Daryl Fougnie

When asked to remember the features of an object, do we retain single point-estimates in memory, or do we retain richer representations, such as probability distributions over feature space? Continuous color report tasks, which require participants to report the exact color of a remembered object, yield a wide distribution of errors. However, it is unclear whether this distribution of errors reflects random errors in point estimates (which would form a distribution when aggregated across trials), or if it reflects the fact that individual memories are probabilistic in nature. To distinguish between these possibilities, we developed a novel paradigm: Participants were shown a set of colorful circles and, after a brief memory delay, asked about the color of a randomly tested item. Rather than reporting a single color, participants placed 10 bets in the color space (each bet comprised 1/18 of the space). Bets could be stacked or moved to a new portion of color space. Points were awarded equal to the number of bets containing the correct color. We discouraged stacking bets by diminishing the value of higher stack levels. We found that participants have considerably richer representations than simple point estimates. The dispersion of bets was correlated with first response accuracy (the less accurate their first response, the more widely they distributed their bets), indicating that participants' internal confidence was guiding bet placement. Furthermore the distribution of bets on a trial was similar in shape to the probability distributions observed across trials, suggesting that an uncertainty distribution may be coded within individual memory representations. Finally, we found that memory performance improved, relative to the first response, when averaging across multiple bets. This performance benefit increased monotonically with each additional bet. Overall, the betting task revealed that memories are more than noisy point estimates, but are surprisingly rich and probabilistic. Meeting abstract presented at VSS 2015

Much is known about visual search for single targets, but relatively little about how participants “forage” for multiple targets. One important question is how long participants will search before moving to a new display. Evidence suggests that participants should leave when intake drops below the average rate (“optimal foraging,” Charnov, 1976). However, the real world has temporal structure (e.g., seasons) that could influence behavior. Does it matter if winter is coming and the next display will be worse than the last? We gave participants a series of search displays and asked them to collect targets as fast as possible. Target density was structured—rising and falling systematically across trials. We measured the duration for which participants foraged in each display (trials were terminated by participants). Foraging behavior was affected by temporal structure—counter to a simple optimal foraging account, observers searched displays longer when quality was falling compared to rising (Experiments 1 and 2). Additionally, we found that temporal structure altered explicit predictions about display quality (Experiment 2). These results demonstrate that foraging theories need to consider richer models of observers' representations of the world.

The MemToolbox is a collection of MATLAB functions for modeling visual working memory. In support of its goal to provide a full suite of data analysis tools, the toolbox includes implementations of popular models of visual working memory, real and simulated data sets, Bayesian and maximum likelihood estimation procedures for fitting models to data, visualizations of data and fit, validation routines, model comparison metrics, and experiment scripts. The MemToolbox is released under the permissive BSD license and is available at http://memtoolbox.org.

Influential theories of visual working memory have proposed that the basic units of memory are integrated object representations. Key support for this proposal is provided by the same object benefit: It is easier to remember multiple features of a single object than the same set of features distributed across multiple objects. Here, we replicate the object benefit but demonstrate that features are not stored as single, integrated representations. Specifically, participants could remember 10 features better when arranged in 5 objects compared to 10 objects, yet memory for one object feature was largely independent of memory for the other object feature. These results rule out the possibility that integrated representations drive the object benefit and require a revision of the concept of object-based memory representations. We propose that working memory is object-based in regard to the factors that enhance performance but feature based in regard to the level of representational failure.

Working memory is a mental storage system that keeps task-relevant information accessible for a brief span of time, and it is strikingly limited. Its limits differ substantially across people but are assumed to be fixed for a given person. Here we show that there is substantial variability in the quality of working memory representations within an individual. This variability can be explained neither by fluctuations in attention or arousal over time, nor by uneven distribution of a limited mental commodity. Variability of this sort is inconsistent with the assumptions of the standard cognitive models of working memory capacity, including both slot- and resource-based models, and so we propose a new framework for understanding the limitations of working memory: a stochastic process of degradation that plays out independently across memories.

The world is composed of features and objects and this structure may influence what is stored in working memory. It is widely believed that the content of memory is object-based: Memory stores integrated objects, not independent features. We asked participants to report the color and orientation of an object and found that memory errors were largely independent: Even when one of the object's features was entirely forgotten, the other feature was often reported. This finding contradicts object-based models and challenges fundamental assumptions about the organization of information in working memory. We propose an alternative framework involving independent self-sustaining representations that may fail probabilistically and independently for each feature. This account predicts that the degree of independence in feature storage is determined by the degree of overlap in neural coding during perception. Consistent with this prediction, we found that errors for jointly encoded dimensions were less independent than errors for independently encoded dimensions.

There is considerable debate on whether working memory (WM) storage is mediated by distinct subsystems for auditory and visual stimuli (Baddeley, 1986) or whether it is constrained by a single, central capacity-limited system (Cowan, 2006). Recent studies have addressed this issue by measuring the dual-task cost during the concurrent storage of auditory and visual arrays (e.g., Cocchini, Logie, Della Sala, MacPherson, & Baddeley, 2002; Fougnie & Marois, 2006; Saults & Cowan, 2007). However, studies have yielded widely different dual-task costs, which have been taken to support both modality-specific and central capacity-limit accounts of WM storage. Here, we demonstrate that the controversies regarding such costs mostly stem from how these costs are measured. Measures that compare combined dual-task capacity with the higher single-task capacity support a single, central WM store when there is a large disparity between the single-task capacities (Experiment 1) but not when the single-task capacities are well equated (Experiment 2). In contrast, measures of the dual-task cost that normalize for differences in single-task capacity reveal evidence for modality-specific stores, regardless of single-task performance. Moreover, these normalized measures indicate that dual-task cost is much smaller if the tasks do not involve maintaining bound feature representations in WM (Experiment 3). Taken together, these experiments not only resolve a discrepancy in the field and clarify how to assess the dual-task cost but also indicate that WM capacity can be constrained both by modality-specific and modality-independent sources of information processing.

An influential theory suggests that integrated objects, rather than individual features, are the fundamental units that limit our capacity to temporarily store visual information (S. J. Luck & E. K. Vogel, 1997). Using a paradigm that independently estimates the number and precision of items stored in working memory (W. Zhang & S. J. Luck, 2008), here we show that the storage of features is not cost-free. The precision and number of objects held in working memory was estimated when observers had to remember either the color, the orientation, or both the color and orientation of simple objects. We found that while the quantity of stored objects was largely unaffected by increasing the number of features, the precision of these representations dramatically decreased. Moreover, this selective deterioration in object precision depended on the multiple features being contained within the same objects. Such fidelity costs were even observed with change detection paradigms when those paradigms placed demands on the precision of the stored visual representations. Taken together, these findings not only demonstrate that the maintenance of integrated features is costly; they also suggest that objects and features affect visual working memory capacity differently.