Investigating the Time Course of Part-Based and Holistic Processing in Face Perception

The effect of processing partial information in dynamic face perception

Face perception is a major topic in vision research. Most previous research has concentrated on (holistic) spatial representations of faces, often with static faces as stimuli. However, faces are highly dynamic stimuli containing important temporal information. How sensitive humans are regarding temporal information in dynamic faces is not well understood. Studies investigating temporal information in dynamic faces usually focus on the processing of emotional expressions. However, faces also contain relevant temporal information without any strong emotional expression. To investigate cues that modulate human sensitivity to temporal order, we utilized muted dynamic neutral face videos in two experiments. We varied the orientation of the faces (upright and inverted) and the presence/absence of eye blinks as partial dynamic cues. Participants viewed short, muted, monochromic videos of models vocalizing a widely known text (National Anthem). Videos were played either forward (in the correct temporal order) or backward. Participants were asked to determine the direction of the temporal order for each video, and (at the end of the experiment) whether they had understood the speech. We found that face orientation, and the presence/absence of an eye blink affected sensitivity, criterion (bias) and reaction time: Overall, sensitivity was higher for upright compared to inverted faces, and in the condition where an eye blink was present compared to the condition without an eye blink. Reaction times were mostly faster in the conditions with higher sensitivity. A bias to report inverted faces as 'backward' observed in Experiment I, where upright and inverted faces were presented randomly interleaved within each block, was absent when presenting upright and inverted faces in different blocks in Experiment II. Language comprehension results revealed that there was higher sensitivity when understanding the speech compared to not understanding the speech in both experiments. Taken together, our results showed higher sensitivity with upright compared to inverted faces, suggesting that the perception of dynamic, task-relevant information was superior with the canonical orientation of the faces. Furthermore, partial information coming from eye blinks, in addition to mouth movements, seemed to play a significant role in dynamic face perception, both when faces were presented upright and inverted. We suggest that studying the perception of facial dynamics beyond emotional expressions will help us to better understand the mechanisms underlying the temporal integration of facial information from different -partial and holistic- sources, and that our results show how different strategies, depending on the available information, are employed by human observers when judging the temporal order of faces.

The Role of Visual Factors in Dyslexia

What are the causes of dyslexia? Decades of research reflect a determined search for a single cause where a common assumption is that dyslexia is a consequence of problems with converting phonological information into lexical codes. But reading is a highly complex activity requiring many well-functioning mechanisms, and several different visual problems have been documented in dyslexic readers. We critically review evidence from various sources for the role of visual factors in dyslexia, from magnocellular dysfunction through accounts based on abnormal eye movements and attentional processing, to recent proposals that problems with high-level vision contribute to dyslexia. We believe that the role of visual problems in dyslexia has been underestimated in the literature, to the detriment of the understanding and treatment of the disorder. We propose that rather than focusing on a single core cause, the role of visual factors in dyslexia fits well with risk and resilience models that assume that several variables interact throughout prenatal and postnatal development to either promote or hinder efficient reading.

Influence of Perceptual Discriminability and Spatial Distance on Holistic Processing of Faces

Background: Holistic processing is defined as the perceptual integration of facial features, and plays an important role in face recognition. While researchers recognize the crucial role played by holistic processing in face perception, a complete delineation of the underlying mechanisms is impending. Very few studies have examined the effects of perceptual discrimination and spatial perception on holistic processing. Hence, the present study aimed to examine the influence of perceptual discrimination and spatial perception on face recognition.

Methods: We conducted two experiments by manipulating the perceptual discriminability of the target (the top-half faces) and non-target face (the bottom-half faces) parts in the composite-face task and examined how perceptual discriminability may affect holistic processing of faces.

Results: The results of Experiment 1 illustrated that holistic processing was modulated by the perceptual discriminability of the face. Furthermore, differential patterns of perceptual discriminability with the target and non-target parts suggested that different mechanisms may be responsible for the influence of target and non-target parts on face perception. The results of Experiment 2 illustrated that holistic processing was modulated by spatial distance between two faces, implicating that feature-by-feature strategy might decrease the magnitude of holistic processing.

Conclusion: The results of the present study suggest that holistic processing may lead to augmented perception effect exaggerating the differences between the two faces and may also be affected by the feature-by-feature strategy.

Quantifying machine influence over human forecasters

Crowdsourcing human forecasts and machine learning models each show promise in predicting future geopolitical outcomes. Crowdsourcing increases accuracy by pooling knowledge, which mitigates individual errors. On the other hand, advances in machine learning have led to machine models that increase accuracy due to their ability to parameterize and adapt to changing environments. To capitalize on the unique advantages of each method, recent efforts have shown improvements by “hybridizing” forecasts—pairing human forecasters with machine models. This study analyzes the effectiveness of such a hybrid system. In a perfect world, independent reasoning by the forecasters combined with the analytic capabilities of the machine models should complement each other to arrive at an ultimately more accurate forecast. However, well-documented biases describe how humans often mistrust and under-utilize such models in their forecasts. In this work, we present a model that can be used to estimate the trust that humans assign to a machine. We use forecasts made in the absence of machine models as prior beliefs to quantify the weights placed on the models. Our model can be used to uncover other aspects of forecasters’ decision-making processes. We find that forecasters trust the model rarely, in a pattern that suggests they treat machine models similarly to expert advisors, but only the best forecasters trust the models when they can be expected to perform well. We also find that forecasters tend to choose models that conform to their prior beliefs as opposed to anchoring on the model forecast. Our results suggest machine models can improve the judgment of a human pool but highlight the importance of accounting for trust and cognitive biases involved in the human judgment process.