Affordances Modulate the Perception of Object Saturation and Orientation in a Change Detection Task When Bottom-Up Attentional Strategies are Minimized

doi:10.21203/rs.3.rs-4613499/v1

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Affordances Modulate the Perception of Object Saturation and Orientation in a Change Detection Task When Bottom-Up Attentional Strategies are Minimized

https://doi.org/10.21203/rs.3.rs-4613499/v1

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The visual system uses high-level knowledge to predict and constrain incoming sensory signals to increase the speed and efficiency of object recognition and action. Thus, we hypothesized that graspable objects would bias lower-level visual processing toward action-relevant object features (e.g., orientation) while non-graspable objects would bias toward less action-relevant features (e.g., saturation). Participants viewed images of objects that did or did not afford grasping and were located close to or far from the participant. Participants indicated, via a right-handed key press, whether the object decreased, didn’t change, or increased, in orientation or saturation. Participants were faster and more accurate at detecting changes in orientation versus saturation, graspable versus non-graspable objects, and far versus close objects, but no interaction effects were found. Upon eliminating objects that were easily parsed into their constituent elements, and thus encouraged bottom-up attentional strategies that circumvent top-down processes, participants were more accurate at detecting orientation changes in graspable objects and faster at detecting saturation changes in non-graspable objects. The results provide partial support for the hypothesis that object affordances modulate low-level visual processes in an action-relevant manner but highlight the importance of accounting for individual differences in attentional strategies when interpreting experimental results from visual cognition tasks.

Biological sciences/Psychology

Biological sciences/Psychology/Human behaviour

Biological sciences/Neuroscience/Sensory processing

Biological sciences/Neuroscience/Visual system/Object vision

The human visual system operates in both a bottom-up and top-down manner. In the bottom-up hierarchical model of vision, two subcortical visual pathways—the magnocellular and parvocellular pathways— send retinal inputs to the lateral geniculate nucleus in the thalamus and then to different layers of the primary visual cortex (Felleman & Van Essen, 1991). The rapid magnocellular neurons project primarily to the dorsal stream in the parietal lobe that enables the production of visually-guided actions towards objects (Kaplan & Shapley, 1986). In contrast, the slow parvocellular neurons project primarily to the ventral stream in the temporal lobe that enables conscious object perception and recognition (Goodale & Milner, 1992). The dorsal and ventral streams have been deemed the “vision-for-action” and “vision-for-perception” pathways, respectively (Goodale & Milner, 1992). At the same time, the visual system also engages top-down processes that enable bidirectionality between higher and lower-level visual areas, such that signals concerning high-level knowledge about the identity, function, or motor possibilities (i.e., affordances) of an object can project from higher brain areas, such as the frontal lobe, to earlier visual areas via “feedback” or “re-entrant” fibers to predict and constrain the processing of incoming sensory signals to increase the speed and efficiency with which objects can be recognized and/or acted upon (Gilbert & Li, 2013).

Magnetoencephalography (MEG) recordings have demonstrated that magnocellular projections to the dorsal stream may play a key role in the initial processing of high-level object knowledge that enables top-down processing in the visual system (Liu et al., 2017). Within 100 milliseconds of viewing an object, neural activity initiates in the intraparietal sulcus (IPS) and then propagates in a top-down manner backwards along the dorsal stream (Liu et al., 2017). It is proposed that this IPS activity reflects the rapid arrival of low spatial frequency (LSF) object information (e.g., general shape) via magnocellular projections (Skottun & Skoyles, 2011). The IPS uses this information to create a coarse global template or ‘gist’ representation of the object (Chan et al., 2013; Liu et al., 2017). The template then constrains the processing of high spatial frequency (HSF) object features (i.e., sharp edges, fine contour, saturation) processed more slowly by parvocellular neurons in earlier visual areas and the ventral stream (Kauffmann et al., 2015). In this way, the brain uses the “gist” representation to create perceptual predictions of the object’s identity which facilitates rapid recognition of the correct object while minimizing extraneous processing of irrelevant object features and identities inconsistent with the “gist” template (Trapp & Bar, 2015).

Affordances refer to the idea that some objects are associated with distinct motor action plans (Garrisons-Vásquez & Schubö, 2014). Both the dorsal and ventral streams play a role in processing knowledge related to object affordances and thereby enable visually-guided behaviours (Mizelle et al., 2013); however, the two streams, generally process different types of affordances. Functional affordances pertain to specific goal-directed actions evoked by an object. For example, a hammer is associated with the functional affordances of grasping and swinging. Physical affordances pertain to the physical features of the object that enable the actualization of said actions. For example, the hammer has a long narrow handle that enables grasping (Mizelle et al., 2013). This distinction is supported by electroencephalography (EEG) studies demonstrating that top-down processes can be elicited by both semantic knowledge about an object’s function (e.g., mugs hold liquid for us to drink from) as well as sensorimotor representations related to an object’s actions (e.g., reaching for a mug and bringing it to your mouth to drink; Foerster & Goslin, 2021). Functional magnetic resonance imaging (fMRI) has revealed that functional affordances and physical affordances are processed by the ventral and dorsal streams, respectively (Mizelle et al., 2013). Thus, it has been suggested that objects that afford action may be able to activate top-down processes that would bias lower-level visual processing to facilitate the refinement of features associated with physical affordances, such as orientation (Chan et al., 2013). Orientation is considered a pure physical affordance, and its strength as an affordance intensifies when objects possess a higher-level feature that is intuitively tied to action, such as a handle (Symes et al., 2007). Further support for this view is derived from fMRI studies that have detected orientation sensitivity in the dorsal stream but not the ventral stream, and only when the object being viewed is graspable (Rice et al., 2007).

