Mixed-effects models are a powerful tool for modeling fixed and random effects simultaneously, but do not offer a feasible analytic solution for estimating the probability that a test correctly ...rejects the null hypothesis. Being able to estimate this probability, however, is critical for sample size planning, as power is closely linked to the reliability and replicability of empirical findings. A flexible and very intuitive alternative to
analytic
power solutions are
simulation-based
power analyses. Although various tools for conducting simulation-based power analyses for mixed-effects models are available, there is lack of guidance on how to appropriately use them. In this tutorial, we discuss how to estimate power for mixed-effects models in different use cases: first, how to use models that were fit on available (e.g. published) data to determine sample size; second, how to determine the number of stimuli required for sufficient power; and finally, how to conduct sample size planning without available data. Our examples cover both linear and generalized linear models and we provide code and resources for performing simulation-based power analyses on openly accessible data sets. The present work therefore helps researchers to navigate sound research design when using mixed-effects models, by summarizing resources, collating available knowledge, providing solutions and tools, and applying them to real-world problems in sample sizing planning when sophisticated analysis procedures like mixed-effects models are outlined as inferential procedures.
Researchers have shown that people often miss the occurrence of an unexpected yet salient event if they are engaged in a different task, a phenomenon known as inattentional blindness. However, ...demonstrations of inattentional blindness have typically involved naive observers engaged in an unfamiliar task. What about expert searchers who have spent years honing their ability to detect small abnormalities in specific types of images? We asked 24 radiologists to perform a familiar lung-nodule detection task. A gorilla, 48 times the size of the average nodule, was inserted in the last case that was presented. Eighty-three percent of the radiologists did not see the gorilla. Eye tracking revealed that the majority of those who missed the gorilla looked directly at its location. Thus, even expert searchers, operating in their domain of expertise, are vulnerable to inattentional blindness.
In sentence processing, semantic and syntactic violations elicit differential brain responses observable in event-related potentials: An N400 signals semantic violations, whereas a P600 marks ...inconsistent syntactic structure. Does the brain register similar distinctions in scene perception? To address this question, we presented participants with semantic inconsistencies, in which an object was incongruent with a scene's meaning, and syntactic inconsistencies, in which an object violated structural rules. We found a clear dissociation between semantic and syntactic processing: Semantic inconsistencies produced negative deflections in the N300-N400 time window, whereas mild syntactic inconsistencies elicited a late positivity resembling the P600 found for syntactic inconsistencies in sentence processing. Extreme syntactic violations, such as a hovering beer bottle defying gravity, were associated with earlier perceptual processing difficulties reflected in the N300 response, but failed to produce a P600 effect. We therefore conclude that different neural populations are active during semantic and syntactic processing of scenes, and that syntactically impossible object placements are processed in a categorically different manner than are syntactically resolvable object misplacements.
How does one find objects in scenes? For decades, visual search models have been built on experiments in which observers search for targets, presented among distractor items, isolated and randomly ...arranged on blank backgrounds. Are these models relevant to search in continuous scenes? This article argues that the mechanisms that govern artificial, laboratory search tasks do play a role in visual search in scenes. However, scene-based information is used to guide search in ways that had no place in earlier models. Search in scenes might be best explained by a dual-path model: a ‘selective’ path in which candidate objects must be individually selected for recognition and a ‘nonselective’ path in which information can be extracted from global and/or statistical information.
Successful adaptive behavior requires efficient attentional and locomotive systems. Previous research has thoroughly investigated how we achieve this efficiency during natural behavior by exploiting ...prior knowledge related to targets of our actions (e.g., attending to metallic targets when looking for a pot) and to the environmental context (e.g., looking for the pot in the kitchen). Less is known about whether and how individual nontarget components of the environment support natural behavior. In our immersive virtual reality task, 24 adult participants searched for objects in naturalistic scenes in which we manipulated the presence and arrangement of large, static objects that anchor predictions about targets (e.g., the sink provides a prediction for the location of the soap). Our results show that gaze and body movements in this naturalistic setting are strongly guided by these anchors. These findings demonstrate that objects auxiliary to the target are incorporated into the representations guiding attention and locomotion.
