Neutral auditory words immediately followed by painful electric shock may show reduced next-day recollection.

In this study, we investigated the effect of experimentally delivered acute pain on memory. Twenty-five participants participated in experimental sessions on consecutive days. The first session involved a categorization task to encourage memory encoding. There were two conditions, presented in randomized order, in which participants listened to a series of words, which were repeated three times. In one condition, one-third of the word items were immediately followed by a painful electrical shock. This word-shock pairing was consistent across repetition and the pain-paired items were presented unpredictably. In the other condition, all word items were not associated with pain. Response times over these repeated presentations were assessed for differences. Explicit memory was tested the following day, employing a Remember-Know assessment of word recognition, with no shocks employed. We found evidence that recollection may be reduced for pain-paired words, as the proportion of correct Remember responses (out of total correct responses) was significantly lower. There were no significant reductions in memory for non-pain items that followed painful stimulation after a period of several seconds. Consistent with the experience of pain consuming working memory resources, we theorize that painful shocks interrupt memory encoding for the immediately preceding experimental items, due to a shift in attention away from the word item.

Memory for non-painful auditory items is influenced by whether they are experienced in a context involving painful electrical stimulation.

In this study, we sought to examine the effect of experimentally induced somatic pain on memory. Subjects heard a series of words and made categorization decisions in two different conditions. One condition included painful shocks administered just after presentation of some of the words; the other condition involved no shocks. For the condition that included painful stimulations, every other word was followed by a shock, and subjects were informed to expect this pattern. Word lists were repeated three times within each condition in randomized order, with different category judgments but consistent pain-word pairings. After a brief delay, recognition memory was assessed. Non-pain words from the pain condition were less strongly encoded than non-pain words from the completely pain-free condition. Recognition of pain-paired words was not significantly different than either subgroup of non-pain words. An important accompanying finding is that response times to repeated experimental items were slower for non-pain words from the pain condition, compared to non-pain words from the completely pain-free condition. This demonstrates that the effect of pain on memory may generalize to non-pain items experienced in the same experimental context.

Manipulating memory efficacy affects the behavioral and neural profiles of deterministic learning and decision-making.

When making a decision, we have to identify, collect, and evaluate relevant bits of information to ensure an optimal outcome. How we approach a given choice can be influenced by prior experience. Contextual factors and structural elements of these past decisions can cause a shift in how information is encoded and can in turn influence later decision-making. In this two-experiment study, we sought to manipulate declarative memory efficacy and decision-making in a concurrent discrimination learning task by altering the amount of information to be learned. Subjects learned correct responses to pairs of items across several repetitions of a 50- or 100-pair set and were tested for memory retention. In one experiment, this memory test interrupted learning after an initial encoding experience in order to test for early encoding differences and associate those differences with changes in decision-making. In a second experiment, we used fMRI to probe neural differences between the two list-length groups related to decision-making across learning and assessed subsequent memory retention. We found that a striatum-based system was associated with decision-making patterns when learning a longer list of items, while a medial cortical network was associated with patterns when learning a shorter list. Additionally, the hippocampus was exclusively active for the shorter list group. Altogether, these behavioral, computational, and imaging results provide evidence that multiple types of mnemonic representations contribute to experienced-based decision-making. Moreover, contextual and structural factors of the task and of prior decisions can influence what types of evidence are drawn upon during decision-making.

Neural signatures of experience-based improvements in deterministic decision-making.

Feedback about our choices is a crucial part of how we gather information and learn from our environment. It provides key information about decision experiences that can be used to optimize future choices. However, our understanding of the processes through which feedback translates into improved decision-making is lacking. Using neuroimaging (fMRI) and cognitive models of decision-making and learning, we examined the influence of feedback on multiple aspects of decision processes across learning. Subjects learned correct choices to a set of 50 word pairs across eight repetitions of a concurrent discrimination task. Behavioral measures were then analyzed with both a drift-diffusion model and a reinforcement learning model. Parameter values from each were then used as fMRI regressors to identify regions whose activity fluctuates with specific cognitive processes described by the models. The patterns of intersecting neural effects across models support two main inferences about the influence of feedback on decision-making. First, frontal, anterior insular, fusiform, and caudate nucleus regions behave like performance monitors, reflecting errors in performance predictions that signal the need for changes in control over decision-making. Second, temporoparietal, supplementary motor, and putamen regions behave like mnemonic storage sites, reflecting differences in learned item values that inform optimal decision choices. As information about optimal choices is accrued, these neural systems dynamically adjust, likely shifting the burden of decision processing from controlled performance monitoring to bottom-up, stimulus-driven choice selection. Collectively, the results provide a detailed perspective on the fundamental ability to use past experiences to improve future decisions.

Content-specific evidence accumulation in inferior temporal cortex during perceptual decision-making.

During a perceptual decision, neuronal activity can change as a function of time-integrated evidence. Such neurons may serve as decision variables, signaling a choice when activity reaches a boundary. Because the signals occur on a millisecond timescale, translating to human decision-making using functional neuroimaging has been challenging. Previous neuroimaging work in humans has identified patterns of neural activity consistent with an accumulation account. However, the degree to which the accumulating neuroimaging signals reflect specific sources of perceptual evidence is unknown. Using an extended face/house discrimination task in conjunction with cognitive modeling, we tested whether accumulation signals, as measured using functional magnetic resonance imaging (fMRI), are stimulus-specific. Accumulation signals were defined as a change in the slope of the rising edge of activation corresponding with response time (RT), with higher slopes associated with faster RTs. Consistent with an accumulation account, fMRI activity in face- and house-selective regions in the inferior temporal cortex increased at a rate proportional to decision time in favor of the preferred stimulus. This finding indicates that stimulus-specific regions perform an evidence integrative function during goal-directed behavior and that different sources of evidence accumulate separately. We also assessed the decision-related function of other regions throughout the brain and found that several regions were consistent with classifications from prior work, suggesting a degree of domain generality in decision processing. Taken together, these results provide support for an integration-to-boundary decision mechanism and highlight possible roles of both domain-specific and domain-general regions in decision evidence evaluation.

