Emily J. A-Izzeddin

How do we make sense of something we’ve never seen before? Classifying objects into superordinate classes like ‘animal’ is a key step in interpreting novel experiences, but is challenging because radically different items (e.g., octopus, rabbit) must somehow be grouped together. In general, no single feature is shared by all members. We reasoned that to classify or imagine novel items from outside the distribution of previous experiences, observers parse objects into meaningful component features that they can mentally recombine (‘compositionality’). To test this, we asked participants to draw familiar and novel members of nine superordinate object classes. We then asked other participants to classify the drawings, and mark and label their defining ‘parts’. We find that human classification performance is well predicted by a Bayesian classifier that optimally combines the part labels, suggesting humans can create and classify out-of-distribution experiences through a compositional generative representation of object features.

The mechanisms by which humans perceptually organize individual regions of a visual scene to generate a coherent scene representation remain largely unknown. Our perception of statistical regularities has been relatively well-studied in simple stimuli, and explicit computational mechanisms that use low-level image features (e.g., luminance, contrast energy) to explain these perceptions have been described. Here, we investigate to what extent observers can effectively use such low-level information present in isolated naturalistic scene regions to facilitate associations between said regions. Across two experiments, participants were shown an isolated reference patch, then required to select which of two subsequently presented patches came from the same scene as the reference (two-alternative forced choice method). In Experiment 1, participants made their judgments based on unaltered image patches, and were consistently above chance when performing such association judgments. Additionally, participants' responses were well-predicted by a generalized linear multilevel model using predictors based on low-level feature similarity metrics (specifically, pixel-wise luminance and phase-invariant structure correlations). In Experiment 2, participants were presented with unaltered image regions, thresholded image regions, or regions reduced to only their edge content. Performance for thresholded and edge regions was significantly poorer than for unaltered image regions. Nonetheless, the model still correlated well with participants' judgments. Our findings suggest that image region associations can be accounted for using low-level feature correlations, suggesting such basic features are strongly associated with those underlying judgments made for complex visual stimuli.

The mechanisms by which humans perceptually organise individual regions of a visual scene to generate a coherent scene representation remain largely unknown. Our perception of statistical regularities has been relatively well-studied in simple stimuli, and explicit computational mechanisms that use low-level image features (e.g., luminance, contrast energy) to explain these perceptions have been described. Here, we investigate to what extent observers can effectively use such low-level information present in isolated naturalistic scene regions to facilitate associations between said regions. Across two experiments, participants were shown an isolated standard patch, then required to select which of two subsequently presented patches came from the same scene as the standard (2AFC). In Experiment 1, participants were consistently above chance when performing such association judgements. Additionally, participants’ responses were well-predicted by a generalised linear multilevel model (GLMM) employing predictors based on low-level feature similarity metrics (specifically, pixel-wise luminance and phase-invariant structure correlations). In Experiment 2, participants were presented with thresholded image regions, or regions reduced to only their edge content. Their performance was significantly poorer when they viewed unaltered image regions. Nonetheless, the model still correlated well with participants’ judgments. Our findings suggest that image region associations can be reduced to low-level feature correlations, providing evidence for the contribution of such basic features to judgements made on complex visual stimuli.

Humans display marked changes to their perceptual experience of a stimulus following prolonged or repeated exposure to a preceding stimulus. A well-studied example of such perceptual adaptation is the tilt-aftereffect. Here, prolonged exposure to one orientation leads to a shift in the perception of subsequent orientations. Such a capacity to adapt suggests the visual system is dynamically tuned to our current visual environment. However, it remains unclear to what extent adaptation occurs in response to systematic features in naturalistic scenes. We therefore investigated orientation adaptation in response to natural viewing of filtered live-action film stimuli. Within a session, participants freely viewed 45 minutes of a film which had been filtered to include increased contrast energy within a specified orientation band (0°, 45°, 90°, or 135°; i.e., the adaptor). To measure adaptation effects, the film was intermittently interrupted to have participants perform a simple orientation judgement task. Having participants complete behavioural trials throughout the testing session, including 45 minutes of total adaptation time, allowed investigation of the accumulation of response biases and changes in such biases over the course of the session. We found participants exhibited stronger adaptation effects in response to cardinal adaptors compared to obliques. However, overall adaptation effects were weaker than those observed under typical tilt-aftereffect paradigms. Further, within a single session, adaptation effects developed inconsistently. The current findings therefore demonstrate a resistance to adaptation in response to naturalistic viewing conditions, suggesting barriers to understanding perceptual adaptation as experienced in nature.

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Decision confidence plays a critical role in humans’ ability to make adaptive decisions in a noisy perceptual world. Despite its importance, there is currently little consensus about the computations underlying confidence judgements in perceptual decisions. In order to better understand these mechanisms, in this study we sought to address the extent to which confidence is informed by a naturalistic prior probability distribution. Contrary to previous research, we did not require participants to internalise the parameters of an arbitrary prior distribution. Instead we used a novel psychophysical paradigm which allowed us to capitalise on probability distributions of low-level image features in natural scenes, which are well-known to influence perception. Participants reported the subjective upright of naturalistic image target patches, and then reported their confidence in their orientation responses. We used computational modelling to relate the statistics of the low-level features in the targets to the distribution of these features across many natural images. As expected, we found that participants used an internalised prior of the regularities of low-level natural image statistics to inform their perceptual judgements. Critically, we also show that the same low-level image statistics predict participants’ confidence judgements. Overall, our study highlights the importance of using naturalistic task designs that capitalise on existing, long-term priors to further our understanding of the computational basis of confidence.

Humans have well-documented priors for many features present in nature that guide visual perception. Despite being putatively grounded in the statistical regularities of the environment, scene priors are frequently violated due to the inherent variability of visual features from one scene to the next. However, these repeated violations do not appreciably challenge visuo-cognitive function, necessitating the broad use of priors in conjunction with context-specific information. We investigated the trade-off between participants’ internal expectations formed from both longer-term priors and those formed from immediate contextual information using a perceptual inference task and naturalistic stimuli. Notably, our task required participants to make perceptual inferences about naturalistic images using their own internal criteria, rather than making comparative judgements. Nonetheless, we show that observers’ performance is well approximated by a model that makes inferences using a prior for low-level image statistics, aggregated over many images. We further show that the dependence on this prior is rapidly re-weighted against contextual information, whether relevant or irrelevant. Our results therefore provide insight into how apparent high-level interpretations of scene appearances follow from the most basic of perceptual processes, which are grounded in the statistics of natural images.