Our perception of objects is influenced, not only by our pre-existing knowledge about objects, but also by knowledge about the interactions that can occur between objects and our own bodies (Riener, 2019). This is known as embodied perception, wherein our ability to interact with our surroundings impacts how we perceive the world around us (Riener, 2019). Although an object may normally be associated with strong affordances – think again of grasping and swinging a hammer – the extent to which the object can modulate lower-level visual processes in a top-down manner may depend on whether the object is located within a region of space that enables one to tangibly act on those affordances (Abrams et al., 2008). This view is supported by findings that participants direct more visuospatial attention towards objects with affordances that are immediately graspable (Garrido-Vasquez & Schubö, 2014); the salience of object affordances such as graspability, appear to be enhanced when the object is presented within peri-personal (PP) space, that is, the space within an individual’s reach (Klassen et al., 2023); and stimuli presented in perihand space are more likely to undergo processing in the dorsal ‘vision-for-action’ stream (Serino, 2019).

Neurophysiological research with macaques indicates that there are multiple representations of PP space associated with different parts of the body and that PP space neurons are multisensory. That is, they respond to tactile stimuli on specific parts of the body in addition to visual stimuli located near the face, neck, arm, and hands (Rizzolatti et al., 1981). These visuotactile neurons are located in the parietal cortex, the putamen, and the premotor cortex (Graziano & Gross, 1993) and their activity drives neurons in motor areas thereby priming motor actions (Rizzolatti et al., 1981). This suggests that objects that afford action and are also located in PP space may enhance dorsal stream activity via top-down processes thereby strengthening perception of lower-level visual features and physical affordances relevant for acting on the object, such as the object’s orientation (Agauas & Thomas, 2019; Kelly & Brockmole, 2014; Makin et al., 2012). Behavioural support for this idea is derived from studies indicating that objects that afford grasping actions and are presented near the hand bias visual processing toward the magnocellular pathway which projects primarily to the dorsal stream (Chan et al., 2013; Dubbelde & Shomstein, 2022). In contrast, stimuli in extrapersonal (EP) space are less likely to activate multisensory neurons or PP space representations associated with particular body parts (Serino, 2019). Since stimuli in PP space are processed by different brain networks than stimuli in EP space it seems plausible that even if an object is graspable it may not be able to bias visual processing toward the dorsal stream as strongly if it is located in EP space, compared to if it were located in PP space.

The aim of the present study was to determine whether high-level knowledge concerning an object's action possibilities and location relative to the body could bias lower-level visual processing in a manner that would selectively favour action production by the dorsal stream or object recognition by the ventral stream. Specifically, we hypothesized that objects with weak affordances located in EP space would activate top-down processes that bias early visual processing in favour of lower-level visual features relevant for object perception (e.g., saturation) at the expense of features relevant for visually-guided action (e.g., orientation). Furthermore, we hypothesized that objects with strong affordances and located in PP space would activate top-down processes that bias early visual processing in favour of lower-level visual features relevant for visually-guided action (e.g., orientation) at the expense of features relevant for object perception (e.g., saturation). Thus, we expected that participants would be faster and more accurate at detecting a change in the orientation of an object that is both graspable and located near the body. At the same time, they would be quicker and more accurate at detecting a change in the saturation of an object that is non-graspable and located far from the body.

To test these hypotheses, participants completed a visual change detection task where they viewed graspable and non-graspable stimuli located in PP or EP space. Each stimulus underwent a change in either saturation or orientation and participants indicated the type of change that they observed by pressing a key on a keyboard. The extent to which object Graspability, Location, and Change Type modulated the Accuracy and Reaction time of the participants’ responses was analyzed using repeated measures ANOVAs.

Participants

Ninety-eight introductory psychology students were recruited online through Sona Systems (https://www.sona-systems.com; age range 18–45, M = 20.578, SD = 4.168, male, n = 25, female, n = 57, other, n = 1). Individuals with known sensorimotor or neurological disorders severely affecting their vision or motor control were excluded. Outliers (n = 5) were defined by Accuracy scores lower than 33% in any single condition and excluded from statistical analysis along with left-handed (n = 8) and ambidextrous (n = 2) participants. Participants received 2% bonus credit towards their introductory psychology course as compensation. All participants provided voluntary, informed consent prior to participation via a virtual form. All experimental methods adhered to the Tricouncil Policy Statement 2 (TCPS 2) for the ethical conduct of research involving humans and the study protocols were approved by the Thompson Rivers University Human Research Ethics Board prior to experimental testing and data collection. The study was registered as a part of the Open Science Framework (osf.io) prior to data analysis, but during early data collection. Materials and data for this study are available at: https://osf.io/tay8m/.

