What representations and computations underpin the contribution of the hippocampus to generalization and inference?

Empirical research and theoretical accounts have traditionally emphasized the function of the hippocampus in episodic memory. Here we draw attention to the importance of the hippocampus to generalization, and focus on the neural representations and computations that might underpin its role in tasks such as the paired associate inference (PAI) paradigm. We make a principal distinction between two different mechanisms by which the hippocampus may support generalization: an encoding-based mechanism that creates overlapping representations which capture higher-order relationships between different items [e.g., Temporal Context Model (TCM): Howard et al., 2005]—and a retrieval-based model [Recurrence with Episodic Memory Results in Generalization (REMERGE): Kumaran and McClelland, in press] that effectively computes these relationships at the point of retrieval, through a recurrent mechanism that allows the dynamic interaction of multiple pattern separated episodic codes. We also discuss what we refer to as transfer effects—a more abstract example of generalization that has also been linked to the function of the hippocampus. We consider how this phenomenon poses inherent challenges for models such as TCM and REMERGE, and outline the potential applicability of a separate class of models—hierarchical Bayesian models (HBMs) in this context. Our hope is that this article will provide a basic framework within which to consider the theoretical mechanisms underlying the role of the hippocampus in generalization, and at a minimum serve as a stimulus for future work addressing issues that go to the heart of the function of the hippocampus

It's In My Eyes, but It Doesn't Look that Way to Me

In this issue of Neuron, Hannula and Ranganath provide striking evidence that hippocampal activity predicts eye movements that reveal memory for the past even when participants' overt memory decisions are in error. Their findings bear on an ongoing debate about the relationship between mnemonic awareness and hippocampal function

The neurobiology of reference-dependent value computation

A key focus of current research in neuroeconomics concerns how the human brain computes value. Although, value has generally been viewed as an absolute measure (e.g., expected value, reward magnitude), much evidence suggests that value is more often computed with respect to a changing reference point, rather than in isolation. Here, we present the results of a study aimed to dissociate brain regions involved in reference-independent (i.e., “absolute”) value computations, from those involved in value computations relative to a reference point. During functional magnetic resonance imaging, subjects acted as buyers and sellers during a market exchange of lottery tickets. At a behavioral level, we demonstrate that subjects systematically accorded a higher value to objects they owned relative to those they did not, an effect that results from a shift in reference point (i.e., status quo bias or endowment effect). Our results show that activity in orbitofrontal cortex and dorsal striatum track parameters such as the expected value of lottery tickets indicating the computation of reference-independent value. In contrast, activity in ventral striatum indexed the degree to which stated prices, at a within-subjects and between-subjects level, were distorted with respect to a reference point. The findings speak to the neurobiological underpinnings of reference dependency during real market value computations

Language models show human-like content effects on reasoning

Abstract reasoning is a key ability for an intelligent system. Large language models achieve above-chance performance on abstract reasoning tasks, but exhibit many imperfections. However, human abstract reasoning is also imperfect, and depends on our knowledge and beliefs about the content of the reasoning problem. For example, humans reason much more reliably about logical rules that are grounded in everyday situations than arbitrary rules about abstract attributes. The training experiences of language models similarly endow them with prior expectations that reflect human knowledge and beliefs. We therefore hypothesized that language models would show human-like content effects on abstract reasoning problems. We explored this hypothesis across three logical reasoning tasks: natural language inference, judging the logical validity of syllogisms, and the Wason selection task (Wason, 1968). We find that state of the art large language models (with 7 or 70 billion parameters; Hoffman et al., 2022) reflect many of the same patterns observed in humans across these tasks -- like humans, models reason more effectively about believable situations than unrealistic or abstract ones. Our findings have implications for understanding both these cognitive effects, and the factors that contribute to language model performance

An Unexpected Sequence of Events: Mismatch Detection in the Human Hippocampus

The ability to identify and react to novelty within the environment is fundamental to survival. Computational models emphasize the potential role of the hippocampus in novelty detection, its unique anatomical circuitry making it ideally suited to act as a comparator between past and present experience. The hippocampus, therefore, is viewed to detect associative mismatches between what is expected based on retrieval of past experience and current sensory input. However, direct evidence that the human hippocampus performs such operations is lacking. We explored brain responses to novel sequences of objects using functional magnetic resonance imaging (fMRI), while subjects performed an incidental target detection task. Our results demonstrate that hippocampal activation was maximal when prior predictions concerning which object would appear next in a sequence were violated by sensory reality. In so doing, we establish the biological reality of associative match-mismatch computations within the human hippocampus, a process widely held to play a cardinal role in novelty detection. Our results also suggest that the hippocampus may generate predictions about how future events will unfold, and critically detect when these expectancies are violated, even when task demands do not require it. The present study also offers broader insights into the nature of essential computations carried out by the hippocampus, which may also underpin its unique contribution to episodic memory

The Hippocampus and Dopaminergic Midbrain: Old Couple, New Insights

Humans have a natural ability to gain new insights by generalizing from previous experience. In this issue of Neuron, Shohamy and Wagner reveal how generalizations naturally emerge during associative learning through a partnership between putatively dopaminergic circuitry in the midbrain and the hippocampus

Correction: An Unexpected Sequence of Events: Mismatch Detection in the Human Hippocampus.

[This corrects the article DOI: 10.1371/journal.pbio.0040424.]