How the Human Brain Represents Perceived Dangerousness or "Predacity" of Animals

Abstract

Common or folk knowledge about animals is dominated by three dimensions: (1) level of cognitive complexity or "animacy;" (2) dangerousness or "predacity;" and (3) size. We investigated the neural basis of the perceived dangerousness or aggressiveness of animals, which we refer to more generally as "perception of threat." Using functional magnetic resonance imaging (fMRI), we analyzed neural activity evoked by viewing images of animal categories that spanned the dissociable semantic dimensions of threat and taxonomic class. The results reveal a distributed network for perception of threat extending along the right superior temporal sulcus. We compared neural representational spaces with target representational spaces based on behavioral judgments and a computational model of early vision and found a processing pathway in which perceived threat emerges as a dominant dimension: whereas visual features predominate in early visual cortex and taxonomy in lateral occipital and ventral temporal cortices, these dimensions fall away progressively from posterior to anterior temporal cortices, leaving threat as the dominant explanatory variable. Our results suggest that the perception of threat in the human brain is associated with neural structures that underlie perception and cognition of social actions and intentions, suggesting a broader role for these regions than has been thought previously, one that includes the perception of potential threat from agents independent of their biological class.

Significance statement: For centuries, philosophers have wondered how the human mind organizes the world into meaningful categories and concepts. Today this question is at the core of cognitive science, but our focus has shifted to understanding how knowledge manifests in dynamic activity of neural systems in the human brain. This study advances the young field of empirical neuroepistemology by characterizing the neural systems engaged by an important dimension in our cognitive representation of the animal kingdom ontological subdomain: how the brain represents the perceived threat, dangerousness, or "predacity" of animals. Our findings reveal how activity for domain-specific knowledge of animals overlaps the social perception networks of the brain, suggesting domain-general mechanisms underlying the representation of conspecifics and other animals.

Keywords: MVPA; STATIS; categories; fMRI; representation similarity analysis; vision.

Figure 1.

a, The stimuli were images from 12 animal categories spanning a 3 × 2 experimental design with three taxonomic groups (mammals, reptiles, and bugs) and two levels of threat (high and low). b, While undergoing fMRI scanning, subjects saw the stimuli presented in sets of three images in brief succession while they monitored whether the images all came from the same animal category. Category-specific brain responses were estimated using the GLM with regressors modeling the encoding trials that contained all the same category. Catch trials (i.e., trials that contained an oddball category) were infrequent and were not included in the subsequent analyses.

Figure 2.

A, Map showing the mean group accuracy for better-than-chance classification (chance, 0.5) for high versus low threat. Pattern classification was performed using a linear SVM classifier within a surface-based searchlight. The accuracy values passing the group result threshold of t₍₁₁₎ > 3 ranged from ∼0.53 to 0.58. B, Group searchlight t map for better-than-chance classification (chance, 0.33) for taxonomic class controlling for threat. Significantly above chance accuracies ranged from 0.38 to 0.63. Maps in A and B are thresholded at t₍₁₁₎ > 3, which has a p value = 0.006, uncorrected for multiple comparisons.

Figure 3.

Maps showing the clusters found by clustering the surviving surface nodes from the searchlight maps (Fig. 2) based on the similarity between local representational similarity matrices measured using a searchlight for each surface node. In a second step, the spatial clustering was applied to identify anatomically adjacent groups of surface nodes. A, The threat map yielded seven clusters. B, The taxonomy map yielded 10 clusters.

Figure 4.

MDS for threat cluster 7 (STSa; A) and taxonomy cluster 5 (LOC; right; B). A, Top, Three-dimensional plot showing the first three PCs from STATIS for STSa, color coded for low (blue) and high (red) threat. The middle and bottom plots show the second and third dimensions, respectively, plotted against the first dimension. B, Top, Three-dimensional plot showing the first three PCs for the anterior LOC, color coded for mammals (brown), reptiles and amphibians (green), and bugs (purple). Middle and bottom plots show the second and third dimensions, respectively, plotted against the first dimension. The ellipses show 95% confidence intervals for the values of the factor scores based on 1000 bootstrap resamplings of the subjects. Tau is the percentage of variance accounted by each PC.

Figure 5.

DMs for threat (THREAT), taxonomy (TAX), and the visual model (VIS). THREAT and TAX were derived from behavioral judgments collected using AMT. For THREAT, participants were instructed to make similarity judgments based on how dangerous the animal is, whereas for TAX, subjects were instructed to make judgments based on what kind of animal was depicted. VIS was based on features from a computational neural model (HMAX; Serre et al., 2007) to model the visual response to the stimulus images. The THREAT model was not correlated with TAX (r = 0.01) or VIS (r = −0.02), and TAX and VIS were moderately correlated with each other (r = 0.33). Noise ceiling calculations based on bootstrap resampling of behavioral subjects (for THREAT and TAX) or stimuli (for VIS) revealed maximum expected correlations with these models to be bounded by r = 0.99, r = 0.99, and r = 0.88 for THREAT, TAX, and VIS, respectively. Model DMs are shown on the same scale normalized over the interval zero to one.

Figure 6.

Classical (metric) MDS showing the factor scores on the first two PCs for distance matrices defined between clusters and predictive models. A, Factor scores for the first two PCs for the MDS solution calculated from the pairwise DM defined over the seven threat clusters (circles) and three models (squares). Red arrow corresponds to increasing similarity with THREAT along the posterior–anterior axis of the right STS. B, Factor scores for the first two PCs for the MDS solution calculated from the pairwise DM defined over 10 taxonomy clusters (diamonds) and three models (squares). The green arrow corresponds to increasing similarity with TAX from EV to LOC. The solutions in A and B were calculated separately.

Figure 7.

A, The semipartial correlations between the three models and the four surviving threat clusters. B, Semipartial correlations between the models and seven clusters visible along the right lateral surface. Error bars indicate 95% confidence intervals based on bootstrap resampling of the subjects.