Design

A 2 x 2 x 2 repeated measures design was used to examine the effects of object Graspability (graspable vs. non-graspable), Location (PP space vs. EP space), and Change Type (orientation vs. saturation) on participant Accuracy and Reaction Times in a visual change detection task. Location and Change Type were presented in a blocked and counterbalanced fashion while object Graspability (i.e., graspable and non-graspable objects) varied randomly throughout the task. Participant Accuracy rates and Reaction Times served as dependent variables. Accuracy was calculated by dividing the participants’ number of correct responses by the total number of responses in a specific block (i.e., 96 trials) and reported as a proportion. To calculate Reaction Time, participants’ response times (in milliseconds) were averaged for correct trials only. If a participant did not respond during a trial, that trial was treated as an incorrect trial for the Accuracy calculation and excluded entirely from the Reaction Time calculation.

Procedure

Stimuli and materials. The visual stimuli consisted of four graspable objects and four non-graspable objects. The base images for the eight stimuli were compiled from one of two royalty-free image websites (https://www.freeimages.com, https://www.unsplash.com). The stimulus objects were extracted from these images and imposed onto a white background using Adobe Photoshop (https://www.adobe.com) to reduce visual distractions. Graspable and non-graspable objects were matched based on the shape of their silhouettes and the compatibility of their real-life sizes (hose vs. branches, ping-pong paddle vs. fan, umbrella vs. birdbath, water bottle vs. cactus; Fig. 1). Graspable stimuli were defined as objects with affordance that are commonly and easily acted upon with a single hand. Orientation variations of each object were created in Adobe Photoshop such that each object was rotated to 10, 24, 38, 52, 66, and 80° angles (Fig. 2, top); the starting position (i.e., 0°) for the stimuli was with the top of the object pointing directly to the left. Saturation variations were created by adjusting the images to 0, 24, 38, 52, 66, and 80% saturation levels (Fig. 2, bottom); the determined saturation starting point (i.e., 0%) for each object occurred by setting the R, G, and B values to 0. A translucent colour mask was added to the fan and birdbath to ensure consistent presentation of saturation changes among the objects. To control for differences in retinal image size across the two Location conditions, a visual angle calculator (https://elvers.us/perception/visualAngle/) was used to determine the size of the visual stimuli required to maintain a consistent visual angle (i.e., 8.578°) in both Location conditions (i.e., 6 cm in PP space versus 34.5 cm in EP space). The task was created and presented using the stimulus presentation software, E-Prime 3.0 (https://pstnet.com/). The task was completed in full-screen mode on a desktop computer – a Dell OptiPlex 5090 – and an ASUS Widescreen LCD Monitor with a 31.5 in. monitor and resolution of 2560 x 1440 and a Razer Blackwidow V3 TKL keyboard with an 8000 Hz poll rate.

Experimental task. The study took place in the Early Childhood Education Centre at Thompson Rivers University located in Kamloops, British Columbia where participants completed a custom visual change detection task on a desktop computer. The entire task consisted of 384 trials divided into two counterbalanced blocks of 192 trials. One block of trials required participants to detect only orientation changes in the objects. The other block required participants to detect only saturation changes in the objects. An optional 10-minute break was granted between the two blocks. Within each of the two blocks, participants completed one half of the trials (96 trials) with the computer monitor located ~ 40 cm from the retina (PP space; Fig. 3a), and the other half of the trials with the monitor located ~ 228 cm from the retina (EP space; Fig. 3b) in a counterbalanced manner. Within each block an equal frequency of graspable and non-graspable stimuli were presented in a randomized order. Participants made a single right-handed keyboard press to indicate whether they perceived a change in the object’s orientation or saturation during each trial.

Before starting the task, participants were instructed to sit in a specific chair position and hold a constant posture so that the retinal size of the images presented on the monitor remained relatively consistent and accurate across the two Location conditions. Participants completed the digital informed consent form and demographic questionnaire and were then instructed to place their right index finger over the left arrow key, middle finger over the down arrow key, and ring finger over the right arrow key on the keyboard (Fig. 3c). Their left hand was to remain in their lap. For the orientation condition, participants were instructed to press the left arrow key if the object rotated counter-clockwise, down arrow key if there was no change, and right arrow key if the object rotated clockwise. For the saturation condition, participants were instructed to press the left arrow key if the object decreased in saturation, down arrow key if there was no change, and right arrow key if the object increased in saturation. If participants were unsure of the change that occurred, they were to make no key response and let the response screen time out. Participants completed six practice trials (two for each possible key response) before each block of trials. The practice trials were identical to the PP space trials, described below, with the exception that if participants responded incorrectly, they were shown the correct answer and key press accompanied by images of the target and test objects for that specific trial.

