Performance of typical and superior face recognizers on a novel interactive face matching procedure

Unfamiliar simultaneous face matching is error prone. Reducing incorrect identification decisions will positively benefit forensic and security contexts. The absence of view-independent information in static images likely contributes to the difficulty of unfamiliar face matching. We tested whether a novel interactive viewing procedure that provides the user with 3D structural information as they rotate a facial image to different orientations would improve face matching accuracy. We tested the performance of ‘typical’ (Experiment 1) and ‘superior’ (Experiment 2) face recognizers, comparing their performance using high-quality (Experiment 3) and pixelated (Experiment 4) Facebook profile images. In each trial, participants responded whether two images featured the same person with one of these images being either a static face, a video providing orientation information, or an interactive image. Taken together, the results show that fluid orientation information and interactivity prompt shifts in criterion and support matching performance. Because typical and superior face recognizers both benefited from the structural information provided by the novel viewing procedures, our results point to qualitatively similar reliance on pictorial encoding in these groups. This also suggests that interactive viewing tools can be valuable in assisting face matching in high-performing practitioner groups.     

Using a Fork as a Hairbrush: Investigating Dual Routes to Release from Functional Fixedness

Functional fixedness involves difficulty with conceptualizing creative object uses. When it obstructs problem‐solving, individuals must reframe their approach. We examined how different training techniques—chunk decomposition (i.e., considering an object’s basic parts and physical properties) and constraint relaxation (i.e., considering an object’s different functions)—might rely upon different routes to creative reframing. Additionally, we investigated how different forms of cognitive load interact with these dual routes. Participants learned one of three techniques. Chunk decomposition participants created object breakdown diagrams; constraint relaxation participants created object functions lists; and free association (control) participants wrote a word that they associated with each of several concrete nouns. After training, participants attempted to solve five functional fixedness problems. E1 investigated how increasing germane cognitive load via either direct or indirect prompting affected training transfer. Experiment 2 investigated how reducing extraneous cognitive load by providing no transfer instructions and using an eye‐closure strategy. Across both experiments, results supported differences in accuracy and response latency by training. However, chunk decomposition and constraint relaxation did not follow the same pattern, suggesting different mechanisms of the effect. We discuss possible applications to increase innovation in real‐world domains such as education, business, and engineering.     

Making AI meaningful again

Synthese - Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s, but this enthusiasm was tempered by a long interlude of frustration when genuinely...