The strength of gradually accruing probabilistic evidence modulates brain activity during a categorical decision.

The evolution of neural activity during a perceptual decision is well characterized by the evidence parameter in sequential sampling models. However, it is not known whether accumulating signals in human neuroimaging are related to the integration of evidence. Our aim was to determine whether activity accumulates in a nonperceptual task by identifying brain regions tracking the strength of probabilistic evidence. fMRI was used to measure whole-brain activity as choices were informed by integrating a series of learned prior probabilities. Participants first learned the predictive relationship between a set of shape stimuli and one of two choices. During scanned testing, they made binary choices informed by the sum of the predictive strengths of individual shapes. Sequences of shapes adhered to three distinct rates of evidence (RoEs): rapid, gradual, and switch. We predicted that activity in regions informing the decision would modulate as a function of RoE prior to the choice. Activity in some regions, including premotor areas, changed as a function of RoE and response hand, indicating a role in forming an intention to respond. Regions in occipital, temporal, and parietal lobes modulated as a function of RoE only, suggesting a preresponse stage of evidence processing. In all of these regions, activity was greatest on rapid trials and least on switch trials, which is consistent with an accumulation-to-boundary account. In contrast, activity in a set of frontal and parietal regions was greatest on switch and least on rapid trials, which is consistent with an effort or time-on-task account.

Prior probability and feature predictability interactively bias perceptual decisions.

Anticipating a forthcoming sensory experience facilitates perception for expected stimuli but also hinders perception for less likely alternatives. Recent neuroimaging studies suggest that expectation biases arise from feature-level predictions that enhance early sensory representations and facilitate evidence accumulation for contextually probable stimuli while suppressing alternatives. Reasonably then, the extent to which prior knowledge biases subsequent sensory processing should depend on the precision of expectations at the feature level as well as the degree to which expected features match those of an observed stimulus. In the present study we investigated how these two sources of uncertainty modulated pre- and post-stimulus bias mechanisms in the drift-diffusion model during a probabilistic face/house discrimination task. We tested several plausible models of choice bias, concluding that predictive cues led to a bias in both the starting-point and rate of evidence accumulation favoring the more probable stimulus category. We further tested the hypotheses that prior bias in the starting-point was conditional on the feature-level uncertainty of category expectations and that dynamic bias in the drift-rate was modulated by the match between expected and observed stimulus features. Starting-point estimates suggested that subjects formed a constant prior bias in favor of the face category, which exhibits less feature-level variability, that was strengthened or weakened by trial-wise predictive cues. Furthermore, we found that the gain on face/house evidence was increased for stimuli with less ambiguous features and that this relationship was enhanced by valid category expectations. These findings offer new evidence that bridges psychological models of decision-making with recent predictive coding theories of perception.

High quality but limited quantity perceptual evidence produces neural accumulation in frontal and parietal cortex.

Goal-directed perceptual decisions involve the analysis of sensory inputs, the extraction and accumulation of evidence, and the commitment to a choice. Previous neuroimaging studies of perceptual decision making have identified activity related to accumulation in parietal, inferior temporal, and frontal regions. However, such effects may be related to factors other than the integration of evidence over time, such as changes in the quantity of stimulus input and in attentional demands leading up to a decision. The current study tested an accumulation account using 2 manipulations. First, to test whether patterns of accumulation can be explained by changes in the quantity of sensory information, objects were revealed with a high quality but consistent quantity of evidence throughout the trial. Imaging analysis revealed patterns of accumulation in frontal and parietal regions but not in inferior temporal regions. This result supports a framework in which evidence is processed in sensory cortex and integrated over time in higher order cortical areas. Second, to test whether accumulation signals are driven by attentional demands, task difficulty was increased on some trials. This manipulation did not affect the nature of accumulating functional magnetic resonance imaging signals, indicating that accumulating signals are not necessarily driven by changes in attentional demand.

Effects of task-set adoption on ERP correlates of controlled and automatic recognition memory.

Successful memory retrieval depends not only on memory fidelity but also on the mental preparedness on the part of the subject. ERP studies of recognition memory have identified two topographically distinct ERP components, the FN400 old/new effect and the late posterior component (LPC) old/new effect, commonly associated with familiarity and recollection, respectively. Here we used a task-switching paradigm to examine the extent to which adoption of a retrieval task-set influences FN400 and LPC old/new effects, in light of the presumption that recollection, as a control process, relies on the adoption of a retrieval task-set, but that familiarity-based retrieval does not. Behavioral accuracy indicated that source memory (experiment 2), but not item recognition (experiment 1), improved with task-set adoption. ERP data demonstrated a larger LPC on stay trials when a task-set had been adopted even with a simple recognition memory judgment. We conclude that adopting a retrieval task-set impacts recollection memory but not familiarity. These data indicate that attentional state immediately prior to retrieval can influence objective measures of recollection memory.