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Analogy is considered a core cognitive capacity that enables relational reasoning. It has been commonly believed that relational reasoning can be tested by the four-term analogy task of type “what is to c as b is to a?” or “a:b::c:?” in short. In this study, we challenge this common belief by investigating an alternative hypothesis that this classic task is solvable only by attributional reasoning on attributional similarities, rather than relational reasoning on the relation of the two relations. A word vector model was employed to demonstrate how these two types of reasoning were performed in the classic three-stem analogy task “a:b::c:?” and the two-stem analogy task “a:b::?:?”. The results showed that the two-stem analogy task was solvable only using relational reasoning, while the three-stem analogy task was solvable using both types of reasoning. It suggests that the two-stem analogy task is a better test for relational reasoning.

CogSci 2023

Two-stem Analogy Task is a Better Test for Relational Reasoning

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The 45th Annual Meeting of the Cognitive Science Society will take place in Sydney, Australia as a modified hybrid conference. Presentations from the main-session room will be live-streamed every day for all events. This will include all invited talks and award sessions. Recordings of all CogSci 2023 sessions will be made available for on-demand viewing after the conference.<br>
The in-person component will be held at the International Convention Centre Sydney at Darling Harbour. The Conference will take place Wednesday July 26th – Saturday July 29th, 2023 (Wednesday, July 26th is the Workshops Day).

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<br>[**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2023/07/COgSci-PAAG-V2.pdf)
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#### **CogSci 2023 Program Book**
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The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

Analogical reasoning is central to learning and thought with a key focus on similarity in which the same system of relations holds across different sets of elements. The current research investigated both first-order relation and second-order relation, represented in the combination pattern of symbols following the syntactic rules. The primary goal was to specify the neural substrates for abstract syntactic rule analogy.Using fMRI, the study identifies brain regions involved in this task, including left rostrolateral prefrontal cortex (rlPFC), left dorsolateral prefrontal cortex (dlPFC), and right precuneus. The results of this study reveal that although analogical reasoning of syntactic rules recruits similar brain regions as those in visuospatial and semantic analogy, their activation patterns are not the same. The role of rlPFC, the core area for analogical reasoning, needs to be reconsidered, and the complexity of precuneus is worth more attention.

Neural Correlates of Analogical Reasoning of Syntactic Rules

Detecting covariation in sequential events provides us with a powerful means of inferring the causal structure of our world. However, people often overestimate the causal relationship between unrelated events, a phenomenon referred to as illusory causation. This tendency is greatest when the putative effect occurs frequently; the widely replicated outcome density (OD) effect. Most laboratory research on illusory causation and the OD effect has focused on possible causes of a positive outcome, such as a drug that causes patient recovery. Despite its relevance, relatively few studies have examined illusory causation in cases where a cue is hypothesized to generate an unfavorable (negative) outcome, such as a drug that produces unwanted side effects. Here, we directly compared how people develop illusory beliefs about the generation of positive versus negative outcomes. We presented all participants with a drug treatment that was hypothesized to cause high readings of a fictitious cell count (but had no effect on cell count across a series of learning trials). We manipulated whether a high cell count occurred frequently or infrequently and whether a high cell count should be considered a beneficial medical outcome or an undesirable side effect. We found consistent evidence of an OD effect but no effect of the valence of the high cell count outcome. This suggests that illusory beliefs are not controlled by the desirability of the cause-effect relationship. We discuss implications for theories of applied causal reasoning.

Learning about Benefits and Side Effects of a Bogus Treatment are Similarly Influenced by the Frequency of the Outcome Occurring

Most research on causal judgment operates on an implicit premise: that the only relevant candidates for actual causation are individual variables, corresponding to the distinct nodes of a causal system. Instead, we claim that causal judgment can be about plural causes featuring more than one variable. We show that plural cause judgments are influenced by the prior probabilities of events just like other actual causation judgments. We further show that this dependence on probabilities cannot be explained away by supposing that subjects estimate the strength of a plural cause by combining their estimates for the individual variables that feature in it. Instead, plural causes appear to be apprehended holistically, as the object of an ad hoc computation, providing evidence that the notion of plural cause is a category of human judgment in its own right.

Plural causes in causal judgment

Predictive coding theories argue that recent experience establishes expectations that generate prediction errors when violated. Neural correlates of prediction errors have been widely reported, but their perceptual consequences remain unknown. We had observers perform a dual-report task on the orientation of briefly presented gratings within predictable or random sequences. Observers first made a speeded response to categorize grating orientation (clockwise or counterclockwise from vertical), then reproduced the orientation without time pressure by rotating a bar. This allowed us to separately assess response speed and accuracy for the same stimulus. Critically, on half the trials, the target orientation was unexpected, due to the spatiotemporal structure established by the preceding gratings. Observers responded faster and more accurately to gratings that were unexpected. These findings reveal the behavioural consequences of prediction error and indicate that unexpected events are prioritized by the visual system both in terms of processing speed and representational fidelity.

