Distinct competitive impacts of palatability of taste stimuli on sampling dynamics during a preference test.

Food or taste preference tests are analogous to naturalistic decisions in which the animal selects which stimuli to sample and for how long to sample them. The data acquired in such tests, the relative amounts of the alternative stimuli that are sampled and consumed, indicate the preference for each. While such preferences are typically recorded as a single quantity, an analysis of the ongoing sampling dynamics producing the preference can reveal otherwise hidden aspects of the decision-making process that depend on its underlying neural circuit mechanisms. Here, we perform a dynamic analysis of two factors that give rise to preferences in a two-alternative task, namely the distribution of durations of sampling bouts of each stimulus and the likelihood of returning to the same stimulus or switching to the alternative-that is, the transition probability-following each bout. The results of our analysis support a specific computational model of decision making whereby an exponential distribution of bout durations has a mean that is positively correlated with the palatability of that stimulus but also negatively correlated with the palatability of the alternative. This impact of the alternative stimulus on the distribution of bout durations decays over a timescale of tens of seconds, even though the memory of the alternative stimulus lasts far longer-long enough to impact the transition probabilities upon ending bouts. Together, our findings support a state transition model for bout durations and suggest a separate memory mechanism for stimulus selection. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Parametric shift from rational to irrational decisions in mice.

In the classical view of economic choices, subjects make rational decisions evaluating the costs and benefits of options in order to maximize their overall income. Nonetheless, subjects often fail to reach optimal outcomes. The overt value of an option drives the direction of decisions, but covert factors such as emotion and sensitivity to sunk cost are thought to drive the observed deviations from optimality. Many questions remain to be answered as to (1) which contexts contribute the most to deviation from an optimal solution; and (2) the extent of these effects. In order to tackle these questions, we devised a decision-making task for mice, in which cost and benefit parameters could be independently and flexibly adjusted and for which a tractable optimal solution was known. Comparing mouse behavior with this optimal solution across parameter settings revealed that the factor most strongly contributing to suboptimal performance was the cost parameter. The quantification of sensitivity to sunk cost, a covert factor implicated in our task design, revealed it as another contributor to reduced optimality. In one condition where the large reward option was particularly unattractive and the small reward cost was low, the sensitivity to sunk cost and the cost-led suboptimality almost vanished. In this regime and this regime only, mice could be viewed as close to rational (here, 'rational' refers to a state in which an animal makes decisions basing on objective valuation, not covert factors). Taken together, our results suggest that "rationality" is a task-specific construct even in mice.

Spatiotemporal discrimination in attractor networks with short-term synaptic plasticity.

We demonstrate that a randomly connected attractor network with dynamic synapses can discriminate between similar sequences containing multiple stimuli suggesting such networks provide a general basis for neural computations in the brain. The network contains units representing assemblies of pools of neurons, with preferentially strong recurrent excitatory connections rendering each unit bi-stable. Weak interactions between units leads to a multiplicity of attractor states, within which information can persist beyond stimulus offset. When a new stimulus arrives, the prior state of the network impacts the encoding of the incoming information, with short-term synaptic depression ensuring an itinerancy between sets of active units. We assess the ability of such a network to encode the identity of sequences of stimuli, so as to provide a template for sequence recall, or decisions based on accumulation of evidence. Across a range of parameters, such networks produce the primacy (better final encoding of the earliest stimuli) and recency (better final encoding of the latest stimuli) observed in human recall data and can retain the information needed to make a binary choice based on total number of presentations of a specific stimulus. Similarities and differences in the final states of the network produced by different sequences lead to predictions of specific errors that could arise when an animal or human subject generalizes from training data, when the training data comprises a subset of the entire stimulus repertoire. We suggest that such networks can provide the general purpose computational engines needed for us to solve many cognitive tasks.