Modeling human syllogistic reasoning in Soar

1989

Abstract: Soar is an architecture for general intelligence that has been proposed as a unified theory of human cognition (UTC)(Newell, 1989) and has been shown to be capable of supporting a wide range of intelligent behavior. Polk & Newell (1988) showed that a Soar theory could account for human data in syllogistic reasoning. In this paper, we begin to generalize this theory into a unified theory of immediate reasoning based on Soar and some assumptions about subjects' representation and knowledge.

2020

Multiple cognitive theories make conflicting explainations for human reasoning on syllogistic problems. The evaluation and comparison of these theories can be performed by conceiving them as predictive models. Model evaluation often employs static sets of predictions rather than full implementations of the theories. However, most theories predict different responses depending on the state of their internal parameters. Disregarding the theories’ capabilities to adapt parameters to different reasoners leads to an incomplete picture of their predictive power. This article provides parameterized algorithmic formalizations and implementations of some syllogistic theories regarding the syllogistic single-response task. Evaluations reveal a substantial improvement for most cognitive theories being made adaptive over their original static predictions. The best performing implementations are PHM, mReasoner and Verbal Models, which almost reach the MFA benchmark. The results show that there e...

Cognitive Science, 2017

A recent meta-analysis (Khemlani & Johnson-Laird, 2012) about psychological experiments of syllogistic reasoning demonstrates that the conclusions drawn by human reasoners strongly deviate from conclusions of classical logic. Moreover, none of the current cognitive theories predictions fit reliably the empirical data. In this paper, we show how human syllogistic reasoning can be modeled under a new cognitive theory, the Weak Completion Semantics. Our analysis based on computational logics identifies seven principles necessary to draw the inferences. Hence, this work contributes to a computational foundation of cognitive reasoning processes.

2015

A recent meta-study shows that the conclusions driven by human reasoners in psychological experiments about syllogistic reasoning are not the conclusions predicted by classical first-order logic. Moreover, current cognitive theories deviate significantly from the empirical data. In the following, three important cognitive approaches are presented and compared to predictions made by a new approach to model human reasoning tasks, viz. the weak completion semantics. Open questions and implications are discussed.

Psychology of reasoning. Theoretical and historical …, 2004

2012

One approach in pursuit of general intelligent agents has been to concentrate on the underlying cognitive architecture, of which Soar is a prime example. In the past, Soar has relied on a minimal number of architectural modules together with purely symbolic representations of knowledge. This paper presents the cognitive architecture approach to general intelligence and the traditional, symbolic Soar architecture. This is followed by major additions to Soar: nonsymbolic representations, new learning mechanisms, and long-term memories.

2020

The prevailing focus on aggregated data and the lacking groupto-individual generalizability it entails have recently been identified as a major cause for the low performance of cognitive models in the field of syllogistic reasoning research. This article attempts to add to the discussion about the performance of current syllogistic reasoning models by considering the parameterization capabilities some cognitive models offer. To this end, we propose a model evaluation setting targeted specifically toward analyzing the capabilities of a model to fine-tune its inferential mechanisms to individual human reasoning data. This allows us to (1) quantify the degree to which models are able to capture individual human reasoning behavior, (2) analyze the efficiency of the parameters used by models, and (3) examine the functional differences between the prediction capabilities of competing models on a more detailed level. We apply this method to two state-of-the-art models for syllogistic reaso...

1989

Abstract: Soar is a theory of the human cognitive architecture. We present here the Soar theory of taking instructions for immediate reasoning tasks, which involve extracting implicit information from simple situations in a few tens of seconds. This theory is realized in a computer system that comprehends simple English instructions and organizes itself to perform a required task. Comprehending instructions produces a model of future behavior that is interpretively executed to yield task behavior.

Cognitive Science, 2018

To this day, a great variety of psychological theories of reasoning exist aimed at explaining the underlying cognitive mechanisms. The high number of different theories makes a rigorous comparison of cognitive theories necessary. The present article proposes to use Multinomial Processing Trees to compare two of the most prominent theories of syllogistic reasoning: the Mental Models Theory and the Probability Heuristics Model. For this, we reanalyzed data from a meta-analysis on six studies about syllogistic reasoning. We evaluate both models with respect to their overall fit to the data by means of G2, AIC, BIC, and FIA, and on a parametric level. Our comparison indicates that a MMT-variant, though having more parameters, is slightly better on all criteria except of the BIC. Yet, none of the two models, realized as MPTs, is clearly superior. We outline the impact of the different theoretical principles and discuss implications for modeling syllogistic reasoning.