During each trial, participants viewed a consistent sequence of four screens. The first screen presented a central fixation cross, followed by the target image, a visual mask, and a test image, during which the participant was supposed to respond with a keypress (Fig. 4). If the participant did not respond during the test image, the experiment automatically progressed to the next trial, and no response was recorded. If they did respond, either a red X or a green checkmark flashed in the center of the screen for 200 ms, before the experiment progressed. The target images consisted of a single object presented at its four intermediate variations (i.e., 24, 38, 52, 66° or %). The upper- and lowermost (i.e., 10 and 80° or %) extremes of the object variations were never used as target images. The test image never differed from the target image by more than one increment of variation such that there were only three possible test images for each target image. For example, if the target image was an object at a 24° angle, the three possible test images for that target would be 14° lower, no change, or 14° higher. The test image was identical to the target image aside from any increases or decreases (or no change) in either Change Type. In the saturation condition, both target and test images were always presented upright (as shown in Fig. 1). In the orientation condition, both target and test images were always presented at an 80% saturation level (as shown in Fig. 1). After completing the task, participants completed an open-ended survey using Google Forms (https://www.google.ca/forms/) to record their experience and provide the experimenters with feedback.

Statistical Analyses

Statistical analyses were conducted using the Statistical Package for the Social Sciences (SPSS) and occurred in three stages. For the initial analysis two separate repeated-measures ANOVAs were used to determine the influence of the three independent variables, Graspability (graspable vs. non-graspable), Location (PP space vs EP space), and Change Type (orientation vs. Saturation) on each of the dependent variables of participant Accuracy and Reaction Time. Second, an exploratory item analysis was conducted to examine whether participant responses varied according to specific objects. For this analysis two repeated-measures ANOVAs were conducted, similar to above, but the Graspability factor was replaced with an Item factor consisting of eight levels, one level for each of the objects used in the study. Third, based on the findings from the exploratory analysis, as well as feedback from the participant surveys, the ping-ping paddle was excluded from the two original repeated-measures ANOVAs to generate the final results. All results in the text are reported as means ± standard error. All results depicted in graphical format are reported as means ± within-subjects standard error.

Initial Analysis

Accuracy

The results for the dependent variable of Accuracy are summarized in Table 1. The statistical analysis revealed significant main effects of Change Type, F(1,82) = 39.78, p = .001, η_p² = .13; Graspability, F(1,82) = 4.21, p = .043, η_p² = .05; and Location, F(1,82) = 12.26, p = .001, η_p² = .33 (Fig. 5). Thus, participants were more accurate at detecting orientation changes (M = .77, SE = .011) compared to saturation changes (M = .71, SE = .009), regardless of object location or graspability. They were also slightly better at identifying changes in graspable objects (M = .75, SE = .008) compared to non-graspable objects (M = .74, SE = .009) regardless of change type or object location. They were also more accurate at identifying changes in objects located in EP space (M = .75, SE = .008) compared to PP space (M = .73, SE = .009) regardless of change type or object graspability. There were no significant two-way interactions and the predicted three-way interaction between Graspability, Location, and Change Type, F(1,82) = 0.22, p = .641, η_p² = .003, was also not significant (Fig. 5).

Reaction Time

The results for the dependent variable of Reaction Time are summarized in Table 2. The results revealed a non-significant but trending main effect of Location, F(1,82) = 3.50, p = .065, η_p² = .04, for Reaction Time. There were also significant main effects of Change Type, F(1,82) = 30.17, p = .001, η_p² = .27, and Graspability, F(1,82) = 12.10, p = .001, η_p² = .13 (Fig. 6). Regardless of object location or graspability, participants responded more quickly to orientation (M = 803.59 ms, SE = 16.91) than saturation changes (M = 869.939 ms, SE = 13.83). Similarly, participants responded more quickly to changes in graspable objects (M = 829.60 ms, SE = 14.35) compared to non-graspable objects (M = 843.931 ms, SE = 14.373). There were no significant two-way interactions, but the three-way interaction between Graspability, Location, and Change Type for Reaction Time, F(1,82) = 3.90, p = .052, η_p² = .05 was trending towards significance but not in the manner predicted (Fig. 6).

Exploratory Item Analysis

As participants’ ability to detect orientation and saturation changes did not seem to be modulated by object Graspability or Location as originally predicted, we decided to investigate whether their performance varied according to the individual objects used in the study. To do this we calculated each participant’s Relative Accuracy (i.e., difference in accuracy for orientation versus saturation trials) and Relative Reaction Time (i.e., difference in reaction time for orientation versus saturation trials) scores for each object while collapsing across Location conditions. Relative Accuracy scores were calculated by subtracting each participant’s saturation accuracy score from their orientation accuracy score for each object. Relative Reaction Times were calculated by subtracting each participant’s average orientation reaction time from their average saturation reaction time for each object.

