Nicholas D. Lange

Implications of cognitive load for hypothesis generation and probability judgment.

We tested the predictions of HyGene (Thomas et al., 2008) that both divided attention at encoding and judgment should affect the degree to which participants’ probability judgments violate the principle of additivity. In two experiments, we showed that divided attention during judgment leads to an increase in subadditivity, suggesting that the comparison process for probability judgments is capacity limited. Contrary to the predictions of HyGene, a third experiment revealed that divided attention during encoding leads to an increase in later probability judgment made under full attention. The effect of divided attention during encoding on judgment was completely mediated by the number of hypotheses participants generated, indicating that limitations in both encoding and recall can cascade into biases in judgments.

Temporal Dynamics of Hypothesis Generation: The Influences of Data Serial Order, Data Consistency, and Elicitation Timing

The pre-decisional process of hypothesis generation is a ubiquitous cognitive faculty that we continually employ in an effort to understand our environment and thereby support appropriate judgments and decisions. Although we are beginning to understand the fundamental processes underlying hypothesis generation, little is known about how various temporal dynamics, inherent in real world generation tasks, influence the retrieval of hypotheses from long-term memory. This paper presents two experiments investigating three data acquisition dynamics in a simulated medical diagnosis task. The results indicate that the mere serial order of data, data consistency (with previously generated hypotheses), and mode of responding influence the hypothesis generation process. An extension of the HyGene computational model endowed with dynamic data acquisition processes is forwarded and explored to provide an account of the present data.

Data acquisition dynamics and hypothesis generation

When formulating explanations for the events we witness in the world, temporal dynamics govern the hypotheses we generate. In our view, temporal dynamics influence beliefs over three stages: data acquisition, hypothesis generation, and hypothesis maintenance and updating. This paper presents experimental and computational evidence for the influence of temporal dynamics on hypothesis generation through dynamic working memory processes during data acquisition in a medical diagnosis task. We show that increasing the presentation rate of a sequence of symptoms leads to a primacy effect, which is predicted by the dynamic competition in working memory that dictates the weights allocated to individual data in the generation process. Individual differences observed in hypothesis generation are explained by differences in working memory capacity. Finally, in a simulation experiment we show that activation dynamics at data acquisition also accounts for results from a related task previously held to support capacity-unlimited diagnostic reasoning.

Catching a glimpse of working memory: top-down capture as a tool for measuring the content of the mind

This article outlines a methodology for probing working memory (WM) content in high-level cognitive tasks (e.g., decision making, problem solving, and memory retrieval) by capitalizing on attentional and oculomotor biases evidenced in top-down capture paradigms. This method would be of great use, as it could measure the information resident in WM at any point in a task and, hence, track information use over time as tasks dynamically evolve. Above and beyond providing a measure of information occupancy in WM, such a method would benefit from sensitivity to the specific activation levels of individual items in WM. This article additionally forwards a novel fusion of standard free recall and visual search paradigms in an effort to assess the sensitivity of eye movements in top-down capture, on which this new measurement technique relies, to item-specific memory activation (ISMA). The results demonstrate eye movement sensitivity to ISMA in some, but not all, cases.

Using a model of hypothesis generation to predict eye movements in a visual search task.

We used a model of hypothesis generation (called HyGene; Thomas, Dougherty, Sprenger, & Harbison, 2008) to make predictions regarding the deployment of attention (as assessed via eye movements) afforded by the cued recall of target characteristics before the onset of a search array. On each trial, while being eyetracked, participants were first presented with a memory prompt that was diagnostic regarding the target’s color in a subsequently presented search array. We assume that the memory prompts led to the generation of hypotheses (i.e., potential target characteristics) from long-term memory into working memory to guide attentional processes and ocular–motor behavior. However, given that multiple hypotheses might be generated in response to a prompt, it has been unclear how the focal hypothesis (i.e., the hypothesis that exerts the most influence on search) affects search behavior. We tested two possibilities using first fixation data, with the assumption that the first item fixated within a search array was the focal hypothesis. We found that a model assuming that the first item generated into working memory guides overt attentional processes was most consistent with the data at both the aggregate and single-participant levels of analysis.

