The contrast sensitivity function (CSF) predicts functional vision better than acuity, but long testing times prevent its psychophysical assessment in clinical and practical applications. This study presents the quick CSF (qCSF) method, a Bayesian adaptive procedure that applies a strategy developed to estimate multiple parameters of the psychometric function (A. B. Cobo-Lewis, 1996; L. L. Kontsevich & C. W. Tyler, 1999). Before each trial, a one-step-ahead search finds the grating stimulus (defined by frequency and contrast) that maximizes the expected information gain (J. V. Kujala & T. J. Lukka, 2006; L. A. Lesmes et al., 2006), about four CSF parameters. By directly estimating CSF parameters, data collected at one spatial frequency improves sensitivity estimates across all frequencies. A psychophysical study validated that CSFs obtained with 100 qCSF trials (~10 min) exhibited good precision across spatial frequencies (SD < 2–3 dB) and excellent agreement with CSFs obtained independently (mean RMSE = 0.86 dB). To estimate the broad sensitivity metric provided by the area under the log CSF (AULCSF), only 25 trials were needed to achieve a coefficient of variation of 15–20%. The current study demonstrates the method’s value for basic and clinical investigations. Further studies, applying the qCSF to measure wider ranges of normal and abnormal vision, will determine how its efficiency translates to clinical assessment.

Four experiments examined how faces compete with physically salient stimuli for the control of attention in 4-month-old, 6-month-old, and 8-month-old infants (N = 117 total). Three computational models were used to quantify physical salience. We presented infants with visual search arrays containing a face and familiar object(s), such as shoes and flowers. Six- and 8-month-old infants looked first and longest at faces; their looking was not strongly influenced by physical salience. In contrast, 4-month-old infants showed a visual preference for the face only when the arrays contained 2 items and the competitor was relatively low in salience. When the arrays contained many items or the only competitor was relatively high in salience, 4-month-old infants’ looks were more often directed at the most salient item. Thus, over ages of 4 to 8 months, physical salience has a decreasing influence and faces have an increasing influence on where and how long infants look.

The visual system efficiently processes complex and redundant information in a scene despite its limited capacity. One strategy for coping with the complexity and redundancy of a scene is to summarize it by using average information. However, despite its importance, the mechanism of averaging is not well understood. Here, a distributed attention model of averaging is proposed. Human percept for an object can be disturbed by various sources of internal noise, which can occur either before (early noise) or after (late noise) forming an ensemble perception. The model assumes these noises and reflects noise cancellation by averaging multiple items. The model predicts increased precision for more items with decelerated increments for large set-sizes resulting from late noise. Importantly, the model incorporates mechanisms of attention, which modulate each item's contribution to the averaging process. The attention in the model also results in saturation of performance increments for small set-sizes because the amount of attention allocated to each item is greater for small set-sizes than for large set-sizes. To evaluate the proposed model, a psychophysical experiment was conducted in which observers' ability to discriminate average sizes of two displays was measured. The observers' averaging performance increased at a decreasing rate with small set-sizes and it approached an asymptote for large set-sizes. The model accurately predicted the observed pattern of data. It provides a theoretical framework for interpreting behavioral data and leads to an understanding of the characteristics of ensemble perception.

Motivated by Signal Detection Theory (SDT), we developed a family of novel adaptive methods that estimate the sensitivity threshold—the signal intensity corresponding to a pre-defined sensitivity level (d′ = 1)—in Yes-No (YN) and Forced-Choice (FC) detection tasks. Rather than focus stimulus sampling to estimate a single level of %Yes or %Correct, the current methods sample psychometric functions more broadly, to concurrently estimate sensitivity and decision factors, and thereby estimate thresholds that are independent of decision confounds. Developed for four tasks—(1) simple YN detection, (2) cued YN detection, which cues the observer's response state before each trial, (3) rated YN detection, which incorporates a Not Sure response, and (4) FC detection—the qYN and qFC methods yield sensitivity thresholds that are independent of the task's decision structure (YN or FC) and/or the observer's subjective response state. Results from simulation and psychophysics suggest that 25 trials (and sometimes less) are sufficient to estimate YN thresholds with reasonable precision (s.d. = 0.10–0.15 decimal log units), but more trials are needed for FC thresholds. When the same subjects were tested across tasks of simple, cued, rated, and FC detection, adaptive threshold estimates exhibited excellent agreement with the method of constant stimuli (MCS), and with each other. These YN adaptive methods deliver criterion-free thresholds that have previously been exclusive to FC methods.

Recent evidence indicates that emotion enhances contrast thresholds in subsequent visual perception (Phelps, Ling, & Carrasco, 2006), and perceptual sensitivity for low-spatial-frequency but not high-spatial-frequency targets (Bocanegra & Zeelenberg, 2009b). However, these studies just report responses to various frequencies at a fixed contrast level or responses to various contrasts at a fixed frequency. In the current study, we measured the full contrast sensitivity function as a function of emotional arousal in order to investigate potential interactions between spatial frequency and contrast. We used a Bayesian adaptive inference with a trial-to-trial information gain strategy (Lesmes, Lu, Baek, & Albright, 2010) and a fear-conditioned stimulus to manipulate arousal level. The spatial frequency at which people showed peak contrast sensitivity shifted to lower spatial frequencies in the arousing condition compared with the non-arousing condition and people had greater contrast sensitivity function bandwidth in the arousing than in the non-arousing condition.