplay Between The Pressurmentioning

confidence: 98%

“…On the opposite, the ELMO model (Juslin et al, 2001) emphasizes that the effect is due to decision-making, i.e., people first probe the better learned rule (about the more frequent category) and/or the most similar one (depending on the test case) and if the probe does not match, they just choose the other category. Unfortunately, both explanations have been criticized for being far from flawless (Winman et al, 2003; Johansen et al, 2007; O’Bryan et al, 2017).…”

Section: Verification Simulations – the Interplay Between The Pressurmentioning

confidence: 99%

Relation-Based Categorization and Category Learning as a Result From Structural Alignment. The RoleMap Model

Petkov

Petrova

2019

Front. Psychol.

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Relational categories are structure-based categories, defined not only by their internal properties but also by their extrinsic relations with other categories. For example, predator could not be defined without referring to hunt and prey. Even though they are commonly used, there are few models taking into account any relational information. A category learning and categorization model aiming to fill this gap is presented. Previous research addresses the hypothesis that the acquisition and the use of relational categories are underlined by structural alignment. That is why the proposed RoleMap model is based on mechanisms often studied as the analogy-making sub-processes, developed on a suitable for this cognitive architecture. RoleMap is conceived in such a way that relation-based category learning and categorization emerge while other tasks are performed. The assumption it steps on is that people constantly make structural alignments between what they experience and what they know. During these alignments various mappings and anticipations emerge. The mappings capture commonalities between the target (the representation of the current situation) and the memory, while the anticipations try to fill the missing information in the target, based on the conceptual system. Because some of the mappings are highly important, they are transformed into a distributed representation of a new concept for further use, which denotes the category learning. When some knowledge is missing in the target, meaning it is uncategorized, that knowledge is transferred from memory in the form of anticipations. The wining anticipation is transformed into a category member, denoting the act of categorization. The model’s behavior emerges from the competition between these two pressures – to categorize and to create new categories. Several groups of simulations demonstrate that the model can deal with relational categories in a context-dependent manner and to account for single-shot learning, challenging most of the existing approaches to category learning. The model also simulates previous empirical data pointing to the thematic categories and to the puzzling inverse base-rate effect. Finally, the model’s strengths and limitations are evaluated.

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“…Typical explanations of the inverse base-rate effect rely on prioritised attention to Cue C during training (Kruschke, 1996, 2001a). There are of course competing explanations for the effect (e.g., O’Bryan et al, 2018); however, the current article will focus primarily on these attentional accounts. Due to the relative frequency of AB-O1 trials, the association between both cues and O1 is learned well.…”

mentioning

confidence: 99%

“…There are of course competing explanations for the effect (e.g. O'Bryan et al, 2018), however the current paper will focus primarily on these attentional accounts. Due to the relative frequency of AB-O1 trials, the association between both cues and O1 is learned well.…”

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confidence: 99%

Attention biases in the inverse base-rate effect persist into new learning

Don

Livesey

2021

Quarterly Journal of Experimental Psychology

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The inverse base-rate effect is a tendency to predict the rarer of two outcomes when presented with cues that make conflicting predictions. Attention-based accounts of the effect appeal to prioritised attention to predictors of rare outcomes. Changes in the processing of these cues are predicted to increase the rate at which they are learned about in the future (i.e., their associability). Our previous work has shown that the development of the inverse base-rate effect is accompanied by greater overt attention to the rare predictor while participants made predictions, and during feedback, and these biases change in different ways depending on the stage of training and global base-rate differences. It is unknown whether these gaze patterns reflect the manner in which cues are prioritised for learning or are merely a consequence of learning what the cues predict. This study tested whether the associability of common and rare predictors differed, and if so, how this difference changed as a function of training length and the presence of base-rate differences in the outcomes. Experiment 1 tested cue associability using a second learning task presented after either short or long training. The results suggest an associability advantage for rare predictors that weakens with extended training and is not strongly affected by the presence of global base-rate differences. However, Experiment 2 showed a clear effect of base-rate differences on choice after very brief training, indicating that attention biases as measured by associability change are not sufficient to produce the inverse base-rate effect.

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Model-based fMRI Reveals Dissimilarity Processes Underlying Base Rate Neglect

Cited by 4 publications

References 76 publications

Brain Mechanisms of Concept Learning

Brain Mechanisms of Concept Learning

Relation-Based Categorization and Category Learning as a Result From Structural Alignment. The RoleMap Model

Attention biases in the inverse base-rate effect persist into new learning

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