Increasing stimulus similarity drives nonmonotonic representational change in hippocampus

Studies of hippocampal learning have obtained seemingly contradictory results, with manipulations that increase coactivation of memories sometimes leading to differentiation of these memories, but sometimes not. These results could potentially be reconciled using the nonmonotonic plasticity hypothesis, which posits that representational change (memories moving apart or together) is a U-shaped function of the coactivation of these memories during learning. Testing this hypothesis requires manipulating coactivation over a wide enough range to reveal the full U-shape. To accomplish this, we used a novel neural network image synthesis procedure to create pairs of stimuli that varied parametrically in their similarity in high-level visual regions that provide input to the hippocampus. Sequences of these pairs were shown to human participants during high-resolution fMRI. As predicted, learning changed the representations of paired images in the dentate gyrus as a U-shaped function of image similarity, with neural differentiation occurring only for moderately similar images.

A cognitive neuroscience perspective on insight as a memory process: Searching for the solution.

What are the cognitive and brain processes that lead to an insight? This is one of two chapters on "A cognitive neuroscience perspective on insight as a memory process" to be published in the "Routledge International Handbook of Creative Cognition" by L. J. Ball & F. Valleé-Tourangeau (Eds.). In this chapter, we will describe the insight solution process from a neurocognitive perspective. Inspired by cognitive theories, we translate some of insight's main cognitive subprocesses (problem representation, search, representational change, solution) into related neurocognitive ones and summarize them in a descriptive framework. Those described processes focus primarily on verbal insight and are explained using the remote associates task. In this task, the solver is provided with several problem elements (e.g. drop, coat, summer) and needs to find the (remotely related) target that matches those cues (e.g., rain). In a nutshell, insight is the consequence of a problem-solving process where the target is encoded in long-term memory but cannot be retrieved at first because the relationship between the problem elements and the target is unknown, precluding a simple memory search. Upon problem display, the problem elements and a whole network of associated concepts are automatically activated in long-term memory in distinct areas of the brain representing those concepts (=problem representation). Insight is assumed to occur when automatic processes suddenly activate the target after control processes associated with inferior frontal gyrus and anterior cingulate cortex activation manage to overcome prior knowledge and/or perceptual constraints by revising the current activation pattern (=representational change). The next chapter (https://psyarxiv.com/bevjm) will focus on the role of insight problem solving for long-term memory formation.

Increasing stimulus similarity drives nonmonotonic representational change in hippocampus

Studies of hippocampal learning have obtained seemingly contradictory results, with manipulations that increase coactivation of memories sometimes leading to differentiation of these memories, but sometimes not. These results could potentially be reconciled using the nonmonotonic plasticity hypothesis, which posits that representational change (memories moving apart or together) is a U-shaped function of the coactivation of these memories during learning. Testing this hypothesis requires manipulating coactivation over a wide enough range to reveal the full U-shape. To accomplish this, we used a novel neural network image synthesis procedure to create pairs of stimuli that varied parametrically in their similarity in high-level visual regions that provide input to the hippocampus. Sequences of these pairs were shown to human participants during high-resolution fMRI. As predicted, learning changed the representations of paired images in the dentate gyrus as a U-shaped function of image similarity, with neural differentiation occurring only for moderately similar images.

From “Hmmm…” to “Aha!”: Emotional Monitoring of Representational Change

There are two common approaches to researching insight: the study of the emotional response to a solution (Aha! experience) and the study of the restructuring of representations. The relationship between them can be found by comparing functions they perform relative to each other. For the experimental investigation of insight, problems that are typically being used can be solved within a little amount of time and are highly similar in their structure. We believe that such laboratory designs of the tasks often lead to researchers missing out on the moments of impasse and initial restructuring of the search space. In the current study, using the method of multimodal corpora constructed from individual solutions, we gained partial confirmation of the key statements of the model of emotional regulation of the representational change. According to the model, an insight solution process is accompanied by emotions regulating the process of representational change. A feeling of impasse is a response to the lack of progress towards the solution. An Aha! experience appears in response to solvers performing actions that bring them a huge step closer to the solution of a problem. We believe that these emotional responses are experienced before the solution reaches consciousness and they motivate the solver to adapt their search space accordingly. The model we propose is a development of the ideas of Ya.A. Ponomarev on the role of emotions in regulating of insight problem solving andmodel of M. Ollinger and colleagues describing the phases of insight problem solving.