Associative activation is commonly assumed to rely on associative strength, such that if A is strongly associated with B, B is activated whenever A is activated. We challenged this assumption by examining whether the activation of associations is state dependent. In three experiments, subjects performed a free-association task while the level of a simultaneous load was manipulated in various ways. In all three experiments subjects in the low-load conditions provided significantly more diverse and original associations compared with subjects in the high-load conditions, who exhibited high consensus. In an additional experiment, we found increased semantic priming of immediate associations under high load and of remote associations under low load. Taken together, these findings imply that activation of associations is an exploratory process by default, but is narrowed to exploiting the more immediate associations under conditions of high load. We propose a potential mechanism for processing associations in exploration and in exploitation modes, and suggest clinical implications.
Perceptual decisions are biased by recent perceptual history—a phenomenon termed 'serial dependence.' Here, we investigated what aspects of perceptual decisions lead to serial dependence, and disambiguated the influences of low-level sensory information, prior choices and motor actions. Participants discriminated whether a brief visual stimulus lay to left/right of the screen center. Following a series of biased ‘prior’ location discriminations, subsequent ‘test’ location discriminations were biased toward the prior choices, even when these were reported via different motor actions (using different keys), and when the prior and test stimuli differed in color. By contrast, prior discriminations about an irrelevant stimulus feature (color) did not substantially influence subsequent location discriminations, even though these were reported via the same motor actions. Additionally, when color (not location) was discriminated, a bias in prior stimulus locations no longer influenced subsequent location discriminations. Although low-level stimuli and motor actions did not trigger serial-dependence on their own, similarity of these features across discriminations boosted the effect. These findings suggest that relevance across perceptual decisions is a key factor for serial dependence. Accordingly, serial dependence likely reflects a high-level mechanism by which the brain predicts and interprets new incoming sensory information in accordance with relevant prior choices.
Perceptual decisions are biased by recent perceptual history -a phenomenon termed 'serialdependence.' Using a visual location discrimination task, we investigated what aspects of perceptual decisions lead to serial dependence, and disambiguated the influences of low-level sensory information, prior choices and motor actions on subsequent perceptual decisions.Following several biased (prior) location discriminations, subsequent (test) discriminations were biased toward the prior choices, even when reported via different motor actions, and when prior and test stimuli differed in color. By contrast, biased discriminations about an irrelevant stimulus feature did not substantially influence subsequent location discriminations. Additionally, biased stimulus locations, when color was discriminated, no longer substantially influenced subsequent location decisions. Hence, the degree of relevance between prior and subsequent perceptual decisions is a key factor for serial-dependence. This suggests that serial-dependence reflects a high-level mechanism by which the brain predicts and interprets incoming sensory information in accordance with relevant prior choices.
Contextual associations facilitate object recognition in human vision. However, the role of context in artificial vision remains elusive as does the characteristics that humans use to define context. We investigated whether contextually related objects (bicycle-helmet) are represented more similarly in convolutional neural networks (CNNs) used for image understanding than unrelated objects (bicycle-fork). Stimuli were of objects against a white background and consisted of a diverse set of contexts (N=73). CNN representations of contextually related objects were more similar to one another than to unrelated objects across all CNN layers. Critically, the similarity found in CNNs correlated with human behavior across three experiments assessing contextual relatedness, emerging significant only in the later layers. The results demonstrate that context is inherently represented in CNNs as a result of object recognition training, and that the representation in the later layers of the network tap into the contextual regularities that predict human behavior.
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