Unexpected visual events elicit faster and more accurate behavioural responses

Seeking plausible models for brain computation has been a continuing effort in brain encoding and decoding. Most prior works have mapped the association between stimulus representation from language models and fMRI brain activity using ridge regression. However, these models are not biologically plausible from the perspective of representing neural dynamics of the brain underlying the fMRI recordings. In this work, our primary motivation is to challenge ridge regression models with simple neural architectures such as echo state network (ESNs) and long short-term memory (LSTMs) on the brain encoding task that requires full-sentence processing in the task of reading short sentences. We explore various pre-trained Transformer language models for computing sentence representations and predict the fMRI brain activity from simple neural architectures that include initial layers with random parameterization and that do not require explicit training. Experiment results show that (i) ESNs with online learning can accurately predict the fMRI brain activity comparable to ridge regression models, (ii) Both cell-state (internal memory representation related to long term memory) and out-gate (related to short term memory) of LSTM display an equal level performance during short sentences in random LSTMs, (iii) left hemisphere language area has higher predictive brain activity versus right hemisphere language area, (iv) ESNs with online learning yield superior performance over offline learning, indicating the biological plausibility of ESNs and the cognitive process of sentence reading, and (v) among all the variants of transformer models, Longformer features facilitate better accuracy when utilized with both ridge regression and ESN online learning models. The proposed framework that combines input featurization, dynamic memory and learning modules offers a flexible, biologically plausible architecture for investigating brain encoding in neuroscience.

Brain Encoding using Randomized Recurrent Networks

A common claim by advocates of embodied, dynamical approaches is that action and perception are “coupled.” On the face of it, this claim may not seem controversial, after all many researchers working in mainstream computationalist neuroscientific approaches also talk about the “coupling” of perception and action. Our goal here is to clarify the relation between these claims of perception-action coupling stemming from dynamical and from computational perspectives. Examining the empirical evidence that computationalists and dynamicists invoke to support their claims we conclude that, despite using similar terminology, they mean entirely different and incompatible things. Still, we propose that both approaches can, at least to some extent, accommodate the evidence invoked by the other. This suggests that the evidence should not, on its own, be used to argue in favor of one approach against the other, and that the disagreement is of a philosophical nature rather than an empirical one.

Perception-Action Coupling and the Dynamicist/Computationalist Divide

Effectively updating one's beliefs requires sufficient empirical evidence (i.e., data) and the computational capacity to process it. Yet both data and computational resources are limited for human minds. Here, we study the problem of belief updating under limited data and limited computation. Using information theory to characterize constraints on computation, we find that the solution to the resulting optimization problem links the data and computational limitations together: when computational resources are tight, agents may not be able to integrate new empirical evidence. The resource-rational belief updating rule we identify offers a novel interpretation of conservative Bayesian updating.

Computation-Limited Bayesian Updating

Assigning category labels to examples varying along a continuous dimension exaggerates perceived differences between members on opposite sides of category boundaries. Using social categories, we investigated how contrast may guide representation of not only features that differentiate between groups but also features that are neither diagnostic nor correlated across trained examples. In a classification task, participants learned which residence hall to correctly assign students varying along three psychological traits (academic, athletic, social). The same target category was learned alongside one of two contrast categories with either higher or lower values along one diagnostic dimension. After learning, participants provided estimates of average trait values for each dorm. Predicted contrast effects were found along diagnostic dimensions but contrast also influenced memory for non-diagnostic and uncorrelated dimensions, presumably based on assumptions about general co-occurrence of features. These findings have implications for how stereotypes are learned and applied and how illusory correlations are perpetuated.

Contrast Categories Elicit Illusory Correlations When Learning Social Groups

Categorization is ubiquitous in human cognition and society, and impacts how we perceive and understand the world. In reflecting the needs and perspectives of their creators, no categorization system is entirely objective, and inbuilt biases can have harmful social consequences. Here, we propose methods for quantifying three kinds of category biases in hierarchical category systems. We present a study on two widely used library classification systems (the DDC and LCC) as large-scale examples of human categorization, and use our methods to quantify bias towards content associated with western (vs non-western) concepts in topic areas including history and religion. We find consistent evidence for western bias and show that the DDC tends to exhibit more western bias than the LCC. Our methods are general, and can be used to survey biases across topic areas, bias attributes, and hierarchical category systems.

Quantifying Bias in Library Classification Systems

In categorization research, competing theories are typically compared by fitting their predictions to participant’s responses on a set of test items. The theory that best matches each participant's responses is identified as the strategy the participant is most likely employing. Researchers face considerable difficulty in selecting the best-fitting model due to several factors. In this study, we show the frailty of this approach. Due to pervasive model mimicry and across the similarity- and rule-based models, typical categorization task designs fail to reliably distinguish strategies. Some design modifications that might help are counter-indicated on practical grounds (e.g., carry-over effects); other possible means of improving strategy identification are also discussed.

Next from CogSci 2023

Neural Correlates of Analogical Reasoning of Syntactic Rules