Using neural response properties to draw the distinction between modal and amodal representations

ABSTRACT

Barsalou has recently argued against the strategy of identifying amodal neural representations by using their cross-modal responses (i.e., their responses to stimuli from different modalities). I agree that there are indeed modal structures that satisfy this “cross-modal response” criterion (CM), such as distributed and conjunctive modal representations. However, I argue that we can distinguish between modal and amodal structures by looking into differences in their cross-modal responses. A component of a distributed cell assembly can be considered unimodal because its responses to stimuli from a given modality are stable, whereas its responses to stimuli from any other modality are not (i.e., these are lost within a short time, plausibly as a result of cell assembly dynamics). In turn, conjunctive modal representations, such as superior colliculus cells in charge of sensory integration, are multimodal because they have a stable response to stimuli from different modalities. Finally, some prefrontal cells constitute amodal representations because they exhibit what has been called ‘adaptive coding’. This implies that their responses to stimuli from any given modality can be lost when the context and task conditions are modified. We cannot assign them a modality because they have no stable relation with any input type.

Abbreviatons: CM: cross-modal response criterion; CCR: conjuntive cross-modal representations; fMRI: functional magnetic resonance imaging; MVPA: multivariate pattern analysis; pre-SMA: pre-supplementary motor area; PFC: prefrontal cortex; SC: superior colliculus; GWS: global workspace     

Moving words: dynamic representations in language comprehension

Eighty-two participants listened to sentences and then judged whether two sequentially presented visual objects were the same. On critical trials, participants heard a sentence describe the motion of a ball toward or away from the observer (e.g., “The pitcher hurled the softball to you”). Seven hundred and fifty milliseconds after the offset of the sentence, a picture of an object was presented for 500 ms, followed by another picture. On critical trials, the two pictures depicted the kind of ball mentioned in the sentence. The second picture was displayed 175 ms after the first. Crucially, it was either slightly larger or smaller than the first picture, thus suggesting movement of the ball toward or away from the observer. Participants responded more quickly when the implied movement of the balls matched the movement described in the sentence. This result provides support for the view that language comprehension involves dynamic perceptual simulations.     

Memory for goals: an activation‐based model

Goal‐directed cognition is often discussed in terms of specialized memory structures like the “goal stack.” The goal‐activation model presented here analyzes goal‐directed cognition in terms of the general memory constructs of activation and associative priming. The model embodies three predictive constraints: (1) the interference level, which arises from residual memory for old goals; (1) the strengthening constraint, which makes predictions about time to encode a new goal; and (3) the priming constraint, which makes predictions about the role of cues in retrieving pending goals. These constraints are formulated algebraically and tested through simulation of latency and error data from the Tower of Hanoi, a means‐ends puzzle that depends heavily on suspension and resumption of goals. Implications of the model for understanding intention superiority, postcompletion error, and effects of task interruption are discussed.