Figure 7 depicts the average Relative Accuracy (top) and Relative Reaction Time (bottom) scores for each object in the study. The x-axis represents participants’ relative performance across both Change Type conditions. The farther the bar extends to the right the better the participants’ orientation detection relative to saturation detection and vice versa. We had expected that all graspable objects (gray bars) would elicit better orientation detection relative to saturation detection, and thus that the bars for graspable objects would extend farther to the right than for non-graspable objects. While this appeared to generally be the case, the ping-pong paddle was a major exception and deviated significantly from our expectations. Although it was arguably the most obviously “graspable” object in the entire stimulus array, participants were both faster and more accurate at detecting saturation compared to orientation changes in the ping-pong paddle. As discussed farther below, this alluded to the possibility that participants may have been using unforeseen bottom-up attentional strategies to complete the task that disproportionately impeded their ability to detect orientation changes in the ping-pong paddle relative to the other objects in the array. As such, we subsequently excluded the ping-pong analysis from the final repeated-measures ANOVAs.

Final Analysis (excluding the ping-pong paddle)

Accuracy

The results for the dependent variable of Accuracy, excluding the ping-pong paddle, are summarized in Table 3. The statistical analysis revealed significant main effects of Location, F(1,82) = 11.086, p = .001, η_p² = .12 and Change Type, F(1,82) = 56.408, p = .001, η_p² = .41 (Fig. 8). Therefore, participants were more accurate at detecting orientation (M = .78, SE = .011) compared to saturation changes (M = .70, SE = .009), and at detecting changes when the objects were located in EP (M = .75, SE = .009) compared to PP (M = .73, SE = .010) space. Notably, this analysis revealed a significant two-way interaction between Graspability and Change Type, F(1,82) = 11.179, p = .001, η_p² = .120 (Fig. 8). Follow-up pairwise comparisons revealed that participants were significantly more accurate at detecting orientation changes in graspable (M = 0.79, SE = .012) compared to non-graspable objects (M = .77, SE = .012). They did not, however, display any significant difference in accuracy when detecting saturation changes in non-graspable (M = .71, SE = .010) compared to graspable objects (M = .69, SE = .009; Fig. 8). The three-way interaction between Graspability, Location, and Change Type, F(1,82) = 0.22, p = .641, η_p² = .003 was not significant indicating that the two-way interaction between object graspability and change type were consistent regardless of whether the object images were located near or far from the participant.

Reaction Time

The results for the dependent variable of Reaction Time, excluding the ping-pong paddle, are summarized in Table 4. Similar to the results for Accuracy, there were significant main effects of Location, F(1,82) = 4.003, p = .049, η_p² = .049; and Change Type, F(1,82) = 37.182, p = .001, η_p² = .309 (Fig. 9). Therefore, participants were faster at detecting orientation changes (M = 806.910, SE = 16.790) relative to saturation changes (M = 884.590, SE = 14.184), and at detecting both types of changes when the objects were located in EP space (M = 836.793, SE = 13.317) compared to PP space (M = 854.708, SE = 16.269). There was also a significant two-way interaction between Graspability and Change Type, F(1,82) = 7.110, p = .01, η_p² = .079 (Fig. 9). Follow-up pairwise comparisons revealed that participants were faster at detecting saturation changes in non-graspable (M = 877.224, SE = 13.754) compared to graspable (M = 891.956, SE = 13.754). They did not, however, display any significant difference in reaction time when detecting orientation changes in graspable (M = 803.182, SE = 16.800) compared to non-graspable (M = 810.638, SE = 17.353) objects (Fig. 8). The three-way interaction between Graspability, Location, and Change Type, F(1,82) = 1.800, p = .183, η_p² = .021 was not significant, indicating that the two-way interaction between object graspability and change type was consistent regardless of whether the objects were located near or far from the participant.

Based on the top-down model of visual processing, we hypothesized that pre-existing knowledge that an object affords grasping, and is located within a graspable region of space, would lead to a selective bias in low-level visual processing that favours other features highly relevant for action production by the dorsal stream, such as orientation, at the expense of features more relevant for object recognition by the ventral stream, such as saturation. Participants completed a visual change detection task on a monitor located close to or far from their body. The monitor depicted images of individual objects that either strongly afforded grasping or did not. Participants indicated via a key press with their right hand when they perceived a change in the object’s orientation or saturation. The results provide partial support for the hypotheses in that, once stimuli that encouraged bottom-up attentional strategies were at least partially accounted for, participants were more accurate at identifying orientation changes when the object was graspable, but they were faster at identifying changes in saturation when the object was non-graspable. These results lend support to the idea that high-level knowledge of object affordances can selectively bias low-level visual processing in favour of visual features more relevant for either action production by the dorsal stream or object recognition by the ventral stream.

