Strategies for memory-based decision making: Modeling behavioral and neural signatures within a cognitive architecture.

How do people use memories to make inferences about real-world objects? We tested three strategies based on predicted patterns of response times and blood-oxygen-level-dependent (BOLD) responses: one strategy that relies solely on recognition memory, a second that retrieves additional knowledge, and a third, lexicographic (i.e., sequential) strategy, that considers knowledge conditionally on the evidence obtained from recognition memory. We implemented the strategies as computational models within the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture, which allowed us to derive behavioral and neural predictions that we then compared to the results of a functional magnetic resonance imaging (fMRI) study in which participants inferred which of two cities is larger. Overall, versions of the lexicographic strategy, according to which knowledge about many but not all alternatives is searched, provided the best account of the joint patterns of response times and BOLD responses. These results provide insights into the interplay between recognition and additional knowledge in memory, hinting at an adaptive use of these two sources of information in decision making. The results highlight the usefulness of implementing models of decision making within a cognitive architecture to derive predictions on the behavioral and neural level.

Framing From Experience: Cognitive Processes and Predictions of Risky Choice.

A framing bias shows risk aversion in problems framed as "gains" and risk seeking in problems framed as "losses," even when these are objectively equivalent and probabilities and outcomes values are explicitly provided. We test this framing bias in situations where decision makers rely on their own experience, sampling the problem's options (safe and risky) and seeing the outcomes before making a choice. In Experiment 1, we replicate the framing bias in description-based decisions and find risk indifference in gains and losses in experience-based decisions. Predictions of an Instance-Based Learning model suggest that objective probabilities as well as the number of samples taken are factors that contribute to the lack of framing effect. We test these two factors in Experiment 2 and find no framing effect when a few samples are taken but when large samples are taken, the framing effect appears regardless of the objective probability values. Implications of behavioral results and cognitive modeling are discussed.

Cognitive Modeling at ICCM: State of the Art and Future Directions.

The goal of cognitive modeling is to build faithful simulations of human cognition. One of the challenges is that multiple models can often explain the same phenomena. Another challenge is that models are often very hard to understand, explore, and reuse by others. We discuss some of the solutions that were discussed during the 2015 International Conference on Cognitive Modeling.

Listen up, eye movements play a role in verbal memory retrieval.

People fixate on blank spaces if visual stimuli previously occupied these regions of space. This so-called "looking at nothing" (LAN) phenomenon is said to be a part of information retrieval from internal memory representations, but the exact nature of the relationship between LAN and memory retrieval is unclear. While evidence exists for an influence of LAN on memory retrieval for visuospatial stimuli, evidence for verbal information is mixed. Here, we tested the relationship between LAN behavior and memory retrieval in an episodic retrieval task where verbal information was presented auditorily during encoding. When participants were allowed to gaze freely during subsequent memory retrieval, LAN occurred, and it was stronger for correct than for incorrect responses. When eye movements were manipulated during memory retrieval, retrieval performance was higher when participants fixated on the area associated with to-be-retrieved information than when fixating on another area. Our results provide evidence for a functional relationship between LAN and memory retrieval that extends to verbal information.

Memory activation and the availability of explanations in sequential diagnostic reasoning.

In the field of diagnostic reasoning, it has been argued that memory activation can provide the reasoner with a subset of possible explanations from memory that are highly adaptive for the task at hand. However, few studies have experimentally tested this assumption. Even less empirical and theoretical work has investigated how newly incoming observations affect the availability of explanations in memory over time. In this article we present the results of 2 experiments in which we address these questions. While participants diagnosed sequentially presented medical symptoms, the availability of potential explanations in memory was measured with an implicit probe reaction time task. The results of the experiments were used to test 4 quantitative cognitive models. The models share the general assumption that observations can activate and inhibit explanations in memory. They vary with respect to how newly incoming observations affect the availability of explanations over time. The data of both experiments were predicted best by a model in which all observations in working memory have the same potential to activate explanations from long-term memory and in which these observations do not decay. The results illustrate the power of memory activation processes and show where additional deliberate reasoning strategies might come into play.