Affective and contextual values modulate spatial frequency use in object recognition

Abstract

Visual object recognition is of fundamental importance in our everyday interaction with the environment. Recent models of visual perception emphasize the role of top-down predictions facilitating object recognition via initial guesses that limit the number of object representations that need to be considered. Several results suggest that this rapid and efficient object processing relies on the early extraction and processing of low spatial frequencies (LSF). The present study aimed to investigate the SF content of visual object representations and its modulation by contextual and affective values of the perceived object during a picture-name verification task. Stimuli consisted of pictures of objects equalized in SF content and categorized as having low or high affective and contextual values. To access the SF content of stored visual representations of objects, SFs of each image were then randomly sampled on a trial-by-trial basis. Results reveal that intermediate SFs between 14 and 24 cycles per object (2.3–4 cycles per degree) are correlated with fast and accurate identification for all categories of objects. Moreover, there was a significant interaction between affective and contextual values over the SFs correlating with fast recognition. These results suggest that affective and contextual values of a visual object modulate the SF content of its internal representation, thus highlighting the flexibility of the visual recognition system.

How does information from low and high spatial frequencies interact during scene categorization?

“…It is important to note that the cut-off frequency used to filter LSF scenes in the present study (4 cpd, i.e., 24 cpi) was relatively high as compared to the ones used in previous studies (usually around 2 cpd; see Kauffmann, Bourgin, et al, 2015;Mu & Li, 2013;Schyns & Oliva, 1994). LSF scenes in the present study thus included a rather large part of the scene spatial frequency spectrum including low to intermediate spatial frequencies and therefore contained the most diagnostic features for scene and object categorization, which have be found to lie at 0-4 cpd (see Caplette et al, 2014;McCotter et al, 2005). Therefore, it is likely that the advantage for LSF processing observed in the present study also reflects the fact that the LSF scenes contained the most relevant information for categorization.…”

“…It has been shown that spatial frequencies below 2 cpd are diagnostic to perform basic-level categorization of scenes (e.g., forest, highway, mountain; McCotter, Gosselin, Sowden, & Schyns, 2005). However, intermediate spatial frequencies of 2.3-4 cpd would be required for basic-level categorization of objects (Caplette et al, 2014). Importantly, most of these studies focused on the preferential use, the relevance, or the diagnosticity of a specific spatial frequency band for a given timescale or task constraint.…”

“…Although the prominent role of LSF information for rapid scene categorization has been extensively investigated and documented Schyns & Oliva, 1994; for a review see Hegdé, 2008;Kauffmann et al, 2014), there is also considerable experimental evidence of a predominant processing of HSF information even for very short stimuli exposure duration (Campagne et al, 2016;Harel & Bentin, 2009;Morrison & Schyns, 2001;Rotshtein, Schofield, Funes, & Humphreys, 2010;Schyns, 1998;Schyns & Oliva, 1999). Furthermore, many studies have shown that the use of spatial frequency information during the processing of complex stimuli such as scenes is highly flexible and depends on many factors such as stimulus exposure duration (Schyns & Oliva, 1994), category (e.g., Awasthi, Sowman, Friedman, & Williams, 2013;Collin & McMullen, 2005;Rotshtein et al, 2010;Vannucci, Viggiano, & Argenti, 2001), or task constraints (Abrams, Barbot, & Carrasco, 2010;Campagne et al, 2016;Caplette, West, Gomot, Gosselin, & Wicker, 2014;Fradcourt, Peyrin, Baciu, & Campagne, 2013;Morrison & Schyns, 2001;Ozgen, Payne, Sowden, & Schyns, 2006;Schyns & Oliva, 1999;Sowden, Özgen, Schyns, & Daoutis, 2003). For example, different spatial frequency bands would be used according to their diagnosticity to categorize a specific visual stimulus.…”

Object expectations alter information use during visual recognition

“…Sparse matrices were also z-scored within each trial. Only spatial frequencies up to 64 cycles per image (cpi) were analyzed, since higher SFs typically do not contribute to accurate object recognition (Caplette et al, 2016;Caplette, West, Gomot, Gosselin & Wicker, 2014;Gold, Bennett & Sekuler, 1999).…”

“…The mechanisms responsible for the modulation of object recognition by expectations remain unclear. Previous studies have reported that expectations can induce an increase in sensitivity (Cheadle, Egner, Wyart, Wu & Summerfield, 2015;Stein & Peelen, 2015;Wyart et al, 2012), and that they can preactivate internal representations (Cheadle et al, 2015;Kok, Failing & de Lange, 2012;2014;Wyart et al, 2012). During the recognition of complex objects, expectations might also alter the timecourse of the use of a subset of features.…”