The present study featured several strengths that allowed us to investigate the top-down influence of object affordances on participants’ ability to detect a change in object orientation or saturation. First, the orientation variations used in the present study were designed to address ceiling effects observed in previous research from our lab (Bamford et al., in prep). Bamford et al., included orientation variations where the stimuli were presented upright on a distinct base. The everyday familiarity of these object positions may have enhanced participant detection of orientation changes, as deviations from these “ideal” positions would have been readily apparent. In the present study, none of the orientation variations assumed an obvious position (i.e., 0, 90, 180°) thereby eliminating this ceiling effect in the orientation condition. Second, participants in the present study responded with their right-hand only as early IPS activity and associated top-down processes (Liu et al., 2017), as well as processes involved in the planning and execution of motor actions (Janssen et al., 2010), are known to be lateralized to the left hemisphere, which controls the right hand in right-handed participants. Third, the stimuli used in the present study were matched to control for complex influences of object animacy and real-world size on ventral stream processing (Konkle & Caramazza, 2013). For instance, objects that have a small real-world size may be more readily connected to reach and grasp networks whereas objects that have a large real-world size may be more readily connected to navigational networks in the brain (Konkle & Caramazza, 2013). Thus, failing to control for the real-world size of stimuli could lead to a confound between object graspability and object real-world size. However, this only seems to apply to inanimate objects (Konkle & Caramazza, 2013), or perhaps more accurately, non-motile stimuli (Shatek et al., 2022; Yorek et al., 2009). Thus, to avoid confounds involving real-world size, we chose objects that were all conceptually inanimate (e.g., non-motile) and that were matched according to comparable real-world size. Fourth, the stimuli in the present study consisted of photorealistic images of real objects. Previous research has found that stimuli composed of mostly LSF information, like line drawings (Salmon et al., 2014), tend to bias visual processing toward the magnocellular pathway, while stimuli composed of mostly HSF tend to bias processing towards the parvocellular pathway (Goffaux et al., 2011; Kauffmann et al., 2014). Thus, the stimuli used in the present, which contained a mix of both LSF and HSF features, should have reduced these lower-level bottom-up biases, helping to ensure that any differences in participant responses to the graspable vs. non-graspable stimuli could be more readily attributed to differences in higher-level knowledge of object affordances.

Still, there are some limitations that should be considered. Although all the objects in the present study are common and readily identifiable to most people, we did not directly assess individual participants’ familiarity with the objects nor their confidence in their ability to use the graspable objects. Participants that have never seen or used a graspable object may be unable to access top-down affordances associated with the object, and as a result the brain may potentially bias processing towards the ventral stream (Riener, 2019) instead of the dorsal stream as predicted. Additionally, prior experience actually interacting with physical objects may be especially critical for strengthening object affordances when visual information is relatively unreliable (Takahashi & Watt, 2012). Thus, the fact that we did not prime the participants by having them physically interact with the stimulus objects may have weakened the graspability manipulation in the present study. This may have been further exasperated by the fact that the objects were presented as 2 dimensional (2D) images rather than real tangible objects. Gomez et al. (2017) found that 2D images are less capable of drawing participants’ attention and producing action responses. Thus, future research should aim to increase the power of the graspability manipulation by priming participants to interact with real world objects and using either real, 3D virtual, or 3D photographs of images instead of 2D images.

Despite the fact that the difficulty of the orientation detection task was increased relative to previous research (e.g., Bamford et al., in prep), the initial results of the present study revealed that participants were still consistently faster and more accurate at detecting changes in object orientation compared to saturation, regardless of whether the object was graspable or not. There may be multiple reason for this. First, LSF features that are highly related to pure physical affordances like orientation (Skottun & Skoyles, 2011; Wang et al., 2024), are rapidly transmitted to the extrastriate cortex via the magnocellular pathway, which, as mentioned previously, is thought to contribute to the rapid formation of a gist representation of the object’s shape (Chan et al., 2013; Kveraga et al., 2007; Liu et al., 2017; Trapp & Bar, 2015). It is possible that this gist representation may be sufficient to enable a fast and accurate judgement about a change in the object’s orientation but not saturation. Relatedly, Nilsson (2022) suggests that the evolution of visual processing began with basic “ancient vision” and progressed into more sophisticated “object vision”. Visual processing related to orientation detection encompasses a form of ancient vision where low resolution inputs are sufficient to enable the detection of changes in an object's outline. However, visual processing related to object recognition (e.g., saturation detection) is more optically sophisticated and evolved later. In the same vein, Montare (2016) provides evidence for an evolutionarily ancient primary reaction time system, that is highly attuned and rapidly responsive to objects in motion (which an object that undergoes a change in orientation may be perceived to be), versus a more recently evolved secondary reaction time system, that is slower and more highly attuned to colour changes in motionless objects (like, for example, objects that undergo a change in saturation). Together, these works suggest that the process of natural selection may have favored the earlier and more robust evolution of a visual system highly attuned and responsive to objects in motion. Consequently, all of the objects in the present study, when they underwent a change in orientation, may have been perceived as being in motion and consequently elicited more accurate and rapid responses from evolutionarily ancient visual systems compared to when the same objects simply underwent a change in colour saturation, but did not appear to move.