Explicit awareness supports conditional visual search in the retrieval guidance paradigm

In four experiments we explored whether participants would be able to use probabilistic prompts to simplify perceptually demanding visual search in a task we call the retrieval guidance paradigm. On each trial a memory prompt appeared prior to (and during) the search task and the diagnosticity of the prompt(s) was manipulated to provide complete, partial, or non-diagnostic information regarding the targets color on each trial (Experiments 1-3). In Experiment 1 we found that the more diagnostic prompts was associated with faster visual search performance. However, similar visual search behavior was observed in Experiment 2 when the diagnosticity of the prompts was eliminated, suggesting that participants in Experiment 1 were merely relying on base rate information to guide search and were not utilizing the prompts. In Experiment 3 participants were informed of the relationship between the prompts and the color of the target and this was associated with faster search performance relative to Experiment 1, suggesting that the participants were using the prompts to guide search. Additionally, in Experiment 3 a knowledge test was implemented and performance in this task was associated with qualitative differences in search behavior such that participants that were able to name the color(s) most associated with the prompts were faster to find the target than participants who were unable to do so. However, in Experiments 1-3 diagnosticity of the memory prompt was manipulated via base rate information, making it possible that participants were merely relying on base rate information to inform search in Experiment 3. In Experiment 4 we manipulated diagnosticity of the prompts without manipulating base rate information and found a similar pattern of results as Experiment 3. Together, the results emphasize the importance of base rate and diagnosticity information in visual search behavior. In the General discussion section we explore how a recent computational model of hypothesis generation (HyGene; Thomas, Dougherty, Sprenger, & Harbison, 2008), linking attention with long-term and working memory, accounts for the present results and provides a useful framework of cued recall visual search.

Using the memory activation capture (MAC) procedure to investigate the temporal dynamics of hypothesis generation

Research investigating top-down capture has demonstrated a coupling of working memory content with attention and eye movements. By capitalizing on this relationship, we have developed a novel methodology, called the memory activation capture (MAC) procedure, for measuring the dynamics of working memory content supporting complex cognitive tasks (e.g., decision making, problem solving). The MAC procedure employs briefly presented visual arrays containing task-relevant information at critical points in a task. By observing which items are preferentially fixated, we gain a measure of working memory content as the task evolves through time. The efficacy of the MAC procedure was demonstrated in a dynamic hypothesis generation task in which some of its advantages over existing methods for measuring changes in the contents of working memory over time are highlighted. In two experiments, the MAC procedure was able to detect the hypothesis that was retrieved and placed into working memory. Moreover, the results from Experiment 2 suggest a two-stage process following hypothesis retrieval, whereby the hypothesis undergoes a brief period of heightened activation before entering a lower activation state in which it is maintained for output. The results of both experiments are of additional general interest, as they represent the first demonstrations of top-down capture driven by participant-established WM content retrieved from long-term memory.

Does constraining memory maintenance reduce visual search efficiency

We examine whether constraining memory retrieval processes affects performance in a cued recall visual search task. In the visual search task, participants are first presented with a memory prompt followed by a search array. The memory prompt provides diagnostic information regarding a critical aspect of the target (its colour). We assume that upon the presentation of the memory prompt, participants retrieve and maintain hypotheses (i.e., potential target characteristics) in working memory in order to improve their search efficiency. By constraining retrieval through the manipulation of time pressure (Experiments 1A and 1B) or a concurrent working memory task (Experiments 2A, 2B, and 2C), we directly test the involvement of working memory in visual search. We find some evidence that visual search is less efficient under conditions in which participants were likely to be maintaining fewer hypotheses in working memory (Experiments 1A, 2A, and 2C), suggesting that the retrieval of representations from long-term memory into working memory can improve visual search. However, these results should be interpreted with caution, as the data from two experiments (Experiments 1B and 2B) did not lend support for this conclusion.