Andrew Wismer

ON THE HUNT: VISUAL SEARCH FOR CAMOUFLAGED TARGETS IN REALISTIC ENVIRONMENTS

Visual search tasks commonly involve manipulating the number of targets and distractors to change difficulty levels and observe differences in reaction time and accuracy. What happens when the search background itself serves as a distractor? By creating a target that has been camouflaged to match the environment, we observed RT and accuracy both with and without a target preview. We hypothesized that 1) as target size decreased (increasing difficulty), RT would increase and accuracy would decrease, and 2) that the target preview would not aid in search. Our results support for our first hypothesis, and partially support our second. Reaction times were unaffected by the availability of the target preview, but search accuracy displayed a small cost. Overall, our findings suggest that search for camouflaged targets in the real world, in some cases, is predicated on a categorical representation of the target.

Implicit learning mediates base rate acquisition in perceptual categorization

We explored the possibility, suggested by Koehler (Behavioral and Brain Sciences, 19, 1-53, 1996; also Spellman Behavioral and Brain Sciences, 19, 38, 1996), that implicit learning mediates the influence of base-rates on category knowledge acquired through direct experience. In two experiments, participants learned simple perceptual categories with unequal base-rates (i.e., presentation frequency). In Experiment 1, participants received either response training or observational training. In Experiment 2, participants received response training with either immediate or delayed feedback. In previous studies, observational training and delayed feedback training have been shown to disrupt implicit learning. We found that base-rate influence was weaker in these conditions when category discriminability was low (i.e., when category membership was difficult to determine). This conclusion was based on signal detection β values as well as decision-bound modeling results. Because these disruptions to implicit learning attenuate the base-rate effect, we conclude that implicit learning does indeed underlie the influence of base-rates learned through direct experience. This suggests that the implicit learning system postulated by the COVIS theory of categorization (Ashby, Alfonso-Reese, Turken, & Waldron Psychological Review, 105, 442-481, 1998) may be involved in developing sensitivity to category base-rates.

On the Hunt: Searching for Poorly Defined Camouflaged Targets.

As camouflaged targets share visual characteristics with the environment within which they are embedded, searchers rarely have access to a perfect visual template of such targets. Instead, they must rely on less specific representations to guide search. Although search for camouflaged and non-specified targets have both received attention in the literature, to date they have not been explored in a combined context. Here we introduce a new paradigm for characterizing behavior during search for camouflaged targets in natural scenes, while also exploring how the fidelity of the target template affects search processes. Search scenes were created from forest images, with targets a distortion (varied size) of that image at a random location. In Experiment 1 a preview of the target was provided; in Experiment 2 there was no preview. No differences were found between experiments on nearly all measures. Generally, reaction times and accuracy improved with familiarity on the task (more so for small targets). Analysis of eye movements indicated that performance benefits were related to improvements in both Search and Target Verification time. Combined, our data suggest that search for camouflaged targets can be improved over a short time-scale, even when targets are poorly defined.

A Workload Comparison During Anatomical Training with a Physical or Virtual Model.

Recent research argues for the supplementation of traditional anatomical training with emerging three-dimensional visualization technologies (3DVTs); however, little is known regarding the effect these technologies have on learner workload. In this experiment, sixty-one participants studied gross brain anatomy using either a plastic physical model (PM; n = 29) or models presented in virtual reality (VR; n = 32). Participants were fitted with a functional near-infrared spectroscopy (fNIRS) sensor, worn on the prefrontal cortex. fNIRS measures regional saturation of oxygen (RSO2) and is indicative of workload. Participants then completed a pre-knowledge test on human brain anatomy. Participants were given 10 min to use the provided 3DVT to study 16 anatomical brain structures. Following the study period, participants completed additional surveys measuring workload, newly acquired anatomical knowledge, and cognitive resources used. Overall, anatomical knowledge increased at post-test and the change was no different between PM and VR conditions. Participants in the PM condition reported significantly higher levels of spatial workload, mental demand, and frustration. RSO2 values suggest left hemispheric increases from baseline during learning for the VR condition, but decreases for the PM condition. No other measures revealed differences between the two conditions. These results provide support for the supplementation of traditional anatomical training techniques with virtual reality technology as a way of alleviating workload. Further research is needed to explain the link between workload and performance in anatomical knowledge acquisition.

Base-rate sensitivity through implicit learning

Two experiments assessed the contributions of implicit and explicit learning to base-rate sensitivity. Using a factorial design that included both implicit and explicit learning disruptions, we tested the hypothesis that implicit learning underlies base-rate sensitivity from experience (and that explicit learning contributes comparatively little). Participants learned to classify two categories of simple stimuli (bar graph heights) presented in a 3:1 base-rate ratio. Participants learned either from “observational” training to disrupt implicit learning or “response” training which supports implicit learning. Category label feedback on each trial was followed either immediately or after a 2.5 second delay by onset of a working memory task intended to disrupt explicit reasoning about category membership feedback. Decision criterion values were significantly larger following response training, suggesting that implicit learning underlies base-rate sensitivity. Disrupting explicit processing had no effect on base-rate learning as long as implicit learning was supported. These results suggest base-rate sensitivity develops from experience primarily through implicit learning, consistent with separate learning systems accounts of categorization.