In contrast to our initial predictions, participants were typically better at detecting changes in both orientation and saturation when the objects were located beyond reach in EP space. These results conflict with previous findings. For example, Kelly and Brockmole (2014) found that participants’ orientation memory improved in PP space, while parvocellular colour memory improved in EP space, suggesting that objects beyond PP space should bias visual processing toward features relevant for perception (e.g., saturation). One reason for this discrepancy may be related to the fact that in the present study, when the object images were presented in EP space, their physical size, relative to the size of the display monitor, was greatly increased to control for retinal image size across the PP and EP space conditions. It may have been the case that in the EP condition participants were better able to compare the orientation and colour saturation of the test images to the black borders surrounding the display monitor. If this were the case, it would indicate that participants were using an unexpected bottom-up attentional strategy that would improve detection of both orientation and saturation changes in EP compared to PP space. Future research utilizing eyetracking could be used to confirm whether or not participants spend more time looking at the display borders in the EP compared to PP condition. Alternatively, Serino et al. (2007) found changes in bimodal receptive field properties following tool use (e.g. cane), whereby the cane extended their action space (PP space). This form of tool embodiment can expand PP space up to 240 cm (Seraglia et al., 2012). In the present study, the use of a wired keyboard that participants were aware they could use to manipulate a monitor located beyond reach may have increased the size of their PP space representation, essentially eliminating the EP space condition completely.

Regardless of object Graspability or Location, participants were typically faster and more accurate at detecting orientation changes, with the exception of the ping-pong paddle for which participants were better at detecting saturation changes (Fig. 7). This was highly surprising as the paddle was arguably the most “graspable” object in the entire stimulus array and was expected to elicit strong bias towards orientation detection. A potential explanation for its exceptionalism could be identified in the participant feedback survey. Multiple participants reported that they used bottom-up strategies to complete the task. This was particularly true for the orientation trials. For example, one participant reported: “orientation tasks were easier if I focused on the straight parts of objects (e.g., the straight blue line in the middle of the paddle’s handle).” The fact that most of the objects contained distinct visual features in their upper regions (e.g., the straight edge atop the birdbath) may have enabled participants to use the bottom-up strategy of focusing on orientation changes in these specific visual features. Notably, attention to specific features in the upper portion of the stimulus would likely enhance performance on the orientation task because when the object rotates clockwise the attended feature appears to move to the right, which is associated with a congruent right-sided key press (Fig. 8, top). As the top of the ping-paddle did not contain any distinct visual features to aid orientation detection, attention to the paddle’s handle in the lower portion of the stimulus, would have produced the opposite outcome; a clockwise rotation in the paddle would cause the attended feature (the handle) to appear to move to the left, which would be associated with an incongruent left-sided key press (Fig. 8, bottom). This likely explains the decreased orientation detection performance for the ping-pong paddle.

Consequently, the original statistical analyses were re-run without the paddle. This revealed that participants were more accurate at detecting orientation changes in graspable objects, but faster at detecting saturation changes in non-graspable objects (Figs. 9 & 10). These results provide partial support for our original hypotheses and align with recent research, which suggests that viewing objects, or images of objects, that afford action biases neural processing towards the magnocellular pathway (Chan et al., 2013; Dubbelde & Shomstein, 2022) and the dorsal stream (Almeida et al., 2014; Noppeney et al., 2006; Rice et al., 2007), and can also modulate visuomotor processing in a top-down manner (Foerster & Goslin, 2021). It also aligns with research indicating that planning and/or execution of specific actions towards a real object can bias visual processing towards other features of that object in visual search tasks (e.g., Bekkering & Neggers, 2002; Wykowska et al., 2009, 2011).

In sum, the present research extends previous work on top-down visual processing by providing behavioural evidence that high-level knowledge of an object’s affordances can bias low-level visual processing in favour of other action-relevant features (e.g., orientation), possibly to facilitate subsequent action production by the dorsal stream, potentially at the expense of less action-relevant features (e.g., saturation) used by the ventral stream for object recognition. However, humans are opportunistic and will adopt a variety of attentional strategies, including those of a bottom-up nature, to successfully complete visual cognition tasks which can substantially obscure experimental results. Thus, future research should consider including manipulations that prime top-down knowledge of object affordances to encourage top-down processing, while fully controlling for lower-level visual features and incorporating catch trials that require participants to attend to the whole stimulus object rather than specific visual features in order to prevent them from adopting bottom-up attentional strategies to complete the task. Furthermore, development of an inventory of the different bottom-up strategies that participants are prone to using in different visual cognition tasks will enable more powerful experimental designs that effectively prevent participants from employing bottom-up strategies while increasing the overall salience of top-down processing.