One might assume that familiarity with a scene or previous encounters with objects embedded in a scene would benefit subsequent search for those items. However, in a series of experiments we show ...that this is not the case: When participants were asked to subsequently search for multiple objects in the same scene, search performance remained essentially unchanged over the course of searches despite increasing scene familiarity. Similarly, looking "at" target objects during previews, which included letter search, 30 seconds of free viewing, or even 30 seconds of memorizing a scene, also did not benefit search "for" the same objects later on. However, when the same object was searched for again memory for the previous search was capable of producing very substantial speeding of search despite many different intervening searches. This was especially the case when the previous search engagement had been active rather than supported by a cue. While these search benefits speak to the strength of memory-guided search when the same search target is repeated, the lack of memory guidance during initial object searches--despite previous encounters with the target objects--demonstrates the dominance of guidance by generic scene knowledge in real-world search. (Contains 14 figures and 6 tables.)
It usually only takes a single glance to categorize our environment into different scene categories (e.g. a kitchen or a highway). Object information has been suggested to play a crucial role in this ...process, and some proposals even claim that the recognition of a single object can be sufficient to categorize the scene around it. Here, we tested this claim in four behavioural experiments by having participants categorize real-world scene photographs that were reduced to a single, cut-out object. We show that single objects can indeed be sufficient for correct scene categorization and that scene category information can be extracted within 50 ms of object presentation. Furthermore, we identified object frequency and specificity for the target scene category as the most important object properties for human scene categorization. Interestingly, despite the statistical definition of specificity and frequency, human ratings of these properties were better predictors of scene categorization behaviour than more objective statistics derived from databases of labelled real-world images. Taken together, our findings support a central role of object information during human scene categorization, showing that single objects can be indicative of a scene category if they are assumed to frequently and exclusively occur in a certain environment.
You Think You Know Where You Looked? You Better Look Again Võ, Melissa L.-H; Aizenman, Avigael M; Wolfe, Jeremy M
Journal of experimental psychology. Human perception and performance,
10/2016, Letnik:
42, Številka:
10
Journal Article
Recenzirano
Odprti dostop
People are surprisingly bad at knowing where they have looked in a scene. We tested participants' ability to recall their own eye movements in 2 experiments using natural or artificial scenes. In ...each experiment, participants performed a change-detection (Exp.1) or search (Exp.2) task. On 25% of trials, after 3 seconds of viewing the scene, participants were asked to indicate where they thought they had just fixated. They responded by making mouse clicks on 12 locations in the unchanged scene. After 135 trials, observers saw 10 new scenes and were asked to put 12 clicks where they thought someone else would have looked. Although observers located their own fixations more successfully than a random model, their performance was no better than when they were guessing someone else's fixations. Performance with artificial scenes was worse, though judging one's own fixations was slightly superior. Even after repeating the fixation-location task on 30 scenes immediately after scene viewing, performance was far from the prediction of an ideal observer. Memory for our own fixation locations appears to add next to nothing beyond what common sense tells us about the likely fixations of others. These results have important implications for socially important visual search tasks. For example, a radiologist might think he has looked at "everything" in an image, but eye tracking data suggest that this is not so. Such shortcomings might be avoided by providing observers with better insights of where they have looked.
The study presented here provides researchers with a revised list of affective German words, the Berlin Affective Word List Reloaded (BAWL-R). This work is an extension of the previously published ...BAWL (Võ, Jacobs, & Conrad, 2006), which has enabled researchers to investigate affective word processing with highly controlled stimulus material. The lack of arousal ratings, however, necessitated a revised version of the BAWL. We therefore present the BAWL-R, which is the first list that not only contains a large set of psycholinguistic indexes known to influence word processing, but also features ratings regarding emotional arousal, in addition to emotional valence and imageability. The BAWL-R is intended to help researchers create stimulus material for a wide range of experiments dealing with the affective processing of German verbal material.
Abstract Interacting with objects in our environment requires determining their locations, often with respect to surrounding objects (i.e., allocentrically). According to the scene grammar framework, ...these usually small, local objects are movable within a scene and represent the lowest level of a scene’s hierarchy. How do higher hierarchical levels of scene grammar influence allocentric coding for memory-guided actions? Here, we focused on the effect of large, immovable objects ( anchors) on the encoding of local object positions. In a virtual reality study, participants (n = 30) viewed one of four possible scenes (two kitchens or two bathrooms), with two anchors connected by a shelf, onto which were presented three local objects (congruent with one anchor) (Encoding). The scene was re-presented (Test) with 1) local objects missing and 2) one of the anchors shifted (Shift) or not (No shift). Participants, then, saw a floating local object (target), which they grabbed and placed back on the shelf in its remembered position (Response). Eye-tracking data revealed that both local objects and anchors were fixated, with preference for local objects. Additionally, anchors guided allocentric coding of local objects, despite being task-irrelevant. Overall, anchors implicitly influence spatial coding of local object locations for memory-guided actions within naturalistic (virtual) environments.
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