In an applied sense, many new technologies aim to mimic the human visual system including drone-based delivery services, autonomous vehicles, and facial recognition systems. If these technologies are developed with an over emphasis on the bottom-up model of visual processing it could lead to technologies that have functional flaws with a potential for grave implications. Insight into how top-down processing operates can help to ensure sound technological advancement in machines that must recognize and interact with objects in the same manner that humans do. It may also aid other fields, like clinical psychology and rehabilitative medicine, by helping to inform the development of therapies that aim to restore functional vision in patients with cerebral visual impairment. Additionally, a better understanding of top-down processing and “gist” object recognition could aid researchers in diagnosing and developing treatments for individuals with schizophrenia and other hallucinatory disorders, which may be related to an imbalance in bottom-up and top-down processing (Adamek et al., 2022).

Author Contribution

All authors confirm responsibility for the following: study conception and design, data collection, analysis and interpretation of results, and manuscript preparation. Funding acquisition: JMK.

Acknowledgement

This research was supported by the Natural Sciences and Engineering Research Council of Canada (JMK) (Grant no. RGPIN-2017-05995) and Thompson Rivers University. Their commitment to advancing scientific inquiry has been instrumental in driving our research forward. We gratefully acknowledge the support of participants who generously volunteered their time and cooperation to participate in this study.

Data Availability

The datasets generated and/or analysed during the current study are available at Open Science Framework, https://osf.io/tay8m/. Participant identities are confidential.

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Table 1.

ANOVA results for the dependent variable of Accuracy

Statistical Effects for Accuracy	df	F value	p value	Partial η²
Main Effects
Graspability	1,82	4.205	0.043*	0.049
Location	1,82	12.261	0.001***	0.130
Change Type	1,82	39.779	0.001***	0.327

Two-Way Interactions
Graspability X Location	1,82	0.107	0.744	0.001
Graspability X Change Type	1,82	0.020	0.888	0.000
Location X Change Type	1,82	2.464	0.120	0.029

Three-Way Interaction
Graspability X Location X Change Type	1,82	0.219	0.641	0.003

Note. The within-subject variables that reached statistical significance were the main effects of Location and Change Type on Accuracy, both with a p-value < 0.001 as well as Graspability, with a p-value < 0.05.

Table 2.

ANOVA results for the dependent variable of Reaction Time

Statistical Effects for Reaction Time	df	F value	p value	Partial η²
Main Effects
Graspability	1,82	12.095	0.001***	0.129
Location	1,82	3.496	0.065	0.041
Change Type	1,82	30.166	0.001***	0.269

Two-Way Interactions
Graspability X Location	1,82	0.071	0.791	0.001
Graspability X Change Type	1,82	0.003	0.957	0.000
Location X Change Type	1,82	1.756	0.189	0.021

Three-Way Interaction
Graspability X Location X Change Type	1,82	3.901	0.052^~	0.045

Note. The within-subject variables that reached statistical significance were the main effects of Graspability, and Change Type on Reaction Time, both with a p-value < 0.001.

Table 3.

ANOVA results for the dependent variable of Accuracy (excluding ping-pong paddle)

Statistical Effects for Accuracy	df	F value	p value	Partial η²
Main Effects
Graspability	1,82	1.050	0.309	0.013
Location	1,82	11.086	0.001***	0.119
Change Type	1,82	56.408	0.001***	0.408

Two-Way Interactions
Graspability X Location	1,82	0.139	0.710	0.002
Graspability X Change Type	1,82	11.179	0.001***	0.120
Location X Change Type	1,82	3.225	0.076	0.038

Three-Way Interaction
Graspability X Location X Change Type	1,82	0.819	0.368	0.010

Note. The within-subject variables that reached statistical significance were the main effects of Location, and Change Type on Accuracy, both with a p-value < 0.001. There was a significant two-way interaction between Graspability and Change Type, with a p-value < 0.001.

Table 4.

ANOVA results for the dependent variable of Reaction Time (excluding ping-pong paddle)

Statistical Effects for Reaction Time	df	F value	p value	Partial η²
Main Effects
Graspability	1,82	0.637	0.427	0.008
Location	1,82	4.003	0.049*	0.046
Change Type	1,82	37.182	0.001***	0.309

Two-Way Interactions
Graspability X Location	1,82	0.830	0.365	0.010
Graspability X Change Type	1,82	7.110	0.009**	0.079
Location X Change Type	1,82	2.646	0.108	0.031

Three-Way Interaction
Graspability X Location X Change Type	1,82	1.800	0.183	0.021

Note. The within-subject variables that reached statistical significance was the main effect of Change Type on Reaction Time, with a p-value < 0.001. There was a significant two-way interaction between Graspability and Change Type, with a p-value < 0.01.

No competing interests reported.

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Affordances Modulate the Perception of Object Saturation and Orientation in a Change Detection Task When Bottom-Up Attentional Strategies are Minimized

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Accuracy

Reaction Time

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Accuracy

Reaction Time

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